Gall is a painkiller, vinegar/wine also

the 3-repetition is important

he doesn't believe, but 3 times he tries to save Christ.

crucially this is not theological repentance. Repentance: putting your life in the hands of God, turning towards Jesus Christ, surrendering. Different from remorse.

The tragedy of Judas: he punishes himself eternally.

in light of Peter's betrayal of Jesus, Judas is also very sad. They both betray him. Even if Judas is more active. Peter still trusts Jesus' love after, whereas Judas does not.

apparently Friend only in Matthews

4 months of wages, $25k.

EAs getting first calss tickets and working in constellation.

Also used to justify the wealth of Roman church

Markus: hard work is important. You have to work otherwise you'll go to hell. Also prepare for the day of judgements.

this is prophesy of the temple's destruction, and then you'll have to run away. If about temple's destruction, it's already passed.

But maybe this is about the endtimes

Temple destruction: 70 AD matthew: 80-90 ad

only priests can go in usually. after destruction

Classic biblical justification for separation of church and state. But jesus is just avoiding being roasted by the Pharisees, so that might not be literal. so rather maybe it's "you can give this to Caesar but it's effectively worthless." Not obvious he believes church would be nonpolitical.

Pantheism?

The devil has angels? I thought it would map to "demons"

I am tyding up the root folder for the indy0.ppergos.me sebsite and reading ino things

At first glance, this article appears to be an objective, educational guide about Malaysia's 2025 single-use plastic ban. However, critical analysis reveals it is a clever piece of B2B Content Marketing disguised as compliance advice. The manufacturer (Enrich Package) leverages the 'Zero Single-Use Plastics' roadmap to create regulatory anxiety among retailers, only to position their own petroleum-based and oxo-biodegradable plastics as the 'sustainable solution.' This is a sophisticated form of Greenwashing, using policy loopholes and misleading terminology to sell products that ultimately continue to harm the environment, risking violations of the Consumer Protection Act 1999."

Quem souber de mais informações sobre a origem de Kuidá e seus multiplos sentidos, por favor, esteja à vontade para partilhar!!!

Grades perhaps tell me what you know; experience shows me what you can do.

It's not AI Literacy, it's the durable skills.

Detection of Greenwashing: Promoting "Oxo-biodegradable bags" is misleading. These plastics fragment into microplastics rather than fully decomposing, which is a common form of greenwashing.

Legal & Ethical Risk: This violates Section 10 of the Consumer Protection Act 1999 (Malaysia) by making misleading representations about the product's environmental benefits.

Feels like Schellabarger is channelling his perception of WWII with Andrea here. Maybe he earnestly believes that the destruction and double-dealing of the war were necessary for the grand new age that followed it.

Gorgeous imagery of a garden.

Elegant summary.

Love this description of Andrea's clothing.

Hilarious, right after Camilla shows up.

Ficção como produtora de um tipo de verdade.

asd

allation

testing

Unlike the Israelites in Ezra 3 v 12 the home Believers long for is in Heaven and is a yearning for the future not the past because the future will be better than anything mankind could have built in the past (all of it will pale in comparison to heaven).

大多数人认为OpenAI在AI应用领域处于绝对领先地位，但作者指出Anthropic在企业采用率上已经超过了OpenAI。这一观点与主流认知相悖，暗示市场格局可能正在发生重大变化，挑战了OpenAI作为AI领域领导者的传统叙事。

大多数人认为AI公司的增长是渐进式的，而非指数级的。作者提到的Anthropic收入在几个月内增长五倍，这一速度远超传统科技公司的增长轨迹，挑战了人们对AI商业化和市场扩张速度的常规认知，暗示AI经济可能比预期更具爆发性。

大多数人认为AI主要应用于内容创作或客户服务，而非高度敏感的财务报告领域。这一观点暗示AI在金融领域的应用比公众普遍认知的要深入得多，可能颠覆了人们对AI应用边界的传统理解，同时也引发了关于AI在关键决策中角色的伦理问题。

大多数人认为中国在AI领域落后于美国，但作者认为中国AI实验室已经建立了效率护城河，这可能与主流认知相反。这一观点挑战了西方媒体对中国AI发展的普遍叙事，暗示中国可能通过效率优势而非纯粹的技术创新来定义未来AI市场的发展方向。

大多数人认为AI革命创造了广泛的中产阶级机会，作者认为AI热潮实际上创造了极少数超级富豪，而大多数人即使在高薪工作中也难以积累可观的财富。

大多数人认为技术人才在AI时代会通过适应和学习而增值，作者认为许多软件工程师感到他们的核心技能正在贬值，导致职业前景不明和深度职业倦怠。

大多数人认为AI技术要么是创造机会的积极力量，要么是威胁就业的消极因素，但作者认为AI同时扮演着双重矛盾角色——既是少数人的财富彩票，又是多数人职业安全的威胁。

大多数人认为科技行业虽有差距但总体向上，作者认为AI热潮中的结果差距是有史以来最严重的，因为只有极少数人获得巨额财富，而大多数人即使在高薪工作中也难以实现财务自由。

大多数人认为AI热潮带来了普遍的乐观情绪和机会，但作者认为即使在科技行业内，AI热潮的氛围也不佳，因为财富分配极不均衡，导致许多人感到焦虑和不满。

大多数人认为解决数学难题需要人类数学家的直觉、创造力和深度思考。但作者认为一个没有专门针对数学训练的通用AI模型能够独立解决长期存在的开放问题，这挑战了人类创造力在数学研究中的核心地位，暗示AI可能拥有类似人类的原创思维能力。

大多数人认为代数数论中的高级概念（如无限类域塔和Golod-Shafarevich理论）与欧几里得平面中的几何问题几乎没有关联。但作者认为这些代数数论工具竟然能应用于解决离散几何问题，揭示了数学领域之间意想不到的深刻联系，挑战了学科界限的传统认知。

大多数人认为解决复杂的数学问题需要专门训练的数学系统或针对特定问题的定制化AI模型。但作者认为一个通用推理模型就能解决离散几何中的核心问题，这挑战了AI在专业领域应用的常规认知，表明通用AI可能比专用系统更有突破性。

Can you provide a source for 'where most of the animal-suffering of conventional meat sits'? E.g., what share of chicken consumption is 'whole cut'? What share is on-bone? What about shrimp consumption globally -- whole vs ground up/paste.

See previous discussion. This actually makes it MORE interesting to study, and offers more potential for displacement, the case in which it seemed that only prior vegetarians were buying this stuff

this doesn't seem to follow from the previous sentence

Think about this more and state in a a more reasoned logical way. Note that we're largely thinking about price here (as well as taste, nutrition and availability). We're largely focused on the the impact of cost and price on consumption and substitution. In fact, skeptics were saying that "we don't care too much about substitution and price impacts because 1. it has such a low market share and 2. it's not taste or nutrition comparable."

Looks like circular reasoning here. I'm not sure that this 'finding' is meaningful. It might need rephrasing

At the end, it doesn't quite make sense. If all PBM buyers (or consumers) were previously vegetarians, then the displacement would be close to zero. If they all only ate meat or PBM and consumed the exact same amount of protein every day, the displacement would be 100%. So it's not the 'dual buyers' that makes displacement less than 100% per se. The question is to what extent consumers, whether vegetarians or omnivores, are buying PBM 'instead of meat' or 'instead of other vegetarian/vegan food'.

• Material in what sense? How much money? How many animal lives saved, animal pain-years avoided, etc.?

I'd say "evidence suggesting that it may be a binding constraint" ... people may report one thing, but actually something else could be fundamentally behind their decision, perhaps even something ~subliminal that they can't identify themselves.

skip this sentence

Give a comparison to some number for global conventional meat and seafood retail sales.

An important fact, but a little bit strange to mix in here. It really belongs in the section below. ... tit's not quantitative either

I would like to also see the statistics for the premium-quality plant-based beef in the U.S. - Impossible beef and Beyond beef. This would take cheaper (gardein etc) versions into account

do we have any reasonable evidence/good estimates on the PB share of each category (e.g., PB sausages/total sausages) in Germany, EU, or any important coungtries other than USA?

? Have you really checked all other countries? Are you saying the US is a leader as well as Germany? That doesn't comport with my casual empiricism.

provide hyperlinks

"materially" is a bit subjective; give numbers (other than patties). & the headline Should also report on the taste comparablility

skip This last bit, it's tpo AI, "not this but that" and the patties are a distraction.

Consider whether this is really overlapping the bit in italics just after the title.

"reformed categories" Is not clear here. Give an example number (other than patties)

"Shows" It is too strong. Maybe quotes suggest, but even then you're not really providing transparent reasoning here.

That's a big if - we need evidence. That is presumably the ceiling on the substitution.

This is too strong. It's a bit of an objective statement to say "non-trivial". The one-to-one substitution seems like a rather strong claim. You haven't presented much evidence about this. In fact, I'd like to see more evidence about the dual buyers and studies on the likelihood that this is attracting people who would otherwise be buying vegan or dairy products rather than animal products.

That's just a simple subtraction of 20% -11%? Because it's also possible that more than 9% stopped but some new users entered.

Are you sure about bratwurst? What's your source for this?

"headroom" is a strange word here - is there a better word, or should you define this in a tooltip?

This is high value. Put something about this in the overview.

What about the ladder? Do we have any data on the ladder that would be particularly interesting?

I'm puzzled why this is so much higher than Germany -- this should be investigated and discussed. 6% is surprisingly high, and I would expect Germany to be among the highest for European countries.

Leave the 'patty' figure out -- put that whole discussion in a tooltip. Add back some numbers about EU or German share of some other relevant categories like sausage (or what's the highest penetration category other than patties?)

6:30 transformational leadership style versus transactional leadership style

schwache führer gegen starke führer

unterschwellige manipulation versus klare befehle

7:22 aber gerade das wichtige, mit den Leuten zu reden, auf die Leute einzugehen, genau das muss ich mir halt dann irgendwann selbst erarbeiten -- das heißt also, du lernst zwar viel Theorie, die du auch in der Praxis anwenden kannst, du lernst aber auch Sachen nicht, die du eigentlich unbedingt brauchst. -- gerade das wichtigste lerne ich nicht. und das ist bitter, und das ist auch ein großer Kritikpunkt, der sich aber nicht so schnell ändern wird, weil es eine festgesteckte Lobby gibt, die den Studiengang in eine bestimmte Richtung treibt, und da ist es eben wirklich schwer gegen anzukommen.

BOOM! micdrop.

das ist doch der elefant im raum: theorie und praxis werden erstickt durch machtstrukturen, aber diese machtstrukturen wollen unsichtbar bleiben (soft power, scheinheilig, neoliberal, the hidden hand, geheimgesellschaften). also jeder der irgendwie für das system arbeitet ist befangen, abhängig, nicht neutral, biased, erpresst, "hat seine seele verkauft", ist unfrei, muss dem befehl von oben folgen, ist eingespannt in eine militärische hierarchie, der ist teil der mafia, teil der verschwörung, der ist mittäter bei einem organisierten verbrechen (gegen die natur, gegen die realität).

genau deswegen: fick die uni. genau deswegen: schule macht dumm. genau deswegen: einzelgänger und privatlehrer sind gefährlich für das system. genau deswegen: null kooperation mit dem system. genau deswegen: wir müssen uns selber helfen. genau deswegen: es gibt keine politische lösung.

Typewriter Video Series - Episode 207: Canon Typestar 5<br /> by [[Joe Van Cleave]]

Instruction manual shows use of the typewriter in someone's lap.

Doesn't have clean plastic at the top for ripping off pages cleanly.

The 5 is missing a few features that were found on the 4.

Nog een nadeel van de voorgestelde implementatie, we moeten het profiel van de Patient uitbreiden. Alsjeblieft niet doen!

deze beschrijving is onnodig complex door de implementatie-keuze om de datum van laatste betrokkenheid in een extensie op te slaan. Kunnen we deze beschrijving terug brengen naar een functionele specificatie, dat zal de beschrijving sterk vereenvoudigen.

Bij elk van deze punten zijn er ook tegenargumenten: - Elke wijziging van de datum leidt nu ook tot een **update ** van de patient resource, wat leidt tot erg veel extra versies van de Patient-resource. Dat terwijl database-queries (zeker voor IRIS) juist goedkoop zijn. - De tegenhanger van dat applicaties zichzelf ook met deze datum mogen bemoeien is dat elke applicatie nu ook bewust dat moet doen om te voorkomen dat die datum onbewust wordt overschreven of een onterechte waarde krijgt. Je kunt in mijn beleving niet allebei! Mijn advies is om alleen events te definiëren die de datum beïnvloeden, en op basis daarvan de datum af te leiden. - Voor mij is niet helder welke business-requirement hierom vraagt. Als er een wens is om extern te kunnen zien wat de datum van laatste betrokkenheid is kan die als een readonly-extensie worden toegevoegd bij het opvragen. Lijkt me goed om te bespreken!

Zie ook mijn commentaar op https://vzvznl.github.io/Koppeltaal-2.0-FHIR/opschoning-patient-data.html#startmoment-bewaartermijn-moet-eenduidig-zijn.

Tijdens ons vorige overleg is expliciet gezegd dat dit niet zo zou zijn.Maar als het wel gewenst moet expliciet worden gemaakt wat met "activiteit" wordt bedoeld.

temporary, passing, or staying only for a short time

a feeling of annoyance, embarrassment, or disappointment caused by a failure, mistake, or unexpected outcome

revisar hasta acá

Another part of the special cases:

High-End Camera and Lighting Kits are another special case. High-End Camera and Lighting kits are part of the reserve equipment, but they have special conditions. Patrons who use the high-end camera and lighting kits MUST go through the online and in person trainings. Access to these trainings can be found on the RMC website. Then, after completing the trainings, the patron can book their desired kit ahead of time through LibCal.

When a patron who has completed the trainings and booked a a high-end camera or lighting kit comes to the desk, the first thing you do is check out the kit like any other reserve equipment. Using LibCal, you click the box by their name, and then you click the large blue check out button at the bottom of the page.

But , 3D printing passes are a special case. You ALSO have to check them out in with a paper form. Paper forms can be found on the left hand side of the front RMC desk. Take a blank form from the center slot. Have the patron read the entire form and fill out the missing information. We must have their name, computing ID, kit number, and dated signature to circulate high-end equipment.

Then, alongside with the patron, go through the kit and make sure that ALL of the pieces of the equipment are in the kit. If there is a piece missing, make a note of it on the paper form AND fill out the equipment repair form. If all of the pieces are there, the patron should initial for the equipment. An RMC staff member will have to sign the equipment out paper form, then they are ready to go!

When checking high-end cameras and lighting kits back in, you must go through the same process. First, you open LibCal. Under the Check In tab, find the patron's name, press the checkbox by it, and click the large blue Check In button at the bottom of the page. After using LibCal, you must ALSO fill out the return section of the paper form. First, you go through the kit alongside the patron, just like before, and make sure that all of the pieces of the equipment are still there. If any pieces are missing, extend the patron's checkout, and allow them to go home and grab any pieces they forgot to bring. If any pieces are broken or they can't find it, fill out the equipment repair form and contact a supervisor immediately. An RMC staff member will have to sign the equipment back in on the paper form. Then the patron is all set!

Add section on "special cases"

3D Printing Passes The 3D printers are part of the reserve equipment, but they have special conditions. Patrons who use the 3D printing studio MUST go through the online and in person trainings. Access to these trainings can be found on the RMC website. Then, after completing the trainings, the patron can book the 3D printer ahead of time through LibCal.

When a patron who has completed the trainings and booked a 3D printer comes to the desk, the first thing you do is check out the 3D printing pass like any other reserve equipment. Using LibCal, you click the box by their name, and then you click the large blue check out button at the bottom of the page.

But , 3D printing passes are a special case. You ALSO have to check them out in Workflows. Find which printer they have booked on LibCal, and grab the printing pass that corresponds with the correct printer. Then you shift over to Workflows. Have the patron scan their ID, and then scan the barcode on the printing pass. Let the patron know when the printing pass is due back to the RMC desk.

When checking 3D printing passes back in, you must go through the same process. First, you open LibCal. Under the Check In tab, find the patron's name, press the checkbox by it, and click the large blue Check In button at the bottom of the page. After using LibCal, you must ALSO discharge the 3D printing pass in Workflows. Click the Discharge tab on the left hand side of the page, and scan the barcode on the 3D printing pass.

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

Learn more at Review Commons

Reply to the reviewers

1. General Statements

We thank the reviewers for their constructive evaluation of our manuscript. We are pleased by the overwhelmingly positive consensus regarding the quality and significance of our data. In particular, the reviewers highlighted that this is a "nice, clean study with interesting data" and noted that our in vivo functional genetic findings in the Drosophila wing are "clearly a strength" that "moves the paper beyond cell-culture correlations" to provide a "simple, straightforward take-home message".

The principal critique across the reports concerns the extent of direct mechanistic evidence linking Groucho (Gro) to regulation of the early elongation checkpoint. Several reviewers suggested additional genomic experiments, including RNA-seq, PRO-seq, or Pol II ChIP approaches, to further examine transcription and pausing behaviour. However, we would like to flag up that genomic datasets addressing these questions across multiple Drosophila cell lines have already been published previously, including work from our own group and others.

The primary objective of the current study is therefore not to replicate these existing genomic analyses, but rather to build directly upon them. We identify a consistent genomic association between Gro and pausing/elongation factors across cell types. Importantly, we extend these findings beyond genomic correlations through in vivo genetic analysis in the developing Drosophila wing.

1. Description of the planned revisions

• *

• *

Reviewer 1

The figures and text could lay out the logic of the genetic interactions for non-Drosophila readers. For example, the comparison of single and double copies of Gro-RNAi to combinatorial knockdowns, when it is additive, and when it is interpreted as synergistic.

The statistical analyses presented in Figure 5C, including Fisher’s exact tests comparing phenotype distributions between genotypes, were intended to address the distinction between additive and synergistic genetic interactions. However, we agree that the presentation of these comparisons could potentially be made clearer for readers less familiar with Drosophila genetic interaction assays. We would therefore be open to revising the presentation of Figure 5 and the accompanying explanatory text following editorial guidance and with consideration of the intended readership of the eventual journal.

The statistical analysis of the phenotype distributions should be shown more clearly (Fig. 5B).

Figure 5B is intended to present the distribution of observed phenotypic classes and does not include statistical comparisons. A similar analysis has been published for experiments looking at the phenotypes of moderate Groucho overexpression in the wing in the presence of HDAC inhibitors (Winkler et al., 2010 doi.10.1371/journal.pone.0010166). Statistical analyses of the genetic interaction experiments are presented separately in 5C. We therefore believe the current presentation of Figure 5B is appropriate for illustrating phenotype frequencies rather than statistical inference, but we will consider moving this panel to the Supplementary material.

Minor comments

-Figure 5 would gain clarity if the phenotype classes/panel letters were shown more clearly on the images. -The legends of the wing figures should be expanded, especially for readers outside the Drosophila field. -"in vivo" should be italicised consistently.

We agree that clearer labelling of phenotype classes, panel annotations and expanded figure legends could improve the accessibility of Figure 5, particularly for readers less familiar with Drosophila wing phenotypes and genetic interaction assays. We would therefore be open to revising the presentation of this figure and its accompanying legends in a future revised version.

We thank the reviewer for noting the typographical inconsistency of italics for in vivo. This will be corrected during manuscript revision and proofing.

__Reviewer #2 __

Reviewer #2 (Significance (Required)):

I think this is nice little paper providing a simple, straightforward take-home message. It does not conceptually shake the world, and the evidence consists of (nice) correlations, with no direct proof put forward for the conclusions. I am not a Drosophila geneticist but probably rather an 'expert' on basic transcription mechanisms. I think the data in the paper are of high quality, if limited in scope, and that the conclusions are supported by the results, but I do not think the results or conclusions will have a big audience. Having said that, I found it interesting to learn about this group of repressors and their likely mode of action.

On the other hand, it is worth emphasizing that proteins such as NELF and CDK9 would arguably be expected to be found at very many genes, as promoter-proximal pausing does exist at a plethora of genes, also genes that are house-keeping genes, ie not regulated by cell type or stimuli. So, lots of genes with pausing are not regulated by modulation of pausing. So, basically, the fact that knockdown of the repressor Groucho and loss of pausing is additive does not in my opinion necessarily mean that Groucho works by stabilizing pausing. Although it is admittedly a reasonably assumption, Groucho could also work by repressing transcription initiation; the genetic outcomes of 'double relief' would be the same, ie higher transcription levels. I think a brief comment to this effect might be appropriate, especially in the absence of (difficult to obtain) direct evidence that the transcription initiation step is not affected by Groucho.

While we agree that the current study does not directly exclude possible effects of Groucho on transcription initiation, previously published work has already provided evidence arguing against repression by Groucho occurring primarily through inhibition of transcription initiation or prevention of pre-initiation complex assembly. Groucho-bound transcriptional start sites were previously shown to retain RNAP II occupancy, active chromatin features, and detectable basal transcriptional activity despite repression (Kaul et al., 2014).

To acknowledge this possibility and explain why it is unlikely, we will add the sentence “While effects on transcription initiation cannot be completely excluded, previous work argues against Gro repressing transcription primarily through inhibition of transcription initiation. Gro-bound promoters remain accessible, overlap RNAP II occupancy, and retain active chromatin features and basal transcriptional activity” to the start of the third paragraph of the Discussion.

Reviewer #3

The methods section is lacking details on how ChIP-seq was performed in the BG3 cell line. The methods section does a good job of indicating how the data were processed. Information on the antibodies and conditions used is critical, as is whether spike-in controls were used.

The generation of the ChIP-seq data from BG3 cells has already been published. __We will add the line “The production of ChIP-seq datasets for Gro binding in Kc167, S2R+ and BG3 cells has been described elsewhere (Kaul, Schuster and Jennings, 2014; Bar-Cohen et al., 2023)” in the Analysis of ChIP-seq data subsection of the Methods. __

1. Description of analyses that authors prefer not to carry out

• *

__Reviewer #1 __ Major comments 1. The main weakness is the lack of a mechanistic link between Gro and the early elongation checkpoint. This is really the main point for this reviewer. The manuscript builds an interesting model, and the data support a functional connection between Gro and pausing-related factors, but the mechanistic link is absent. At present, the paper relies on co-localisation of ChIP peaks and genetic interaction in vivo. This is interesting and supportive, but with several possible interpretations. The title and some parts of the text are thus a bit stronger than what is directly demonstrated. Two possibilities could be proposed: either tone down the mechanistic claim or strengthen it experimentally. A more direct assay of pause release or productive elongation after Gro depletion at endogenous targets would be highly valuable. For example, Gro-KD followed by Pol II Ser2-P ChIP, or promoter vs. gene body analysis on Gro-bound genes, ideally comparing genes with Gro at TSS vs. not-TSS, would greatly support the proposed model. If the assay is established, this seems feasible in about 4 months.

We thank the reviewer for this thoughtful comment. We agree that the current study does not directly measure genome-wide RNAP II pause release following Gro depletion. However, several key observations linking Gro with promoter-proximal pausing have already been published and are summarised in the Introduction. Previous work demonstrated that Gro occupancy correlates with paused genes and that depletion of Gro reduces RNAP II pausing and increases elongating RNAP II at the endogenous E(spl)mbeta-HLH locus, an established target gene of Groucho-mediated repression (Kaul et al., 2014; doi.10.1371/journal.pgen.1004595). We also note that several of the experiments proposed by the reviewer have already been addressed in previous work. Specifically, Kaul et al. (2014) demonstrated that Gro depletion increases elongating RNAP II (Ser2-P) at the endogenous E(spl)mbeta-HLH locus while total promoter-associated RNAP II occupancy remains largely unchanged. Promoter versus gene body analyses in that study further supported a role for Gro in regulating progression through the early elongation checkpoint rather than transcription initiation.

The aim of the current manuscript was therefore to build upon these earlier mechanistic and genomic observations by asking whether the relationship between Gro and pausing-associated factors extends across multiple cell types and whether it has functional significance in vivo. By integrating comparative genomic analyses with sensitised developmental genetic assays in the wing, we provide evidence that Gro functionally interacts with multiple regulators of the early elongation checkpoint during development.

The bioinformatic part could be strengthened on "distinct TF repertoires" between cell types.The authors interpret the cell type-specific Gro recruitment as reflecting distinct transcription factor repertoires in BG3, Kc167 and S2R+ cells. This is interesting, but not really shown. To make this point more strongly, the author could provide a map of TF expression across different cell types, especially for the TFs corresponding to the enriched motifs they discuss. Otherwise, this remains speculative.In line, the manuscript discusses enriched motifs in BG3 and compares them to Kc167 and S2R+ cells, but this remains a bit descriptive. A clearer side-by-side comparison would strengthen the paper. This is particularly relevant to the motifs used in interpreting cell type-specific recruitment.

