Change font again, to the font of "our simple booking process" header

All of these 1,2,3,4 squares, please change the font to match the font in the above header of "our simple booking process"

Let's write "email" on the space that's on the left from the word "SEND"

for - example - collaboration - AI - china proposed

for - best action - AI - cSTP

for - quote - more intelligent species is better species?

for - - balancing power with counter power

for - futures - AI -Terminator

for - quote - EO Wilson - pace of technology - compare - quotes - EO Wilson - Ronald Wright

for - quote - change happens when

for - alternative to self replicating AI - narrow ai

for - quote - incentive - outcome

for - application - cSTP

for - book - The Anxious Generation

for - movie - the social dilemma

for - futures - the internet - possible world

for - progress trap - education -advocate

for - collective (bottom up) action - AI - cISTP - AI

for - ai - 3rd perspective

for - history - AI - Anthropic - safety first

for - chatbait

for - progress trap - AI designed to be affirming

for - progress trap - AI - therapist - subtle attachment

for - collective human technology project - Deep Humanity

for - definition - under the hood bias

for - clarion call - speak out on tech stewardship

for - governance - failure - no adults in the room

for - quote - AI - default reckless path - The default path is companies racing to release - the most powerful inscrutable uncontrollable technology we've ever invented - with the maximum incentive to cut corners on safety. - Rising energy prices, depleting jobs,, creating joblessness, creating security risks, deep fakes. That is the default outcome

for - quote - AI reckless path - narrow path to AI future, rather than the default reckless one

for - progress trap - AI

for - progress trap - NAFTA

for - forte - comparison - foreign immigrants Vs AI immigrants - sorry about foreign immigrants - should be more worried about AI immigrants

for - social media - progress trap

for - AI - Deep Humanity - the sacred

for - AI - immortality project

There’s No Doubt About It. The Great MAGA Crack-Up Has Begun<br /> by [[Michael Tomasky]] in The New Republic<br /> accessed on 2025-11-28T12:40:54

Frontoparietal and frontostriatal refer to two distinct but interconnected neural networks in the brain. The frontoparietal circuit, which involves the frontal and parietal lobes, is crucial for executive functions such as cognitive control, working memory, and attention. The frontostriatal circuit connects the frontal lobe (including the prefrontal cortex) with the striatum (part of the basal ganglia) and is involved in motor control, reward processing, and habit formation

This is important, when measuring volume as this is often misconstrued as frequency or amount, in the case of elections, limiting analysis to text or language, this assumption can be mistakenly made.

Strong justification for my multi-modal research, as it will investigate whether these visual components were manipulated or not?

Will the same results persist when I narrow it down for the 2024 U.S. presidential election?

If it was the case that broadcast news was bias, the bias in this context would be seen to lean more Republican than it would Democrat. In the 2024 U.S. election, it was the case that coverage (number currently unknown) would have featured Trump more as he was running for second term, said newsworthy outrageous things such as Mexicans eating Americans cats and dogs and so on. But then again, literature has pointedly contended that women, more specifically WOC are more likely to be covered as they are breaking gender boundaries. Is this the case? Where does the visual bias tend to lie more?

This chapter measures visual bias that occurs in broadcast news. Examining the visual packaging of general election news, this chapter aims to explain how and why this trend exist including the argument of journalist occasionally submitting to control of image handlers to construct the visual portrayal of politicians. In the context of my study, and research question "how do conservative and liberal news outlets represent Kamala Harris race and gender in the 2024 presidential election?" a much larger debate comes into play here, whether broadcast news can be visually bias, and if so how as this chapter aims to answer. Considering the first debate between Trump and Harris took place on ABC News , a more left centrist, the criticism from right-wing news channel Fox alongside Trump's populist perspective that Harris was not challenged or questioned hard enough, will make for an interesting analysis of visual bias and whether this is what occured in this election

In short, the entire argument of this chapter is that visuals have been disregarded as having poor cultural capital. However, neuroscience research has illustrated that when watching news reports, visuals are highly remembered, in particularly in political events such as political leaders. For example, face processing may occur as cues can be reveal about leadership or evoke emotional response. In sum, images are compelling are build a better narrative than lingustics alone.

What's the purpose of this image? What does it relate to? It has no obvious illustrative function.

How would the participants in your workshop know how to leverage this web resource via the "keywords" they have generated? There's no causal or relational link between their "keywords" and this resource. What do you expect them to do?

This doesn't work. These materials need to be provided when they are required (e.g. in Step 3). There's a bigger issue, however, which is that the reference materials will need quite some time to read and digest. They would have to be read and understood by the peers long before the workshop started. There's also the additional (major) issue that only 3 of the 7 weblinks you give are open sources, so only 3 are admissiable as part of this OER.

It doesn't, at least not here. It's not clear what the learner is supposed to do as there are no resources to work with here,

This is not open access - you need to remove it from the OER as it's not an OER.

This is not open access - you need to remove it from the OER as it's not an OER.

This is not open access - you need to remove it from the OER as it's not an OER.

???? This doesn't make any sense. There are no websites listed above Step 4

Such as? Again, your OER toolkit needs to begin this process, it can't be left entirely to the learner.

Which peers? You do not specify if the workshop is peer led. Who would be a "peer" for such a workshop? You need to be clear about this.

What's the purpose of this image? What does it relate to? It has no obvious illustrative function.

What's the purpose of this image? What does it relate to? It has no obvious illustrative function.

This open toolkit provides access to multiple websites and resources concerning arts careers. By tracking keywords, it aims to reshape your understanding of career opportunities within the arts sector and identify effective solutions to address your specific “keyword” challenges.

This isn't a step, it's a statement of intent - it's what the toolkit aims to do. Step 3 needs to explain how to track the keywords, why they are being "tracked" and to what ends they are being tracked...

"Sticky notes" need to be listed on an inventory of materials (= "ingredients" when we did the "Make Gold" workshop). The materials inventory needs to be at the top of the toolkit.

The keywords need to be capture while you are talking - that means either you a) make your own notes as you speak OR b) someone else is the 'scribe' and they make notes of the keywords on sticky notes.

Neither you/the scribe will be able to do this unless you first have a full inventory of materials required for the workshop that includes pens, stickynotes, paper, etc.

I've never tried Wexford before either, but often those sorts of products are mass produced in China by one company and just re-labeled for half a dozen different companies, so searching around may find something similar under a different name.

I will say that some of the ones you listed tend to be the cheapest, lower quality cards I've run across. I use the Amazon Basics a lot, but primarily because they had a sale on their bricks of 500 cards a year or two back and I picked up 20 of them for $2.50 each.

Oxford cards are some of the smoother (inexpensive) cards I've tried in the past, but even their paper quality has shifted a bit over the past 15 years.

If you're doing 3x5 cards in blank, Brodart's library catalog cards are of a much higher quality and durability without breaking the bank and they're wonderfully smooth as well. https://www.shopbrodart.com/

Stockroom plus has some great quality, smooth cards, but I've only ever seen them in gridded format and never plain or lined: https://www.amazon.com/Grid-Index-Cards-Inches-White/dp/B08BJ11LWC/

Notsu also has some high quality smooth cards, but I don't think I've seen them in lined format and they can tend toward being very expensive.

If you have the funds and want something incredibly smooth, try the Exacompta Bristol cards made by Clairefontaine. Their manufacturing process is dramatically different and they're incredibly smooth, particularly for fountain pen use. The downside is that they can be almost 3 times more expensive than other brands. They do carry their cards in a wide variety of sizes and formats though.

One of these days I ought to lay out a grid of the more common cards and do some more serious reviews.

reply to https://old.reddit.com/r/indexcards/comments/1p8xog6/looking_for_index_card_recommendations_similar_to/

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Genomics.<br /> See the peer reviews and the recommendation.

eLife Assessment

This important study provides convincing evidence that glucosylceramide synthase (GlcT), a rate-limiting enzyme for glycosphingolipid (GSL) production, plays a role in the differentiation of intestinal cells. Mutations in GlcT compromise Notch signaling in the Drosophila intestinal stem cell lineage, resulting in the formation of enteroendocrine tumors. Further data suggest that a homolog of glucosylceramide synthase also influences Notch signaling in the mammalian intestine. While the outstanding strengths of the initial genetic and downstream pathway analyses are noted, there are minor weaknesses in the data regarding the potential role of this pathway in Delta trafficking. Nevertheless, this study opens the way for future mechanistic studies addressing how specific lipids modulate Notch signalling activity.

Reviewer #1 (Public review):

Summary:

From a forward genetic mosaic mutant screen using EMS, the authors identify mutations in glucosylceramide synthase (GlcT), a rate-limiting enzyme for glycosphingolipid (GSL) production, that result in ee tumors. Multiple genetic experiments strongly support the model that the mutant phenotype caused by GlcT loss is due to by failure of conversion of ceramide into glucosylceramide. Further genetic evidence suggests that Notch signaling is comprised in the ISC lineage and may affect endocytosis of Delta. Loss of GlcT does not affect wing development or oogenesis, suggesting tissue-specific roles for GlcT. Finally, an increase in goblet cells in UGCG knockout mice, not previously reported, suggests a conserved role for GlcT in Notch signaling in intestinal cell lineage specification.

Strengths:

Overall, this is a well-written paper with multiple well-designed and executed genetic experiments that support a role for GlcT in Notch signaling in the fly and mammalian intestine. The authors have addressed my concerns from the prior review.

Reviewer #2 (Public review):

Summary:

This study genetically identifies two key enzymes involved in the biosynthesis of glycosphingolipids, GlcT and Egh, act as tumor suppressors in the adult fly gut. Detailed genetic analysis indicates that a deficiency in Mactosyl-ceramide (Mac-Cer) is causing tumor formation. Analysis of a Notch transcriptional reporter further indicates that the lack of Mac-Ser is associated with reduced Notch activity in the gut, but not in other tissues.

Addressing how a change in the lipid composition of the membranes might lead to defective Notch receptor activation, the authors studied the endocytic trafficking of Delta and claimed that internalized Delta appeared to accumulate faster into endosomes in the absence of Mac-Cer. Further analysis of Delta steady state accumulation in fixed samples suggested a delay in the endosomal trafficking of Delta from Rab5+ to Rab7+ endosomes, which was interpreted to suggest that the inefficient, or delayed, recycling of Delta might cause a loss in Notch receptor activation.

Finally, the histological analysis of mouse guts following the conditional knock-out of the GlcT gene suggested that Mac-Cer might also be important for proper Notch signaling activity in that context.

Strengths:

The genetic analysis is of high quality. The finding that a Mac-Cer deficiency results in reduced Notch activity in the fly gut is important and fully convincing.

The mouse data, although preliminary, raised the possibility that the role of this specific lipid may be conserved across species.

Author response:

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

Reviewer #1 (Public review):

Summary:

From a forward genetic mosaic mutant screen using EMS, the authors identify mutations in glucosylceramide synthase (GlcT), a rate-limiting enzyme for glycosphingolipid (GSL) production, that result in EE tumors. Multiple genetic experiments strongly support the model that the mutant phenotype caused by GlcT loss is due to by failure of conversion of ceramide into glucosylceramide. Further genetic evidence suggests that Notch signaling is comprised in the ISC lineage and may affect the endocytosis of Delta. Loss of GlcT does not affect wing development or oogenesis, suggesting tissue-specific roles for GlcT. Finally, an increase in goblet cells in UGCG knockout mice, not previously reported, suggests a conserved role for GlcT in Notch signaling in intestinal cell lineage specification.

Strengths:

Overall, this is a well-written paper with multiple well-designed and executed genetic experiments that support a role for GlcT in Notch signaling in the fly and mammalian intestine. I do, however, have a few comments below.

Weaknesses:

(1) The authors bring up the intriguing idea that GlcT could be a way to link diet to cell fate choice. Unfortunately, there are no experiments to test this hypothesis.

We indeed attempted to establish an assay to investigate the impact of various diets (such as high-fat, high-sugar, or high-protein diets) on the fate choice of ISCs. Subsequently, we intended to examine the potential involvement of GlcT in this process. However, we observed that the number or percentage of EEs varies significantly among individuals, even among flies with identical phenotypes subjected to the same nutritional regimen. We suspect that the proliferative status of ISCs and the turnover rate of EEs may significantly influence the number of EEs present in the intestinal epithelium, complicating the interpretation of our results. Consequently, we are unable to conduct this experiment at this time. The hypothesis suggesting that GlcT may link diet to cell fate choice remains an avenue for future experimental exploration.

(2) Why do the authors think that UCCG knockout results in goblet cell excess and not in the other secretory cell types?

This is indeed an interesting point. In the mouse intestine, it is well-documented that the knockout of Notch receptors or Delta-like ligands results in a classic phenotype characterized by goblet cell hyperplasia, with little impact on the other secretory cell types. This finding aligns very well with our experimental results, as we noted that the numbers of Paneth cells and enteroendocrine cells appear to be largely normal in UGCG knockout mice. By contrast, increases in other secretory cell types are typically observed under conditions of pharmacological inhibition of the Notch pathway.

(3) The authors should cite other EMS mutagenesis screens done in the fly intestine.

To our knowledge, the EMS screen on 2L chromosome conducted in Allison Bardin’s lab is the only one prior to this work, which leads to two publications (Perdigoto et al., 2011; Gervais, et al., 2019). We have now included citations for both papers in the revised manuscript.

(4) The absence of a phenotype using NRE-Gal4 is not convincing. This is because the delay in its expression could be after the requirement for the affected gene in the process being studied. In other words, sufficient knockdown of GlcT by RNA would not be achieved until after the relevant signaling between the EB and the ISC occurred. Dl-Gal4 is problematic as an ISC driver because Dl is expressed in the EEP.

This is an excellent point, and we agree that the lack of an observable phenotype using NRE-Gal4 could be due to delayed expression, which may result in missing the critical window required for effective GlcT knockdown. Consequently, we cannot rule out the possibility that GlcT also plays a role in early EBs or EEPs. We have revised the manuscript to soften this conclusion and to include this alternative explanation for the experiment.

(5) The difference in Rab5 between control and GlcT-IR was not that significant. Furthermore, any changes could be secondary to increases in proliferation.

We agree that it is possible that the observed increase in proliferation could influence the number of Rab5+ endosomes, and we will temper our conclusions on this aspect accordingly. However, it is important to note that, although the difference in Rab5+ endosomes between the control and GlcT-IR conditions appeared mild, it was statistically significant and reproducible. In our revised experiments, we have not only added statistical data and immunofluorescence images for Rab11 but also unified the approaches used for detecting Rab-associated proteins (in the previous figures, Rab5 was shown using U-Rab5-GFP, whereas Rab7 was detected by direct antibody staining). Based on this unified strategy, we optimized the quantification of Dl-GFP colocalization with early, late, and recycling endosomes, and the results are consistent with our previous observations (see the updated Fig. 5).

Reviewer #2 (Public review):

Summary:

This study genetically identifies two key enzymes involved in the biosynthesis of glycosphingolipids, GlcT and Egh, which act as tumor suppressors in the adult fly gut. Detailed genetic analysis indicates that a deficiency in Mactosyl-ceramide (Mac-Cer) is causing tumor formation. Analysis of a Notch transcriptional reporter further indicates that the lack of Mac-Ser is associated with reduced Notch activity in the gut, but not in other tissues.

Addressing how a change in the lipid composition of the membranes might lead to defective Notch receptor activation, the authors studied the endocytic trafficking of Delta and claimed that internalized Delta appeared to accumulate faster into endosomes in the absence of Mac-Cer. Further analysis of Delta steady-state accumulation in fixed samples suggested a delay in the endosomal trafficking of Delta from Rab5+ to Rab7+ endosomes, which was interpreted to suggest that the inefficient, or delayed, recycling of Delta might cause a loss in Notch receptor activation.

Finally, the histological analysis of mouse guts following the conditional knock-out of the GlcT gene suggested that Mac-Cer might also be important for proper Notch signaling activity in that context.

Strengths:

The genetic analysis is of high quality. The finding that a Mac-Cer deficiency results in reduced Notch activity in the fly gut is important and fully convincing.

The mouse data, although preliminary, raised the possibility that the role of this specific lipid may be conserved across species.

Weaknesses:

This study is not, however, without caveats and several specific conclusions are not fully convincing.

First, the conclusion that GlcT is specifically required in Intestinal Stem Cells (ISCs) is not fully convincing for technical reasons: NRE-Gal4 may be less active in GlcT mutant cells, and the knock-down of GlcT using Dl-Gal4ts may not be restricted to ISCs given the perdurance of Gal4 and of its downstream RNAi.

As previously mentioned, we acknowledge that a role for GlcT in early EBs or EEPs cannot be completely ruled out. We have revised our manuscript to present a more cautious conclusion and explicitly described this possibility in the updated version.

Second, the results from the antibody uptake assays are not clear.: i) the levels of internalized Delta were not quantified in these experiments; ii) additionally, live guts were incubated with anti-Delta for 3hr. This long period of incubation indicated that the observed results may not necessarily reflect the dynamics of endocytosis of antibody-bound Delta, but might also inform about the distribution of intracellular Delta following the internalization of unbound anti-Delta. It would thus be interesting to examine the level of internalized Delta in experiments with shorter incubation time.

We thank the reviewer for these excellent questions. In our antibody uptake experiments, we noted that Dl reached its peak accumulation after a 3-hour incubation period. We recognize that quantifying internalized Dl would enhance our analysis, and we will include the corresponding statistical graphs in the revised version of the manuscript. In addition, we agree that during the 3-hour incubation, the potential internalization of unbound anti-Dl cannot be ruled out, as it may influence the observed distribution of intracellular Dl. We therefore attempted to supplement our findings with live imaging experiments to investigate the dynamics of Dl/Notch endocytosis in both normal and GlcT mutant ISCs. However, we found that the GFP expression level of Dl-GFP (either in the knock-in or transgenic line) was too low to be reliably tracked. During the three-hour observation period, the weak GFP signal remained largely unchanged regardless of the GlcT mutation status, and the signal resolution under the microscope was insufficient to clearly distinguish membrane-associated from intracellular Dl. Therefore, we were unable to obtain a dynamic view of Dl trafficking through live imaging. Nevertheless, our Dl antibody uptake and endosomal retention analyses collectively support the notion that MacCer influences Notch signaling by regulating Dl endocytosis.

Overall, the proposed working model needs to be solidified as important questions remain open, including: is the endo-lysosomal system, i.e. steady-state distribution of endo-lysosomal markers, affected by the Mac-Cer deficiency? Is the trafficking of Notch also affected by the Mac-Cer deficiency? is the rate of Delta endocytosis also affected by the Mac-Cer deficiency? are the levels of cell-surface Delta reduced upon the loss of Mac-Cer?

Regarding the impact on the endo-lysosomal system, this is indeed an important aspect to explore. While we did not conduct experiments specifically designed to evaluate the steady-state distribution of endo-lysosomal markers, our analyses utilizing Rab5-GFP overexpression and Rab7 staining did not indicate any significant differences in endosome distribution in MacCer deficient conditions. Moreover, we still observed high expression of the NRE-LacZ reporter specifically at the boundaries of clones in GlcT mutant cells (Fig. 4A), indicating that GlcT mutant EBs remain responsive to Dl produced by normal ISCs located right at the clone boundary. Therefore, we propose that MacCer deficiency may specifically affect Dl trafficking without impacting Notch trafficking.

In our 3-hour antibody uptake experiments, we observed a notable decrease in cell-surface Dl, which was accompanied by an increase in intracellular accumulation. These findings collectively suggest that Dl may be unstable on the cell surface, leading to its accumulation in early endosomes.

Third, while the mouse results are potentially interesting, they seem to be relatively preliminary, and future studies are needed to test whether the level of Notch receptor activation is reduced in this model.

In the mouse small intestine, Olfm4 is a well-established target gene of the Notch signaling pathway, and its staining provides a reliable indication of Notch pathway activation. While we attempted to evaluate Notch activation using additional markers, such as Hes1 and NICD, we encountered difficulties, as the corresponding antibody reagents did not perform well in our hands. Despite these challenges, we believe that our findings with Olfm4 provide an important start point for further investigation in the future.

Reviewer #3 (Public review):

Summary:

In this paper, Tang et al report the discovery of a Glycoslyceramide synthase gene, GlcT, which they found in a genetic screen for mutations that generate tumorous growth of stem cells in the gut of Drosophila. The screen was expertly done using a classic mutagenesis/mosaic method. Their initial characterization of the GlcT alleles, which generate endocrine tumors much like mutations in the Notch signaling pathway, is also very nice. Tang et al checked other enzymes in the glycosylceramide pathway and found that the loss of one gene just downstream of GlcT (Egh) gives similar phenotypes to GlcT, whereas three genes further downstream do not replicate the phenotype. Remarkably, dietary supplementation with a predicted GlcT/Egh product, Lactosyl-ceramide, was able to substantially rescue the GlcT mutant phenotype. Based on the phenotypic similarity of the GlcT and Notch phenotypes, the authors show that activated Notch is epistatic to GlcT mutations, suppressing the endocrine tumor phenotype and that GlcT mutant clones have reduced Notch signaling activity. Up to this point, the results are all clear, interesting, and significant. Tang et al then go on to investigate how GlcT mutations might affect Notch signaling, and present results suggesting that GlcT mutation might impair the normal endocytic trafficking of Delta, the Notch ligand. These results (Fig X-XX), unfortunately, are less than convincing; either more conclusive data should be brought to support the Delta trafficking model, or the authors should limit their conclusions regarding how GlcT loss impairs Notch signaling. Given the results shown, it's clear that GlcT affects EE cell differentiation, but whether this is via directly altering Dl/N signaling is not so clear, and other mechanisms could be involved. Overall the paper is an interesting, novel study, but it lacks somewhat in providing mechanistic insight. With conscientious revisions, this could be addressed. We list below specific points that Tang et al should consider as they revise their paper.