The interpretation that cell type-specific Gro recruitment reflects differences in transcription factor repertoires is based on several previously established observations already described in the manuscript. BG3 cells are derived from the larval CNS, whereas Kc167 and S2R+ cells are embryonic haemocyte-like lines (Cherbas et al., 2011; doi.10.1101/gr.112961.110). Transcriptomic analyses have further shown that these Drosophila cell lines maintain stable and distinct lineage-associated transcriptional identities, including differences in transcription factor expression (Cherbas et al., 2011). Given the diversity of transcription factors known to recruit Gro, the observed cell-type-specific binding patterns and motif enrichments are consistent with the distinct lineage-associated transcriptional programmes previously described for these cell lines.

1. Several overlap analyses could be discussed more in depth. A few statements feel too strong for the actual percentages. For example, the GAF overlap in BG3 is around 51% genome-wide and 56% at TSS, which is meaningful, but not especially high. The text already states that it is not universal, and this point could be discussed more clearly.

We note that the manuscript already explicitly states that overlap between Gro and GAF is not universal. Given the diversity of factors known to recruit Gro and the broad genomic distribution of GAF, we consider overlap frequencies of approximately 50% to represent a substantial association, particularly at transcription start sites. Importantly, the interpretation does not rely on complete co-occupancy between these factors, but rather on the observation that Gro-bound regions show significant enrichment for multiple factors associated with promoter-proximal pausing across different cell types.

Similarly, for the UpSet plot, the wording around the "most frequent" combination could be toned down, because this is not a dominant pattern.

The statement that the overlap between Gro, Nelf-E, GAF, Cdk9 and RNAP II represents the “most frequent” combination refers specifically to the relative frequency of the intersection categories within the UpSet analysis. In this context, the overlap between all five factors represents the largest intersection category identified (306 of 649 Gro peaks), with the next most frequent category containing substantially fewer peaks (90 of 649). We therefore feel that the current wording accurately describes the distribution observed in the analysis.

More generally, I think the manuscript needs a clearer quantitative breakdown of TSS versus non-TSS peaks for the overlap analyses with NELF, GAF, Cdk9 and CycT. Several interpretations depend on this distinction, and right now, this is not always clear enough.

The overlap analyses presented in Figure 3 explicitly distinguish between TSS and non-TSS peaks, and the corresponding quantitative overlap frequencies are described in the Results section. We do not consider that additional breakdowns are required for interpretation of the current data as this distinction is already incorporated into both the analyses and figure presentation.

The "enhancer chromatin" interpretation is interesting, but not fully integrated with the genomic distribution. The observation that Gro is enriched in open enhancer-type chromatin is interesting and supports the idea that Gro does not act mainly through classical repressed chromatin. However, Gro peaks are also enriched at promoters and introns, and this reviewer feels that the manuscript does not fully connect these observations. Where are these enhancer-type peaks located exactly? Are they often intronic? Can this be correlated with the distribution of Gro peaks? This would help the reader and also strengthen the discussion because intronic Gro peaks are present in the data, but are not well integrated into the model.

In the current manuscript, “enhancer chromatin” refers to chromatin states defined by combinations of enhancer-associated histone modifications, including H3K4me1, H3K27ac and H3K56ac as defined by Skalska et al.,2015 (doi.10.15252/embj.201489923), rather than exclusively to distal intergenic regulatory regions. As described in the chromatin-state analysis, these enhancer-associated chromatin signatures do occur at intronic regulatory regions, including regions classified as active intron chromatin. We therefore do not consider the enrichment of Gro peaks at promoters, enhancers and intronic regions to be mutually exclusive observations within this framework.

Intronic enhancer localisation is common in Drosophila, where the compact organisation of the genome results in many developmental regulatory elements residing within introns (Arnold et al., 2013; doi.10.1126/science.1232542). We therefore consider the presence of Gro peaks within intronic regions to be fully consistent with the observed enrichment of Gro binding within enhancer-associated chromatin states.

The in vivo part is a strength, but some important points need clarification.The in vivo section is a clear highlight of the manuscript. It gives functional relevance to the model and moves the paper beyond cell-culture correlations. That said, a few points need to be clearer:-RNAi efficiency is not clear for the tested genes, especially the pausing factors. This is important because the differential effects between NELF subunits could simply reflect differences in knockdown efficiency.

While differences in RNAi efficiency could potentially contribute to variation in phenotype strength between individual knockdowns, multiple biological explanations could also account for the differing effects observed between NELF subunits, including differences in protein stability, residual complex activity, or subunit-specific functions. Importantly, the central conclusion of the manuscript does not depend on quantitative comparison of phenotype strength between individual NELF components, but rather on the observation that perturbation of multiple pausing-associated factors genetically interacts with Gro in vivo.

If RNAi validation is possible with existing reagents, this seems realistic within 3 months.

The manuscript focuses on the genetic interactions observed between Gro and pausing-associated factors in vivo rather than on quantitative comparison between individual RNAi lines. As no specific validation experiments were proposed, we are not currently planning additional RNAi validation analyses for the present study.

The discussion could be expanded, especially because the mechanism is not fully shown.Since the direct mechanism is still missing, the discussion could compensate. Right now, the proposed model is interesting, but it still leaves many open questions. For example:-Is Gro affecting the recruitment or activity of elongation factors?-Could looping or enhancer-promoter communication contribute?-How should the intronic Gro peaks be interpreted in the model?-In the wing, could the phenotype be discussed more mechanistically, in light of what is already known about Gro and derepression of vein-promoting genes?For example, a model figure could help here.

We thank the reviewer for these thoughtful suggestions.

Several of the points raised by the reviewer are discussed in the manuscript already. For example, we discuss the possibility that Gro influences the activity or recruitment of elongation-associated factors. We agree that enhancer-promoter communication and chromatin looping are a plausible component of this mechanism. As the Drosophila genome is compact and intronic enhancers are highly prevalent, topological looping provides a clear physical framework for how Gro molecules distributed at non-TSS sites regulate promoter-proximal machinery. Indeed, we have previously published this model (Kaul, Schuster, and Jennings, 2015; see Figure 1C; doi.10.1080/21541264.2014.1000709). Our current in vivo and genomic findings build directly upon this model, suggesting that within these established looped configurations, Gro acts locally to interface with and stabilize the pausing machinery.

With respect to the wing phenotypes, the Discussion focuses primarily on the interpretation of the observed genetic interactions between Gro and pausing-associated factors rather than on defining the precise downstream target genes contributing to vein phenotypes. We agree that additional mechanistic dissection of these developmental phenotypes would be interesting. However, this would require a substantial expansion of the study into the detailed developmental and signalling mechanisms underlying vein specification, which lies beyond the primary focus of the current manuscript.

OPTIONAL: It would be interesting to know whether the same peak distribution / functional logic is observed in mammalian TLE orthologs. This is not essential for the current conclusions, but it would broaden the impact.

Determining whether similar genomic distributions and functional relationships are conserved for mammalian TLE orthologues will be an important future project. However, relatively little comparable genome-wide TLE occupancy data are currently available, meaning that such analyses would require a substantial independent undertaking beyond the scope of the present study.

Minor comments -Please explain why promoters were defined as {plus minus}250 bp from the TSS. This seems rather narrow.

Promoters were defined as ±250 bp from annotated transcription start sites. This window size is commonly used in Drosophila genomic studies, where the compact organisation of the genome means that broader windows frequently overlap adjacent genes.

-Please clarify why S2R+ cells are included in the comparative part but are not followed in the same way in some downstream analyses.

S2R+ cells were included in the comparative analyses to determine which aspects of Gro recruitment were shared across multiple cell types and which were cell-type specific. Some downstream analyses focused on BG3 and Kc167 cells because these lines had the most extensive corresponding datasets available for the chromatin and pausing-factor analyses performed in the current study.

__Reviewer #3 __ Here Martínez Quiles and Jennings investigate the role of the Groucho repressor in BG3 cells. This extends a previous study that used S2R+ cells, published previously by one of the authors, as well as Kc167 cells. They find that Gro is recruited to gene promoters in a cell-type-specific manner. Gro associates with open chromatin, is mostly associated with enhancer regions, and is primarily excluded from regions of the genome that are repressed by Polycomb. After studying its function in cell culture, the authors investigate the role of Gro in a wing-specific background. The findings here are mostly correlative, showing that loss of Gro results in stronger phenotypic defects when combined with loss of factors including NELF-B or NELF-D, LARP7, and bin3. They propose that Gro acts to attenuate gene expression during early gene expression. This claim would be greatly strengthened if the authors provided RNA-seq data in addition to the ChIP-seq data shown in this manuscript, especially to examine gene expression patterns among the different cell lines studied here. At present, this is a correlative study that does not illuminate the mechanism of Gro in directly regulating promoter-proximal pausing or RNA polymerase behavior.

We thank the reviewer for this suggestion. However, extensive transcriptomic analyses of Drosophila cell lines, including Kc167, S2R+ and BG3-derived lines, have already been published (Cherbas et al., 2011), together with RNA-seq analyses following Gro depletion (Kaul et al., 2014). In addition, the association between Gro occupancy and paused genes has also been reported previously (Kaul et al., 2014; Chambers et al., 2017; doi. 10.1186/s12864-017-3589-6).

While additional RNA-seq analyses could further characterise transcriptional differences between cell lines, RNA-seq alone would not directly determine whether altered transcript levels arise specifically through changes in promoter-proximal pausing, as opposed to effects on transcription initiation, transcript stability, or indirect downstream regulatory effects. We therefore do not consider additional RNA-seq analyses necessary to support the central conclusions of the present study.

Figure 2-3: For the ChIP-seq data, scale the y-axis in the same manner to better understand enrichment between the samples.

These ChIP-seq datasets were generated independently using different antibodies and experimental conditions, direct comparison of enrichment magnitudes across datasets would not be biologically meaningful. Accordingly, our analyses focus on significant peak calls and overlap relationships rather than relative signal intensity. Applying identical y-axis scaling across all tracks would obscure significant enrichment in several datasets and could therefore be misleading.

RNA-seq data between different cell lines would greatly enhance the authors findings or Pro-Seq to really show a relationship with Gro binding and promoter proximal pausing.

We note that RNA-seq datasets for Gro depletion in Kc167 and S2R+ cells have already been published previously (Kaul et al., 2014), together with evidence linking Gro occupancy to paused genes (Kaul et al., 2014; Chambers et al., 2017). We therefore do not consider that additional RNA-seq analysis would substantially strengthen the central conclusions of the current manuscript.

Moreover, RNA-seq alone cannot distinguish if altered transcript abundance reflects changes in promoter-proximal pausing from other mechanisms influencing transcript abundance. While PRO-seq approaches could provide further mechanistic information regarding RNAPII dynamics, such experiments are beyond the scope of the present study.

This study helps to further clarify how Gro binds DNA in different cell types and indicates that may intersect with factors involved in promoter proximal pausing. The study is highly correlative and would require additional work to show a mechanistic link between Gro and transcription attenuation due to promoter proximal pausing.

While we agree that PRO-seq approaches could provide additional mechanistic information regarding RNAPII dynamics, establishing an appropriate experimental and analytical framework for these analyses would require a substantial extension beyond the scope of the present study. In addition, several aspects of the relationship between Gro occupancy, transcriptional repression, and promoter-proximal pausing that underpin these suggestions have already been addressed in previously published work, including RNA-seq analyses following Gro depletion (Kaul et al., 2014), evidence linking Gro occupancy with paused genes (Kaul et al., 2014; Chambers et al., 2017), and studies demonstrating that Gro-mediated repression does not occur through inhibition of pre-initiation complex assembly. The current manuscript is therefore intended to build upon these existing findings by integrating comparative genomic analyses with new in vivo genetic interaction data.

• *

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Referee #3

Evidence, reproducibility and clarity

Here Martínez Quiles and Jennings investigate the role of the Groucho repressor in BG3 cells. This extends a previous study that used S2R+ cells, published previously by one of the authors, as well as Kc167 cells. They find that Gro is recruited to gene promoters in a cell-type-specific manner. Gro associates with open chromatin, is mostly associated with enhancer regions, and is primarily excluded from regions of the genome that are repressed by Polycomb. After studying its function in cell culture, the authors investigate the role of Gro in a wing-specific background. The findings here are mostly correlative, showing that loss of Gro results in stronger phenotypic defects when combined with loss of factors including NELF-B or NELF-D, LARP7, and bin3. They propose that Gro acts to attenuate gene expression during early gene expression. This claim would be greatly strengthened if the authors provided RNA-seq data in addition to the ChIP-seq data shown in this manuscript, especially to examine gene expression patterns among the different cell lines studied here. At present, this is a correlative study that does not illuminate the mechanism of Gro in directly regulating promoter-proximal pausing or RNA polymerase behavior.

Major comments:

Figure 2-3: For the ChIP-seq data, scale the y-axis in the same manner to better understand enrichment between the samples.

The methods section is lacking details on how ChIP-seq was performed in the BG3 cell line. The methods section does a good job of indicating how the data were processed. Information on the antibodies and conditions used is critical, as is whether spike-in controls were used.

RNA-seq data between different cell lines would greatly enhance the authors findings or Pro-Seq to really show a relationship with Gro binding and promoter proximal pausing.

Significance

This study helps to further clarify how Gro binds DNA in different cell types and indicates that may intersect with factors involved in promoter proximal pausing. The study is highly correlative and would require additional work to show a mechanistic link between Gro and transcription attenuation due to promoter proximal pausing.

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Referee #2

Evidence, reproducibility and clarity

This paper describes experiments designed to determine the mechanism of repression by the Groucho co-repressor in flies. The authors first characterize DNA binding by Groucho by ChIP-Seq analysis. This turns out to be consistent with recruitment driven by cell-type specific transcription factors. Nevertheless, its distribution across genomic features is similar across cell types, with enrichment in promoters and introns. It appears to bind in regions otherwise transcriptionally active (ie 'open chromatin'), rather than chromatin that is compacted and repressed. This suggest that Groucho regulates transcription through promoters or promoter-proximal pausing rather than by reducing chromatin accessibility. Groucho binding overlaps with NELF and GAF binding, seemingly consistent with a role in regulating pausing. However, Gro binding was also observed at promoters where P-TEFb components are detected, arguing against Gro repressing transcription P-TEFb exclusion from pausing sites. The authors next switched to investigating the consequences of Groucho kd and tested the idea that co-depletion of pausing factors might inform about the manner of gene repression, the idea being that if Groucho attenuates transcription by promoting or stabilizing promoter proximal pausing, then partial reduction of the pausing factors it affects should enhance the Groucho knock-down phenotype. Interestingly, simultaneous knock-down of Groucho and GAF resulted in enhanced patterning defects relative to Groucho knock-down alone, with the severity of the phenotypes resembling that observed upon increasing Groucho knock-down. Similarly, the knock-down of either Nelf-B or Nelf-D significantly enhanced Groucho phenotype. Finally, Kd of regulators of the pausing regulator CDK9 were tested. The 7SK snRNA complex inhibits CDK9, so any treatment leading to less 7SK will free CDK9 to positively affect pausing release. Larp kd fits that category as it directly leads to less 7SK and thus more CDK9 activity, while Bin3 kd results in less 5'-methyl capping, and thus more 7SK destabilization (less 7SK), again freeing CDK9 from inhibition - so, increasing pause release (like Nelf kd). Gratifyingly, this separate way of de-regulating/decreasing pausing again had an additive effect to Groucho depletion. Together, these genetic data thus overall support the idea that the (non-chromatin regulating) repressor Groucho works by stabilizing pausing complexes at specific genes.

Significance

I think this is nice little paper providing a simple, straightforward take-home message. It does not conceptually shake the world, and the evidence consists of (nice) correlations, with no direct proof put forward for the conclusions. I am not a Drosophila geneticist but probably rather an 'expert' on basic transcription mechanisms. I think the data in the paper are of high quality, if limited in scope, and that the conclusions are supported by the results, but I do not think the results or conclusions will have a big audience. Having said that, I found it interesting to learn about this group of repressors and their likely mode of action.

On the other hand, it is worth emphasizing that proteins such as NELF and CDK9 would arguably be expected to be found at very many genes, as promoter-proximal pausing does exist at a plethora of genes, also genes that are house-keeping genes, ie not regulated by cell type or stimuli. So, lots of genes with pausing are not regulated by modulation of pausing. So, basically, the fact that knockdown of the repressor Groucho and loss of pausing is additive does not in my opinion necessarily mean that Groucho works by stabilizing pausing. Although it is admittedly a reasonably assumption, Grouch could also work by repressing transcription initiation; the genetic outcomes of 'double relief' would be the same, ie higher transcription levels. I think a brief comment to this effect might be appropriate, especially in the absence of (difficult to obtain) direct evidence that the transcription initiation step is not affected by Groucho.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript entitled "the co-repressor Groucho limits progression through the early transcription elongation checkpoint in vivo", the authors study how the co-repressor Groucho (Gro) may repress transcription in Drosophila. They combine Gro ChIP-seq analysis in BG3 cells with published data from Kc167 and S2R+ cells, chromatin-state and overlap analyses with pausing/elongation factors, and functionally link these interactions in vivo by genetic interaction assays in the wing. The manuscript shows that Gro recruitment is largely cell type-specific, while Gro binding is detected as discrete peaks with similar genomic distribution across cell types. Gro peaks are enriched in open enhancer-type chromatin and overlap with factors linked to promoter-proximal pausing. In vivo, knock-down (KD) of several pausing-related factors enhances the gro RNAi phenotype in the wing. Overall, this is a nice, clean study with interesting data, and the in vivo findings are clearly a strength. However, the mechanistic link between Gro and the early elongation checkpoint remains unclear, and several bioinformatics and presentation points could be strengthened.

Major comments

1. The main weakness is the lack of a mechanistic link between Gro and the early elongation checkpoint. This is really the main point for this reviewer. The manuscript builds an interesting model, and the data support a functional connection between Gro and pausing-related factors, but the mechanistic link is absent. At present, the paper relies on co-localisation of ChIP peaks and genetic interaction in vivo. This is interesting and supportive, but with several possible interpretations. The title and some parts of the text are thus a bit stronger than what is directly demonstrated. Two possibilities could be proposed: either tone down the mechanistic claim or strengthen it experimentally. A more direct assay of pause release or productive elongation after Gro depletion at endogenous targets would be highly valuable. For example, Gro-KD followed by Pol II Ser2-P ChIP, or promoter vs. gene body analysis on Gro-bound genes, ideally comparing genes with Gro at TSS vs. not-TSS, would greatly support the proposed model. If the assay is established, this seems feasible in about 4 months.
2. The bioinformatic part could be strengthened on "distinct TF repertoires" between cell types. The authors interpret the cell type-specific Gro recruitment as reflecting distinct transcription factor repertoires in BG3, Kc167 and S2R+ cells. This is interesting, but not really shown. To make this point more strongly, the author could provide a map of TF expression across different cell types, especially for the TFs corresponding to the enriched motifs they discuss. Otherwise, this remains speculative. In line, the manuscript discusses enriched motifs in BG3 and compares them to Kc167 and S2R+ cells, but this remains a bit descriptive. A clearer side-by-side comparison would strengthen the paper. This is particularly relevant to the motifs used in interpreting cell type-specific recruitment.
3. Several overlap analyses could be discussed more in depth. A few statements feel too strong for the actual percentages. For example, the GAF overlap in BG3 is around 51% genome-wide and 56% at TSS, which is meaningful, but not especially high. The text already states that it is not universal, and this point could be discussed more clearly. Similarly, for the UpSet plot, the wording around the "most frequent" combination could be toned down, because this is not a dominant pattern. More generally, I think the manuscript needs a clearer quantitative breakdown of TSS versus non-TSS peaks for the overlap analyses with NELF, GAF, Cdk9 and CycT. Several interpretations depend on this distinction, and right now, this is not always clear enough.
4. The "enhancer chromatin" interpretation is interesting, but not fully integrated with the genomic distribution. The observation that Gro is enriched in open enhancer-type chromatin is interesting and supports the idea that Gro does not act mainly through classical repressed chromatin. However, Gro peaks are also enriched at promoters and introns, and this reviewer feels that the manuscript does not fully connect these observations. Where are these enhancer-type peaks located exactly? Are they often intronic? Can this be correlated with the distribution of Gro peaks? This would help the reader and also strengthen the discussion because intronic Gro peaks are present in the data, but are not well integrated into the model.
5. The in vivo part is a strength, but some important points need clarification. The in vivo section is a clear highlight of the manuscript. It gives functional relevance to the model and moves the paper beyond cell-culture correlations. That said, a few points need to be clearer:
   • RNAi efficiency is not clear for the tested genes, especially the pausing factors. This is important because the differential effects between NELF subunits could simply reflect differences in knockdown efficiency.
   • The figures and text could lay out the logic of the genetic interactions for non-Drosophila readers. For example, the comparison of single and double copies of Gro-RNAi to combinatorial knockdowns, when it is additive, and when it is interpreted as synergistic.
   • The statistical analysis of the phenotype distributions should be shown more clearly (Fig. 5B). If RNAi validation is possible with existing reagents, this seems realistic within 3 months.
6. The discussion could be expanded, especially because the mechanism is not fully shown. Since the direct mechanism is still missing, the discussion could compensate. Right now, the proposed model is interesting, but it still leaves many open questions. For example:
   • Is Gro affecting the recruitment or activity of elongation factors?
   • Could looping or enhancer-promoter communication contribute?
   • How should the intronic Gro peaks be interpreted in the model?
   • In the wing, could the phenotype be discussed more mechanistically, in light of what is already known about Gro and derepression of vein-promoting genes? For example, a model figure could help here.

OPTIONAL:

It would be interesting to know whether the same peak distribution / functional logic is observed in mammalian TLE orthologs. This is not essential for the current conclusions, but it would broaden the impact.

Minor comments

• Please explain why promoters were defined as {plus minus}250 bp from the TSS. This seems rather narrow.
• Please clarify why S2R+ cells are included in the comparative part but are not followed in the same way in some downstream analyses.
• Figure 5 would gain clarity if the phenotype classes/panel letters were shown more clearly on the images.
• The legends of the wing figures should be expanded, especially for readers outside the Drosophila field.
• "in vivo" should be italicised consistently.

Referee cross-commenting

My main concerns are broadly echoed by Reviewer 2, notably regarding the need to clarify the level of mechanistic support for the proposed model. Reviewer 3 also raises related points about the correlative nature of the evidence. Overall, I think the reports converge on the need to better align the conclusions with the current data, while recognising the value of the functional in vivo results, though with different levels of requested additional analysis.

Significance

General assessment

This is a nice paper, with clean data and an interesting model. The strongest point is the attempt to connect the Gro genomic localisation with functional interaction in a developmental context. The observation that Gro is found in open enhancer-type chromatin, together with the in vivo genetic interactions, makes the study significant. The main limitation is that the mechanistic link is still missing. Overall, this reviewer finds the study convincing as a functional and descriptive paper but less convincing as a mechanistic one.

Advance

The study extends previous work on Gro by comparing several cell types and by adding in vivo genetic data in the wing. The main advance is thus conceptual and functional: it supports the idea that Gro acts in concert with the pausing/elongation machinery rather than simply through repressed chromatin. However, the mechanistic advance remains limited because a direct link to the early elongation checkpoint has not yet been demonstrated. This is the main thing preventing the paper from being stronger.

Audience

This reviewer feels that the manuscript will mainly interest a specialised basic research audience: scientists working on transcriptional regulation, co-repressors, RNA polymerase II pausing, chromatin regulation, and Drosophila developmental genetics. It can also be relevant to those broadly interested in Gro/TLE biology.

Expertise

This reviewer's expertise includes gene regulation and its nuclear organisation, transcriptional/co-transcriptional and post-transcriptional regulations, transcription factors biology, and Drosophila genetics. This reviewer is comfortable evaluating the developmental genetics, the conceptual aspect, and the interpretation of genomic analyses, but has less competence in evaluating bioinformatic ChIP-seq processing pipelines.

Het opslaan van een afgeleide datum in de Patient-resource heeft nogal wat gevolgen, onder andere dat er heel veel versies van de Patient-resource zullen ontstaan. Ook moeten alle aangesloten applicaties nu expliciet logica toevoegen om om te gaan met deze extensie om te voorkomen dat deze wordt overschreven.

Het opnieuw bepalen van deze datum is minder complex vergeleken met alle logica die nodig is om bij de verschillende triggers steeds de Patient resource te updaten.

Kortom, ik vind dit geen goed idee.

Não eram totalmente iniciantes. Porém não confirma se já haviam feito a atividade em questão do sprint.

Alguns testes práticos que para avaliar o nível de overtraining (excesso de treino e falta de recuperação) individual.

crítica aos métodos existentes de medir a recuperação.

Replace

skip/replace

Skip

Skip

Remove or replace with something else

REmove this

We can remove this

We'd like to replace this with a "how to find equipment in the vault" video but I'd like to leave this as a helpful bonus video for students.