Strengths:

The genetic screen is excellent.

The basic characterization of GlcT phenotypes is excellent, as is the downstream pathway analysis.

Weaknesses:

(1) Lines 147-149, Figure 2E: here, the study would benefit from quantitations of the effects of loss of brn, B4GalNAcTA, and a4GT1, even though they appear negative.

We have incorporated the quantifications for the effects of the loss of brn, B4GalNAcTA, and a4GT1 in the updated Figure 2.

(2) In Figure 3, it would be useful to quantify the effects of LacCer on proliferation. The suppression result is very nice, but only effects on Pros+ cell numbers are shown.

We have now added quantifications of the number of EEs per clone to the updated Figure 3.

(3) In Figure 4A/B we see less NRE-LacZ in GlcT mutant clones. Are the data points in Figure 4B per cell or per clone? Please note. Also, there are clearly a few NRE-LacZ+ cells in the mutant clone. How does this happen if GlcT is required for Dl/N signaling?

In Figure 4B, the data points represent the fluorescence intensity per single cell within each clone. It is true that a few NRE-LacZ+ cells can still be observed within the mutant clone; however, this does not contradict our conclusion. As noted, high expression of the NRE-LacZ reporter was specifically observed around the clone boundaries in MacCer deficient cells (Fig. 4A), indicating that the mutant EBs can normally receive Dl signal from the normal ISCs located at the clone boundary and activate the Notch signaling pathway. Therefore, we believe that, although affecting Dl trafficking, MacCer deficiency does not significantly affect Notch trafficking.

(4) Lines 222-225, Figure 5AB: The authors use the NRE-Gal4ts driver to show that GlcT depletion in EBs has no effect. However, this driver is not activated until well into the process of EB commitment, and RNAi's take several days to work, and so the author's conclusion is "specifically required in ISCs" and not at all in EBs may be erroneous.

As previously mentioned, we acknowledge that a role for GlcT in early EBs or EEPs cannot be completely ruled out. We have revised our manuscript to present a more cautious conclusion and described this possibility in the updated version.

(5) Figure 5C-F: These results relating to Delta endocytosis are not convincing. The data in Fig 5C are not clear and not quantitated, and the data in Figure 5F are so widely scattered that it seems these co-localizations are difficult to measure. The authors should either remove these data, improve them, or soften the conclusions taken from them. Moreover, it is unclear how the experiments tracing Delta internalization (Fig 5C) could actually work. This is because for this method to work, the anti-Dl antibody would have to pass through the visceral muscle before binding Dl on the ISC cell surface. To my knowledge, antibody transcytosis is not a common phenomenon.

We thank the reviewer for these insightful comments and suggestions. In our in vivo experiments, we observed increased co-localization of Rab5 and Dl in GlcT mutant ISCs, indicating that Dl trafficking is delayed at the transition to Rab7⁺ late endosomes, a finding that is further supported by our antibody uptake experiments. We acknowledge that the data presented in Fig. 5C are not fully quantified and that the co-localization data in Fig. 5F may appear somewhat scattered; therefore, we have included additional quantification and enhanced the data presentation in the revised manuscript.

Regarding the concern about antibody internalization, we appreciate this point. We currently do not know if the antibody reaches the cell surface of ISCs by passing through the visceral muscle or via other routes. Given that the experiment was conducted with fragmented gut, it is possible that the antibody may penetrate into the tissue through mechanisms independent of transcytosis.

As mentioned earlier, we attempted to supplement our findings with live imaging experiments to investigate the dynamics of Dl/Notch endocytosis in both normal and GlcT mutant ISCs. However, we found that the GFP expression level of Dl-GFP (either in the knock-in or transgenic line) was too low to be reliably tracked. During the three-hour observation period, the weak GFP signal remained largely unchanged regardless of the GlcT mutation status, and the signal resolution under the microscope was insufficient to clearly distinguish membrane-associated from intracellular Dl. Therefore, we were unable to obtain a dynamic view of Dl trafficking through live imaging. Nevertheless, our Dl antibody uptake and endosomal retention analyses collectively support the notion that MacCer influences Notch signaling by regulating Dl endocytosis.

(6) It is unclear whether MacCer regulates Dl-Notch signaling by modifying Dl directly or by influencing the general endocytic recycling pathway. The authors say they observe increased Dl accumulation in Rab5+ early endosomes but not in Rab7+ late endosomes upon GlcT depletion, suggesting that the recycling endosome pathway, which retrieves Dl back to the cell surface, may be impaired by GlcT loss. To test this, the authors could examine whether recycling endosomes (marked by Rab4 and Rab11) are disrupted in GlcT mutants. Rab11 has been shown to be essential for recycling endosome function in fly ISCs.

We agree that assessing the state of recycling endosomes, especially by using markers such as Rab11, would be valuable in determining whether MacCer regulates Dl-Notch signaling by directly modifying Dl or by influencing the broader endocytic recycling pathway. In the newly added experiments, we found that in GlcT-IR flies, Dl still exhibits partial colocalization with Rab11, and the overall expression pattern of Rab11 is not affected by GlcT knockdown (Fig. 5E-F). These observations suggest that MacCer specifically regulates Dl trafficking rather than broadly affecting the recycling pathway.

(7) It remains unclear whether Dl undergoes post-translational modification by MacCer in the fly gut. At a minimum, the authors should provide biochemical evidence (e.g., Western blot) to determine whether GlcT depletion alters the protein size of Dl.

While we propose that MacCer may function as a component of lipid rafts, facilitating Dl membrane anchorage and endocytosis, we also acknowledge the possibility that MacCer could serve as a substrate for protein modifications of Dl necessary for its proper function. Conducting biochemical analyses to investigate potential post-translational modifications of Dl by MacCer would indeed provide valuable insights. We have performed Western blot analysis to test whether GlcT depletion affects the protein size of Dl. As shown below, we did not detect any apparent changes in the molecular weight of the Dl protein. Therefore, it is unlikely that MacCer regulates post-translational modifications of Dl.

Author response image 1.

To investigate whether MacCer modifies Dl by Western blot,(A) Four lanes were loaded: the first two contained 20 μL of membrane extract (lane 1: GlcT-IR, lane 2: control), while the last two contained 10 μL of membrane extract (B) Full blot images are shown under both long and shortexposure conditions.

(8) It is unfortunate that GlcT doesn't affect Notch signaling in other organs on the fly. This brings into question the Delta trafficking model and the authors should note this. Also, the clonal marker in Figure 6C is not clear.

In the revised working model, we have explicitly described that the events occur in intestinal stem cells. Regarding Figure 6C, we have delineated the clone with a white dashed line to enhance its clarity and visual comprehension.

(9) The authors state that loss of UGCG in the mouse small intestine results in a reduced ISC count. However, in Supplementary Figure C3, Ki67, a marker of ISC proliferation, is significantly increased in UGCG-CKO mice. This contradiction should be clarified. The authors might repeat this experiment using an alternative ISC marker, such as Lgr5.

Previous studies have indicated that dysregulation of the Notch signaling pathway can result in a reduction in the number of ISCs. While we did not perform a direct quantification of ISC numbers in our experiments, our Olfm4 staining—which serves as a reliable marker for ISCs—demonstrates a clear reduction in the number of positive cells in UGCG-CKO mice.

The increased Ki67 signal we observed reflects enhanced proliferation in the transit-amplifying region, and it does not directly indicate an increase in ISC number. Therefore, in UGCG-CKO mice, we observe a decrease in the number of ISCs, while there is an increase in transit-amplifying (TA) cells (progenitor cells). This increase in TA cells is probably a secondary consequence of the loss of barrier function associated with the UGCG knockout.

eLife Assessment

This paper reports a valuable finding that gastric fluid DNA content can be used as a potential biomarker for human gastric cancer. The evidence supporting the claims of the authors is solid, although an inclusion of explanations for the methodological limitations, moderate diagnostic performance, and the unexpected survival correlation would have strengthened the study. The work will be of interest to medical biologists working in the field of gastric cancer.

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn

Reviewer #2 (Public review):

Summary:

The authors investigated whether the total DNA concentration in gastric fluid (gfDNA), collected via routine esophagogastroduodenoscopy (EGD), could serve as a diagnostic and prognostic biomarker for gastric cancer. In a large patient cohort (initial n=1,056; analyzed n=941), they found that gfDNA levels were significantly higher in gastric cancer patients compared to non-cancer, gastritis, and precancerous lesion groups. Unexpectedly, higher gfDNA concentrations were also significantly associated with better survival prognosis and positively correlated with immune cell infiltration. The authors proposed that gfDNA may reflect both tumor burden and immune activity, potentially serving as a cost-effective and convenient liquid biopsy tool to assist in gastric cancer diagnosis, staging, and follow-up.

Strengths:

This study is supported by a robust sample size (n=941) with clear patient classification, enabling reliable statistical analysis. It employs a simple, low-threshold method for measuring total gfDNA, making it suitable for large-scale clinical use. Clinical confounders, including age, sex, BMI, gastric fluid pH, and PPI use, were systematically controlled. The findings demonstrate both diagnostic and prognostic value of gfDNA, as its concentration can help distinguish gastric cancer patients and correlates with tumor progression and survival. Additionally, preliminary mechanistic data reveal a significant association between elevated gfDNA levels and increased immune cell infiltration in tumors (p=0.001).

Weaknesses:

The study has several notable weaknesses. The association between high gfDNA levels and better survival contradicts conventional expectations and raises concerns about the biological interpretation of the findings. The diagnostic performance of gfDNA alone was only moderate, and the study did not explore potential improvements through combination with established biomarkers. Methodological limitations include a lack of control for pre-analytical variables, the absence of longitudinal data, and imbalanced group sizes, which may affect the robustness and generalizability of the results. Additionally, key methodological details were insufficiently reported, and the ROC analysis lacked comprehensive performance metrics, limiting the study's clinical applicability.

Author response:

Public Reviews:

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

We are grateful for this astute remark. A comparison of gfDNA concentration among the diagnostic groups indicates a trend of increasing values as the diagnosis progresses toward malignancy. The observed values for the diagnostic groups are as follows:

Author response table 1.

The chart below presents the statistical analyses of the same diagnostic/tumor-stage groups (One-Way ANOVA followed by Tukey’s multiple comparison tests). It shows that gastric fluid gfDNA concentrations gradually increase with malignant progression. We observed that the initial tumor stages (T0 to T2) exhibit intermediate gfDNA levels, which in this group is significantly lower than in advanced disease (p = 0.0036), but not statistically different from non-neoplastic disease (p = 0.74).

Author response image 1.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

We appreciate the attention to detail regarding the numbers analyzed in the manuscript. Importantly, the results are meaningful because the number of subjects in each group is comparable (T0-T2, N = 65; T3, N = 91; T4, N = 63). The mean gastric fluid gfDNA values (ng/µL) increase with disease stage (T0-T2: 15.12; T3-T4: 30.75), and both are higher than the mean gfDNA values observed in non-neoplastic disease (10.81 ng/µL for N+PD and 10.10 ng/µL for PN). These subject numbers in each diagnostic group accurately reflect real-world data from a tertiary cancer center.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

Histopathological analyses were performed throughout the study not only for the initial diagnosis of tissue biopsies, but also for the classification of Lauren’s subtypes, tumor staging, and the assessment of the presence and extent of immune cell infiltrates. Regarding the time of disease onset, this variable is inherently unknown--by definition--at the time of a diagnostic EGD. While the prognosis definition is indeed straightforward, we believe that a simple, cost-effective, and practical approach is advantageous for patients across diverse clinical settings and is more likely to be effectively integrated into routine EGD practice.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

We wish to reinforce that EGD, along with conventional histopathology, remains the gold standard for gastric cancer evaluation. EGD under sedation is routinely performed for diagnosis, and the collection of gastric fluids for gfDNA evaluation does not affect patient comfort. Thus, while gfDNA analysis was evidently not intended as a diagnostic EGD and biopsy replacement, it may provide added prognostic value to this exam.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

We are grateful for these comments and apologize for the clerical oversight. All figures, tables, titles and figure legends have now been double-checked.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn.

We assume that the unusual wording remark regarding “overall logicality” pertains to the rationale and/or reasoning of this investigational study. Our working hypothesis was that during neoplastic disease progression, tumor cells continuously proliferate and, depending on various factors, attract immune cell infiltrates. Consequently, both tumor cells and immune cells (as well as tumor-derived DNA) are released into the fluids surrounding the tumor at its various locations, including blood, urine, saliva, gastric fluids, and others. Thus, increases in DNA levels within some of these fluids have been documented and are clinically meaningful. The concurrent observation of elevated gastric fluid gfDNA levels and immune cell infiltration supports the hypothesis that increased gfDNA—which may originate not only from tumor cells but also from immune cells—could be associated with better prognosis, as suggested by this study of a large real-world patient cohort.

In summary, we thank Reviewer #1 for his time and effort in a constructive critique of our work.

Reviewer #2 (Public review):

Summary:

The authors investigated whether the total DNA concentration in gastric fluid (gfDNA), collected via routine esophagogastroduodenoscopy (EGD), could serve as a diagnostic and prognostic biomarker for gastric cancer. In a large patient cohort (initial n=1,056; analyzed n=941), they found that gfDNA levels were significantly higher in gastric cancer patients compared to non-cancer, gastritis, and precancerous lesion groups. Unexpectedly, higher gfDNA concentrations were also significantly associated with better survival prognosis and positively correlated with immune cell infiltration. The authors proposed that gfDNA may reflect both tumor burden and immune activity, potentially serving as a cost-effective and convenient liquid biopsy tool to assist in gastric cancer diagnosis, staging, and follow-up.

Strengths:

This study is supported by a robust sample size (n=941) with clear patient classification, enabling reliable statistical analysis. It employs a simple, low-threshold method for measuring total gfDNA, making it suitable for large-scale clinical use. Clinical confounders, including age, sex, BMI, gastric fluid pH, and PPI use, were systematically controlled. The findings demonstrate both diagnostic and prognostic value of gfDNA, as its concentration can help distinguish gastric cancer patients and correlates with tumor progression and survival. Additionally, preliminary mechanistic data reveal a significant association between elevated gfDNA levels and increased immune cell infiltration in tumors (p=0.001).

Reviewer #2 has conceptually grasped the overall rationale of the study quite well, and we are grateful for their assessment and comprehensive summary of our findings.

Weaknesses:

(1) The study has several notable weaknesses. The association between high gfDNA levels and better survival contradicts conventional expectations and raises concerns about the biological interpretation of the findings.

We agree that this would be the case if the gfDNA was derived solely from tumor cells. However, the findings presented here suggest that a fraction of this DNA would be indeed derived from infiltrating immune cells. The precise determination of the origin of this increased gfDNA remains to be achieved in future follow-up studies, and these are planned to be evaluated soon, by applying DNA- and RNA-sequencing methodologies and deconvolution analyses.

(2) The diagnostic performance of gfDNA alone was only moderate, and the study did not explore potential improvements through combination with established biomarkers. Methodological limitations include a lack of control for pre-analytical variables, the absence of longitudinal data, and imbalanced group sizes, which may affect the robustness and generalizability of the results.

Reviewer #2 is correct that this investigational study was not designed to assess the diagnostic potential of gfDNA. Instead, its primary contribution is to provide useful prognostic information. In this regard, we have not yet explored combining gfDNA with other clinically well-established diagnostic biomarkers. We do acknowledge this current limitation as a logical follow-up that must be investigated in the near future.

Moreover, we collected a substantial number of pre-analytical variables within the limitations of a study involving over 1,000 subjects. Longitudinal samples and data were not analyzed here, as our aim was to evaluate prognostic value at diagnosis. Although the groups are imbalanced, this accurately reflects the real-world population of a large endoscopy center within a dedicated cancer facility. Subjects were invited to participate and enter the study before sedation for the diagnostic EGD procedure; thus, samples were collected prospectively from all consenting individuals.

Finally, to maintain a large, unbiased cohort, we did not attempt to balance the groups, allowing analysis of samples and data from all patients with compatible diagnoses (please see Results: Patient groups and diagnoses).

(3) Additionally, key methodological details were insufficiently reported, and the ROC analysis lacked comprehensive performance metrics, limiting the study's clinical applicability.

We are grateful for this useful suggestion. In the current version, each ROC curve (Supplementary Figures 1A and 1B) now includes the top 10 gfDNA thresholds, along with their corresponding sensitivity and specificity values (please see Suppl. Table 1). The thresholds are ordered from-best-to-worst based on the classic Youden’s J statistic, as follows:

Youden Index = specificity + sensitivity – 1 [Youden WJ. Index for rating diagnostic tests. Cancer 3:32-35, 1950. PMID: 15405679]. We have made an effort to provide all the key methodological details requested, but we would be glad to add further information upon specific request.

Dates are correct: she started with neo-adjuvant treatment, then after a year they discovered a bone metastasis but they still decided to do a hygenic mastectomy and axillary clearance

Learn how to integrate AI into your web application using the MERN stack. This guide covers key concepts, tools, and best practices for building an AI-powered web app with MongoDB, Express, React, and Node.js.

On the erosion of middle class America. Poverty line is around 140k if actual costs taken into account. The 1960s benchmark assumed cost of food to be 1/3 of overall costs. Now it's 7% or so, meaning 1/15 of overall costs. This pushes up the poverty line to 5 times the level used, or some 150k USD pa

Example of a proxy being used as 'measurement' and the assumptions in a proxy never re-evaluated.

would be good to have some answers to the exercises. stuck on number 4

eLife Assessment

This study concerns how macaque visual cortical area MT represents stimuli composed of more than one speed of motion. The study is valuable because little is known about how the visual pathway segments and preserves information about multiple stimuli, and the study involves perceptual reports from both humans and one monkey regarding whether there are one or two speeds in the stimulus. The study presents compelling evidence that (on average) MT neurons shift from faster-speed-takes-all at low speeds to representing the average of the two speeds at higher speeds. Ultimately, this study raises intriguing questions about how exactly the response patterns in visual cortical area MT might preserve information about each speed, since such information could potentially be lost in an average response as described here, depending on assumptions about how MT activity is evaluated by other visual areas.

Reviewer #1 (Public review):

Summary:

Most studies in sensory neuroscience investigate how individual sensory stimuli are represented in the brain (e.g., the motion or color of a single object). This study starts tackling the more difficult question of how the brain represents multiple stimuli simultaneously and how these representations help to segregate objects from cluttered scenes with overlapping objects.

Strengths:

The authors first document the ability of humans to segregate two motion patterns based on differences in speed. Then they show that a monkey's performance is largely similar; thus establishing the monkey as a good model to study the underlying neural representations.

Careful quantification of the neural responses in the middle temporal area during the simultaneous presentation of fast and slow speeds leads to the surprising finding that, at low average speeds, many neurons respond as if the slowest speed is not present, while they show averaged responses at high speeds. This unexpected complexity of the integration of multiple stimuli is key to the model developed in this paper.

One experiment in which attention is drawn away from the receptive field supports the claim that this is not due to the involuntary capture of attention by fast speeds.

A classifier using the neuronal response and trained to distinguish single speed from bi-speed stimuli shows a similar overall performance and dependence on the mean speed as the monkey. This supports the claim that these neurons may indeed underlie the animal's decision process.

The authors expand the well-established divisive normalization model to capture the responses to bi-speed stimuli. The incremental modeling (eq 9 and 10) clarifies which aspects of the tuning curves are captured by the parameters.

Reviewer #3 (Public review):

Summary:

This study concerns how macaque visual cortical area MT represents stimuli composed of more than one speed of motion.

Strengths:

The study is valuable because little is known about how the visual pathway segments and preserves information about multiple stimuli. The study presents compelling evidence that (on average) MT neurons shift from faster-speed-takes-all at low speeds to representing the average of the two speeds at higher speeds. An additional strength of the study is the inclusion of perceptual reports from both humans and one monkey participant performing a task in which they judged whether the stimuli involved one vs two different speeds. Ultimately, this study raises intriguing questions about how exactly the response patterns in visual cortical area MT might preserve information about each speed, since such information is potentially lost in an average response as described here.

Reviewing Editor comment on revised version:

The remaining concern was resolved.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #3 (Recommendations for the authors):

The authors have done an excellent job of addressing most comments, but my concerns about Figure 5 remain. I appreciate the authors' efforts to address the problem involving Rs being part of the computation on both the x and y axes of Figure 5, but addressing this via simulation addresses statistical significance but overlooks effect size. I think the authors may have misunderstood my original suggestion, so I will attempt to explain it better here. Since "Rs" is an average across all trials, the trials could be subdivided in two halves to compute two separate averages - for example, an average of the even numbered trials and an average of the odd numbered trials. Then you would use the "Rs" from the even numbered trials for one axis and the "Rs" from the odd numbered trials for the other. You would then plot R-Rs_even vs Rf-Rs_odd. This would remove the confound from this figure, and allow the text/interpretation to be largely unchanged (assuming the results continue to look as they do).