Need to update pic

Just need to update after we decide what's in this one.

Remove/update

Remove or update

I dont know this word

Christ is pictured as a hero who just endured a tough battle for humanity.

creates a sorrowful and mournful tone, emphasizing how tragic the Crucifixion was.

Reveals the speaker's strong commitment and faith towards the cross. The tree of victory symbolizes salvation and shows how the speaker sees the cross and hope

Emphasizes hope and salvation as a theme towards the end of the poem. Even after suffering and death, Christ still gives hope and promises happiness in heaven.

Uses a first person POV that makes the poem more personal and emotional.

Here, Christ is characterizes as a brave and strong hero rather than a weak person, The poem represents Jesus as like a warrior.

Connects directly to the Christian's belief in Jesus's crucifixion and resurrection. The poem emphasizes not only Christ's suffering, but also his victory over death.

This develops the theme of salvation and redemption. The poem suggests that faith and Christ's sacrifice are the path toward eternal life and heaven.

The vivid imagery helps readers pictures the cross changing between beauty and suffering. The contrast between the blood and treasure shows both the pain of the crucifixion and the glory connected to Christ's sacrifice

The author personifies the cross by giving it the ability to speak and tell its own story. This makes the crucifixion feel a lot more personal and readers hear directly form the cross that saw Christ's suffering.

Symbolizes both suffering and victory throughout the poem. Even though it was originally an object death and torture, it later becomes a symbol of salvation, hope, and faith

Reveals the sorrowful and emotional tone of the poem. The speaker feels fear and sadness while witnessing the suffering connected to the cross, emphasizing the seriousness of Christ's sacrifice.

代码本身无法直接推断动态变量old和val会不相同，因此必须加一句DEPENDENCE告诉HLS一定不会发生先写后读导致读到数据过期的情况，所以II可以是1，否则II必须为2

@federico.canosa Please, put this on the assingment

https://bafybeid6iaeij4fy4zx5cg4q6olrgzxmmxqcytb7gxwwhzupo6wwta5v4a.ipfs.dweb.link?filename=Introduction%EF%BC%9A%20Brewster%20Kahle%20%E2%80%93%20Locking%20the%20Web%20Open%20%E2%80%93%20a%20Call%20for%20a%20New%2C%20Decentralized%20Web%20%EF%BC%9A%20Free%20Download%2C%20Borrow%2C%20and%20Streaming%20%EF%BC%9A%20Internet%20Archive%20(5_21_2026%202%EF%BC%9A37%EF%BC%9A57%20PM).html

vieo

video-DWeb Summit, Lock the Web Open-2016

/💻/thinkpad/🧊/me/📓/2026/5/2

indy.dweb.archive-search?q=khale+locking+the+web+opens@google

Companies are facing legal action for claiming their operations are 'carbon neutral'. This proves that green marketing misconduct has serious legal implications.

The tactic of using words such as 'sustainable aviation' and 'lowest emission' without solid evidence is an element of greenwashing that clearly aims to build a false 'green' image.

This is a legal and ethical risk (legal risk). The authorities (ASA) banned this airline's advertisement for manipulating environmental sustainability messages which mislead consumers.

2.45$

test

期望定义式

期望在Y取随机变量而非某个值时，也变成了随机变量

非负定矩阵也称半正定矩阵，positive semi-definite matrix，缩写 PSD 即一个对称矩阵A，对于任意向量有\(\mathbf{x}^T A \mathbf{x} \geq 0\)

其中严格大于0即为正定矩阵PD 严格定义中写为A是n×n实对称矩阵

eLife Assessment

The revised version of the paper demonstrates that a genetic code expansion to tag two ALS proteins associated with stress granules is useful and convincingly evaluates the utility of the genetic code expansion in this context. The data is solid and demonstrates the feasibility of using ANAP-fluorescence for live cell imaging.

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells and primary mouse cortical neurons. By incorporating the intrinsically fluorescent noncanonical amino acid Anap at carefully selected sites, the authors enable live-cell and neuronal visualization of protein localization, stress-induced redistribution, and dynamic behavior without the structural and functional compromises often associated with large fluorescent protein tags. The work provides technical framework that will be useful for live imaging of tagged proteins.

Strengths:

A key strength is the demonstration of the specificity of the Anap fluorescence signal through appropriate controls and the agreement between Anap labeling and antibody-based detection across multiple cell types, including primary neurons. The ability to visualize stress-induced redistribution of both G3BP1 and TDP 43 in living cells highlights the practical value of this approach.<br /> The functional validation of TDP 43-Anap is compelling. The rescue of both cell viability and RNA splicing defects in TDP 43 knockout models provides evidence that Anap incorporation preserves core protein functions. This is important, as functional disruption is a central concern for any alternative tagging strategy applied to aggregation-prone or RNA-binding proteins.

Weaknesses:

While some inherent limitations of genetic code expansion remain (e.g., variable amber suppression efficiency and the inability to directly assess endogenous protein behavior), these are acknowledged and discussed appropriately. Importantly, these limitations do not undermine the central contributions of the study.

Reviewer #2 (Public review):

Summary:

In this manuscript, Chen and colleagues describe a novel means of labeling two RNA binding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically-fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP-43 was observed in cultures of mouse primary neurons.

Genetic code expansion and non-canonical amino acid labeling have many advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap, and follow labeled proteins in living cells with and without stress.

I suspect that this method could be incredibly valuable to many investigators studying the dynamics and interactions of proteins that are difficult to label or detect by conventional methods.

Author response:

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

eLife Assessment

Amyotrophic lateral sclerosis (ALS) affects nerve cells in the brain and spinal cord. The authors' approach to use genetic code expansion to tag two ALS proteins associated with stress granules has value and should be useful in the ALS field. Parts of the work are well done, but there are concerns that the evidence is incomplete overall, and additional controls would strengthen the study.

We thank the editors and reviewers for their thoughtful assessment and for highlighting the potential value of applying genetic code expansion (GCE) to study ALSassociated proteins involved in stress granule biology. Our goal in this work was to establish and validate a minimally perturbative labeling strategy using the noncanonical amino acid Anap to monitor the localization and stress-dependent behavior of TDP-43 and G3BP1.

We agree that additional controls can further strengthen the conclusions. In the revised manuscript, we have clarified the experimental design and added essential controls to better support the reliability of the Anap labeling approach (Supplementary Fig. 1).

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies, and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons. This is a useful study that demonstrates the utility of ANAP tagging to evaluate ALS proteins.

We sincerely thank the reviewer for the positive assessment of our work and for recognizing the utility of the Anap-based GCE system for studying ALS-associated proteins.

Strengths:

Rescue of cell survival by ANAP-tagged TDP-43 is compelling

We appreciate the reviewer’s highlighting of this point. Demonstrating that TDP43-Anap can rescue cell survival was an important validation in our study, as it indicates that incorporation of the noncanonical amino acid does not substantially disrupt the biological function of TDP-43. Additionally, we also tested the RNA splicing function recovery potency of TDP-43-Anap. As shown in Fig. 1K and 1L, a recovery of expression of PFKP, a protein undergoing cryptic exon when TDP-43 lost its function [1], was observed when expressing TDP-43-Anap in TDP-43 knockout Hela cells.

Weaknesses:

While the ANAP-tagged proteins had similar distributions to antibody staining, there were some discrepancies that may be more explained by the technique than by novel findings, as the authors suggested. The inclusion of additional controls to evaluate this would be helpful.

This is a helpful suggestion. To ensure that the fluorescence signal observed in our experiments was specifically derived from site-specific Anap incorporation rather than background fluorescence, we performed three control conditions. Specifically, we tested: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. These control experiments were performed for both TDP-43 and G3BP1, and no observable fluorescence signal was detected under any of these conditions (Supplementary Fig. 1). We have clarified this control experiment in the revised manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, Chen and colleagues describe a novel means of labeling two RNAbinding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP43 were observed in cultures of mouse primary neurons.

We are grateful for the reviewer’s accurate summary of our study and recognition of the value of GCE strategy for labeling the RNA-binding proteins G3BP1 and TDP-43.

Strengths:

Genetic code expansion and non-canonical amino acid labeling have quite a few advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap and follow labeled proteins in living cells with and without stress.

We acknowledge the reviewer’s comment on the advantages of GCE-based noncanonical amino acid labeling for studying protein dynamics in living cells.

Weaknesses:

The authors do not convincingly leverage the advantages of genetic code expansion in the current study. There is no specific question posed by the authors that can be or is answered using this approach, and several of the experiments lack critical controls. This is also not the first example of TDP-43 labeling by genetic code expansion (see PMID: 38290242). As a result, the study as a whole adds little to our understanding of protein trafficking and behavior under stress.

We thank the reviewer for raising these important points. Although as reviewer mentioned, genetic code expansion has previously been applied to TDP-43 [2], it mainly employed the photocaged lysine incorporation system to optogenetic control of TDP-43 translocation, and the protein was still labeled by mRubby. Our paper has totally different goal, to establish and validate a minimally perturbative labeling strategy using the intrinsically fluorescent noncanonical amino acid Anap to monitor the localization and stress-dependent behavior of both TDP-43 and G3BP1. And our work extends this approach in several important ways.

First, we demonstrate that Anap incorporation enables visualization of stress-dependent redistribution of both TDP-43 and G3BP1, two key proteins involved in stress granule biology. Importantly, we validate this approach across multiple cellular systems, including HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons, which broadens the applicability of this labeling strategy.

Second, we provide functional validation of the Anap-tagged protein, showing that TDP43-Anap rescues both cell survival and RNA splicing activity in TDP-43 knockout cells, including restoration of PFKP expression, a known cryptic exon target of TDP-43. These results support that Anap incorporation does not substantially disrupt protein function.

We performed additional control experiments to ensure the specificity of the labeling system. Specifically, we tested three control conditions: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. These control experiments were performed for both TDP-43 and G3BP1, and no observable fluorescence signal was detected under any of these conditions (Supplementary Fig. 1).

We agree that the manuscript would benefit from clearer articulation of the advantages of genetic code expansion in this context. Accordingly, we have revised the manuscript to more explicitly emphasize how Anap labeling provides a minimally perturbative alternative to large fluorescent protein fusions, which can alter the phase behavior and localization of stress granule proteins.

“Conventional fluorescent protein tags have enabled visualization of TDP-43 and G3BP1 in living cells; however, these approaches can perturb the native biophysical properties of the proteins being studied. For example, GFP or other fluorescently tagged TDP-43 usually requires additional modifications, such as deletion of the nuclear localization signal (NLS) [3, 4], to induce cytoplasmic inclusion formation. Such manipulations introduce non-physiological conditions that may alter the native trafficking and aggregation behavior of TDP-43. As for G3BP1, tags like GFP may also cause unexpected effects on the phase separation or other dynamics of the protein. In contrast, Anap based GCE strategy allows the minimally perturbative labeling and visualization of protein localization and stress-induced redistribution while preserving native protein architecture and function of both proteins. Importantly, the approach provides a generalizable genetically encoded platform for quantitatively examining the behavior of ALS-associated proteins in living cells. By enabling faithful monitoring of protein trafficking and stressgranule dynamics without extensive protein engineering, Anap-based GCE can offer a powerful strategy for probing molecular-scale mechanisms underlying ALS-linked proteinopathies”.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1A

The authors report that the nuclear staining of G3BP1 by ANAP labeling shows the presence of nuclear pools of G3BP1 that aren't detected with antibody staining. However, unspecific nuclear staining by aminoacylated tRNAs bound to synthetases has been described. It would be important to have a control to evaluate for this possibility.

This is an important point. We agree that the nuclear ANAP signal should be carefully controlled to exclude the possibility of nonspecific staining arising from the Anap incorporation machinery itself, such as aminoacylated tRNAs and/or synthetases.

To address this concern, in methods and material part, we note that after DPBS washes to remove excess Anap, cells were incubated in fresh medium for 2 hours to allow sufficient time for the decay of unstable aminoacylated tRNAs, which are generally cleared within minutes to tens of munites [5].

Also, we performed three control conditions for both TDP-43 and G3BP1: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. Under all three conditions, we observed no detectable fluorescence signal (Supplementary Fig. 1).

In addition, as shown in Fig. 1I, the nuclear signal of G3BP1-Anap partially colocalizes with the nuclear signal of TIA-1 in several condensate-like structures. This observation further supports that the nuclear Anap signal reflects protein-associated localization rather than nonspecific fluorescence, as it overlaps with a known RNA-binding protein that can form nuclear condensates under certain conditions.

(2) Figure 1A, 1B

Anap labeling appears to stain fewer cytoplasmic structures compared to antibody staining for both G3BP1 and TDP-43 after sodium arsenite treatment. Quantification would be useful to address whether this is the case. If so, might this be due to unincorporated/truncated proteins competing with Anap-labeled proteins?

We appreciate the reviewer’s helpful suggestion. To address this point, we performed quantitative colocalization analysis using Fiji/ImageJ, calculating the Pearson correlation coefficient (R) for regions of interest between the Anap signal and antibody staining. These analyses indicate a strong overall agreement between the two detection methods under stress conditions, supporting that Anap labeling reliably reports the localization of both G3BP1 and TDP-43 (see Fig1. A, B).

Regarding the possibility that truncated or unincorporated proteins could influence the observed signal, we note that fluorescence from Anap depends on successful amber suppression and incorporation of Anap at the engineered TAG site. Proteins that fail to incorporate Anap, such as truncated products generated by premature termination, would not produce fluorescence, and therefore would not contribute to the Anap signal. Thus, the Anap fluorescence selectively reports the population of successfully labeled full-length proteins, whereas antibody staining detects both labeled and unlabeled protein pools. This difference may partially explain why antibody staining appears to label a larger number of cytoplasmic structures.

(3) Figure 1F

FRAP of G3BP1-GFP in stress granules is slower than in previous publications. The underlying reasons for this should also be addressed.

We thank the reviewer for this important observation. Differences in FRAP recovery kinetics of G3BP1 in stress granules may arise from several experimental variables that are known to influence stress granule dynamics. These include differences in cell type, expression levels of G3BP1-GFP, and imaging or photobleaching parameters. In our experiments, FRAP measurements were performed under specific conditions optimized for our experimental system, which may lead to recovery kinetics that differ from those reported in previous studies.

(4) Figure 1H

A full-size Western blot would be useful to evaluate for amount of truncated protein for G3BP1 and TDP-43. Could truncated proteins be competing with and altering ANAPtagged G3BP1 and TDP-43 localization in response to stress? This should be addressed.

We acknowledge this important point. Full-size Western blotting can provide information on the overall presence of truncated species in the transfected population; however, it represents a bulk measurement and does not capture cell-to-cell variability in amber suppression efficiency at the single-cell level. We therefore cannot exclude the possibility that truncated products are present at varying levels in individual cells and may contribute, directly or indirectly, to differences between antibody staining and Anap fluorescence.

Importantly, we observe that cells with successful Anap incorporation consistently exhibit strong antibody staining for TDP-43 or G3BP1, indicating that full-length protein is the predominant species in these cells. Because Anap fluorescence depends on successful amber suppression, it selectively reports the full-length protein population, whereas truncated products are not detected in the imaging assay. The concordance between Anap fluorescence and antibody staining therefore argues against a major contribution of truncated species to the observed localization patterns (Supplementary Fig. 1).

Accordingly, we interpret the Anap signal as reflecting the localization of successfully labeled full-length protein, while acknowledging that heterogeneity in suppression efficiency is an important limitation of the current approach.

(5) Figure 3

This is a well-designed diagram.

We are grateful for the reviewer’s positive feedback on the diagram and are pleased that the schematic effectively illustrates the experimental design and the principles of the genetic code expansion strategy used in this study.

Reviewer #2 (Recommendations for the authors):

The authors present a one-sided viewpoint concerning the connection between stress granules and disease (lines 45-46). A more balanced discussion is recommended, including data arguing against a role for abnormal stress granules in neurodegeneration.

This is an important suggestion. We agree that the relationship between stress granules and neurodegeneration remains an active area of investigation and that evidence both supporting and questioning a causal role of stress granules in disease has been reported. In the revised manuscript, we have modified the Introduction to provide a more balanced discussion of this topic.

“Altered stress-granule dynamics have been associated with ALS/FTD [6, 7]; however, whether stress granules directly drive neurodegeneration remains debated, as several studies suggest that stress granules primarily function as protective stress responses [8].”

(1) A central rationale for the study is missing. The authors state only that G3BP1 and TDP-43 'undergo dynamic stress-dependent redistribution, making them ideal candidates for minimally invasive, site-specific fluorescent labeling.' Is there a controversy or question that can be resolved using these approaches?

We thank the reviewer for raising this important point. The central motivation of this study is that the dynamic behavior and phase separation properties of stressgranule proteins are highly sensitive to protein modifications and tagging strategies.

“Conventional fluorescent protein tags have enabled visualization of TDP-43 and G3BP1 in living cells; however, these approaches can perturb the native biophysical properties of the proteins being studied. For example, GFP or other fluorescently tagged TDP-43 usually requires additional modifications, such as deletion of the nuclear localization signal (NLS) [3, 4], to induce cytoplasmic inclusion formation. Such manipulations introduce non-physiological conditions that may alter the native trafficking and aggregation behavior of TDP-43. As for G3BP1, tags like GFP may also cause unexpected effects on the phase separation or other dynamics of the protein.”

(2) Related to this, there is little context for how or why genetic code expansion is utilized for these studies

We agree that the rationale for using genetic code expansion should be more clearly explained. In this study, genetic code expansion was employed to enable sitespecific incorporation of the small fluorescent noncanonical amino acid Anap, allowing minimally perturbative labeling of proteins of interest.

“Anap based GCE strategy allows the minimally perturbative labeling and visualization of protein localization and stress-induced redistribution while preserving native protein architecture and function of both proteins. Importantly, the approach provides a generalizable genetically encoded platform for quantitatively examining the behavior of ALS-associated proteins in living cells. By enabling faithful monitoring of protein trafficking and stress-granule dynamics without extensive protein engineering, Anapbased GCE can offer a powerful strategy for probing molecular-scale mechanisms underlying ALS-linked proteinopathies.”

(3) The justification for the criteria for selecting the site for incorporation of non-canonical amino acids in G3BP1 or TDP-43 is missing.

We acknowledge this important comment and agree that the rationale for selecting the incorporation sites should be stated more clearly.

“For TDP-43, the incorporation site was selected to avoid the major functional domains involved in RNA binding, nuclear localization, and aggregation-related behavior, thereby reducing the likelihood that Anap incorporation would perturb its native trafficking or function. For G3BP1, the selected site was chosen to minimize interference with domains important for stress granule assembly, RNA binding, and protein-protein interactions. More generally, we aimed to place the ncAA at positions likely to be solventaccessible and tolerant of substitution, while avoiding highly conserved or functionally essential residues.”

(4) Studies in Figures 1 and 2 lack essential controls, including background signal from Anap in non-transfected cells, or those transfected with plasmids lacking the tRNA or tRS.

This is an important point, also raised by Reviewer 1. To evaluate potential background fluorescence arising from Anap or the labeling system, we performed several control experiments. Specifically, we examined three conditions: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. Under all three conditions, we observed no detectable fluorescence signal (Supplementary Fig. 1).

(5) Another marker of stress granules should be used for confirming the identity of G3BP1-Anap (+) or TDP-43-Anap (+) structures, including TIA1, TAF15, or polyA RNA.

We appreciate this helpful suggestion. To further confirm the identity of the stress granule structures observed in our experiments, we performed colocalization analysis with TIA-1, a well-established marker of stress granules. The results have been included in revised manuscript.

“Additionally, we examined the colocalization of G3BP1-Anap with TIA-1, another established stress granule marker. Under stress conditions, G3BP1-Anap largely colocalized with TIA-1 within stress granules. Interestingly, under basal conditions, the nuclear signal of G3BP1-Anap, which was not detected by antibody staining, appeared to partially colocalize with TIA-1 in several condensate-like structures. (Fig. 1I).”

(6) There is no information on the number of granules bleached or the number of cells selected for FRAP studies. There is no information on the shaded areas in Figure 1F or 1G, and no information on statistical comparisons between regressions in Figure 1F.

We thank the reviewer for pointing out these omissions. We have revised the figure legends to clarify these details.

“One granule from each of three independent cells was selected and photobleached for FRAP analysis.”

“Here, error bars with filled area are used for better data presentation. FRAP recovery curves were compared using two-way ANOVA.”

(7) Protein dynamics measured by FRAP are highly dependent on the concentration and/or expression level of each protein. Because of this, the authors need to control for expression level in all FRAP studies.

We agree that protein concentration and expression level can influence FRAP recovery kinetics. Since Anap incorporation is based on amber suppression, and the suppression rate in each cell varies, so it is difficult to control the expression of Anap labeled proteins, however, to minimize this potential effect, we performed FRAP measurements on cells exhibiting comparable fluorescence intensities, which served as a proxy for similar expression levels of the labeled proteins. In addition, FRAP analyses were conducted on individual granules within cells expressing moderate levels of the protein, avoiding cells with unusually high fluorescence intensity that might reflect overexpression.

Furthermore, fluorescence recovery was normalized to the pre-bleach intensity of the selected granules, which reduces variability arising from differences in overall expression levels between cells.

(8) There is no point of reference for TDP-43-Anap FRAP results in Figure 1G. Additional studies using variants harboring a mutated NLS (mNLS) can be used in place of TDP43-YFP.

This is a helpful suggestion. In response, we have performed additional FRAP experiments using TDP-43^ΔNLS, a commonly used construct that promotes cytoplasmic localization and facilitates analysis of TDP-43 granules. The results from TDP-43^ΔNLS have now been included as a reference for the FRAP measurements of TDP-43-Anap in the revised manuscript (Fig. 1D, 1G).

“We then used YFP-tagged nuclear localization signal (NLS)-deleted TDP-43 (TDP43^ΔNLS-YFP) as a reference and performed FRAP analysis to compare the mobility of TDP-43-Anap and TDP-43^ΔNLS-YFP. Fluorescence recovery of TDP-43-Anap reached ~45% within 20 s after photobleaching, consistent with liquid-like dynamics. In contrast, TDP-43^ΔNLS-YFP showed only ~22% recovery, suggesting more solid-like dynamics (Fig. 1D, 1G). These results are consistent with previous reports describing relatively immobile aggregates formed by TDP-43^ΔNLS4and illustrate the advantage of Anap-based labeling, which preserves native protein properties and enables real-time assessment of protein dynamics without introducing disruptive mutations.”

(9) There is no point of reference for comparing FRAP results from G3BP1-GFP to G3BP1-Anap. What is the 'gold standard'? Without this, it is difficult to conclude that "... Anap labeling better preserved the native mobility and biophysical properties of G3BP1 than the conventional GFP tag."

We acknowledge this important point and agree that there is currently no definitive gold standard for measuring the native mobility of endogenous G3BP1 within stress granules in living cells. Our intention was not to claim that the Anap-labeled protein definitively represents the native state, but rather to compare the relative effects of different labeling strategies.

Thus, we rewrite the sentence as “These results suggest that G3BP1-Anap displays higher mobility compared with G3BP1-GFP, indicating that Anap labeling may provide a less perturbative approach for monitoring G3BP1 dynamics.”

(10) The WB in Figure 1H is overexposed, making it difficult to compare expression levels between WT and V100Anap-transfected cells. In addition, there is no similar assay for confirming G3BP1-Anap expression.

Thank you for pointing this out. In the revised manuscript, we have replaced the image with a properly exposed Western blot to allow clearer comparison of protein expression levels.

In addition, we have now included a corresponding western blot analysis to confirm the expression of G3BP1-Anap in G3BP knockout U2OS cell (Fig. 1H). These results verify that the Anap-labeled proteins are expressed at detectable levels and support the interpretation of the imaging and FRAP experiments.

(11) Although survival studies in Figures 1I and J are promising, a more convincing demonstration of functional replacement of TDP-43 would involve an assessment of cryptic exon splicing, comparing WT to TDP-43 KO, V100Stop- and V100Anaptransfected cells.

This is a valuable suggestion.

“We also evaluated TDP-43-dependent RNA splicing activity by examining the expression of PFKP, a well-established target that undergoes cryptic exon inclusion upon loss of TDP-43 function17. As shown in Figures 1K and 1L, expression of TDP-43Anap in TDP-43 knockout HeLa cells restored PFKP expression, indicating that the Anap-labeled protein retains functional RNA splicing activity. These results demonstrate that TDP-43-Anap is capable of functionally compensating for endogenous TDP-43, supporting that the incorporation of Anap does not substantially disrupt the protein’s biological function.”

(12) Tuj1 staining in Figure 2 is inconsistent and often fails to confirm neuronal identity.

We thank the reviewer for this important comment. We acknowledge that Tuj1 staining in Figure 2 is variable and, in some cases, does not clearly delineate neuronal identity. Notably, the reduced Tuj1 signal is primarily observed in neurons that express Anap-labeled proteins under sodium arsenite treatment, which likely reflects the combined effects of transfection-associated stress and oxidative stress on neuronal morphology and cytoskeletal integrity.