We have added a description and the result of the new analysis (line #321 to #332), and a supplementary figure (Suppl. Fig. 1) (line #1464 to #1477). 

“We calculated 𝑅_𝑠 in the ordinate and abscissa of Figure 5A-E using responses averaged across different subsets of trials, such that 𝑅_𝑠 was no longer a common term in the ordinate and abscissa. For each neuron, we determined 𝑅_𝑠1 by averaging the firing rates of 𝑅_𝑠 across half of the recorded trials, selected randomly. We also determined 𝑅_𝑠2 by averaging the firing rates of 𝑅_𝑠 across the rest of the trials.  We regressed (𝑅 − 𝑅_𝑠1 )  on (𝑅_𝑓 − 𝑅_𝑠2) , as well as (𝑅_𝑠 - 𝑅_𝑠2)  on (𝑅_𝑓 − 𝑅_𝑠1), and repeated the procedure 50 times. The averaged slopes obtained with 𝑅_𝑠 from the split trials showed the same pattern as those using 𝑅_𝑠 from all trials (Table 1 and Supplementary Fig. 1), although the coefficient of determination was slightly reduced (Table 1). For ×4 speed separation, the slopes were nearly identical to those shown in Figure 5F1. For ×2 speed separation, the slopes were slightly smaller than those in Figure 5F2, but followed the same pattern (Supplementary Fig. 1). Together, these analysis results confirmed the faster-speed bias at the slow stimulus speeds, and the change of the response weights as stimulus speeds increased.”

An additional remaining item concerns the terminology weighted sum, in the context of the constraint that wf and ws must sum to one. My opinion is that it is non-standard to use weighted sum when the computation is a weighted average, but as long as the authors make their meaning clear, the reader will be able to follow. I suggest adding some phrasing to explain to the reader the shift in interpretation from the more general weighted sum to the more constrained weighted average. Specifically, "weighted sum" first appears on line 268, and then the additional constraint of ws + wf =1 is introduced on line 278. Somewhere around line 278, it would be useful to include a sentence stating that this constraint means the weighted sum is constrained to be a weighted average.

Thanks for the suggestion. We have modified the text as follows. Since we made other modifications in the text, the line numbers are slightly different from the last version. 

Line #274 to 275: 

“Since it is not possible to solve for both variables, 𝑤_𝑠 and 𝑤_𝑓, from a single equation (Eq. 5) with three data points, we introduced an additional constraint: 𝑤_𝑠 + 𝑤_𝑓 =1. With this constraint, the weighted sum becomes a weighted average.”

Also on line #309:

“First, at each speed pair and for each of the 100 neurons in the data sample shown in Figure 5, we simulated the response to the bi-speed stimuli (𝑅_𝑒) as a randomly weighted average of 𝑅_𝑓 and 𝑅_𝑠 of the same neuron. 

in which 𝑎 was a randomly generated weight (between 0 and 1) for 𝑅_𝑓, and the weights for 𝑅_𝑓 and 𝑅_𝑠 summed to one.”

The exact method to calculate this is not mentioned anywhere in the text.

A variable is a column, an observation a row and different types of observations are in different tables

Tidy data is made up for values, each value is from an observation and each observation has one or more variables

Gălûñ′lătĭ, secondo la mitologia Cherokee, è un mondo creato prima dell'esistenza della Terra.

[https://www.cherokeepins.org/post/cherokee-stories-and-the-birth-of-a-new-world-the-first-fire]

Ani′-Hyûñ′tĭkwălâ′skĭ è il nome, nella mitologia Cherokee, per "The Thunders," il termine plurale per indicare "coloro che hanno dato il fuoco".

[https://www.cherokeepins.org/post/cherokee-stories-and-the-birth-of-a-new-world-the-first-fire]

Kănăne′skĭ Amai′yĕhĭ, the water spider in the Cherokee myth.

[https://www.cherokeepins.org/post/cherokee-stories-and-the-birth-of-a-new-world-the-first-fire]

"Blacksnake the climber" likely refers to various black-colored snakes, particularly the black rat snake, known for their exceptional climbing abilities due to their specialized belly scales. Gûle′gĭ in the Cherokjee myth.

[https://developmentserver.ravenspacepublishing.org/the-cherokee-natural-world/blacksnake-or-rat-snake]

Used in athe Cherokee myth to refer to a barn owl, which is also known as a screech owl.

[https://www.cherokeepins.org/post/cherokee-stories-and-the-birth-of-a-new-world-the-first-fire]

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

Reviewer #1 (Evidence, reproducibility and clarity (Required)): The authors map the ZFP36L1 protein interactome in human T cells using UltraID proximity labeling combined with quantitative mass spectrometry. They optimize labeling conditions in primary T cells, profile resting and activated cells, and include a time course at 2, 5, and 16 hours. They complement the interactome with co-immunoprecipitation in the presence or absence of RNase to assess RNA dependence. They then test selected candidates using CRISPR knockouts in primary T cells, focusing on UPF1 and GIGYF1/2, and report effects on global translation, stress, activation markers, and ZFP36L1 protein levels. The work argues that ZFP36L1 sits at the center of multiple post-transcriptional pathways in T cells (which in itself is not a novel finding) and that UPF1 supports ZFP36L1 expression at the mRNA and protein level. The main model system is primary human T cells, with some data in Jurkat cells.

The core datasets show thousands of identified proteins in total lysates and enriched biotinylated fractions. Known partners from CCR4-NOT, decapping, stress granules, and P-bodies appear, with additional candidates like GIGYF1/2, PATL1, DDX6, and UPF1. Time-resolved labeling suggests shifts in proximity during early activation. Co-IP with and without RNase suggests both RNA-dependent and RNA-independent contacts. CRISPR loss of UPF1 or GIGYF1/2 increases translation at rest and elevates activation markers, and UPF1 loss reduces ZFP36L1 protein and mRNA while MG132 does not rescue protein levels; UPF1 RIP enriches ZFP36L1 mRNA.

Among patterns worth noting are that the activation state drives the principal variance in both proteome and proximity datasets. Deadenylation, decapping, and granule proteins are consistently near ZFP36L1 across conditions, while some contacts dip at 2 hours and recover by 5 to 16 hours. Mitochondrial ribosomal proteins become more proximal later. UPF1 and GIGYF1 show time-linked behavior and RNase sensitivity that fits roles in mRNA surveillance and translational control. These observations support a dynamic hub model, though they remain proximity-based rather than direct binding maps.

We thank the reviewer for their careful reading and thoughtful summary. Please find our point-to point response below.

Major comments

1) The key conclusions are directionally convincing for a broad and dynamic ZFP36L1 neighborhood in human T cells. The data robustly recover established complexes and add plausible candidates. The time-course and RNase experiments strengthen the claim that interactions shift with activation state and RNA context. The functional tests around UPF1 and GIGYF1/2 point to biological relevance. That said, some statements could be qualified. The statement that ZFP36L1 "coordinates" multiple pathways implies mechanism and directionality that proximity data alone cannot prove. I suggest reframing as "positions ZFP36L1 within" or "supports a model where ZFP36L1 sits within" these networks.

We thank this reviewer for considering our data ‘directionally convincing, and robust, adding new plausible candidates as interactors with ZFP36L1’. We agree that the proposed wording is more appropriate and will change it accordingly.

2) UPF1, as an upstream regulator of ZFP36L1 expression, is a promising lead. The reduction of ZFP36L1 protein and mRNA in UPF1 knockout, the non-rescue by MG132, and the UPF1 RIP on ZFP36L1 mRNA together argue that UPF1 influences ZFP36L1 transcript output or processing. This claim would read stronger with one short rescue or perturbation that pins the mechanism. A compact test would be UPF1 re-expression in UPF1-deficient T cells with wild-type and helicase-dead alleles. This is realistic in primary T cells using mRNA electroporation or virus-based systems. Approximate time 2 to 3 weeks, including guide design check and expansion. Reagents and sequencing about 2 to 4k USD depending on donor numbers. This would help separate viability or stress effects from a direct role in ZFP36L1 mRNA handling.

We agree that a rescue experiment with wild-type and helicase-dead UPF1 in UPF1-deficient primary T cells would be interesting. Unfortunately, however, UPF1 knockout T cells are less viable and divide less (Supp Figure 6B), making further manipulations such as re-expression by viral transduction technically impossible. We will clarify this limitation in the Discussion and will more explicitly indicate that UPF1 promotes ZFP36L1 mRNA and protein expression, while acknowledging that the precise mechanistic contribution of UPF1 (e.g. to transcript processing, export, or surveillance) remain to be fully resolved.

3) The inference that ZFP36L1 proximity to decapping and deadenylation complexes reflects pathway engagement is reasonable and, frankly, expected. Still, where the manuscript moves from proximity to function, the narrative works best when supported by orthogonal validation. Two compact additions would raise confidence without opening new lines of work. First, a small set of reciprocal co-IPs for PATL1 or DDX6 at endogenous levels in activated T cells, run with and without RNase, would tie the RNase-class assignments to biochemistry. Second, a short-pulse proximity experiment using a reduced biotin dose and shorter labeling window in activated cells would address whether long incubations drive non-specific labeling. Both are feasible in 2 to 3 weeks with minimal extra cost for antibodies and MS runs if the facility is in-house.

We fully agree with the reviewer that orthogonal biochemical validation is valuable. Therefore, we already combined time-resolved proximity labeling (between 0-2h, 2-5h, and 5-16 hours) with time-resolved ZFP36L1 co-IPs ± RNase, to address the dynamic behavior and potential temporal broadening of the interactome.

As to running reciprocal co-IPs for PATL1 or DDX6: we had in fact already considered to follow up on PATL1. However, we failed to identified specific antibodies, revealing many unspecific bands (see below). As to DDX6, antibodies suitable for IP have been reported, and we can therefore offer such reciprocal IP as requested.

To further address the raised points, we will (i) clarify how we define and interpret RNase-sensitive versus RNase-resistant classes (ii) emphasize that some key factors (including PATL1) are already detected in shorter labeling conditions (2 h) in activated T cells (Fig 4C); and (iii) better highlight that the our data provide strong candidates and pathway hypotheses that warrant further mechanistic experimentation in follow-up studies, when moving from proximity to function.

As to the suggested lowering dose of biotin: As described in Figure S1, this appeared unsuccessful. We owe it to the reported dependence and use of biotin in primary T cells (Ref’s 31-33 of this manuscript). This also included that we could not culture T cells in biotin-free medium prior to labeling, as most protocols would do in cell lines.

The reviewer also suggested shorter labeling times. Please be advised that the labeling times chosen were based on the reported protein induction and activity on target mRNAs: 1) ZFP36L1 expression peaks at 2h of T cell activation (Zandhuis et al. 2025; 0.1002/eji.202451641, Petkau et al. 2024; 10.1002/eji.202350700), 3) shows the strongest effects on T cell function between 4-5h, and displays a late phase of activity at 5-16h (Popovic et al. Cell Reports 2023; 10.1016/j.celrep.2023.112419). We realize that additional explanation is warranted for this rationale, which we will provide.