In addition, transfection efficiency in primary neurons is inherently low and variable, and cells that successfully express the constructs may represent a more stress-sensitive subpopulation, further contributing to variability in staining quality. Despite optimization efforts, these technical constraints limit the consistency of Tuj1 labeling under these experimental conditions.

(13) Close-up images and correlation scatter plots in Figures 1 and 2 do not add very much information.

We thank the reviewer for this comment. To address the reviewer’s concern, we have revised the figure legends to better clarify the purpose of these panels and how they support the quantitative analysis presented in the manuscript.

For scatter plot, “Colocalization threshold analysis was performed in Fiji/ImageJ to calculate the Pearson correlation coefficient (R) for each region of interest (A, B, I, J). The X- and Y-axes represent the fluorescence intensity values of the red and green channels, respectively. When signals are colocalized, pixels with high intensity in one channel correspond to high intensity in the other, forming a diagonal distribution. In contrast, non-colocalized signals cluster along the axes. A higher R value indicates a greater degree of colocalization. Scale bar, 3 μm.”

Same information was added to figure legend of figure 2.

For the scheme, please see line 412-413 in the revised manuscript.

Reference:

(1) Rothstein, J.D. et al. Sporadic ALS induced pluripotent stem cell derived neurons reveal hallmarks of TDP-43 loss of function. Nature Communications 16, 7092 (2025).

(2) Shadish, J.A. & Lee, J.C. Genetically encoded lysine photocage for spatiotemporal control of TDP-43 nuclear import. Biophys Chem 307, 107191 (2024).

(3) Gasset-Rosa, F. et al. Cytoplasmic TDP-43 De-mixing Independent of Stress Granules Drives Inhibition of Nuclear Import, Loss of Nuclear TDP-43, and Cell Death. Neuron 102, 339–357.e337 (2019).

(4) Yan, X. et al. Intra-condensate demixing of TDP-43 inside stress granules generates pathological aggregates. Cell 188, 4123–4140.e4118 (2025).

(5) Walker, S.E. & Fredrick, K. Preparation and evaluation of acylated tRNAs. Methods 44, 81–86 (2008).

(6) Kassouf, T. et al. Targeting the NEDP1 enzyme to ameliorate ALS phenotypes through stress granule disassembly. Science Advances 9, eabq7585 (2023).

(7) Van Nerom, M. et al. C9orf72-linked arginine-rich dipeptide repeats aggravate pathological phase separation of G3BP1. Proceedings of the National Academy of Sciences 121, e2402847121 (2024).

(8) Wolozin, B. & Ivanov, P. Stress granules and neurodegeneration. Nat Rev Neurosci 20, 649–666 (2019).

eLife Assessment

This important study identifies a physiologically relevant interaction between LRRK2 and drebrin, an actin-binding protein crucial for neuronal structure. A solid body of evidence, including multiple cell models, highlights the complexities of how modifiers like BDNF intersect with LRRK2-kinase dependent function, and that many modifiers between AKT and LRRK2 in different cellular pathways are yet to be identified and understood.

Reviewer #1 (Public review):

Summary:

LRRK2 protein is familially linked to Parkinson's disease by the presence of several gene variants that confer a gain-of-function effect on LRRK2 kinase activity.

The authors examine the effects of BDNF stimulation in immortalized neuron-like cells, cultured mouse primary neurons, hIPSC-derived neurons, and brain tissue from genetically modified mice. They examine a LRRK2 regulatory phosphorylation residue, LRRK2 binding relationships, other kinase phosphorylation status, and measures of synaptic structure and function.

Strengths:

The study addresses an important research question: how does a PD-linked protein interact with other proteins, and contribute to responses to a well-characterized neuronal signalling pathway involved in the regulation of synaptic function and cell health.

They employ a range of models and techniques to convincingly demonstrate that BDNF stimulation alters LRRK2 phosphorylation at pS935 and binding to many proteins. Several independent data sets lead to some exciting conclusions.<br /> In this re-revised manuscript, some aspects are very convincing and well validated e.g., drebrin binding to LRRK2, increased by BDNF, and reduced LRRK2 protein levels in young (but not mature) drebrin KO mice. A phosphoproteomic analysis of PD mutant Knock-in mouse brain is included. Overall, the links between LRRK2, LRRK2 activity, and the changes to synaptic molecules, structures, and activity are intriguing.

Weaknesses:

Enthusiasm for the title claim that "LRRK2 regulates synaptic function through BDNF signalling" is tempered by disconnected results across different model systems and inconsistent alterations upon kinase phosphorylation in SHSY5Y cell line and primary neurons. Exciting conclusions are sometimes not consistently supported by the data and/or only conducted in one of the models.

BDNF increasing pS935 LRRK2 is quite well supported in cell lines, as is BDNF regulation of derbrin-LRRK2 binding. However, there is a lack of connection between this result and subsequent alterations to LRRK2 substrates e.g., phosphorylation of Rab GTPases, especially in neurons. Interesting omic data sets are provided, but with very little or no validation. For example, only drebrin protein was assessed in BDNF treatment omic, and the phosphoproteomic analysis of PD mutant Knock-in mouse is stand alone with no validation and G2019S is not explored elsewhere in the study.

The major disconnect this reviewer struggles with is the conclusion that the quite clear data in SHSY5Y cells is the same as that from neurons regarding BDNF / LRRK2 and ERK / Akt. It seems they are not.

ERK and Akt phosphorylation by BDNF is absent in CRISPR KO SHSY5Y cells.<br /> This conclusion is at odds with interpretation of neuronal data. To explain; in div14 neurons, BDNF's transient increase in pLRRK2 is seen and strongly prevented by MLi2. BDNF also increased pAkt & pERK1&2 in WT... but also in LRRK2 KO. Furthermore, this happened in the presence of MLi2 in WT despite no pLRRK2 increase. While the 5min BDNF induced increase to pAkt appears reduced in LKO, the same time BDNF in LKO with MLi2 is as high as WT (in these unquantified examples) and ERK is almost identical. This is described as "significantly reduced" but I see no replicates or quantification, and face value assessment of the blot argues against this.<br /> Thus, there is little or no evidence supporting that LRRK2 activity is involved in BDNF-stimulated increases in pAkt or pERK, upstream, in neurons as neither Mli2 nor KO prevented this.

Synapse markers increased in WT neuron with BDNF treatment which did not happen in LKO neurons. So this process requires pLRRK2, but is unrelated to pAkt or pERK (which do still go up with BDNF in KO)? Similarly, an increase in synaptic activity in WT hiPSC neurons in response to BDNF seems lost in LRRK2 KO hiPSC neurons, although their activity is already increased and depending on the age of the cells the effects were different. Both of these experiments lack supporting evidence by other measures e.g., LRRK2 inhibition effects on BDNF-induced increases in WT and parallel biochemistry of p'd LRRK2, Akt, ERK in WT & KO.

LRRK2 activating Akt1 has been published before (e.g., Ohta 2011 - not cited), but Ohta also conclude that LRRK2 gain of function mutations (more LRRK2 kinase activity) were associated with a reduced ability of LRRK2 to bind AND phosphorylate Akt at the same residue, in contradiction to the mechanism proposed here? This should be discussed. Here the authors also conclude Akt is Upstream of LRRK2. However, it appears from the data here in neurons that pLRRK2 increases in response to BDNF are separate from BDNF signalling to Akt.

Of note, in comparison to bTubulin control, LKO total Akt levels appear consistently higher in this single example blot; a large increase in Akt would skew the ratio down, while absolute levels of pAkt (probably the most important matter for an active enzyme - what is the ratio against total protein stain) are similar or increased. These are major problems for the conclusions as presented.

BDNF increased mEPSC frequency in hIPSC neurons; which didn't happen in LKO, which already had high frequency. Earlier in the manuscript BDNF is shown to alter synapse number in WT but not LKO mouse neurons, but no increase in synapse number was seen following BDNF treatment in any WT or LKO hiPSC neurons +/- BDFN.

If we are to assume that the WT neurons have LRRK2 (not demonstrated), and that LRRK2 KO neurons have similar drebrin (not demonstrated) it is unclear how to interpret this result in the model of BDNF-LRRK2 being upstream of pERK/Akt. There is no evidence that the BDNF increase in WT is blocked by LRRK2 inhibition, nor has it been associated with changes (or not) to pAkt or ERK1, which would be expected in both WT and KO based on Figure 4C.

There are many reports of acute and longer term BDNF application increasing event frequency in brain slices & primary neurons. Overexpression of BDNF in NPCs has also been shown to increase synapse function in hiPSC neurons derived from them. Here, BDNF has an effect on frequency in only one 6 comparisons (3 timepoints, two lines). Is it not concerning that expected BDNF effects occur at only one time point in WT, and that generally a lack of effect is more common both in WT and LKO... is this due to slow appearance of TrkB receptors and degeneration at 90 days?

There are no other data provided to show that BDNF was having a consistent expected effect in human neurons (pAkt, pLRRK2 etc etc), and there is little to link between this data and that in previous figures of the study.

The discussion of some of the weaknesses is mostly fair, asides the disparities noted above which are not.

Reviewer #2 (Public review):

The data show that BDNF regulates the PD-associated kinase LRRK2, they place LRRK2 within well-described BDNF pathways biochemically, and they show that LRRK2 can play a role mediating BDNF-driven synaptic outcomes at excitatory synapses. The chief strength is that the data provide a potential focal point for multiple observations that have been made across many labs. The findings will be of broad interest because LRRK2 has emerged as a protein that is likely to be part of Parkinson's pathology and its normal and pathological actions remain poorly understood.

A major strength of the study is the multiple approaches that were used (biochemistry, bioinformatics, light and electron microscopy and electrophysiology) across different experimental models (cells, primary neurons, human neurons, mice) to identify and examine the impact of BDNF on LRRK2 signaling and functions. Noteworthy is also the employment of LRRK2KO preparations to validate outcomes and to place LRRK2 actions up or downstream.

The demonstration that LRRK2 and drebrin interact directly is important and suggests that other interacting proteins identified biochemically and bioinformatically in the paper will be important to pursue.

Author response:

The following is the authors’ response to the previous reviews

eLife Assessment

This important work begins to understand how BDNF regulates the phosphorylation and activity of LRRK2. The overall strength of evidence has been assessed as compelling, though some claims are only partially supported. The work will be of interest for those that might pursue specific LRRK2 interactions and mutational effects on these pathways as the work continues to develop.

We thank the editors and reviewers for the constructive feedback. We have revised the manuscript to improve clarity, strengthen statistical analysis and increase the western blot sample size in drebrin KO mice.

Public Reviews:

Reviewer #1 (Public review):

Summary:

LRRK2 protein is familially linked to Parkinson's disease by the presence of several gene variants that all confer a gain-of-function effect on LRRK2 kinase activity.

The authors examine the effects of BDNF stimulation in immortalized neuron-like cells, cultured mouse primary neurons, hIPSC-derived neurons, and brain tissue from genetically modified mice. They examine a LRRK2 regulatory phosphorylation residue, LRRK2 binding relationships, and measures of synaptic structure and function.

Strengths:

The study addresses an important research question: how does a PD-linked protein interact with other proteins, and contribute to responses to a well-characterized neuronal signalling pathway involved in the regulation of synaptic function and cell health.

They employ a range of good models and techniques to fairly convincingly demonstrate that BDNF stimulation alters LRRK2 phosphorylation and binding to many proteins. In this revised manuscript, aspects are well validated e.g., drebrin binding, but there is a disconnect between these findings and alterations to LRRK2 substrates. A convincing phosphoproteomic analysis of PD mutant Knock-in mouse brain is included. Overall the links between LRRK2, LRRK2 activity, and the changes to synaptic molecules, structures, and activity are intriguing.

We thank this Reviewer for appreciating our work including the new experiments performed during the revisions.

Weaknesses:

The data sets remain disjointed, conclusions are sweeping, and not always in line with what the data is showing. Validation of 'omics' data is light. Some inconsistencies with the major conclusions are ignored. Several of the assays employed (western blotting especially) are underpowered, findings key to their interpretation are addressed in only one or other of the several models employed, and supporting observations are lacking.

We understand the Reviewer’s points and agree that it is important to increase the sample size (animals) for western blot. In particular, we acknowledge that the initial experiments with the Dbn1 KO mice included only 3 mice, which was insufficient to draw any definitive conclusion on the effect, especially regarding pRab8 levels. In response to this, we have collected additional animals and repeated the experiment with N=7 wild-type and N=7 KO mice (2 months old). Despite a high degree of interindividual variability, we have confirmed that drebin KO mouse brains have reduced levels of pLRRK2 (Author response image 1). In the new figure 2H we included all the replicates (N=7+3 per genotype) for pLRRK2. However, we removed western blot for pRab8 because a new batch of pRab8 antibody did not yield specific results, making it impossible to reassess.

Author response image 1.

Western blot analysis of N=7 WT and N=7 drebrin KO whole brains.

Main Conclusions of Abstract:

(1) Increase in pLRRK2 Ser935 and pRAB after BDNF in SH-SY5Y & mouse neurons

Well supported, but only for pLRRK2 in neurons, why not pERK pAkt & pRab?

The response of pERK and pAKT in neurons is shown in figure 4C. We have repeatedly tried pRab (both pRab8 and pRab10) in primary neurons but with no success. In support of the difficulty in detecting pRab in primary neurons, we are not aware of studies in the literature of western blot analysis of pRabs in primary neuronal cultures. This is likely due to the high levels of PPM1H in neurons as discussed in Berndsen et at, eLife, 2019 (PMID: 31663853).

(2) Omics Proteome remodelling of LRRK2 interactome with BDNF & different in G2019S mouse neurons.

Supports that the phosphoproteome of G2019S is different. Drebrin interaction with LRRK2 very well supported. Link between drebrin and LRRK2 activity somewhat supported (pS935 site), but the consequence (non-specific pRab8) not supported, as there is no evidence of a change in LRRK2 substrate(s).

As discussed above, we removed the pRab8 western blot in figure 2H as we could not confirm with the new set of mice and a new batch of pRab8 antibody.

(3) Golgi 1 month LKO mouse altered dendritic spines, transient at 1m not older.

Supported but very small transient change in spines, disconnected to other results (e.g., drebrin).

We agree with the Reviewer that the observed effect is modest, still we believe it is important to report. As discussed in the discussion, one plausible explanation for the limited magnitude of the effect is functional compensation by LRRK1.

(4) iPSC-derived neurons BDNF increases mEPSC frequency (transient at 70 not 50 or 90 days) in WT not KO "which appear to bypass this regulation through developmental compensation"

Weak, not clear what is being bypassed.

We reviewed the statistical analysis as described below.

Main Conclusions Based on Old and New Figure / Data:

(1) Increase in pLRRK2 Ser935 and pRAB after BDNF in SH-SY5Y & mouse neurons

Well supported, but only for pLRRK2 in neurons, why not ERK Akt & Rab?

The response of pERK and pAKT in neurons is shown in figure 4C. We have repeatedly tried pRab (both pRab8 and pRab10) in primary neurons but with no success. In support of the difficulty in detecting pRab in primary neurons, we are not aware of studies in the literature of western blot analysis of pRabs in primary neuronal cultures. This is likely due to the high levels of PPM1H in neurons as discussed in Berndsen et at, eLife, 2019 (PMID: 31663853).

(2) BDNF promotes LRRK2 interaction with "post-synaptic actin cytoskeleton components"

Tone down, only one postsynaptic validated - drebrin strong BUT CONTRADICTORY; link between drebrin and LRRK2 activity (pS935 site) supported, consequence (non-specific pRab8) broken, no evidence of change in LRRK2 substrate.

As suggested we tone down the paragraph title and changed it as follow: “BDNF stimulates LRRK2 interaction with drebrin, an actin cytoskeletal-associated protein enriched at the postsynapse”. As mentioned above, pRab8 has not been incorporated.

(3) LRRK2 G2019S striatal phosphoproteome is different from WT.

It is different. Where is link to BDNF or Drebrin?

We found that debrin S339 phosphorylation is 3.7 fold higher in G2019S KI mice as compared to WT, suggesting a potential functional connection between LRRK2 and drebrin. However, differences in phosphorylation do not necessarily translate into physiological effects so further validation is required. To test if BDNF can induce S339 drebrin phosphorylation in a LRRK2-dependent manner we plan an in vivo experiment where BDNF is acutely administered to WT vs G2019S-KI mice +/- MLi2 to control for LRRK2 dependency. This is an important experiment to establish the mechanistic link, though it will require sufficient time due to the necessary ethical authorization needed to administer BDNF in the mouse brain.

(4) BDNF signaling is impaired in Lrrk2 knockout neurons

TrkB changes seem higher in SHSY5Y. pAKT impaired, pERK not convincing. Primary neurons Akt slower but it and Erk mostly intact. MLi-2 did not block pAkt or pErk in WT or KO (higher in latter). Whatever is happening in KO, Mli-2 not really blocking effect in WT. If we are to assume that studying the KO was a means to understand LRRK2 function, the authors data should explain why we care if an effect is absent in LKO, if LRRK2 isn't doing the same job in WT?

To further support the conclusion that this effect is reproducible and dependent on LRRK2 kinase activity acting upstream of AKT and ERK signaling, we probed the membranes shown in Figure 1H for phosphorylated and total AKT and ERK1/2. Consistent with our hypothesis, the inhibition of LRRK2 with MLi-2 significantly reduced BDNF-induced AKT and ERK1/2 phosphorylation (Author response image 2).

Author response image 2.

Western blot (same experiments as in figure 1) was performed using antibodies against phosphoThr202/185 ERK1/2, total ERK1/2 and phospho-Ser473 AKT, total AKT protein levels. Retinoic acid-differentiated SH-SY5Y cells stimulated with 100 ng/mL BDNF for 0, 5, 30, 60 mins. MLi-2 was used at 500 nM for 90 mins to inhibit LRRK2 kinase activity.

BDNF increases synaptic puncta in WT not LKO (which start higher?). Is this BDNF increase blocked by LRRK2 inhibition?

This is an important experiment that we plan to investigate in a future study.

(5) Postsynaptic structural changes in Lrrk2 knockout neurons

Golgi impregnation shows some very small spine changes at 1m. Not sustained over age. mRNA changes are very small (10% not even a fold... very weak and should be written as so). Derbrin levels reduced clearly at 1m, but probably also at 4 & 18. Underpowered, disconnected time course from the spine changes.

While differences are small they have been observed in independent sets of mice through qPCR, histology, WB and TEM, supporting the consistency of the effect, although small. For clarity we rescaled the qPCR graphs to 0.

(6) An effect on "spontaneous electrical activity" at Div70

Weak. What is so special at 70 days that means we should be confident in the differences, or be satisfied that the other time points are legitimately ignored? These are 10-11 cells from 3 cultures assayed at 3 time points but only one is presented (rest in supplement). This should be a 2 (time) or 3 way (+culture RM) ANOVA. As it stands, in WT there is a little - no activity at 50 days, little to no at 70 days, and variable to lots or none at 90. BDNF did nothing at 50 or 90 but may have at 70. In KO low activity stable at 50 & 70, tanks at 90. BDNF would seem to have a similar effect on KO at 90 as WT at 70, but as there are only 7 cells it remains inconclusive. Thus the conclusion that BDNF signalling is broken in LKO is not well supported by the ephys data, nor is the BDNF effect in WT cells (even at the 70 day time point) shown to be susceptible to LRRK2 inhibition.

We thank the Reviewer for suggesting a more comprehensive analysis of the data. Following this suggestion, we performed separate two-way ANOVAs (DIV × treatment) for WT and LRRK2 KO neurons. This analysis revealed significant main effects of DIV and BDNF treatment in WT neurons, indicating that synaptic activity increases with neuronal maturation and is globally enhanced by BDNF. In contrast, neither DIV nor BDNF treatment reached statistical significance in LRRK2KO neurons, and no DIV × treatment interaction was observed. These results indicate that BDNFdependent enhancement of synaptic activity is preserved in WT neurons but is lost in the absence of LRRK2. We have now incorporated this analysis into the main figure and removed the individual DIV50 and DIV90 plots from the supplementary material. We also revised the title of the last paragraph to reflect the outcome of this analysis and toned down our interpretation (page 12).

Furthermore, we have added a paragraph to the Discussion section highlighting the limitations of this study. These include the variability observed in protein content and phosphorylation analyses by western blot, as well as the necessity to confirm the electrophysiological findings in larger datasets, including in dopaminergic neurons.

Reviewer #2 (Public review):

The data show that BDNF regulates the PD-associated kinase LRRK2, they place LRRK2 within welldescribed BDNF pathways biochemically, and they show that LRRK2 can play a role mediating BDNFdriven synaptic outcomes at excitatory synapses. The chief strength is that the data provide a potential focal point for multiple observations that have been made across many labs. The findings will be of broad interest because LRRK2 has emerged as a protein that is likely to be part of Parkinson's pathology and its normal and pathological actions remain poorly understood.

We thank this Reviewer for appreciating our work and acknowledging that our findings will be of broad interest.

A major strength of the study is the multiple approaches that were used (biochemistry, bioinformatics, light and electron microscopy and electrophysiology) across different experimental models (cells, primary neurons, human neurons, mice) to identify and examine the impact of BDNF on LRRK2 signaling and functions. Noteworthy is also the employment of LRRK2KO preparations to validate outcomes and to place LRRK2 actions up or downstream.

Thank you to the Reviewer

The demonstration that LRRK2 and drebrin interact directly is important and suggests that other interacting proteins identified biochemically and bioinformatically in the paper will be important to pursue.

We agree with this statement

Some data from different models do not fit well with one another (like mouse and human neurons). This is likely due to inherent differences in the preparations. Since different experiments were carried out on the different preps, however, it is not possible to cross compare. The lack of this information is viewed more as an open question than a cause for concern.

We thank the Reviewer for raising this point. In response, we have added a new section to the Discussion explicitly addressing the limitations of the study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

MLi2 pretreatment experiment is nice. They state in legends BDNF treatment prior to MLi2, they mean prior MLI2 treatment. Or MLi2 pretreatment, prior to BDNF. However, this experiment is hard to interpret as it has no control (non BDNF treated) time course following MLi2, could this be (at least in part) a rebound effect produced by relief of inhibition? This should be discussed if not addressed directly by experiments.

The non BDNF treated group represents the 0 time point. We have specified it in the figure legend. We have excluded the rephosphorylation kinetic after MLi-2 relief because pRabs increase significantly at 5 minutes, far exceeding the control levels. This observation gives us feel confidence that the effect if BDNF dependent.

(1) "As suggested, we performed qPCR and observed that 1 month-old KO midbrain and cortex express lower levels of Dbn1 as compared to WT brains (Figure 5G). This result is in agreement with the western blot data (Figure 5H)."

There is no Fig 5H? 5F? In 5F effect sizes are exaggerated with axes not crossing zero. There is a 10% reduction in mRNA (normally >1 or 2 fold changes would be considered biologically important?). This isn't much change, and should be presented as such. 1 month old WB in G are much more convincing of a reduction of drebrin levels, but what brain region is this from?

We apologize for the error in the rebuttal, where we incorrectly referred to figure 5G (the correct is 5F), while what we called 5H is instead 5G. We checked the labeling in the manuscript text and it is correct.

Following the Reviewer’s important suggestion, we rescaled all plots to start at zero. Although some differences appear relatively modest, they are statistically significant. Importantly, all brains used for qPCR analyses (N = 6 per genotype) were obtained from independent mice. In addition, independent cohorts of mice were used for spine morphology analyses (N = 3 per genotype), TEM analyses (N = 4), and western blot experiments (N = 3). Thus, the overall sample size across approaches is substantial.

WB are from whole brain, now indicated in the figure legend.

All blots are underpowered, especially given what appears to be an age dependent loss of drebrin in both genotypes beset by high variability

(i) Western blots looking at pSer935 and pRab8 (pan Rab) in Dbn1 WT and knockout brains.

"As reported and quantified in Figure 2I, we observed a significant decrease in pSer935 and a trend decrease in pRab8 in Dbn1 KO brains. This finding supports the notion that Drebrin forms a complex with LRRK2 that is important for its activity, e.g. upon BDNF stimulation."

Non-sig data in Fig2I/H and especially Fig5G are important data but hard to interpret because the experiment is underpowered. I am surprised the authors designed studies on an n=3 western blot.

For fig 2 this is a problem if they wish to correlate LRRK2 activity with drebrin. The KO have a clear 50% decrease in LRRK2 pS935 but no change to pRab8(pan).

As discussed above, we increased the sample size by 7 additional mice per genotype (total of 10 brains analyzed).

Why not look at Rab10, and certainly redo with a higher n than three. Of special confusion is the observation that the WT with the highest drebrin levels, is the animal with the lowest pS935 & pRab

As discussed above neither pRab8 nor pRab10 returned convincing results in the new round of western blots. We acknowledge that future experiments should explore the phosphorylation levels of Rab12 which is emerging as a more reliable readout of LRRK2 kinase activity in the brain.

(ii) "Reverse co-immunoprecipitation of YFP-drebrin full-length, N-terminal domain (1-256 aa) and Cterminal domain (256-649 aa) (plasmids kindly received from Professor Phillip R. Gordon-Weeks, Worth et al., J Cell Biol, 2013) with Flag-LRRK2 co-expressed in HEK293T cells. As shown in supplementary Fig. S2C, we confirm that YFP-drebrin binds LRRK2, with the N terminal region of drebrin appearing to be the major contributor to this interaction"

CoIP with drebrin (and fragments) is very convincing.

We thank the Reviewer for his/her comment/feedback

Ephys data, presentation, and response to review is weak.

We reanalyzed the data as suggested by the Reviewer and reviewed the text and interpretation.

Reviewer #2 (Recommendations for the authors):

p. 12, last paragraph. "sealing" should be "ceiling"

We corrected the misspelled word

eLife Assessment

This important study provides solid evidence that early childhood malaria exposure affects the development of antibody responses to unrelated pathogens and vaccine-derived antigens in Kenyan children. The findings are of major public health importance and limitations of the observational study design are properly acknowledged.

Reviewer #2 (Public review):

Summary:

The authors investigated whether early-life malaria exposure has long-term effects on immune responses to unrelated antigens. They leveraged a natural experiment in coastal Kenya where two adjacent communities (Junju and Ngerenya) experienced divergent malaria transmission patterns after 2004. Using 15 years of longitudinal data from 123 children with weekly malaria surveillance and annual serological sampling, they measured antibody responses to multiple pathogens using a protein microarray technology and ELISA.

Strengths:

(1) Extensive longitudinal data collection with weekly malaria surveillance, enabling precise exposure classification.

(2) Use of a natural experiment design that allows for causal inference about malaria's immunological effects.

(3) Broad panel of antigens tested, demonstrating generalized rather than antigen-specific effects.

(4) Within-cohort analysis in Ngerenya controls for geographic and environmental factors.

(5) Validation of key findings using both serologic microarray and ELISA.

(6) Important public health implications for vaccine strategies in malaria-endemic regions.

Weaknesses:

(1) Due to its nature, the study lacks the ability to determine the direction of the associations found between malaria exposure and other IgG levels to unrelated pathogens.

(2) No evaluation of the clinical Implications of the reduced IgG levels observed in the area with high malaria exposure.

Assessment of Claims:

The data appear to support the authors' primary claims. The strength of the evidence is limited by the observational nature of the study and the results should be interpreted in that light. Together with the currently available evidence of P. falciparum's impact on the host's immune function, this natural experiment design provides further evidence for a relationship between early malaria exposure and reduced antibody responses to other pathogens and vaccine-derived antigens. The within-Ngerenya analysis controls for geographic factors and thus enhances the quality of the evidence; there is limited physical, nutritional, and socio-economic information on factors that may have driven the observed changes.

Impact and Utility:

This work has fundamental implications for understanding vaccine effectiveness in malaria-endemic regions and may contribute to inform vaccination strategies. The findings, if confirmed, would suggest that children in areas of high malaria transmission may require modified immunization approaches. The dataset provides a valuable resource for future studies of malaria's immunological legacy.

Context:

This study builds on prior work showing acute immunosuppressive effects of malaria but uniquely attempts to demonstrate the durability of these effects years after exposure. The natural experiment design addresses limitations of previous observational studies by providing a more controlled comparison.

Author response:

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

eLife Assessment

This important study sought to investigate the role that early childhood malaria exposure plays in the development of antibody responses to unrelated pathogens and vaccine-derived antigens in Kenyan children. In this natural experiment, the authors compare antibody levels among children who have been exposed to different levels of malaria transmission by using protein microarray technology. Although the findings are of importance, the evidence remains incomplete, and the analysis would benefit from a more in-depth evaluation of potential confounders. With the appropriate analysis, the findings will be of great interest for global health, immunology, and vaccine development.

We thank the editors for highlighting the need for a more comprehensive evaluation of potential confounding. We agree that this is a critical aspect of the study and have now undertaken additional analyses to address this directly.

The original longitudinal cohort was designed to investigate the acquisition of naturally acquired immunity to malaria and did not include systematic collection of anthropometric/nutritional, environmental or socioeconomic data, precluding direct adjustment for these factors within the primary dataset. However, to assess whether there were population-level differences in these factors, we leveraged contemporaneous hospital-based surveillance data from the same geographic regions, which includes measurements of anthropometry and nutritional status (muac, weight-for-age, and height-for-age) and detailed infection diagnostics.

Using this independent dataset, we fitted mixed-effects regression models adjusting for age, calendar year, and concurrent infections (RSV, parainfluenza, influenza A, human metapneumovirus, OC43). For all three anthropometric indices, we found no evidence of systematic differences between children from Junju and Ngerenya. Adjusted differences were small and centred around zero (muac: −0.12, 95% CI −0.38 to 0.15, weight-for-age: −0.05, −0.28 to 0.19, height-for-age: 0.08, −0.17 to 0.33), with no consistent directional effect.

As the longitudinal cohort was randomly selected from these underlying populations, these findings suggest that the groups were broadly comparable with respect to nutritional status and there were no differences in their exposure to the infections that were included in the analysis. We have incorporated these analyses into the revised manuscript, added a new figure focussed on this analysis -fig. 6, updated the statistical analysis and discussion sections), and believe they substantially strengthen the evidence by addressing a key source of potential confounding.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study shows that childhood malaria can weaken the antibody response to other vaccines and infections. This suggests that early exposure to P. falciparum may have a long-lasting effect on immunity, with implications for vaccine efficacy in endemic areas.

Strengths:

This study stands out for its longitudinal design, the use of robust immunological techniques, and the comparison between areas with different levels of malaria exposure. Its findings reveal that early malaria can weaken the response to childhood vaccines, with important implications for public health in endemic regions.

We thank the reviewer for this comment

Weaknesses:

One of the study's main limitations is the lack of functional data confirming the clinical impact of the low antibody levels. Furthermore, although multiple immune responses were measured, other important components, such as cellular immunity, were not assessed. Furthermore, the results may not be generalizable to other regions.

We thank the reviewer for this important comment and agree that the absence of functional immunological assays is a limitation of the current study. Our analysis was designed to determine whether early-life malaria exposure is associated with durable alterations in antibody responses to unrelated pathogens and vaccine antigens, rather than to establish the downstream functional consequences of these differences. As such, the study is able to demonstrate a broad and persistent attenuation of humoral responses but cannot directly determine whether the lower antibody levels observed translate into reduced neutralising capacity or diminished protection at the individual level.

We have revised the manuscript to make this distinction more explicit. In the revised discussion, we now state that although reduced antibody titres to vaccine-preventable pathogens may have implications for long-term protection, the clinical significance of these differences remains to be established in future studies incorporating functional assays and clinical outcome data.

Reviewer #2 (Public review):

Summary:

The authors investigated whether early-life malaria exposure has long-term effects on immune responses to unrelated antigens. They leveraged a natural experiment in coastal Kenya where two adjacent communities (Junju and Ngerenya) experienced divergent malaria transmission patterns after 2004. Using 15 years of longitudinal data from 123 children with weekly malaria surveillance and annual serological sampling, they measured antibody responses to multiple pathogens using a protein microarray technology and ELISA.

Strengths:

(1) Extensive longitudinal data collection with weekly malaria surveillance, enabling precise exposure classification.

(2) Use of a natural experiment design that allows for causal inference about malaria's immunological effects.

(3) Broad panel of antigens tested, demonstrating generalized rather than antigen-specific effects.

(4) Within-cohort analysis in Ngerenya controls for geographic and environmental factors.

(5) Validation of key findings using both serologic microarray and ELISA.

(6) Important public health implications for vaccine strategies in malaria-endemic regions.

We thank the reviewer for these comments

Weaknesses:

(1) Lack of participants' characteristics (socio-economic, nutritional, physical).

We thank the reviewer for this important comment. We have now included a detailed summary of participant characteristics in Table 1to provide context for the study population. This includes key demographic and longitudinal variables stratified by cohort (Junju and Ngerenya), including sex distribution, age at study entry and exit, duration of follow-up, number of visits per participant, and total serum samples analysed. Detailed data on socio-economic status, nutritional status, and other environmental or physical characteristics were not consistently available across all participants and time points, and therefore could not be included. This has now been explicitly stated as a limitation in the discussion.

(2) Somewhat limited sample size (longitudinal analysis of 123 children total), with further subdivision reducing statistical power for some analyses.

We thank the reviewer for this important observation. The study is based on an intensively followed cohort with weekly malaria surveillance and repeated serological measurements throughout childhood, allowing detailed characterisation of early-life exposure and subsequent immune trajectories. This depth of longitudinal sampling provides resolution that is not achievable in larger cross-sectional studies. We acknowledge that subdivision of the cohort reduces statistical power for some analyses. Nevertheless, the key findings were consistent in several independent comparisons, including a reduction in antibody levels for broad panel of antigens in the malaria endemic setting, within-cohort analyses in Ngerenya that replicated this observation, and the confirmation of results generated on the protein microarray on the ELISA platform. The consistency of these findings across analytical approaches and measurement platforms reduces the likelihood that the observed effects are driven by small-sample variability. We have clarified this point in the revised discussion to emphasise that the strength of the study lies in the depth and longitudinal resolution of the data rather than the absolute sample size.

(3) Potential confounding by unmeasured socioeconomic, nutritional, or environmental factors between communities.

We thank the reviewer for this important point and agree that residual confounding between communities must be considered. As outlined in reponse to the editorial assesment, we have undertaken additional analyses using contemporaneous population-level data from the same regions and found no evidence of systematic differences in anthropometric indices between children from Junju and Ngerenya after accounting for age, calendar year, and concurrent infections, with effect estimates small and crossing zer. In addition, the within-Ngerenya analysis provides an internal comparison within a shared geographic and environmental setting, reducing the likelihood that unmeasured socioeconomic or environmental differences between communities account for the observed associations. The new analyses suggest that major population-level differences in nutritional status or infection burden are unlikely to explain the observed patterns. We have clarified this point in the revised discussion and explicitly acknowledge the possibility of residual confounding.

(4) Lack of ability to determine the direction of the associations found between malaria exposure and other IgG levels to unrelated pathogens.

We agree that, as an observational study, our analysis cannot definitively establish the direction of the association between malaria exposure and antibody responses to unrelated antigens. However, several features of the study design strengthen the inference that early-life malaria exposure contributes to the observed differences. First, malaria exposure was characterised prospectively through intensive weekly surveillance, allowing precise temporal definition of exposure in early childhood. Second, within the Ngerenya cohort, where children were exposed to different levels of malaria due to a rapid decline in transmission, those with even limited early-life exposure exhibited lower antibody responses at 10 years of age than malaria-naïve peers, despite residing in the same geographic and environmental context. In addition, we now show that these differences are not confined to a single timepoint but are evident across the full longitudinal follow-up after adjustment for age and repeated measurements. While we cannot exclude the possibility of residual confounding or bidirectional relationships, the convergence of evidence from the natural experiment design, within-cohort contrasts, and age-adjusted longitudinal analyses supports early-life malaria exposure as a key contributor to long-term differences in antibody responses. We have clarified this in the discussion.

(5) Despite good longitudinal data, the main analysis was conducted as a cross-sectional analysis at age 10 for many comparisons, which limits the understanding of temporal dynamics.

We thank the reviewer for highlighting this point. While age 10 was initially used as a standardised reference point for cross-sectional comparisons, the underlying dataset is longitudinal, with repeated antibody measurements across childhood. To address this more directly, we have now complemented these analyses with antigen-specific mixed-effects regression models incorporating all available longitudinal data, with adjustment for age and a random intercept for repeated measurements within individuals. These models demonstrate that the differences between cohorts are not confined to the age-10 cross-section but are evident in an age-adjusted longitudinal framework for multiple antigens. We have retained the age-10 comparisons for reference, but the primary inference is now based on the longitudinal mixed-effects analyses. These changes are reflected in the revised results and statistical analysis sections. We thank the reviewer for this astute point, which we think has substantially improved the manuscript.

(6) Statistical analysis is limited to univariable comparisons without consideration for confounders or adjusting for multiple comparisons.

We agree that the original analyses relied primarily on univariable comparisons. In the revised manuscript, we have extended the analytical framework to include mixed-effects regression models that account for age effects and repeated measurements within individuals. These models estimate the average age-adjusted difference in antibody responses between cohorts across the full follow-up period. We have also applied false discovery rate (FDR) correction to account for multiple antigen testing. For multiple antigens, the direction and magnitude of cohort differences remain consistent under this approach, strengthening the robustness of the findings beyond the original univariable comparisons. These analyses have been incorporated into the revised results and statistical analysis sections.

(7) No mechanistic understanding of how early malaria exposure creates lasting immunosuppression.

We agree that this study does not directly resolve the mechanistic basis underlying the observed long-term differences in antibody responses. The primary aim of this work was to identify and characterise durable alterations in humoral immune profiles associated with early-life malaria exposure, rather than to define the cellular or molecular pathways involved. However, our findings are consistent with a growing body of experimental and clinical literature suggesting that malaria infection can induce sustained perturbations in B cell and T cell compartments, including the expansion of atypical memory B cells, altered germinal centre responses, and increased regulatory immune activity. These mechanisms have been proposed to impair the generation and maintenance of effective humoral immunity. In the revised discussion, we have clarified that the mechanistic basis of this phenomenon remains to be fully defined and have expanded the discussion of plausible pathways in light of existing literature. We now explicitly position our findings as providing population-level evidence of a durable immunological phenotype that warrants further mechanistic investigation.

(8) No understanding of the clinical Implications of the reduced IgG levels observed in the area with high malaria exposure.

We agree that this study does not directly establish the clinical consequences of the reduced antibody levels observed in malaria-exposed children. The primary objective of this study was to characterise long-term differences in humoral immune profiles associated with early-life malaria exposure, rather than to assess downstream clinical outcomes or functional antibody activity. We have clarified this limitation in the revised discussion. Nevertheless, the breadth and consistency of the observed differences for multiple vaccine-preventable and infectious antigens raise the possibility that early-life malaria exposure may have implications for long-term immune protection. We now emphasise in the revised discussion that future studies incorporating functional assays and clinical outcome data will be required to determine whether these serological differences translate into altered susceptibility to infection or reduced vaccine effectiveness.

Assessment of Claims:

The data appear to support the authors' primary claims, but the strength of the evidence is limited, and the results should be interpreted with caution. Together with the currently available evidence of P. falciparum's impact on the host's immune function, this natural experiment design provides further evidence for a relationship between early malaria exposure and reduced antibody responses. The within-Ngerenya analysis controls for geographic factors and thus enhances the quality of the evidence, however, it still fails to account for the physical, nutritional, and socio-economic factors that may have driven the observed changes. Additionally, the mechanism underlying this effect remains unclear, and the clinical significance of reduced antibody levels is not established.

We thank the reviewer for this assessment and for recognising the strengths of the natural experiment design and within-cohort analyses. We agree that, as an observational study, our findings should be interpreted appropriately. In the revised manuscript, we have undertaken additional analyses and clarifications to strengthen the evidential basis of our conclusions and to address the points raised. To address potential confounding by nutritional and related factors, we analysed contemporaneous hospital-based surveillance data from the same geographic populations since nutritional and socioeconomic variables were not consistently collected during the course of longitudinal follow up. For three independent anthropometric indices of nutrition status (muac, weight-for-age, and height-for-age), we found no evidence of systematic differences between children from Junju and Ngerenya after adjustment for age, calendar year, and concurrent infections. As the longitudinal cohort subjects were randomly drawn from these populations, these findings suggest that the two groups were broadly comparable with respect to early-life growth and nutritional status.

We agree that the mechanistic basis of the observed differences is not resolved in this observational study. We have clarified this point in the revised discussion and expanded our consideration of plausible biological pathways based on existing literature, including perturbations in B cell and T cell compartments. Similarly, we now explicitly state that the clinical implications of reduced antibody levels remain to be determined and will require studies incorporating functional assays and clinical outcomes. We believe these revisions strengthen the manuscript by providing a more comprehensive interpretation of the data.

Impact and Utility:

This work has fundamental implications for understanding vaccine effectiveness in malaria-endemic regions and may contribute to informing vaccination strategies. The findings, if strengthened, would suggest that children in areas of high malaria transmission may require modified immunization approaches. The dataset provides a valuable resource for future studies of malaria's immunological legacy.

We thank the reviewer for this comment

Context:

This study builds on prior work showing acute immunosuppressive effects of malaria but uniquely attempts to demonstrate the durability of these effects years after exposure. The natural experiment design addresses limitations of previous observational studies by providing a more controlled comparison.

We thank the reviewer for this comment

Recommendations for the authors:

Reviewing Editor Comments:

We suggest that further analyses of potential confounders such as anthropometric indices, socioeconomic status, and comorbidities would render the evidence more robust.

We thank the Reviewing Editor for this important suggestion. We agree that careful consideration of potential confounding factors is critical to the interpretation of these findings, and have undertaken additional analyses to address this.

Because anthropometric and related socioeconomic measurements were not collected systematically within the original longitudinal malaria cohort, we assessed potential population-level differences using hospital-based surveillance data from the same geographic regions. This dataset includes measurements of anthropometry (mid-upper arm circumference, weight-for-age, and height-for-age) as well as detailed infection diagnostics in childhood. Using these data, we fitted regression models adjusting for age, calendar year, and concurrent, clinically diagnosed infections. For all three anthropometric indices, we found no evidence of systematic differences between children from Junju and Ngerenya, with effect estimates small and crossing zero (fig. 6). As the longitudinal cohorts were randomly selected from these populations, these findings suggest that the groups were broadly comparable with respect to nutritional status and infection exposure. With respect to socioeconomic status and comorbidities, detailed individual-level data were not available within the longitudinal cohort. However, the within-Ngerenya analysis, where children with differing early-life malaria exposure were compared within the same geographic and environmental setting, provides a complementary control for these factors. We have incorporated these additional analyses and clarifications into the revised manuscript statistical analysis, discussion lines and believe they strengthen the robustness of the findings by addressing key sources of potential confounding.

Reviewer #1 (Recommendations for the authors):

The manuscript is well written, with clear and informative figures that effectively support the findings.

We thank the reviewer for this comment

Suggestions:

(1) Although the study well controlled for malaria exposure, other environmental or infectious factors that influence immunity could be considered:

Nutritional status in childhood (malnutrition impacts immune response), co-infections (helminths, respiratory viruses), socioeconomic differences, or differences in access to health services, even minimal, between Junju and Ngerenya.

We thank the reviewer for highlighting the potential influence of environmental, infectious, and socioeconomic factors on immune responses. We agree that these are important considerations in the interpretation of observational data. To address nutritional status and concurrent infectious exposures, we analysed contemporaneous hospital-based surveillance data from the same geographic populations. This dataset includes measurements of anthropometric indices (mid-upper arm circumference, weight-for-age, and height-for-age) alongside detailed clinical diagnostics for common childhood infections. Using regression models adjusting for age, calendar year, and concurrent infections, we found no evidence of systematic differences in anthropometric profiles between children from Junju and Ngerenya (fig. 6). These findings suggest that the populations from which the longitudinal cohorts were randomly selected were comparable with regard to early-life growth and nutritional status. We agree that we cannot fully exclude the influence of unmeasured factors such as helminth infections, socioeconomic variation, or subtle differences in healthcare access. However, the within-Ngerenya analysis, where children with differing early-life malaria exposure were compared within the same geographic, environmental, and healthcare setting, provides an internal control for many of these factors. The persistence of similar patterns within this setting supports malaria exposure as a key contributor of the observed differences. We have clarified these considerations in the revised discussion and believe that, the additional analyses and within-cohort comparisons strengthen the robustness of our conclusions while acknowledging the limitations inherent to observational studies.

(2) Measurement of other immunological markers:

In addition to IgG, include: B cell subpopulations (naive, memory, atypical), cytokine levels (IL-10, IFN-γ) to characterize the immunological microenvironment.

You could include these recommendations in the text for future studies.

We thank the reviewer for this thoughtful suggestion. We agree that detailed immunophenotyping, including characterisation of B cell subpopulations and cytokine profiles, would provide important insight into the mechanisms underlying the observed differences in antibody responses. In the revised manuscript, we have expanded the discussion to highlight these important avenues for future work, including the potential role of altered B cell subsets (and regulatory or inflammatory cytokine environments in shaping long-term humoral responses).

Reviewer #2 (Recommendations for the authors):

The manuscript is well-written.

We thank the reviewer for this comment

(1) Methodological Clarifications:

Do the authors have any information regarding the characteristics of these children that could be of use in understanding their immune responses better? (e.g., weight, height, BMI, socioeconomic status, HB level, access to health care, etc.).

We thank the reviewer for highlighting the importance of participant characteristics in interpreting immune responses. Anthropometric and related clinical measures were not collected systematically within the original longitudinal malaria cohort, as the study was designed to investigate the acquisition of naturally acquired immunity to malaria.

To address this, we analysed contemporaneous hospital-based surveillance data from the same geographic populations, which include measurements of anthropometric indices (mid-upper arm circumference, weight-for-age, and height-for-age) alongside detailed infection diagnostics. Using regression models adjusting for age, calendar year, and concurrent infections, we found no evidence of systematic differences in anthropometric profiles between children from Junju and Ngerenya (fig. 6) Detailed individual-level data on socioeconomic status, haemoglobin levels, and healthcare access were not available within the longitudinal cohort impeding direct adjustment in the longitudinal cohorts. However, the within-Ngerenya analysis, where children with differing early-life malaria exposure were compared within the same geographic and healthcare setting, provides an internal control for many of these factors. These considerations are now clarified in the revised discussion.

Could the authors provide more detailed statistical analysis, including power calculations and multiple comparison corrections?

In the revised manuscript, we have extended the statistical analysis and now include antigen-specific mixed-effects regression models incorporating all available longitudinal measurements, which is comprehensively described in the statistical analysis section. We have also applied false discovery rate (FDR) correction to account for multiple testing across antigens, and report both unadjusted and FDR-adjusted significance in the revised results. With respect to power, the sample size was determined by the number of children meeting inclusion criteria within the long-term surveillance cohorts in terms of availability of a sufficient number of longitudinal samples. We have clarified this in the revised manuscript.

Clarify the criteria for selecting the 123-child subset from the larger surveillance cohorts.

We thank the reviewer for this comment. The 123 children included in this analysis were selected from the larger surveillance cohorts based on the availability of sufficiently dense longitudinal serum sampling as described above. Specifically, children were required to have at least eight longitudinal samples available in the archive, enabling robust assessment of within-individual antibody trends over time. This criterion was applied to ensure adequate temporal resolution to examine the long-term stability of malaria-associated effects on antibody responses. Children with fewer available samples were therefore excluded, as limited sampling would not allow reliable characterisation of longitudinal patterns. We have clarified these inclusion criteria in the revised manuscript.

(2) Additional Analyses and Data Presentation:

The authors could consider dose-response analyses relating malaria episode frequency/timing to degree of immunosuppression or even AMA-1 IgG levels and degree of immunosuppression. How do they associate over time?

We thank the reviewer for this suggestion. To address this, we examined the relationship between malaria exposure (using cumulative febrile malaria episode count derived from longitudinal surveillance data) and the magnitude of heterologous antibody responses. In mixed-effects models adjusting for age and repeated antibody measurements, higher malaria episode burden was associated with lower antibody responses against multiple antigens (fig 7).

Analyze whether the effects vary by specific age at malaria exposure.

We agree that age at exposure is an important consideration. We have now assessed how the relationship between malaria burden and antibody responses varies with age by including age as a non-linear term and modelling interactions between malaria exposure and age as described above. These analyses did not suggest substantial heterogeneity in the association over age, and therefore we have retained the simpler presentation for clarity.

Provide correlation analyses between different antibody responses to assess whether suppression is generalized.

We have addressed this by modelling responses jointly across a panel of heterologous antigens and by examining antigen-specific associations. The direction of effect was consistent for the majority of antigens, with no evidence of opposing trends, supporting a broad rather than antigen-specific effect.

The authors could consider moving Figures 2a and b to the supplementary material.

We thank the reviewer for this suggestion. We carefully considered whether panels 2a and 2b could be moved to the supplementary material. However, we have retained them in the main text because they provide a simple, intuitive illustration of how AMA1 antibody responses track with malaria exposure at the individual level, complementing the population-level analysis shown in fig. 2c. We feel that this helps establish the biological validity of the microarray platform in a way that is immediately interpretable to the reader, and therefore supports the interpretation of subsequent analyses.