4) Reproducibility is helped by donor pooling, repeated T-cell screens, Jurkat confirmation, and detailed methods including MaxQuant, LIMMA, and supervised patterning. Deposition of MS data is listed. The authors should consider adding a brief, stand-alone analysis notebook in SI or on GitHub with exact filtering thresholds and "shape" definitions, since the supervised profiles are central to claims. This would let others reproduce figures from raw tables with the same code and workflows.

We thank the reviewer for his or her suggestion and we have done as suggested. We will include the following link in the manuscript: https://github.com/ajhoogendijk/ZFP36L1_UltraID

5) Replication and statistics are mostly adequate for discovery proteomics. The thresholds are clear, and PCA and correlation frameworks are appropriate. For functional readouts in edited T cells, please make the number of donors and independent experiments explicit in figure legends, and indicate whether statistics are paired by donor. Where viability differs (UPF1), note any gating strategies used to avoid bias in puromycin or activation marker measurements. These clarifications are quick to add.

Please be advised that the current figure legends already contain the requested information at the bottom (which test used, donor number etc). To highlight this better, we will indicate this point more explicitly in the methods section.

Minor comments 6) The UltraID optimization in primary T cells is useful, but the long 16-hour labeling and high biotin should be framed as a compromise rather than a standard. A short statement about potential off-target labeling during extended incubations would set expectations and justify the RNase and time-course controls.

Please be advised that 1) high biotin was required because primary T cells depend on biotin and 2) increase biotin absorption a 2-7-fold upon activation (Ref 31-33 from the paper). For better time resolution, we included a labeling of 2h (from 0-2h of activation), 3h (from 2-5h) and 9h (from 5-16h) of T cell activation. Nevertheless, we agree that we cannot exclude the risk of off-target labeling, which in fact is inherent to any labeling and pulldown method. We will include such statement in the discussion.

7) The overlap across T-cell screens and with HEK293T APEX datasets is discussed, but a compact quantitative reconciliation would help. A table that lists shared versus cell-type-specific interactors with brief notes on known expression patterns would make this point concrete.

We thank the reviewer for this suggestion. We agree and we will include such table.

8) Figures are generally clear. Where proximity and total proteome PCA are shown, consider adding sample-wise annotations for donor pools and activation time to help readers link variance to biology. Ensure all volcano plots and heatmaps display the exact cutoffs used in text.

We agree that sample-wise annotations would be a nice addition. However, when testing this for e.g. FIgure 1D&E, such differentiation into individual donors becomes illegible due to the many different variables already present. We therefore decided against it.

9) Prior work on ZFP36 family roles in decay, deadenylation via CCR4-NOT, granules, and translational control is cited within the manuscript. In a few places, recent proximity and interactome papers could be more explicitly integrated when comparing overlap, especially where conclusions differ by cell type. A concise paragraph in Discussion that lays out what is truly new in primary T cells would help clarify the contribution of this work to the field.

We appreciate this suggestion and will revise the Discussion accordingly. As to what is new in primary T cells, we would also like to mention that adding H2O2 (required for APEX labeling) to T cells results in immediate cell death can therefore not be employed on T cells. This technical limitation further underscores the valuable contribution of the UltraID-based approach we present here.

Reviewer #1 (Significance (Required)):

Nature and type of advance. The study is a technical and contextual advance in mapping ZFP36L1 proximity partners directly in human primary T cells during activation. The combination of time-resolved labeling and RNase-class assignments is informative. The CRIS PR perturbations provide an initial functional bridge from proximity to phenotype, especially for UPF1.

Context in the literature. ZFP36 family proteins have long been linked to ARE-mediated decay, CCR4-NOT recruitment, and granule localization. The present work confirms those cores and extends them to include decapping and GIGYF1/2-4EHP scaffolds in primary T cells with temporal resolution. The UPF1 link to ZFP36L1 expression adds a plausible surveillance angle that merits follow-up. The cell-type specificity analysis versus HEK293T underscores that proximity networks vary with context.

Audience. Readers in RNA biology, T-cell biology, and proteomics will find the dataset valuable. Groups studying post-transcriptional regulation in immunity can use the resource to prioritize candidate nodes for mechanistic work.

Expertise and scope. I work on post-transcriptional regulation, RNA-protein complexes, and T-cell effector biology. I am comfortable evaluating the conceptual claims, experimental design, and statistical treatment. I am not a mass spectrometry specialist, so I rely on the presented parameters and deposited data for MS acquisition specifics.

To conclude, the manuscript delivers a substantive proximity map of ZFP36L1 in human T cells, with useful temporal and RNA-class information. The UPF1 observations are promising and would benefit from a compact rescue to secure causality. A few minor additions for biochemical validation and transparency in replication would further strengthen the paper.

We thank the reviewer for this comprehensive and constructive assessment. We agree that our study primarily provides a substantive and well-annotated proximity map of ZFP36L1 in human T cells, including temporal and RNA-class information, and that the UPF1 observations constitute a promising lead that merits more detailed mechanistic analysis in follow-up studies.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): The manuscript by Wolkers and colleagues describes the protein interactome of the RNA-binding protein ZFP36L1 in primary human T-cells. There is inherent value in the use of primary cells of human origin, but there is also value in that the study is quite complete, as it is performed in a variety of conditions: T-cells that have been activated or not, at different time points after activation, and by two methods (co-IP and proximity labeling). One might imagine that this basically covers all what can be detected for this protein in T-cells. The authors report a large amount of new interactors involved at all steps in post-transcriptional regulation. In addition, the authors show that UPF1, a known interactor of ZFP36L1, actually binds to ZFP36L1 mRNA and enhances its levels. In sum, the work provides a valuable resource of ZFP36L1 interactors. Yet, how the data add to the mechanistic understanding of ZFP36L1 functions and/or regulation of ZFP36L1 remains unclear.

We thank the reviewer for this enthusiasm on our experimental setups, considering the use of primary T cells of inherent value and our study with the variety of conditions complete.

Major comments: 1) Fig 2: It is confusing that the Pearson correlation to define ZFP36L1 interactors is changed depending on figure panel. In panels A-C, a correlation {greater than or equal to} 0.6 is used, while panel D uses a correlation > 0.5, which changes the nº of interactors. Then, this is changed again in Fig 3A for some cell types but not for others. Why has this been done? It would be better to stick to the same thresholds throughout the manuscript.

Please be advised that different correlation thresholds arise from the composition of the individual datasets: they in depth, number of controls, and the overall dynamic range. The initial proximity labeling experiment (Figure 2A–C) had a higher depth and a larger number of suitable control samples, which allowed us to apply a stricter cutoff (r ≥ 0.6). The time-course experiment and some of the cross-cell-type comparisons have fewer controls and somewhat lower depth, which then required a more permissive threshold (e.g. r > 0.5) to retain known core interactors.

We fully agree that this rationale needs to be explicit. In the revised manuscript we (i) clearly state for each dataset which correlation cutoff is used (ii) emphasize that these thresholds are somewhat arbitrary and should not be directly compared across experiments, and (iii) highlight that our key biological conclusions do not depend on the exact boundary chosen but rather on the consistent enrichment of core complexes and pathways across .

2) Fig 3A: It would be nice to have the information of this Figure panel as a Table (protein name, molecular process(es), known or novel, previously detected in which cells) in addition to the figure.

We agree that this would increase the value of our work as a resource to the community, and we will include such table and merge it with the table Reviewer 1 asked about.

3) Fig 6: To what extent are the effects of UPF1 and GIGFYF1 knock-out on translation and T-cell hyper-activation mediated by ZFP36L1? If deletion of ZFP36L1 itself has no effect on these processes, it seems unlikely that it is involved. In this respect, I am not sure that Fig 6 contributes to the understanding of ZFP36L.

We appreciate this conceptual question. In our dataset, ZFP36L1 knockout affects T-cell activation markers, but does not recapitulate the increased global translation observed upon UPF1 or GIGYF1/2 deletion. We will discuss this finding more explicitly in the Results and Discussion. We discuss the possibility that other ZFP36 family members (e.g. ZFP36/TTP, ZFP36L2) may partially compensate for the absence of ZFP36L1 in some readouts1. Moreover, we will emphasize that at this point it is not clear whether ZFP36L1’s contribution to UPF1 and GIGYF1 protein levels is direct or indirect.

We nonetheless consider Fig. 6 an important component of the story, as it demonstrates that proximity partners emerging from the interactome (UPF1, GIGYF1/2) have measurable functional consequences on T cell activation and translational control, thereby illustrating how the resource can guide mechanistic hypotheses. We will now more carefully phrase this as “first indications of mechanism” and avoid implying that these phenotypes are mediated exclusively via ZFP36L1.

4) Fig 7E: Differences in ZFP36L1 mRNA expression are claimed as a consequence of UPF1 deletion, and indeed there is a clear tendency to reduction of ZFP36L1 mRNA levels upon UPF1 KO. Yet the difference is statistically non-significant. Please, repeat this experiment to increase statistical significance. In addition, a clear discussion on how UPF1 -generally associated to mRNA degradation- contributes to increase ZFP36L1 mRNA levels would be appreciated.

We would like to refrain from including repeats for increasing statistical power. We find similar trends with n=3 at 0h as with n=7 at 3h of activation (Fig. 7E). We rather would like to stress that despite the width overall expression levels which most probably stems from using primary human material, the overall levels of ZFP36L1 mRNA are lower in UPF1 KO T cells. We will include a point on how UPF1 possibly may contribute to the decreased ZFP36L1 mRNA levels, as suggested.

5) Fig 6A: The decrease in global translation by GIGFYF1 knock-out upon activation claimed by the authors is not clear in Fig 6A and is non-significant upon quantification. Please, modify narrative accordingly.

Indeed, this was not phrased well. We will correct our description to match the statistical analysis.

6) Page 6: The authors state 'This included the PAN2/3 complex proteins which trim poly(A) tails prior to mRNA degradation through the CCR4/NOT complex'. To the best of my knowledge, the CCR4/NOT complex does not degrade the body of the mRNA. Both PAN2/3 and CCR4/NOT are deadenylases that function independently.

We thank the reviewer for highlighting this inaccuracy. PAN2/3 and CCR4–NOT are indeed both deadenylase complexes that function independently rather than one acting strictly upstream of the other in degrading the mRNA body. We will correct this statement to that PAN2/3 and CCR4–NOT cooperate in poly(A) tail shortening and do not themselves degrade the mRNA body, which is instead handled by the downstream decay machinery.

7) Please, label all Table sheets. Right now one has to guess what is being shown in most of them. Furthermore, it would be convenient to join all Tables related to the same Figure in one unique Excel with several sheets, rather than having many Tables with only one sheet each.