The authors could consider replacing Figures 3a and b with IgG levels from ALL vaccinated children and ALL non-vaccinated children.

We thank the reviewer for this suggestion. We would like to retain these figures for the same reasons that have been articulated above for figures 2a and b.

(3) Discussion Enhancements:

The authors should consider expanding the discussion to address the limitations of the data more thoroughly, particularly regarding the potential differences between cohorts that could have contributed to the results.

We have expanded the discussion to more explicitly address potential differences between cohorts that could contribute to the observed findings, including nutritional, socioeconomic, and environmental factors.

The discussion needs to acknowledge the lack of directionality for the associations observed. As stated above, although I agree in general terms with the observations that the authors have made, it is not possible to distinguish between a suppressive effect of malaria on immune responses to infection-derived pathogens or a protective effect of malaria that leads to less exposure to infection-derived pathogens (and consequently lower IgG levels). The mechanisms behind these could include things like different health-seeking behaviors or social interactions from kids who have malaria versus those who don't, for example.

We agree that, as an observational study, we cannot definitively establish the direction of the association between malaria exposure and antibody responses to unrelated antigens. We have now clarified this limitation explicitly in the discussion. We acknowledge the alternative interpretations raised by the reviewer, including the possibility that differences in exposure to other pathogens, potentially driven by behavioural, environmental or healthcare-related factors, could contribute to the observed patterns. At the same time, we note that the natural experiment design, prospective malaria exposure classification, and within-Ngerenya comparisons support early-life malaria exposure as a key contributing factor. We have revised the discussion to reflect this balance.

Extend the discussion of potential biological mechanisms underlying durable immunosuppression.

We thank the reviewer for this suggestion. We have expanded the discussion to more fully consider potential biological mechanisms that could underlie the observed long-term differences in antibody responses. Specifically, we now discuss evidence from prior studies indicating that malaria infection can induce sustained alterations in B cell and T cell compartments, including expansion of atypical memory B cells, disruption of germinal centre responses, and increased regulatory immune activity. We position our findings as providing population-level evidence of a durable immunological phenotype, while noting that targeted mechanistic studies will be required to define the underlying pathways.

Extend the discussion around the clinical implications of the observed antibody level differences.

In the revised discussion, we highlight that studies incorporating functional assays and clinical outcome data will be required to determine whether these serological differences translate into altered susceptibility to infection or reduced vaccine effectiveness.

(4) Technical Issues:

Could the authors please:

(1) Clarify microarray data processing and quality control procedures.

We thank the reviewer for this request. We have expanded the methods section to provide additional detail on microarray data processing and quality control procedures.

(2) Provide information on inter-assay variability and batch effects.

We have expanded the methods section to clarify how these were evaluated and addressed. Inter-assay variability was monitored using pooled adult serum included on every slide as a consistent positive control. This allowed us to assess slide-to-slide consistency in signal detection across the full antigen panel. In addition, fluorophore-conjugated IgG and IgA controls were printed directly onto each miniarray to confirm scanner performance independently of antigen–antibody interactions. At the sample level, each specimen was assayed on two independent miniarrays per slide, generating four spatially separated replicate measurements per antigen. Technical variability was quantified using the coefficient of variation (CV), and measurements with CV >20% were excluded from downstream analyses.

(3) Include details on how missing data were handled in longitudinal analyses.

We thank the reviewer for highlighting this point. We have added clarification in the statistical analysis section describing how missing data were handled. Specifically, mixed-effects models were used, which accommodate unbalanced longitudinal data without requiring imputation, allowing all available observations to contribute to the analysis.

(4) Include details of the parameters of the LOWESS analysis shown in Figure 1.

We have expanded the figure 1 legend to include the parameters used for the loess smoothing shown, including the smoothing span.

(5) Include details of the samples used for Figure 3d (Negative and Pooled Adult Serum).

We have clarified in the methods the nature and purpose of the samples used in Figure 3d. The negative control consisted of phosphate-buffered saline applied to a full miniarray in place of serum, allowing assessment of background and non-specific signal in the absence of antibody binding. The pooled adult serum comprised a composite of sera from multiple healthy adults from the same setting and was included as a positive reference sample, expected to contain a broad repertoire of antigen-specific antibodies. These controls were included on each slide to enable interpretation of assay performance, with the negative control defining baseline signal and the pooled adult serum providing a consistent reference for antigen recognition across the microarray.

ist

eLife Assessment

This valuable study identifies a brown adipose tissue-specific heat shock factor 1 (HSF1)-alcohol dehydrogenase 5 (ADH5) axis that regulates oxidative stress and cellular senescence during aging. The authors show that ADH5 deficiency drives BAT dysfunction and contributes to organismal health decline in aged mice. The evidence is convincing, and the work will be of broad interest to the adipose tissue and aging research communities.

Reviewer #1 (Public review):

Sebag et al. addressed the role of ADH5 in BAT in the development of aging and metabolic disarrangements associated with it. This is a follow-up study after the authors' demonstration of the role of BAT ADH5 in glucose homeostasis, obesity, and cold tolerance. By ablating ADH5 specifically in brown adipocytes or pharmacologically modulating ADH5 through activation of its transcription factor, the authors conclude that preservation of BAT function is crucial for healthy aging and ADH5 is causally involved in this process. The topic is appealing given the rise in the aging population and the unclear role of BAT function in this process. Overall, the study uses several techniques and addresses several physiological and molecular manifestations of aging. Therefore, the findings contribute to the growing body of literature pointing to the biological role of BAT activity in aging.

Comments on revised version:

I have no further comments other than to congratulate the authors on the nice piece of work.

Reviewer #2 (Public review):

Summary:

This study investigates the role of the enzyme Alcohol Dehydrogenase 5 (ADH5) in brown adipose tissue (BAT) during aging. BAT is crucial for thermogenesis and energy balance, but its function and mass diminish with age, contributing to metabolic dysfunction and age-related diseases. ADH5, also known as S-nitrosoglutathione reductase, regulates nitric oxide (NO) signaling by removing damaging S-nitrosylation modifications from proteins. The authors show that aging in mice leads to increased protein S-nitrosylation associated with a combination of increased Nos2 expression and reduced ADH5 expression in BAT, resulting in impaired metabolic and cognitive functions. Deletion of ADH5 in BAT accelerates tissue senescence and systemic metabolic decline. Mechanistically, aging suppresses ADH5 via downregulation of heat shock factor 1 (HSF1), a master regulator of protein homeostasis. Importantly, pharmacologically boosting HSF1 improves BAT function and mitigates both metabolic and cognitive declines in aged mice. The findings highlight a critical HSF1-ADH5 pathway in BAT that protects against aging-related dysfunction, suggesting that targeting this pathway may offer new therapeutic strategies for improving metabolic health and cognition during aging.

Strengths:

This research provides insight into the interplay between redox biology, proteostasis, and metabolic decline in aging. By showing that age regulates genes that control SNO status in BAT and further developing a therapy to target ADH5 in BAT to prevent age related decline, the authors have identified a putative mechanism to combat age related decline in BAT function.

Weaknesses:

None identified.

Comments on revised version:

Congratulations to the authors for this interesting manuscript. I don't want to pat myself on the back, but I found the increased Nos2 expression in Figure 1C of the revised manuscript very satisfying, as it reinforces the shift in the regulation of SNO status that happens in BAT with aging. I appreciate the authors addressing this suggestion.

Author response:

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

We sincerely thank the reviewers for their thoughtful and constructive comments. We fully agree that when two independent variables (genotype and age) are being evaluated, the statistical analysis must appropriately account for both factors and their potential interaction. We appreciate the reviewers’ guidance in strengthening the statistical rigor of our study.

In response to this concern, we have carefully reanalyzed the relevant datasets using two-way ANOVA to properly assess the effects of genotype, age, and their interaction. The manuscript, figures, and figure legends have been revised accordingly. Specifically:

Figure 1:

The quantification of p16 expression in Fig. 1F has been reanalyzed using two-way ANOVA. The figure has been replotted, and the corresponding legend has been updated to reflect the revised statistical approach.

Figure 2:

The quantification of AUC in Fig. 2F has been reanalyzed using two-way ANOVA. The figure and legend have been updated accordingly.

Figure 3:

The quantification of F4/80 in Fig. 3C and 3D has been reanalyzed using two-way ANOVA. The figures and corresponding legends have been revised to reflect this updated analysis.

Public Reviews:

Reviewer #1 (Public review):

Sebag et al. addressed the role of ADH5 in BAT in the development of aging and metabolic disarrangements associated with it. This is a follow-up study after the authors' demonstration of the role of BAT ADH5 in glucose homeostasis, obesity, and cold tolerance. By ablating ADH5 specifically in brown adipocytes or pharmacologically modulating ADH5 through activation of its transcription factor, the authors conclude that preservation of BAT function is crucial for healthy aging and ADH5 is causally involved in this process. The topic is appealing given the rise in the aging population and the unclear role of BAT function in this process. Overall, the study uses several techniques, is easy to follow, and addresses several physiological and molecular manifestations of aging. However, the study lacks an appropriate statistical analysis, which severely affects the conclusions of the work. Therefore, interpretation of the findings is limited and must be done with caution.

We sincerely thank the reviewer for their thoughtful and constructive comments. We fully agree that when two independent variables (genotype and age) are being evaluated, the statistical analysis must appropriately account for both factors and their potential interaction. We appreciate the reviewers’ guidance in strengthening the statistical rigor of our study.

Reviewer #2 (Public review):

Weaknesses:

(1) Sex needs to be considered as a biological variable, at a minimum in the reporting of the phenotypes observed in this manuscript, but also potentially by further experimentation. The only mention of sex I could find is that the authors reported the general protein SNO status in BAT is increased with age in male C57Bl/6J mice. Is this also true in female mice?

We thank the reviewer for this insightful comment. In response, we examined whether aging affects Hsf1 and Adh5 transcript levels in wild-type female mice (3 months vs. 19 months). Our analysis did not reveal significant age-associated changes in the expression of either gene. These results have now been incorporated into the revised manuscript and are presented in Figure 4A.

(2) It would be helpful to know the extent of ADH5 loss in the adipose tissue of knockout mice, either by mRNA or by immunoblotting for ADH5. It could also be helpful to know if ADH5 is deleted from the inguinal adipose tissue of these mice, especially since they seem to accumulate fat mass as they age (Figure 2B).

We thank the reviewer for this suggestion. Indeed, we have previously measured ADH5 expression in both brown adipose tissue (BAT) and inguinal white adipose tissue (iWAT). These data were published in Cell Reports (PMID: 3478865).

(3) For Figure 4D, it's not clear how these BAT samples were treated with HSF1A - was this done in vivo or ex vivo?

We thank the reviewer for their thoughtful comment. We have now provided additional methodological details in the revised manuscript. In Figure 4D (current Figure 4E), BAT was collected from wild-type mice and cultured ex vivo as explants. The BAT explants were treated for 24 hours with HSF1A (an HSF1 activator; 20 µM). Following treatment, mRNA levels of the indicated genes were measured by RT-qPCR.

(4) I didn't understand what was on the y-axis in Figure 5A, nor how it was measured.

We apologize for not making these critical points clearer in the initial submission. Figure 5A shows the release profiles of HSF1A from collagen gels with nanoclay (Collagen–NC–HSF1A) and without nanoclay (Collagen–HSF1A), determined using an established standard curve method (Hu et al., PMID: 33225042).

The concentration of HSF1A was quantified by UV–Vis spectroscopy. Briefly, a standard curve for HSF1A was generated by measuring the UV–Vis spectra of HSF1A at known concentrations (1.25, 2.5, 5, 10, and 20 µM) prepared in phosphate-buffered saline (PBS). Collagen gels with or without nanoclay were then fabricated to evaluate the release profile. At predetermined time points (1, 5, 9, 14, and 21 days), the PBS supernatant from each sample was collected and analyzed by UV–Vis spectroscopy. The amount of released HSF1A was calculated using the previously established standard curves. A brief description has now been included in the figure legend.

(6) Figure 1B: What is the age of the positive (ADH5BKO) and negative (Adh5 fl) mice?

We regret that we did not describe our results clearly in the first submission and have included detailed information in the revised manuscript.

(7) Figure 1F: Can you clarify what I'm looking at in the P16ink4a panels? The red staining? Is the blue staining DAPI? This is also a problem in Figures 3C, 3D and 5G, and 5I. Figure 4B looks great - maybe this could be used as an example?

We regret that we did not present results clearly in the first submission and have provided detailed information in these figures in the revised manuscript.

(8) Figure 3B looks a bit odd. Can the approach to measuring IL-1β be clarified, and could the authors explain why they can't show units of mass for IL-1β levels?

We have provided information in the revised manuscript.

(9) What are the levels of nitric oxide synthase in the BAT of the aging model? Since protein S-nitrosylation is regulated by a balance of both, the attribution of greater protein S-nitrosylation to ADH5 is incomplete without determining nitric oxide synthase.

We thank the reviewer for this thoughtful comment. In response, we have now included the analysis of iNOS transcript expression levels in the revised manuscript. These data are presented in Figure 1C.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(2) Presentation of metabolomics is not appropriate. The authors described, using color coding, the metabolites up- or downregulated in the experimental design. However, the current approach does not allow the reader to detect sample size, magnitude of changes, variability of the data, p-values, etc. This approach does not follow the standard practices of scientific rigor and should be modified. Metabolomic data could be uploaded as supplementary data, or a table with all necessary information to allow a full interpretation of the data should be provided.

We have now provided the the metabolimic data in a table format as Figure 3I.

(6) What are the levels of nitric oxide synthase in the BAT of the aging model? Since protein S-nitrosylation is regulated by a balance of both, the attribution of greater protein S-nitrosylation to ADH5 is incomplete without determining nitric oxide synthase.

We thank the reviewer for their thoughtful comment. We have now included iNOS transcript levels expression level in the revised manuscript (Figure 1C).

Minor Comments:

(1) The conclusion of the abstract is somewhat vague. I suggest the authors rewrite it to better recapitulate what was found in the study.

We thank the reviewers for this helpful suggestion. In response, we have revised the Abstract to improve the specificity and clarity of our conclusions.

(2) In the introduction, the authors mention that an increased level of mitochondrial ROS activates UCP1. Given that the evidence for this statement is circumstantial and not supported by the current state-of-the-art (PMID: 28710335), where it is accepted that UCP1 activation diminishes ROS production, I suggest that the authors tone down this statement or at least acknowledge conflicting findings and interpretations.

We thank the reviewer’s insight, we have included this important notion in the introduction.

(3) Figure 2H - It is unclear what this figure (and statistical analysis) represents. Please, improve the description of the experiment and how the data were plotted to reach such a conclusion.

We regret that we did not present results clearly in the first submission. The trend lines show the relationship between body weight and time on rotarod. The P value is the comparison of the slope of the line between Adh5 BKO mice and Adh5 fl/fl mice. The data implicate that the heavier the BKO mouse, the less time spent on the rotarod.

(4) Figure 2M - The unit of LV thickness is missing. Please, provide it. In addition, I am missing the other cardiac parameters obtained from the echocardiogram.

We have included this information in Figure 2M in the revised manuscript.

(5) Figure 2G - I believe force is not the right unit for the grip strength test. Please, revise accordingly.

We regret that we did not describe our results clearly in the first submission. We have corrected this unit in the revised figure.

(6) Figure 3H - What is the unit when reporting mitochondrial area?

We regret that we did not describe our results clearly in the first submission. We have added this information in the revised figure.

(7) Is HFS1 also downregulated in iWAT?

We thank the reviewer for this thoughtful comment. In response, we measured Hsf1 expression in iWAT from young and aged wild-type male mice. Our analysis did not reveal any significant age-associated changes in Hsf1 expression in iWAT. These results have now been included in the revised manuscript (Figure 4C).

(8) Can the authors explain how HFS1 expression increases upon HSF1 activation? I understand ADH5 is controlled by HSF1, but what would control HSF1 itself? Off targets?

We thank the reviewer for this insightful comment. At present, we do not have direct mechanistic evidence to definitively support this notion, and we cannot exclude the possibility of off-target effects of HSF1A. However, previous studies have reported that the HSF1 promoter contains heat shock elements (HSEs) in humans and HSE-like domains in mice. Based on this, we speculate that activated HSF1 may enhance its own transcription through an autoregulatory or positive feedback mechanism.

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eLife Assessment

The study by Reed et al. provides fundamental findings defining the topological changes that occur during tumorigenesis. These compelling findings enhance the understanding of stable long-range connections among genes that reprogram cancer-related functions.

Reviewer #1 (Public review):

Summary:

In their manuscript, Metz Reed and colleagues present an exceptionally thorough analysis of three-dimensional genome reorganization during breast cancer progression using the well-characterized MCF10 model system. The integration of high-resolution Micro-C contact maps with multi-omics profiling provides compelling insights into stage-specific dynamics of chromatin compartments, TAD boundaries, and looping events. The discovery that stable chromatin loops enable epigenetic reprogramming of cancer genes while structural changes selectively drive metastasis-associated pathways represents a significant conceptual advance. This work substantially deepens our understanding of genome topology in malignancy.

Strengths:

This work sets a benchmark for integrative 3D genomics in oncology. Its methodological sophistication and conceptual advances establish a new paradigm for studying nuclear architecture in disease.

Comments on revised version:

The authors made a significant effort to improve the manuscript. My comments were sufficiently addressed.

Reviewer #2 (Public review):

Using the MCF10 breast cancer progression sequence, the authors combined high-resolution Micro-C chromatin conformation capture with RNA-seq and ChIP-seq to depict the sequential reorganization of compartments, topologically associated domains (TADs), and long-range loops in benign, pre-tumor, and metastatic states, and coupled these three-dimensional changes with gene expression and enhancer activity. Four main findings were: (i) chromatin structure was largely quiescent, still limiting gene output differentiation, with upregulated sites being most significantly affected; (ii) enhancer-promoter contact strength covariated with transcriptional amplitude; (iii) 127 genes gained expression with increasing chromatin contact; and (iv) progression-related genes acquired altered histone markers in distal enhancers, which remained connected by stable loops. These conclusions are widely accepted and provide strong justification for the publication of this paper.

Reviewer #3 (Public review):

Summary:

The authors tackle an important problem- that is defining the topological changes that occur during tumorigenesis. To study this, they use an established stepwise cell model of breast cancer. A strength of their study is a careful, robust differential analysis of topological features across each cell state that is presented clearly and rigorously. They define changes in compartmentalization, TAD structure and chromatin looping. Intriguingly, when the authors integrate differential gene expression with chromatin looping, they see that most differentially regulated genes are not involved in loop changes, suggesting that changes in promoter or enhancer chromatin marks may play a bigger role in regulating transcription than differential loops. The differential topology analysis and its integration with transcription is very well done- one of the best versions of this I have read in the 3D genome field! However, the paper is framed largely as a cancer biology study and it teaches us much less about this. I am worried that some of the trends for each topologic feature are not going to be consistent across the pre-malignant-malignant-metastatic spectrum and would like the authors to soften some of their claims a bit regarding how this clarifies our understanding of cancer evolution.

Updated comments on revision:

There are still some issues with this paper. First, it reads descriptively. It is a series of comparisons with limited biologic insight as changes are always seen in genomics and in this case, they're often not tied back to transcription or gene regulation in cancer. Cell lines do not represent cancer faithfully and in this case should not be argued to represent malignant transformation broadly. The authors did not really soften their language as much as I think required. I would caution the authors to further qualify their results in the context of a single, clonal cell line that has undergone stepwise transformation. This is not a patient cohort analysis or frank progression. This matters because there is likely to be much more noise, not pertinent to transformation, in a cell line model. It doesn't negate the validity of the study, but this language should be adjusted appropriately. It was nice to see the authors compare gene expression data from their model to the primary tumor data, however the limited overlap is concerning that at the least patterns of transcriptional regulation in their model are not faithful to primary tumors. If this is the case, it raises concern that the topological changes are also not generalizable to cancer.

The authors declined a number of functional assays to validate their observations (which are purely correlative). And while I see that the burden of extra experiments may be beyond the scope of this study, they must soften their language to justify the observed relationships.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Strengths:

This work sets a benchmark for integrative 3D genomics in oncology. Its methodological sophistication and conceptual advances establish a new paradigm for studying nuclear architecture in disease.

We appreciate the very kind words.

Weaknesses:

Major Issues

(1) Functional tests would strengthen the observed links between structure and gene changes. For example, the COL12A1 gene loop formation correlates with its increased expression. Disrupting this loop using CRISPR-dCas9 at chr6 position 75280 kb could prove whether the loop causes COL12A1 activation. Such experiments would turn strong correlations into clear mechanisms.

We agree that targeted disruption of specific loops such as COL12A1 will be important for functional validation of the causal relationships between enhancer-promoter loop formation/dissipation and changes in gene expression. However, the intent of our current study was to profile changes in genome organization at a global scale to deduce general features of cancer progression-associated changes in genome organization, rather than to explore specific loop interactions. The current findings are a foundation for more targeted functional follow-up studies.

(2) The H3K27ac looping idea needs deeper validation. Data suggests H3K27ac loss weakens loops without affecting CTCF. Testing how cohesin proteins interact with H3K27acmodified sites would clarify this process. Degron systems could rapidly remove H3K27ac to observe real-time effects. Also, the AP-1 motifs found at dynamic loop sites deserve functional tests. Knocking down AP-1 factors might show if they control loop formation.

We agree that modulating histone modifications or transcription factors would provide insights into the underlying mechanisms driving the changes we observed. However, such studies utilizing degrons or small molecule inhibitors that globally knock down either H3K27ac or specific transcription factors are often difficult to interpret. For example, assessing the role of AP-1 factors, as suggested, would be complicated by the variety of AP-1 proteins. In addition, H3K27ac reduction could inhibit loop strength either directly (i.e. by reducing cohesin recruitment) or indirectly (i.e. by reducing gene expression which could in turn affect loop strength). Parsing out the exact relationships between these features will require extensive follow-up work and falls outside of the scope of the current study.

(3) Connecting findings to patient data would boost clinical relevance. The MCF10 model is excellent for controlled studies. Checking if TAD boundary weakening occurs in actual patient metastases would show real-world importance. Comparing primary and metastatic tumor samples from the same patients could reveal new structural biomarkers. If tissue is scarce, testing cancer cells with added stroma cells might mimic tumor environment effects.

We have leveraged publicly available datasets to link the observations from the progression model to clinical samples. Specifically, we have compared our datasets to chromatin organization data in non-cancerous mammary epithelial cells (HMEC), five cell lines representing distinct cancer subtypes ranging from less (luminal) to more aggressive (triple negative, TNBC), as well as tissue samples from TNBC patients with contralateral normal controls. We explored the conservation of both loops and TADs identified in the MCF10 progression system in each of these maps, paying particular attention to how features that are differential between MCF10 cells differ across other cancer cell types. We observe a high degree of conservation of static loops and TAD boundaries among the cancer samples, as well as some degree of cell-specific changes among loops and boundaries that change during MCF10 progression. These findings are included in Supplemental Figures 3 and 4 and are discussed on page 7.

Minor Issues

(1) Adding a clear definition for static loops would help readers. For example, state that static loops show less than 10 percent contact change across replicates.

Static loops are defined as loops with a fold-change of 1.5 or more between any two MCF10 cell lines and an adjusted p-value of less than 0.025 considering change across biological and technical replicates. This definition is stated on page 6).

(2) In the ABC model analysis, removing promoter regions from the enhancer list would focus results on true long-range interactions.

The ABC model already excludes the promoter of each gene. Only self-promoters are excluded, whereas the model allows promoters of other genes to act as potential long-range enhancers of the target gene. We have added text to make this clear (see page 11).

(3) Briefly noting why this study sees TAD weakening while other cancer types show different patterns would provide useful context.

The biological reason for TAD weakening in the MCF10 model is not known, but neither the mechanism for boundary weakening nor the reason for apparently different behavior amongst cancers is known. We expanded the text on this discussion slightly, but we refrain from making any definitive claims. We do note that differences in the types of cancer studied or the methods used for detecting changes in TADs (i.e. different sensitivities and thresholds for detecting change) could be responsible (see page 15). We also mention that the loss of insulation at many TAD boundaries detected in our study are subtle changes in intensity that could be potentially missed if using methods tailored to find more drastic changes in TAD architecture.

Reviewer #2 (Public review):

While the conclusions are broadly supported, methodological and analytical refinements are required.

We appreciate these comments.

(1) Model representativeness. The long-term culture-adapted MCF10 genome harbours extensive aneuploidies and translocations. Validation of key COL12A1/WNT5A loop dynamics in an independent breast-cancer line (e.g., MDA-MB-231, T47D) or in patientderived organoids/PDX models would strengthen generalizability.

Although the generation of Micro-C datasets in additional cell lines is outside of the scope of this study, we used publicly available Hi-C data from triple negative breast cancer (TNBC) progression and patient samples (Kim, Han & Chun et al. 2022) to assess generalizability of the MCF10 model findings. While these maps are lower resolution than the Micro-C maps used in our study, they are of sufficient depth to detect loops at a similar resolution (10 kb). We report these findings in Supplemental Figures 3 and 4 and discuss them on page 7.