We appreciate this suggestion. In the revised supplementary files all table sheets will be clearly labeled to indicate the corresponding figure and dataset, and combined into a single excel file when multiple tables relate to the same figure. We have already done so.

Minor comments: 8) Fig 1E: Shouldn't there be a better separation by biotinylation in the UltraID IP principal component analysis? In theory, only biotinylated proteins should be immunoprecipitated.

In theory this should indeed be the case. However, in practice, pull down experiments always suffer from background stickiness of proteins to tubes, beads etc. Combined, these known background issues highlight the critical addition of control samples, allowing for unequivocal call of proteins that are above background.

In addition, as we indicated in the manuscript, primary T cells depend on Biotin. This prohibited us to use biotin-free medium, even for a short culture period (it resulted in cell death). Such biotin-free culture steps are included in proximity labeling assays performed in cell lines. Owing to the continuous addition of biotin, some of the ‘background’ biotinylation signal may even be ‘real’. Nevertheless, the higher levels of biotin we added during the labeling results in increased signals, and statistical analysis with these controls identifies which of the proteins are above background, irrespective from the source. We will include a short note on this in the manuscript

9) Fig 3B-E: Is the labeling not swapped, top (always +) is Biotin and bottom (- or +) is aCD3/aCD28?

We thank the reviewer for catching this mistake- we have corrected it

10) Fig 7A data is from another paper, so I suggest to move this panel to Supplementary materials.

We respectfully disagree. Please be advised that we reanalysed data from published datasets, that resulted in this figure. Re-analysis is a widely accepted method and certainly used for main figure panels. Our re-analysis from Bestenhorn et al 2025; (10.1016/j.molcel.2025.01.001) confirms that ZFP36L1 interacts with UPF1 and GIGYF1/2 in the RAW 264.7 macrophage cell line, which we consider an important consolidation of our findings. To highlight that this table is a re-analysis of published data, we will include this information (including the reference) below the data. As ‘extracted from Bestenhorn et al'

11) Fig S1A: Why is there so much labeling in the UltraID only lane without biotin?

This is a phenomenon also reported by others (Kubitz et al. 2022; 10.1038/s42003-022-03604-5: Figure 5A). UltraID alone is a small protein of (19.7KD), comparable to TurboID or others (Kubitz et al. 2022; 10.1038/s42003-022-03604-5). If not tethered to a specific compartment, these proximity labeling moieties can diffuse through the cytoplasm, biotinylating any protein they ‘bump’ into. Please be advised that we included this control to show this effect, to substantiate why we use GFP-UltraID- as control, to limit such background effects. To highlight this point better, we will better articulate this reasoning in the results section.

12) Fig S1E: Please, explain better. What is WT?

We thank the reviewer for catching this inconsistency. We will explicitly define “WT” as wild-type primary T cells (non-edited, non-transduced) and clarify how this relates to the other conditions.

13) Fig S4B: Please, explain the labels on top of the shapes.

We will update the figure, explaining how the labels above each shape are chosen (e.g. indicating specific clusters, functional categories, or experimental conditions, as appropriate). This should make the reading more intuitive.

14) Page 3: A time-course of incubation with biotin is lacking in Fig S1B, and thereby it is confusing that the authors direct readers to this figure when an increased to 16h incubation is claimed to be better.

Please be advised that short labeling times yielded disappointing results in primary human T cells. Therefore all first analyses were performed with 16h biotinylation, as depicted in Figure S1B). Only after achieving good results (presented in Figure 1B), we performed time course experiments (presented in __Figure 4, __lowering incubation times to 2h, 3h and 9h). We realize that this is confusing and we will rephrase this point in page 3.

Reviewer #2 (Significance (Required)): Strengths: A thorough repository of ZFP36L1 interactors in primary human T-cells. A valuable resource for the community. Weaknesses: There is little mechanistic insight on ZFP36L1 function or regulation.

We would like to highlight that the purpose of our study was to provide a comprehensive interactome of ZFP36L1, and to study the dynamics of these interactions. In addition to known interactors, we identified novel putative interactors of ZFP36L1. We have indeed not followed up on all interactions, which we consider beyond the scope of this manuscript. Rather, we consider our study as a toolbox for the community, that helps in their studies.

Nevertheless, in Fig 6-7, we show first indications of mechanistic insights on ZFP36L1 interactors, exemplifying how the findings of this resource paper can be used by the community.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The authors have analyzed the interactome of ZFP36L1 in primary human T cells using a biotin-based proximity labeling method. In addition to proteins that are known to interact with ZFP36L1, the authors defined a multitude of novel interactions involved in mRNA decapping, mRNA degradation pathways, translation repressors, stress granule/p-body formation, and other regulatory pathways. Time-lapse proximity labeling revealed that the ZFP36L1 interactome undergoes remodeling during T cell activation. Co-IP for ZFP36L1 executed in the presence/absence of RNA further revealed the interactome and possible regulators of ZFP36L1, including the helicase UPF1. In addition to interacting with ZFP36L1, UPF1 promotes the ZFP36L1 protein expression, seemingly by binding to the ZFP36L1 mRNA transcript, and in some way stabilizing it. This comprehensive interactome map highlights the widespread interactions of ZFP36L1 with proteins of many types, and its potential roles in diverse T cell processes. Although somewhat descriptive, rather than hypothesis-testing, this work represents an important contribution to understanding the potential roles of the ZFP36 family proteins, and sets up many future experiments which could test molecular details.

We thank the reviewer for these thoughtful points, and for recognizing our paper as an important contribution for the field as resource, that should support future experiments.

Major points: 1) Can the authors discuss the specificity of the antibody for ZFP36L1 used in the Co-IP experiments? The antibody listed in Appendix A is abcam catalog number ab42473, although the catalog number for this antibody (unlike the others major ones used) is not listed in the Methods section - please add this to the Methods to make it easier for readers to find this detail. Could this antibody also be immunoprecipitating ZFP36 or ZFP36L2? Other antibodies have had cross-reactivity for the different family members. It is also notable that this antibody has been discontinued by the manufacturer (https://www.abcam.com/en-us/products/unavailable/zfp36l1-antibody-ab42473). Have the authors tried the current abcam anti-ZFP36L1 antibody being sold, catalog number ab230507?

We appreciate the opportunity to clarify this important technical point. We have now added the catalog number (ab42473, Abcam) of the anti-ZFP36L1 antibody used for co-IP to the Methods section, in addition to Appendix A, to facilitate reproducibility. The antibody ab42473 has indeed been discontinued by the manufacturer. We have contacted the manufacturer on multiple occasions with no luck.

We have evaluated multiple alternative anti-ZFP36L1 antibodies, including the currently available Abcam antibody ab230507. In our hands, these alternatives showed weaker or less specific detection of ZFP36L1 compared to the original ZFP36L1 antibody. Only antibody 1A3 recognized ZFP36L1. We therefore used this antibody for the Co-IP. Importantly, even though the signal is lower than the original antibody we used, the migration patterns observed with ab42473 in our co-IP experiments match the expected molecular weight of ZFP36L1 and do not suggest substantial cross-reactivity with ZFP36 or ZFP36L2, which display distinct sizes (we will add the sizes to the WB in figures). We discuss this point briefly in the revised Methods/Results.

2) On this point, the authors report interactions between ZFP36L1 and its related proteins ZFP36 and ZFP36L2 in the Co-IP experiment (Supp 5C). Did these proteins interact in the proximity labeling? Ideally this could be discussed in the Discussion section.

ZFP36 and ZFP36L2 were indeed detected as co-precipitating with ZFP36L1 in the co-IP experiments but were not found as high-confidence interactors in the UltraID proximity labeling datasets. Also in the APEX proximity labeling of Bestehorn et al. In RAW macrophage cells, they did not find ZFP36 or ZFP36L1 to interact with ZFP36L1. * *We now explicitly mention this in the Results and discuss it in the Discussion.

3) Can the authors discuss more fully the limited overlap in identified interactors across the two proximity labeling screens performed in primary T cells (Fig 2C)? Likewise, can the authors comment on the very limited overlap between the screens in T cells and the published ZFP36L1-APEX proximity labelling experiment performed in the HEK293T cell line by Bestehorn et al. (ref 42)? Only 6.8% of proteins found in either T cell screen were found as interactors in this cell line. The authors comment that this may be because "...either expression of certain proteins is cell-type specific, or [because] ZFP36L1 has cell-type specific protein interactions, in addition to its core interactome". While I agree that cell-type specific interactions may be at play, I would think most of the interactors found in the T cell screens are widely expressed proteins necessary for central cell functions.

First, the apparent overlap percentage depends on depth and filtering. As noted above and now detailed in a new Supplementary table, a core set of decapping, deadenylation, and granule-associated factors is consistently recovered across our T-cell screens and the HEK293T APEX dataset. However, beyond this core protein, overlap is reduced, reflecting several factors: (i) differences in expression levels of many interactors between HEK293T cells and primary T cells; (ii) the activation-dependent nature of ZFP36L1 function in T cells, which cannot be fully mimicked in HEK293T; (iii) different proximity labeling enzymes and fusion constructs (APEX vs UltraID, different tags, expression levels); and (iv) distinct experimental designs and control strategies, which influence statistical filtering and the effective “depth” of each interactome.

In the revised Discussion and in the new comparative table, we now emphasize that while many of the ZFP36L1 proximity partners identified in T cells are indeed widely expressed, their effective labeling and enrichment are strongly context dependent. We therefore interpret the relatively limited overlap as highlighting both a robust core interactome and substantial context-specific remodeling, rather than as evidence of artifacts in one or the other dataset.

Minor comments: 4) In Figure 3D, the legend states that black circles indicate significantly enriched proteins in biotin samples, while grey circles indicate non-significant enrichment. However, some genes, including DCP1A, DDX6, YBX1, have black circles in the -biotin group and grey in the +biotin group, which creates confusion in interpretation.

We thank the reviewer for this comment. We have accidentally switched the labeling of biotin and activation as pointed out by reviewer 2. Once this is fixed, this comment will also be fixed.

5) Did the authors find any interactors whose expression is known to be specific to CD4 or CD8 T cells?

In our current dataset we did not identify interactors whose presence was clearly restricted to CD4 or CD8 T-cells. We agree that differential ZFP36L1 interactomes in defined T-cell subsets represent an interesting avenue for future targeted studies and will outline this is the discussion.

Reviewer #3 (Significance (Required)):

The authors present the first comprehensive analysis of the ZFP36L1 interactome in primary T cells. The use of biotin-based proximity labeling enables detection of physiologically relevant interactions in live cells. This approach revealed many novel interactors.