We find that chromatin loops and TAD boundaries detected across the MCF10 system are highly conserved across all other mammary epithelial lines studied. Chromatin loops that were more prominent in MCF10AT1 and MCF10CA1a lines were also significantly stronger in TNBC cells. Insulation score boundaries that were weakened in MCF10CA1a showed strong insulation across all cell lines in TNBC. These findings highlight that different model systems indeed have distinct profiles of structural change, just as they have distinct gene expression profiles.

It is worth noting that direct comparison at individual loci is complicated by variations in gene expression profiles between the MCF10 model and the TNBC progression model; for example, COL12A1 is not significantly upregulated between normal and TNBC tissues in this study (unlike in the TCGA-BRCA data) and is downregulated between HMEC and TNBC cell lines. Regardless, our analysis provides some indication of conserved and divergent features in the various model systems.

(2) The study remains purely correlative; no perturbation experiments are conducted to demonstrate causal roles of chromatin loops on gene expression. CRISPR interference (CRISPR-Cas9-KRAB/HDAC) or enhancer deletion/inversion should be applied to 3-5 pivotal loops (e.g., COL12A1, WNT5A) to test their impact on target-gene expression and cellular phenotypes (e.g., proliferation, migration).

We agree that targeted disruption of specific loops such as COL12A1 will be important for understanding the causal relationships between enhancer-promoter loop formation/dissipation and changes in gene expression. However, the intent of our current study was to profile changes in genome organization at a global scale to deduce general features of cancer progression-associated changes in genome organization, rather than exploring specific loop interactions. The current findings are a foundation for more targeted follow-up functional studies.

(3) The manuscript lacks integration with clinical datasets. Integrate TCGA-BRCA data to assess whether elevated COL12A1/WNT5A expression associates with overall survival (OS) or distant metastasis-free survival (DMFS)

To assess clinical significance of specific loci, we have queried expression of all differentially expressed genes in the MCF10 progression system among TCGA-BRCA expression data. We summarize our findings in Supp. Fig. 5E and discuss them on page 8.

We found that roughly 25% of genes that change in our model also change significantly in breast cancer, but only roughly half of those genes change in the same direction (i.e. up-regulated in MCF10CA1a vs MCF10A, and up-regulated in tumor vs normal samples). Interestingly, there was a higher degree of directional agreement between latechanging genes (i.e. genes that change in MCF10CA1a compared to MCF10A and MCF10AT1) than early-changing genes (i.e. genes that change in MCF10AT1 and MCF10CA1a compared to MCF10A).

We have also explored the impact of select highlighted genes on overall survival (OS). We present these data in Supp. Fig. 6 and discuss it on page 8. While not all genes showcased in this study have a significant impact on overall survival, most trend in the same direction as their differential expression would suggest (i.e. genes more highly expressed in cancer vs tumor also have a hazard ratio above 1).

Reviewer #3 (Public review):

The differential topology analysis and its integration with transcription is very well done- one of the best versions of this I have read in the 3D genome field!

We appreciate the reviewers’ endorsement.

However, the paper is framed largely as a cancer biology study, and it teaches us much less about this. I am worried that some of the trends for each topologic feature are not going to be consistent across the pre-malignant-malignant-metastatic spectrum and would like the authors to soften some of their claims a bit regarding how this clarifies our understanding of cancer evolution.

We agree that the strength of the study lies in its deep mapping of chromatin architecture and the landscape of enhancers and differentially expressed genes, which we hope to use to better understand the relationship between chromatin structure and gene expression, regardless of their cancer relevance. To better relate the findings in the progression system to cancer, we have added new data from direct comparisons of the MCF10 progression system with multiple patient-derived cancer cell lines and cancer tissues. These data are shown in Supp. Fig. 3 and 4 and discussed on p. 7. Regardless, we have softened the claims regarding cancer progression throughout the manuscript.

Weaknesses:

Major Concerns:

(1) The integration of gene expression and chromatin loops is intriguing. The authors' differential analysis, however, omits consideration of genes that are on and simply further upregulated versus genes that transition on/off or off/on. It would be nice to see the authors break out looping patterns for these two different patterns of regulation, as it may be instructive regarding the rules for how EP loops govern transcription.

To address different types of gene expression patterns, we analyzed 108 genes that went from an unexpressed or “off” state (2 or fewer read counts) in one cell line to an expressed “on” state (100 or more read counts) in another, and 111 genes that go from “on” to “high” (1000 or more read counts). We present these data in Supp. Fig. 8 and discuss the findings on page 9. While neither of these genes were enriched for differential loops, a large number overlap with loop anchors. We found a relationship between loop strength and gene expression levels; genes that are more strongly expressed are more likely to overlap with the anchor of a chromatin loop. All gene sets show similar strong trends at distal regulatory regions.

(2) Given the paucity of differential loops at the majority of genes whose expression changes, the authors should examine chromatin subcompartments, as these may associate more with differential transcription.

We present subcompartment analysis in Supp. Fig. 9. Our CALDER compartment calls are qualitative rather than quantitative, so to explore this we examined how compartments change genome-wide and at specific promoters. We show these data in Supp. Fig. 9 and discuss the findings on page 10-11. We see that between any two cell types, a majority of changes occur between closely related subcompartments, i.e. from A.2.2 to A.2.1 (1 step more A-like) or B.1.1 (1 step more B-like). The promoters of differentially expressed genes have minimal subcompartment changes, but genes that shift from on to off have larger changes. Differentially expressed genes with promoters that shift by multiple subcompartments have significant impacts on fold-change, but smaller shifts have minimal impacts on gene expression. In summary, small changes in subcompartments are very common and have little impact on gene expression, while larger changes are infrequent and correlate more strongly with changes in gene expression.

(3) The authors could push their TAD analysis further by integrating it with transcription. Can they look at genes and their enhancers that span these altered boundaries to see if these shifts impact transcription?

We provide this analysis in Supp. Fig. 9. We started, as suggested, by looking at genes with distal enhancers (as determined by the ABC model) that span a single TAD boundary. However, the number of genes that fit this definition was relatively small, so we expanded to look at any genes with promoters in the proximity (50kb) of differential insulation score boundaries, for which we saw the same trends with more robust signal. Our findings are shown in Supp. Fig. 9 and discussed on page 10. We found that genes near weakened boundaries are not enriched for differentially expressed genes, while those near strengthened boundaries are. Comparing the fold-change of genes near strengthened, weakened, and static boundaries showed a significant inverse correlation between boundary strength and gene expression, although effect sizes were small. These results show that changes in TAD boundary insulation have small but noticeable impacts on gene expression.

(4) The progression of cancer critically goes from a benign -> pre-malignant -> malignant -> metastatic series of steps. The AT1 line is described as 'premalignant' and thus the authors' series omits a malignant line. While I think adding such a sample is an unreasonable request at this point (as it would have had to have been studied in 'batch' with these other samples), the authors should acknowledge that they omit this step and spend some time discussing the genetic, morphologic, and phenotypic features for their 3 conditions. The images in Figure 1S aren't particularly useful- they don't tell the reader that these cells are malignant/benign. The karyotypic data are intriguing but not fully analyzed, so it is hard to know what true phenotype these cells represent. For example, malignant means DCIS/invasive carcinoma - so then what does this pre-malignant cell model represent? The described alteration in the AT1 line is a Ras oncogene, so in some sense, the transition to this line really is just +/- Ras. The authors could spend some time thinking about the effects of Ras specifically on the 3D genome.

We have expanded our discussion of the relevance of the MCF10 model on page 4, and the limitations of the model on page 17. The MCF10 progression model has been extensively used by many laboratories, and its properties have been discussed in detail (i.e. Polizzotti et al. 2012). Critically, the MCF10AT1 cell line is the product not only of Ras oncogene expression but then derived from a 100-day-old precancerous lesion that formed a squamous carcinoma in a mouse, and over this time it accumulated additional changes. The MCF10AT1 line is considered pre-malignant as it has accrued critical changes that prepare it for the metastatic transition, but it does not immediately form tumors when injected back into mice. Unlike the MCF10DCIS cell line which is malignant but not metastatic, the more aggressive MCF10CA1a is classified as both malignant and highly metastatic, forming tumors that quickly metastasize to the lungs in mouse xenografts. While both MCF10AT1 and MCF10CA1a are tumorigenic, we acknowledge the lack of a nonmetastatic malignant cell line in the discussion on page 17. We have also provided updated karyotype characterization of the cell lines used in this study in Supp. Fig. 1B and now include full composite karyotypes in the Methods section (page 18).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The reviewer’s recommendations are the same as their public review comments. See our response to the review comments above.

Reviewer #2 (Recommendations for the authors):

(1) If conditions permit, it is recommended that inclusion of primary human mammary epithelial cells (HMECs) to distinguish immortalisation-specific from malignancy-specific 3D changes.

Micro-C data of equal resolution is not available for HMECs. We have, however, incorporated analysis of publicly available deeply sequenced Hi-C data of HMECs into several figures that explore the conservation of loops and TADs in these cells (Supp. Fig. 3 and 4).

We find that chromatin loops and TAD boundaries detected across the MCF10 system are highly conserved across all other mammary epithelial lines studied. Chromatin loops that were more prominent in MCF10AT1 and MCF10CA1a lines were also significantly stronger in TNBC cells. Insulation score boundaries that were weakened in MCF10CA1a showed strong insulation across all cell lines in the TNBC system. These findings highlight that different model systems indeed have distinct profiles of structural change, just as they have distinct gene expression profiles.

(2) The relationship between loop alterations and copy-number variations (CNVs) is not explored. If conditions permit, it is recommended that overlay differential loops with SNP/Indel/CNV data to exclude spurious differences arising from structural alterations.

While we have not conducted an in-depth SNP analysis, we have clarified our discussion of the karyotype analysis on pages 21 and 23 and how we mitigated these effects when identifying differential loops between cell lines.

(3) The horizontal and vertical coordinates of the diagram are difficult to view; it is recommended that the size of the text on the picture be adjusted to ensure that it is clear to read. Some of the text coordinates of the figure are labeled in gray; it is recommended that they be in black.

The clarity of the figures has been improved.

Reviewer #3 (Recommendations for the authors):

I really like this paper. I think if the cancer focus can be down-emphasized (because I'm not fully clear what we've really learned about cancer), then it represents a nice dataset and a thoughtful, comprehensive analysis.

We greatly appreciate the kind words and helpful feedback. The cancer focus has been toned down throughout the manuscript, as suggested.

Minor Concerns:

(1) The authors present a nice summary of the topological changes across samples. However, summary statistics can mask noise/bias and also don't fully convey the effect size of the reported changes. Highlighting individual loci and visualizing these would strengthen the paper and participate in maintaining a high standard for our genomic studies of topology, in which we summarize, but also provide representative examples. I would appreciate seeing more example plots at distinct loci (even if in the supplemental information).

We have included several more example regions in Supp. Fig. 7 and 12, including four looped genes that change similarly between the MCF10 series and TCGA-BRCA data (2 stably looped genes and 2 differentially looped genes, 2 up-regulated and 2 downregulated), and six differentially looped and differentially expressed genes (3 which change in the same direction as the loops, and 3 which change in the opposite direction).

(2) "To identify loops that changed significantly during cancer progression, we assessed changes in contact frequency among every loop in each cell type, correcting for karyotypic differences that result in differences in coverage between cell lines (see Methods)." The Methods section is not adequately explained. Also, could you go a bit deeper to define if these large-scale changes shift the 3D genome specifically? This is hard, but there may be some low-hanging fruit given the otherwise fairly isogenic features in your model.

We have added more detail to the Methods section on pages 21 and 23 on how karyotypic abnormalities were included in our analysis and differential loop calling. A deeper analysis of how large-scale karyotypic changes affect chromatin organization (i.e. through the formation of neoloops and TADs through translocations) is indeed an attractive subject, but due to its complexity requires a separate dedicated study.

(3) "Approximately half of chromatin loops featured some combination of active gene promoters and enhancers within 10kb of loop anchors". The authors have high-resolution topology data and should be more stringent; these features should have to overlap loop anchors or at least use a distance less than 10kb, which, in some sense, forfeits the advantages of high-resolution topology data.

The threshold of 10kb was chosen for several specific reasons: First, the loop sizes detected here are large enough that this relatively large region still represents a small fraction of the loop span, and these regions are reasonably considered anchor-proximal. Second, the loops we detect are non-punctate, both in aggregate analysis (Figure 1H, bottom) and at individual loci (see example regions), showing increased contact frequency among several 5kb or 10kb bins. Therefore, adding 10kb to either side (2 pixels on 5kb maps and 1 pixel on 10kb maps) ensures that the full region of increased contact frequency is included. Finally, ultra-resolution Hi-C data has also shown that loops remain diffuse even with 1kb resolution maps (albeit they do get smaller than the 30kb used here) (Harris & Gu 2023). We have added a brief justification of this overlap size to the text on page 24.

(4) "These results show that not only changes in either contact frequency and enhancer activity correlate with increased gene expression, but they also correlate with each other, suggesting a potentially linked functional role during enhancer-promoter communication." The authors could use this opportunity to disentangle the contributions of loops and chromatin modifications a bit more. The exceptions are of interest - e.g., loop is stable, gene expression changes or loop changes, gene expression does not. Highlighting exemplar cases for these exceptions (rather than just a genomics summary) would be nice to see.

The additional example regions we have included in Supp. Fig. 7 and 12 now showcase a wider variety of scenarios; in addition to more examples of static loops with gene expression changes (Fig. 2, Supp. Fig. 7E-F) and differential loops with matching gene expression changes (Fig. 4, Supp. Fig. 7C-D, Supp. Fig. 12A-C), we now also feature examples of differential loops where gene expression changes in the opposite direction (i.e. a strengthened loop at a down-regulated gene, Supp. Fig. 12D-F).

eLife Assessment

This study reports a novel function for syntaxin 11, a specialized SNARE protein critical for the immune system whose mutations cause familial hemophagocytic lymphohistiocytosis type 4. The data convincingly show that depletion of STX11 impairs store-operated calcium entry in Jurkat T cells and that this defect is recapitulated in primary cells from a patient suffering from the disease; the authors further show that the syntaxin interacts with the pore subunit of the ORAI1 channel and propose that it primes the channel by promoting the assembly of multimers before activation by its endogenous ligand, the ER Ca2+ sensing protein STIM1. This is a conceptually important claim that challenges the prevailing view that all structural transitions in ORAI1 are STIM-driven. The data are high-quality and broadly consistent with the interpretation, but alternative mechanisms for the defects are not considered; additional work should rule out vesicular trafficking, discuss other mechanisms, and address methodological issues.

Reviewer #1 (Public review):

Summary:

Patients with STX11 mutations develop familial hemophagocytic lymphohistiocytosis Type 4, a fatal immune disorder marked by defective T and NK cell cytotoxicity and cytokine storm. The conventional explanation attributes this to impaired cytotoxic granule release, but this has never fully accounted for the broader disease picture. This study proposes an alternative mechanism. The authors show that STX11 is required for store-operated calcium entry through ORAI1 channels, which are essential for both cytotoxic killing and NFAT-driven gene expression in T cells. In STX11-deficient cells, ORAI1 currents drop, NFAT nuclear translocation fails, IL-2 expression is suppressed, and degranulation is impaired. These defects are largely rescued by ionomycin or a constitutively active ORAI1 mutant, placing the primary lesion at calcium signaling rather than the fusion machinery. Mechanistically, STX11 binds the C-terminal tail of ORAI1 via its Habc domain and maintains ORAI1 in a state competent for productive assembly prior to STIM1-dependent gating, a step the authors call "priming."

Strengths:

The paper identifies a novel and disease-relevant role for STX11 in calcium channel regulation and raises the possibility of using channel agonists as a therapeutic strategy in the disease. The biochemical and functional data are of high quality and generally consistent with the interpretation. The proposal that a non-conventional syntaxin directly interacts with ion channels to prime its activation is novel and interesting.

Weaknesses:

For readers to appreciate the value of patient experiments derived from a single individual, the authors should quote prior studies showing that STX11 protein levels are abolished in all known human STX11 mutations. The priming model, while functionally well-supported, rests on indirect structural evidence, and the precise conformational transition involved remains to be defined. These are acknowledged limitations, but alternate mechanisms have not been explored and formally excluded. More direct evidence should be provided to exclude the possibility that STX11 could act as a conventional SNARE and sustain calcium fluxes by promoting the delivery of additional ORAI1 channels from vesicles.

Reviewer #2 (Public review):

Summary:

Vig's lab delineates a critical role for STX11 in CRAC channel function, particularly in the context of the fatal immune disorder familial hemophagocytic lymphohistiocytosis type 4 (FHL4). They demonstrate that Syntaxin 11 directly binds and regulates Orai1, and that STX11 depletion abolishes CRAC currents and downstream signaling. Loss of STX11 reduces IL2 gene expression and impairs degranulation, both of which are rescued by the constitutively active Orai1 mutant H134S, whereas a gain‑of‑function mutant targeting the C‑terminus fails to restore these defects. The authors conclude that STX11 primes Orai1 for optimal local assembly that is independent of STIM1 yet required for CRAC channel gating.

Strengths:

This study is firmly grounded in disease biology and demonstrates that STX11 downregulation leads to profound functional defects. Using a comprehensive suite of methods and analyses, the authors interrogate the co-regulation of STX11 and Orai1 and present a near-complete view of STX11's modulatory role in CRAC channel function and downstream signaling pathways. The figures are clear, and the statistical analyses are rigorous and convincing.

Weaknesses:

The authors conclude that Syntaxin 11 directly binds Orai1. This conclusion is well supported by a multifaceted approach, including co-immunoprecipitation (co-IP), molecular dynamics simulations, co-localization/FRET assays, and targeted mutational analysis-all of which are thoroughly executed. While the interaction appears reasonably strong in co-IP experiments, the STX11-Orai1 interaction is comparatively weaker in pull-down assays, which the authors attribute to instability of the purified His-STX11 protein. A remaining gap is direct evidence of interaction in live cells; this is understandably challenging given that fluorescent tagging of STX11 is not feasible. Fully resolving this question lies beyond the scope of the present study and will require more advanced approaches to capture STX11 binding dynamics.

Author response:

eLife Assessment

This study reports a novel function for syntaxin 11, a specialized SNARE protein critical for the immune system whose mutations cause familial hemophagocytic lymphohistiocytosis type 4. The data convincingly show that depletion of STX11 impairs store-operated calcium entry in Jurkat T cells and that this defect is recapitulated in primary cells from a patient suffering from the disease; the authors further show that the syntaxin interacts with the pore subunit of the ORAI1 channel and propose that it primes the channel by promoting the assembly of multimers before activation by its endogenous ligand, the ER Ca2+ sensing protein STIM1. This is a conceptually important claim that challenges the prevailing view that all structural transitions in ORAI1 are STIM-driven. The data are high-quality and broadly consistent with the interpretation, but alternative mechanisms for the defects are not considered; additional work should rule out vesicular trafficking, discuss other mechanisms, and address methodological issues.

We thank the editor and reviewers for assessing our work. Although significant amount of data in this paper already rule out any potential defects in the vesicular trafficking of Orai1 in cells lacking STX11, we will still include the additional suggested experiments. In the revised version, we will include the various experiments that we had already performed to measure vesicular trafficking and ER-PM junctions in STX11 depleted cells. We will discuss any remaining alternate explanations, include missing methods, quantifications and calibrations, where applicable, and provide response to each of the reviewer’s comments.

Public Reviews:

Reviewer #1 (Public review):

Weaknesses:

For readers to appreciate the value of patient experiments derived from a single individual, the authors should quote prior studies showing that STX11 protein levels are abolished in all known human STX11 mutations. The priming model, while functionally well-supported, rests on indirect structural evidence, and the precise conformational transition involved remains to be defined. These are acknowledged limitations, but alternate mechanisms have not been explored and formally excluded. More direct evidence should be provided to exclude the possibility that STX11 could act as a conventional SNARE and sustain calcium fluxes by promoting the delivery of additional ORAI1 channels from vesicles.

In the revised version, we will include references for the prior STX11 human mutations that have been biochemically characterized till date (Bryceson, Rudd et al. 2007);(Muller, Chiang et al. 2014);(Macartney, Weitzman et al. 2011);(Marsh, Satake et al. 2010). As the reviewer has correctly pointed out, the STX11 protein levels were almost completely abolished in these studies. Therefore, the prior mutations are essentially comparable to the frameshift mutation characterized in this study, in terms of the mechanisms underlying the phenotypic defects reported here versus earlier. From a mechanistic point of view, we believe that our data from even a single FHLH4 patient, where STX11 levels were severely depleted, and additional knockdown studies across three different cell lines, are representative of all STX11 patients that have been reported thus far.

Regarding the Reviewers’ concern that absence of STX11 as a conventional SNARE could affect Orai1 channel delivery from intracellular vesicles. We would like to point out the following:

(1) In Miao et al. 2013 (Miao, Miner et al. 2013), Figure 3C-D, we conclusively showed that expression of a dominant negative mutant of NSF, a non-redundant protein in vesicle trafficking, impaired vesicle trafficking but did not impair SOCE. This experiment had essentially ruled out a role for vesicle trafficking in SOCE. In the same paper, we had also shown that Orai1 levels in the PM do not increase post-store depletion (Figure 3, figure supplement 2).

(2) In this manuscript (Supplementary Figure 3B), we have shown that U2OS cells stably expressing Orai1-BBS-YFP have identical levels of Orai1 in the PM with and without STX11 depletion. This shows that the biosynthesis or delivery of Orai1 to the PM is not affected by STX11 depletion, another broadly classified member of the vesicle trafficking. The levels were also assessed in store-depleted U2OS cells but not included here because in Miao et al. 2013 we had already shown that levels of PM Orai1 are essentially equal in resting and store-depleted cells. In our revised submission, we will include the data from store-depleted cells in U2OS and also repeat this experiment in the other cell types used in this paper. In addition, in our revised submission, we will include three different vesicle trafficking assays performed in STX11 depleted cells.

(3) Most importantly, in Figure 7I-J of this manuscript, we showed that calcium influx from a constitutively active mutant Orai1 (Orai H134S) is identical between STX11 depleted and scramble control cells. If wildtype Orai1 was indeed stuck in vesicles in STX11 depleted cells, then how would H134S Orai1 be able to rescue the defect in SOCE? In fact, the Orai1 mutant calcium flux assays were done using a 20X water objective, to visualize and confirm whether the expression of mutant and WT Orai1 was comparable in the PM. We will include the quantification of PM levels of Orai1 mutants w.r.t WT Orai1 in the revised paper.

(4) We have generated and been using HEK293, U2OS and Jurkat cell lines that stably express fluorescently tagged Orai1 for most of our experiments (Miao, Miner et al. 2013); (Li, Miao et al. 2016);(Ramanagoudr-Bhojappa, Miao et al. 2021). In each case, we have never observed Orai1 in intracellular vesicles with or without store depletion. In all cases, it is constitutively and stably expressed in the PM.

In summary, significant amount of data in this paper already rule out any potential reduction in the PM levels of Orai1 in cells lacking STX11. We will still do the additional experiments suggested by the Reviewer 1.

Regarding STX11 induced precise conformational transition, we are trying to setup collaborations with scientists who might be able to visualize this in vivo.

The readers should note that purification of isolated pore subunits of ion channels followed by crystallization or expression in membranes for cryo-EM is currently considered a gold standard in the analysis of ion channel pore subunits. However, we have shown that ion channels are dynamic macromolecular complexes, in vivo (Li, Miao et al. 2016), where synaptic proteins dynamically bind to induce conformational changes and affect their stoichiometry (Li, Miao et al. 2016). Please also see (Chorev, Baker et al. 2018) and (Dorwart, Wray et al. 2010). More advanced in vivo approaches therefore need to be developed to enable visualization of the dynamics of ion channel macromolecular complexes in the native environment. In the absence of such approaches, the structural insights obtained from detergent purified subunits will remain incomplete and biased.

Reviewer #2 (Public review):

Weaknesses:

The authors conclude that Syntaxin 11 directly binds Orai1. This conclusion is well supported by a multifaceted approach, including co-immunoprecipitation (co-IP), molecular dynamics simulations, co-localization/FRET assays, and targeted mutational analysis-all of which are thoroughly executed. While the interaction appears reasonably strong in co-IP experiments, the STX11-Orai1 interaction is comparatively weaker in pull-down assays, which the authors attribute to instability of the purified His-STX11 protein. A remaining gap is direct evidence of interaction in live cells; this is understandably challenging given that fluorescent tagging of STX11 is not feasible. Fully resolving this question lies beyond the scope of the present study and will require more advanced approaches to capture STX11 binding dynamics.

We thank the reviewer for acknowledging that the above studies will require standardization of advanced techniques which are beyond the scope of the present study. We plan to continue developing methods that will allow us to visualize the binding and unbinding of STX11 to Orai1 in vivo.

References:

Bryceson, Y. T., E. Rudd, C. Zheng, J. Edner, D. Ma, S. M. Wood, A. G. Bechensteen, J. J. Boelens, T. Celkan, R. A. Farah, K. Hultenby, J. Winiarski, P. A. Roche, M. Nordenskjold, J. I. Henter, E. O. Long and H. G. Ljunggren (2007). "Defective cytotoxic lymphocyte degranulation in syntaxin-11 deficient familial hemophagocytic lymphohistiocytosis 4 (FHL4) patients." Blood 110(6): 1906-1915.

Chorev, D. S., L. A. Baker, D. Wu, V. Beilsten-Edmands, S. L. Rouse, T. Zeev-Ben-Mordehai, C. Jiko, F. Samsudin, C. Gerle, S. Khalid, A. G. Stewart, S. J. Matthews, K. Grunewald and C. V. Robinson (2018). "Protein assemblies ejected directly from native membranes yield complexes for mass spectrometry." Science 362(6416): 829-834.

Dorwart, M. R., R. Wray, C. A. Brautigam, Y. Jiang and P. Blount (2010). "S. aureus MscL is a pentamer in vivo but of variable stoichiometries in vitro: implications for detergent-solubilized membrane proteins." PLoS Biol 8(12): e1000555.

Li, P., Y. Miao, A. Dani and M. Vig (2016). "alpha-SNAP regulates dynamic, on-site assembly and calcium selectivity of Orai1 channels." Mol Biol Cell 27(16): 2542-2553.

Macartney, C. A., S. Weitzman, S. M. Wood, D. Bansal, M. Steele, M. Meeths, M. Abdelhaleem and Y. T. Bryceson (2011). "Unusual functional manifestations of a novel STX11 frameshift mutation in two infants with familial hemophagocytic lymphohistiocytosis type 4 (FHL4)." Pediatr Blood Cancer 56(4): 654-657.

Marsh, R. A., N. Satake, J. Biroschak, T. Jacobs, J. Johnson, M. B. Jordan, J. J. Bleesing, A. H. Filipovich and K. Zhang (2010). "STX11 mutations and clinical phenotypes of familial hemophagocytic lymphohistiocytosis in North America." Pediatr Blood Cancer 55(1): 134-140.

Miao, Y., C. Miner, L. Zhang, P. I. Hanson, A. Dani and M. Vig (2013). "An essential and NSF independent role for alpha-SNAP in store-operated calcium entry." Elife 2: e00802.

Muller, M. L., S. C. Chiang, M. Meeths, B. Tesi, M. Entesarian, D. Nilsson, S. M. Wood, M. Nordenskjold, J. I. Henter, A. Naqvi and Y. T. Bryceson (2014). "An N-Terminal Missense Mutation in STX11 Causative of FHL4 Abrogates Syntaxin-11 Binding to Munc18-2." Front Immunol 4: 515.

Ramanagoudr-Bhojappa, R., Y. Miao and M. Vig (2021). "High affinity associations with alpha-SNAP enable calcium entry via Orai1 channels." PLoS One 16(10): e0258670.

must abolish current systems of colonialism. must study the origins of healthcare and what groups of people it benefitted and left out. how did western bio-medical science possibly contribute to men's health problems

how are these investments going to process? in the form of grant proposals, or needs based rewards? how can it be treated less as a competition but rather a financial support for all NGOs and public institiutions?

this movement is very political, and must understand the agendas of each winning party of provinces, municipals, and federal. must understand the the upward trends of what each political party priortizes, how also foreign policies and economics has changed the trajectory of Canadian politics.

Ogichidaa, part of the Anishnaabe language, means a person held in high esteem due to their large heart. This kind of warrior is not one defined by violence but by love, meant to dedicate their entire life to building sustaining and protecting community.

there are different kinds of masculinity representations aside from western or post colonial culture.

Tailoring healthcare and scientific language to speakable and accessible terms for everyone who has not reached into the same depths of academia. For men of different languages and backgrounds who hadn't completed education, still requires a form of information to understand their health issues.

While I understand that this report has posted links to other Indigenous reports. It still needs to mention how colonialism is an ongoing project. How was the kinship broken and why. How does Canadian laws, politics, and social systems affect Indigenous communities? Sixities scoop, residential schools, starlight tours, MMIW, etc.

How can we intersect and connect colonialism to the South Asian and Black diaspora communities in Canada.

3 common factors of men's suicidality 1. drug use 2. being unmarried, single, divorced or widowed 3. depression

Centralizing services for men by connecting with NGOs and cultural communities, keeping a telehealth and in person communication with men.

How can FSP join the services that can promote boys and men's emotional and relational health.

there needs to be research on trans and non-binary people, the gender spectrum in relation to men's health

main focus on men's health

The paper introduces Zman-seq, a novel single-cell genomic technology designed to capture the temporal dynamics of cells in vivo. Traditionally, single-cell RNA sequencing (scRNA-seq) only provides static snapshots of gene expression. Zman-seq overcomes this by introducing fluorescent "time stamps" into circulating immune cells, allowing researchers to track exactly how long these cells have been in a tissue and how their gene expression changes over time.

The researchers applied this technology to study the highly immunosuppressive tumor microenvironment (TME) in glioblastoma (GBM), mapping the rapid trajectory of how competent immune cells are corrupted by the tumor.

Key Findings

Rapid NK Cell Dysfunction: Within just 24 hours of entering the tumor, cytotoxic Natural Killer (NK) cells lose their anti-tumor capabilities and transition into a dysfunctional state, a process heavily driven by TGF-β signaling from the tumor environment.

Monocyte-to-TAM Reprogramming: Over the course of 36 to 48 hours, infiltrating monocytes are instructed by the tumor to differentiate into immunosuppressive tumor-associated macrophages (TAMs). This transition is marked by the upregulation of suppressive checkpoints like Trem2, Il18bp, and Arg1.

TREM2 Immunotherapy: By treating the glioblastoma models with an antagonistic antibody that blocks TREM2 signaling, the researchers successfully disrupted the tumor's influence. Instead of becoming immunosuppressive TAMs, the monocytes were redirected into becoming pro-inflammatory macrophages that fight the tumor.

v_2?

only on the cancer cell line, not on tumour.

Core Objective The study aimed to understand the diverse populations and functions of immune cells (specifically myeloid cells like microglia and macrophages) within the tumor microenvironment of glioblastomas, a type of brain cancer that is notably more prevalent in men.

Methodology The researchers utilized single-cell RNA sequencing (scRNA-seq) to analyze CD11b+ myeloid cells taken from both healthy (naïve) mice and mice bearing GL261 gliomas.

Key Findings

Distinct Cell Populations: The study successfully identified and separated the distinct molecular profiles of resident brain microglia (MG), infiltrating peripheral monocytes/macrophages (Mo/MΦ), and CNS border-associated macrophages (BAMs).

Specific Markers & Spatial Distribution: They validated specific protein markers to tell these cells apart (e.g., Tmem119 for microglia and Gal-3 for monocytes/macrophages). Using these markers, they discovered that microglia tend to surround the tumor's outer edges, whereas infiltrating monocytes/macrophages dive deep into the tumor core.

Tumor-Induced Activation: The tumor environment alters the gene expression of both microglia and infiltrating macrophages. However, the infiltrating macrophages showed a much stronger activation of immunosuppressive genes, suggesting they differentiate into cells that actively help the tumor evade the immune system.

Significant Sex Differences: A major discovery was that glioma-activated microglia and some macrophages in male mice exhibited significantly higher expression of MHCII (major histocompatibility complex II) genes compared to females. The researchers corroborated this finding using data from human diffuse gliomas as well.

replace: "corporate animal welfare campaigns"

eLife Assessment

This valuable study demonstrates that a multi-step differentiation programme in bacteria combining a bistable switch with two quorum-sensing systems is capable of generating autonomous and self-organized spatial patterns. The evidence for the core engineering system supporting patterning across several conditions is convincing, albeit incomplete for the stronger differentiation/maturation claims because the irreversibility of the proposed states is not consistently established, and some modelling and conceptual interpretation details require further clarification.

Reviewer #1 (Public review):

Summary:

This paper by Boni and colleagues presents the engineering of a multi-step differentiation program in Escherichia coli based on synthetic gene circuits. The motivation behind the study was to engineer a system capable of undergoing differentiation in a step-wise manner without the presence of external spatial cues and without inducers added during the differentiation process. To achieve this, the authors created several synthetic gene circuits, one being a toggle switch, and the others being quorum-sensing-mediated gene expression modules. The outputs of the differentiation process are fluorescent proteins, which allowed the authors to quantify the behavior of the system using fluorescence intensity measurements. The authors additionally built a multi-component mathematical model which is able to reproduce the experimental data.

The data presented are convincing and support the claims; the work is well executed.

Strengths:

(1) The differentiation process proceeds autonomously after the initial step in liquid culture in the presence of external inducers.

(2) It is indeed a step-wise process.

(3) The mathematical model predicts the outcome (% of green, blue and red FP-expressing cells in the population) when changing the initial ratio of green:blue FP-expressing cells.

Weaknesses:

(1) No spatial pattern emerges. There are some isolated colonies that turn on the downstream FPs, but I do not see a pattern, really. Nonetheless, some colonies do differentiate (i.e. they turn on additional FPs).

(2) The mathematical model appears somewhat superfluous. While it can clearly reproduce the data, it is not used to make interesting predictions, changing parameters (and not initial conditions) that guide further experimental implementations.

Future directions

The utility of this differentiation process (e.g. in metabolic engineering or for the study of biofilm formation and antibiotic resistance) will become clearer once the FPs are substituted with functional proteins that exert an effect on the cells.

Reviewer #2 (Public review):

In this manuscript, the authors implement a three-step genetic programme in E. coli that converts an initially homogeneous population into spatially structured sender, receiver, and "matured" receiver colonies on agar without externally supplied positional information. They combine a TetR/LacI toggle switch for symmetry breaking, LuxI/LuxR quorum sensing for a paracrine signalling step, and CinI/CinR for an autocrine signalling-like maturation step, and complement the experiments with a mathematical model that qualitatively reproduces pattern formation over a range of initial conditions.

While the article has many strengths such as a clear conceptual framing using Waddington landscapes, a modular and carefully optimised circuit design, thorough experimental characterisation of the toggle and quorum-sensing modules, integration of spatial modelling with experiments, and generally clear writing and figures, I think it will benefit the article to clarify the definition and stability of "differentiated" states, clarify several quantitative and modelling aspects, better explain how fitted curves and promoter engineering were done, and improve some figure design and wording to avoid ambiguity.

Detailed comments below:

(1) P5-8 / and more generally: A major concern is that producing a reporter output is not, by itself, differentiation. For a state to be credibly called "differentiated", it should be stable (self-maintained) over relevant timescales, ideally in the absence of the inducing context. As written, the manuscript sometimes seems to equate cell type with reporter expression. I strongly suggest adding a short subsection explicitly defining state versus output, and for each claimed state, stating whether it is stable/bistable or unstable/reversible, with evidence. Concretely, the authors should enumerate:<br /> a) Toggle-derived sender versus receiver: stable? under what conditions (inducer ranges, hysteresis window)?<br /> b) Paracrine-induced "red" receivers: is this a stable differentiated state, or a context-dependent induction requiring proximity to senders?<br /> c) "Mature" (yellow) state: does it persist after removal from the spatial signal field? If not, it should be described as an induced output programme rather than a mature lineage state.

At present, later sections (and the "maturation" language) risk over-stating what is demonstrated.

(2) Figure 2d: It is unclear whether this panel is intended to be qualitative (schematic/illustrative) or generated from quantitative data. The legend should explicitly state the origin (e.g., representative image, averaged data, simulation output, schematic) and, if quantitative, what was measured, how many replicates, and how the visualisation was constructed.

(3) Figure 2e: The cross-sectional line is described as meant to be comparable, yet the leftmost plot appears to have a different slope from the others. The authors should explain whether this reflects a different scaling/normalisation, a different underlying dataset/condition, or simply a plotting artefact. If these are fitted trends, report the fit function (see also the comment on fitted lines below).

(4) Around P7-8: (saddle/separatrix description): When describing the saddle or separatrix between the two valleys, it would be helpful to briefly connect this more directly to a quantitative dynamical-systems perspective: for instance, the intersection of nullclines and how nullcline geometry changes under IPTG/aTc induction. This will make the landscape picture more complete for readers familiar with the original genetic toggle switch work (Garder et al., 2000).

(5) P9, lines 157-159: The current phrasing ("in absence of noise, the system would be fully deterministic... in living cells, however, stochastic bursts... change the trajectory") risks conflating predicting population-level percentages with predicting colony-level trajectories. It would help to clearly separate (i) the ability to predict the overall fraction of ON/OFF (green/blue) colonies from inducer conditions (which is largely deterministic at the population level) from (ii) the intrinsically stochastic choice of state made by any given founder cell and its colony.

(6) P11, lines 193-195 (promoter engineering): The main text currently only refers to screening variants and choosing pLux76; I suggest briefly stating in the main text (not only in the supplement) what was changed (for example, promoter box variants, core promoter strength modifications) and what design criteria were used (reduced leakiness, increased dynamic range).

(7) Use of fitted lines (Figures 2, 4, 5, 7): Wherever fitted curves are overlaid on data, the asuthors should indicate in the figure legend the explicit form of the fit as well as the fit equation/ parameters. As a reader, it is difficult to interpret what is empirical smoothing versus what is a mechanistic functional form.

(8) P13, lines 232-235: The comparison between induction directly with C6-HSL and induction from sender colonies is qualitative ("significantly smaller range"). The authors should provide distances (for example, in mm) for the induction range in each case and, if possible, approximate total HSL amounts or concentrations, so that the reader can appreciate the magnitude of the difference.

(9) P13, lines 259-262: The authors model the transition to the stationary phase via a monotonically decreasing sigmoid in time for biosynthetic capacity. What is the rationale or literature basis for this approach to model entry into the stationary phase? The authors should cite prior work and clarify why this form is appropriate here, versus alternatives (nutrient diffusion limitation, logistic growth with resource depletion, etc.).

(10) Figure 6c: Are the areas of the plate shown in each column the same field of view across conditions/time, or are these simply representative regions selected per condition (possibly from different plates)? The caption/legend should clarify whether these are matched locations and how images were chosen.

(11) Figure 7a: The combination of solid, dashed, and dash-dot arrows/lines is visually hard to read. I suggest replacing the dash-dot line with a fully dotted line or using different colours (if consistent with journal style) to improve readability.

(12) Figure 7e and similar analyses: The authors should explain in the Methods and/or captions how "distance from sender colonies" is computed when multiple senders exist. Is the distance always measured to the nearest sender, and how are cases handled where a receiver is in the overlapping influence of several senders? This clarification is important for interpreting the fitted curves.

Reviewer #3 (Public review):

This manuscript presents an engineered 3-step circuit in E. coli that combines toggle-switch-based symmetry breaking with quorum-sensing interactions to generate colony-scale spatial patterns. The work is interesting as a synthetic circuit integration study and as a demonstration of self-organized patterning across physically separated colonies. The authors provided a compelling demonstration of the characterization/tuning of parts to guide the overall system engineering. A notable strength is the demonstration that a single circuit can generate a range of self-organized spatial patterns across separate colonies.

However, I think the paper needs to tone down the extent to which the system demonstrates multi-step differentiation or morphogenesis, which is not critical for making the paper valuable. Only the first step of their circuit design (Figure 1), the toggle switch, generates stable alternative states. The latter steps are mainly signal-dependent reporter activation states layered on top of the blue receiver state, rather than true fate transitions. The authors explicitly state that red expression is added without replacing the blue identity, and they also acknowledge that red cells lose their identity upon restreaking unless they remain near sender cells. That substantially weakens the differentiation analogy and makes the Waddington framing too strong.

A related concern is that the 3rd step does not introduce a new spatial organizing rule. The authors show that the second signal remains confined to cells already receiving the first signal, and explicitly conclude that it functions only as an autocrine cue rather than a second paracrine layer. As a result, the 3-step system seems more like an added local readout or maturation layer. Overall, the main 2-step outcome is sparse green sender colonies surrounded by red-expressing blue receivers, with distant receivers remaining blue. That is a valid engineered pattern, but it is still a local, threshold-response circuit architecture.

The autonomy claim should be toned down and stated more precisely. The plate patterning occurs without externally imposed spatial gradients, which is a strength. However, by design, the overall system behavior depends strongly on pre-culture inducer conditions that set the sender:receiver ratio, and this externally imposed history is central to the final pattern. This property is tied to how the circuit is designed where steps 2 and 3 largely respond to symmetry breaking introduced in step 1, which is dependent on both history and initialization on the plate. In particular, currently the pattern formation process is quite variable (e.g. figure 5), depending on how different colonies flip the toggle switch, and consequently, how many become senders and how many become receivers. It would have been fascinating if they could also demonstrate the differentiation within individual colonies, leading to intra-colony patterns. This aspect should at least be discussed.

The mathematical model is useful in guiding both the characterization of parts, modules and the overall system. However, the claims around its quantitative predictive power should also be made narrower. The simulations are built from multiple fitted and partly hand-tuned components, including toggle-switch response curves, colony-growth rules, diffusion, reporter-response functions, and activity decline. This supports a calibrated qualitative reconstruction of the observed patterns, but not a strong predictive or mechanistic validation.

Other specific points:

(1) Given the topic of the work, the authors should cite closely relevant studies in programming pattern formation, including:<br /> Cao et al, Cell 2016 Collective space-sensing coordinates pattern scaling in engineered bacteria<br /> Rajasekaran et al, Cell 2024 A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design<br /> Lu et al, BioRxiv 2024 Discovery of interpretable patterning rules by integrating mechanistic modeling and deep learning

(2) The model assumes identical diffusion coefficients for C6-HSL and C14-HSL despite their substantially different molecular sizes and hydrophobicities. This assumption could distort kinetic lag with differential diffusion in explaining the autocrine confinement of the third step. Its impact should at least be explored in the simulations.

(3) The mCherry response parameters change significantly between the 2-step and 3-step systems. The authors acknowledged this change but did not provide a clear explanation.

(4) The 3-step system is evaluated at only a single condition with no simulation comparison, in contrast to the systematic 11-condition validation of the 2-step system.

eLife Assessment

This important study provides detailed insights into the metabolic states of hemocyte populations across developmental stages and in both physiological and pathological contexts, including during immune challenge. The study provides convincing evidence by comparing the relative utilization of glycolysis and oxidative phosphorylation in Drosophila larval immune cells, and can have implications for metabolic programs that shape immune function in health and disease.

Reviewer #1 (Public review):

Summary:

The metabolic profiles of immune cells under steady-state or immune-activated conditions remain poorly characterized. The authors find that embryonically derived hemocytes in Drosophila larvae predominantly utilize mitochondrial respiration to generate energy and exhibit minimal glycolysis rates under unchallenged conditions. Hemocytes developmentally elevate ATP production rates. Mitochondrial respiration drives metabolic activation in larval hemocytes. More specifically, lamellocytes exhibit unique metabolic activities, including enhanced trehalose catabolism and mitochondrial remodeling, required for their encapsulation response.

Strengths:

The study shows the metabolism that is most likely to operate in different immune cells in Drosophila during development and also during infection. This is related to mitochondrial organization and proliferation and/or differentiation state.

Weaknesses:

Even though there is a rigorous analysis of mitochondrial activity using the Sea Horse analyzer, the analysis of diverse mitochondrial activities in the different immune cell types across development and in infection could be carried out using microscopy. ROS, mitochondrial membrane potential, NADH/+ and FADH/+ levels in vivo are likely to give a more specific readout of change in cellular activities. The activities of mitochondrial fusion and fission need to be collectively tested to understand their role in development and also in infection. The relevance of the change in mitochondrial activity for development or immunity remains to be tested.

Reviewer #2 (Public review):

Summary:

This study presents an analysis of the metabolism of Drosophila larval immune cells during development and activation. The authors compared the utilization of glycolysis and oxidative phosphorylation for energy metabolism. Although this topic has been widely discussed and well-studied in immune cell research, particularly in mammals, it has received little attention in insects. The authors demonstrated that quiescent and activated larval Drosophila immune cells predominantly use mitochondrial oxidative phosphorylation to produce energy. This finding is significant for the emerging field of insect immunometabolism research and is interesting in comparison to mammalian immunity, where immune cell activation is often associated with a shift toward greater reliance on glycolysis.

Strengths:

Using the Agilent Seahorse system, the authors developed and fine-tuned a method to measure the energy metabolism of Drosophila immune cells, obtaining high-quality, robust data. Through genetic manipulations targeting immune cells specifically, they analyzed metabolic changes in cells with different activations, going beyond developmental changes. They convincingly demonstrated ATP production, primarily in the mitochondria of immune cells, at various developmental stages and in various activated states. The results presented mostly support the conclusions drawn. This methodology and its results are valuable for further studies of insect immunometabolism. In a broader context, they are also valuable for comparing the metabolism of immune cells across different animal groups.

Weaknesses:

The genetic manipulations used were suitable for obtaining immune cells of various types and activation states, such as proliferation, differentiation, and immune activation. However, this method has limitations: the mixture of different cell types was always analyzed, and the specific type of interest was often a minority cell population. Had the other cells remained in their initial control state, the observed change in metabolism could have been primarily attributed to the desired cell type. However, the remaining cells that did not transform into the desired type were also usually influenced or activated in some way, making it difficult to determine to which group the observed change should be attributed. For example, consider the induction of lamellocyte differentiation using Hml>Hop[tum]. There are approximately 1,000 lamellocytes per larva, but according to Supplementary Figure 4, there are still about 5,000 Hml+ cells, and even these cells have activated Jak/Stat signaling. Therefore, it can be assumed that they are also activated. After a real infection, the proportion of lamellocytes is greater, but the remaining plasmatocytes are also activated. The authors should mention these limitations more clearly. However, as the authors correctly note, solving this problem will require single-cell approaches, which current technologies still limit. I see this as a problem when interpreting the proliferation effect. The crucial question is what percentage of the analyzed cells induced by Hml>Ras[V12] were actually in the division stage. Not all hemocytes are Hml+, so not all are induced. Of those that are induced, how many are in the division stage at the time of analysis? Meanwhile, those that were not dividing at that moment also had activated Ras, which triggers many processes besides division. Information on what percentage of the analyzed cells were dividing is missing. This information is important because the finding that dividing Drosophila immune cells primarily use mitochondria and oxidative phosphorylation to produce ATP contrasts with the debated significance of the Warburg effect in dividing mammalian cells. This finding would be significant, but unfortunately, it is not robustly supported by the presented data.

Reviewer #3 (Public review):

Summary :

This study investigates the metabolic profiles of hemocytes across multiple stage/conditions and suggests that hemocytes act as regulators of metabolism rather than merely receivers of metabolic cues. The authors show that hemocytes rely primarily on mitochondrial respiration, which is further enhanced during proliferation in development or upon genetic manipulation of plasmatocytes, but not crystal cells.

Metabolic respiration is also activated in lamellocytes, and this activation correlates with changes in mitochondrial morphology. The authors further attempt to identify mechanisms underlying this activation, proposing that mitochondrial fission may contribute to the ability of lamellocytes to encapsulate wasp eggs.

Strengths:

This work provides detailed and valuable insights into the metabolic phenotypes of hemocyte populations at different developmental stages and under both physiological and pathological conditions. The authors perform a longitudinal assessment of hemocyte metabolism and compare metabolic states across contexts.

Importantly, they provide evidence that hemocytes regulate metabolism to perform essential immunological functions, such as wasp egg encapsulation. This reinforces the view that hemocytes are key regulators and communicators that adapt their metabolic programs according to developmental and environmental demands.

Weaknesses:

The results presented are insightful, although several controls and validations could strengthen the conclusions. It would be preferable to also include responder transgenes alone as a control for leakiness, and the scRNA-seq findings would benefit from in vivo validation.

Some conclusions appear inconsistent or insufficiently supported. For instance, although mitochondrial respiration in plasmatocytes peaks at 96 h AEL, this increase is not accompanied by detectable mitochondrial rearrangement, which remains constant between 96 h AEL and 120 h AEL.

In general, the authors should temper some statements or provide further data.

💻/thinkpad/🧊/me/📓/2026/5/2/1

indy.dweb.archive-search?q=what+is+kill+chain+palantir@brave

DOI: 10.64898/2026.05.13.724989

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DOI: 10.64898/2026.05.13.724989

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DOI: 10.64898/2026.05.13.724989

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DOI: 10.64898/2026.05.13.724804

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DOI: 10.64898/2026.05.11.724407

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DOI: 10.64898/2026.05.11.724407

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DOI: 10.3390/cells15100906

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