Strengths include the overall richness of the dataset, and the hypothesis-provoking experiments that could follow in the future. Limitations include somewhat limited overlap with a published proximity labeling dataset from performed in a different cell line, suggesting that there may be artifacts in one or both datasets.

The audience for this article would include those interested broadly in RNA binding proteins and those interested in post-transcriptional and translational regulation.

I have immunology expertise on T cell activation and differentiation and expertise on transcriptional and post-transcriptional regulation of gene expression in T cells.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The authors have analyzed the interactome of ZFP36L1 in primary human T cells using a biotin-based proximity labeling method. In addition to proteins that are known to interact with ZFP36L1, the authors defined a multitude of novel interactions involved in mRNA decapping, mRNA degradation pathways, translation repressors, stress granule/p-body formation, and other regulatory pathways. Time-lapse proximity labeling revealed that the ZFP36L1 interactome undergoes remodeling during T cell activation. Co-IP for ZFP36L1 executed in the presence/absence of RNA further revealed the interactome and possible regulators of ZFP36L1, including the helicase UPF1. In addition to interacting with ZFP36L1, UPF1 promotes the ZFP36L1 protein expression, seemingly by binding to the ZFP36L1 mRNA transcript, and in some way stabilizing it. This comprehensive interactome map highlights the widespread interactions of ZFP36L1 with proteins of many types, and its potential roles in diverse T cell processes. Although somewhat descriptive, rather than hypothesis-testing, this work represents an important contribution to understanding the potential roles of the ZFP36 family proteins, and sets up many future experiments which could test molecular details.

Major points:

1) Can the authors discuss the specificity of the antibody for ZFP36L1 used in the Co-IP experiments? The antibody listed in Appendix A is abcam catalog number ab42473, although the catalog number for this antibody (unlike the others major ones used) is not listed in the Methods section - please add this to the Methods to make it easier for readers to find this detail. Could this antibody also be immunoprecipitating ZFP36 or ZFP36L2? Other antibodies have had cross-reactivity for the different family members. It is also notable that this antibody has been discontinued by the manufacturer (https://www.abcam.com/en-us/products/unavailable/zfp36l1-antibody-ab42473). Have the authors tried the current abcam anti-ZFP36L1 antibody being sold, catalog number ab230507?

2) On this point, the authors report interactions between ZFP36L1 and its related proteins ZFP36 and ZFP36L2 in the Co-IP experiment (Supp 5C). Did these proteins interact in the proximity labeling? Ideally this could be discussed in the Discussion section.

3) Can the authors discuss more fully the limited overlap in identified interactors across the two proximity labeling screens performed in primary T cells (Fig 2C)? Likewise, can the authors comment on the very limited overlap between the screens in T cells and the published ZFP36L1-APEX proximity labelling experiment performed in the HEK293T cell line by Bestehorn et al. (ref 42)? Only 6.8% of proteins found in either T cell screen were found as interactors in this cell line. The authors comment that this may be because "...either expression of certain proteins is cell-type specific, or [because] ZFP36L1 has cell-type specific protein interactions, in addition to its core interactome". While I agree that cell-type specific interactions may be at play, I would think most of the interactors found in the T cell screens are widely expressed proteins necessary for central cell functions.

Minor comments:

4) In Figure 3D, the legend states that black circles indicate significantly enriched proteins in biotin samples, while grey circles indicate non-significant enrichment. However, some genes, including DCP1A, DDX6, YBX1, have black circles in the -biotin group and grey in the +biotin group, which creates confusion in interpretation.

5) Did the authors find any interactors whose expression is known to be specific to CD4 or CD8 T cells?

Significance

The authors present the first comprehensive analysis of the ZFP36L1 interactome in primary T cells. The use of biotin-based proximity labeling enables detection of physiologically relevant interactions in live cells. This approach revealed many novel interactors.

Strengths include the overall richness of the dataset, and the hypothesis-provoking experiments that could follow in the future. Limitations include somewhat limited overlap with a published proximity labeling dataset from performed in a different cell line, suggesting that there may be artifacts in one or both datasets.

The audience for this article would include those interested broadly in RNA binding proteins and those interested in post-transcriptional and translational regulation.

I have immunology expertise on T cell activation and differentiation and expertise on transcriptional and post-transcriptional regulation of gene expression in T cells.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The manuscript by Wolkers and colleagues describes the protein interactome of the RNA-binding protein ZFP36L1 in primary human T-cells. There is inherent value in the use of primary cells of human origin, but there is also value in that the study is quite complete, as it is performed in a variety of conditions: T-cells that have been activated or not, at different time points after activation, and by two methods (co-IP and proximity labeling). One might imagine that this basically covers all what can be detected for this protein in T-cells. The authors report a large amount of new interactors involved at all steps in post-transcriptional regulation. In addition, the authors show that UPF1, a known interactor of ZFP36L1, actually binds to ZFP36L1 mRNA and enhances its levels. In sum, the work provides a valuable resource of ZFP36L1 interactors. Yet, how the data add to the mechanistic understanding of ZFP36L1 functions and/or regulation of ZFP36L1 remains unclear.

Major comments:

1) Fig 2: It is confusing that the Pearson correlation to define ZFP36L1 interactors is changed depending on figure panel. In panels A-C, a correlation {greater than or equal to} 0.6 is used, while panel D uses a correlation > 0.5, which changes the nº of interactors. Then, this is changed again in Fig 3A for some cell types but not for others. Why has this been done? It would be better to stick to the same thresholds throughout the manuscript.

2) Fig 3A: It would be nice to have the information of this Figure panel as a Table (protein name, molecular process(es), known or novel, previously detected in which cells) in addition to the figure.

3) Fig 6: To what extent are the effects of UPF1 and GIGFYF1 knock-out on translation and T-cell hyper-activation mediated by ZFP36L1? If deletion of ZFP36L1 itself has no effect on these processes, it seems unlikely that it is involved. In this respect, I am not sure that Fig 6 contributes to the understanding of ZFP36L.

4) Fig 7E: Differences in ZFP36L1 mRNA expression are claimed as a consequence of UPF1 deletion, and indeed there is a clear tendency to reduction of ZFP36L1 mRNA levels upon UPF1 KO. Yet the difference is statistically non-significant. Please, repeat this experiment to increase statistical significance. In addition, a clear discussion on how UPF1 -generally associated to mRNA degradation- contributes to increase ZFP36L1 mRNA levels would be appreciated.

5) Fig 6A: The decrease in global translation by GIGFYF1 knock-out upon activation claimed by the authors is not clear in Fig 6A and is non-significant upon quantification. Please, modify narrative accordingly.

6) Page 6: The authors state 'This included the PAN2/3 complex proteins which trim poly(A) tails prior to mRNA degradation through the CCR4/NOT complex'. To the best of my knowledge, the CCR4/NOT complex does not degrade the body of the mRNA. Both PAN2/3 and CCR4/NOT are deadenylases that function independently.

7) Please, label all Table sheets. Right now one has to guess what is being shown in most of them. Furthermore, it would be convenient to join all Tables related to the same Figure in one unique Excel with several sheets, rather than having many Tables with only one sheet each.

Minor comments:

8) Fig 1E: Shouldn't there be a better separation by biotinylation in the UltraID IP principal component analysis? In theory, only biotinylated proteins should be immunoprecipitated.

9) Fig 3B-E: Is the labeling not swapped, top (always +) is Biotin and bottom (- or +) is aCD3/aCD28?

10) Fig 7A data is from another paper, so I suggest to move this panel to Supplementary materials.

11) Fig S1A: Why is there so much labeling in the UltraID only lane without biotin?

12) Fig S1E: Please, explain better. What is WT?

13) Fig S4B: Please, explain the labels on top of the shapes.

14) Page 3: A time-course of incubation with biotin is lacking in Fig S1B, and thereby it is confusing that the authors direct readers to this figure when an increased to 16h incubation is claimed to be better.

Significance

Strengths: A thorough repository of ZFP36L1 interactors in primary human T-cells. A valuable resource for the community.

Weaknesses: There is little mechanistic insight on ZFP36L1 function or regulation.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

The authors map the ZFP36L1 protein interactome in human T cells using UltraID proximity labeling combined with quantitative mass spectrometry. They optimize labeling conditions in primary T cells, profile resting and activated cells, and include a time course at 2, 5, and 16 hours. They complement the interactome with co-immunoprecipitation in the presence or absence of RNase to assess RNA dependence. They then test selected candidates using CRISPR knockouts in primary T cells, focusing on UPF1 and GIGYF1/2, and report effects on global translation, stress, activation markers, and ZFP36L1 protein levels. The work argues that ZFP36L1 sits at the center of multiple post-transcriptional pathways in T cells (which in itself is not a novel finding) and that UPF1 supports ZFP36L1 expression at the mRNA and protein level. The main model system is primary human T cells, with some data in Jurkat cells.

The core datasets show thousands of identified proteins in total lysates and enriched biotinylated fractions. Known partners from CCR4-NOT, decapping, stress granules, and P-bodies appear, with additional candidates like GIGYF1/2, PATL1, DDX6, and UPF1. Time-resolved labeling suggests shifts in proximity during early activation. Co-IP with and without RNase suggests both RNA-dependent and RNA-independent contacts. CRISPR loss of UPF1 or GIGYF1/2 increases translation at rest and elevates activation markers, and UPF1 loss reduces ZFP36L1 protein and mRNA while MG132 does not rescue protein levels; UPF1 RIP enriches ZFP36L1 mRNA.

Among patterns worth noting are that the activation state drives the principal variance in both proteome and proximity datasets. Deadenylation, decapping, and granule proteins are consistently near ZFP36L1 across conditions, while some contacts dip at 2 hours and recover by 5 to 16 hours. Mitochondrial ribosomal proteins become more proximal later. UPF1 and GIGYF1 show time-linked behavior and RNase sensitivity that fits roles in mRNA surveillance and translational control. These observations support a dynamic hub model, though they remain proximity-based rather than direct binding maps.

Major comments

The key conclusions are directionally convincing for a broad and dynamic ZFP36L1 neighborhood in human T cells. The data robustly recover established complexes and add plausible candidates. The time-course and RNase experiments strengthen the claim that interactions shift with activation state and RNA context. The functional tests around UPF1 and GIGYF1/2 point to biological relevance. That said, some statements could be qualified. The statement that ZFP36L1 "coordinates" multiple pathways implies mechanism and directionality that proximity data alone cannot prove. I suggest reframing as "positions ZFP36L1 within" or "supports a model where ZFP36L1 sits within" these networks.

UPF1, as an upstream regulator of ZFP36L1 expression, is a promising lead. The reduction of ZFP36L1 protein and mRNA in UPF1 knockout, the non-rescue by MG132, and the UPF1 RIP on ZFP36L1 mRNA together argue that UPF1 influences ZFP36L1 transcript output or processing. This claim would read stronger with one short rescue or perturbation that pins the mechanism. A compact test would be UPF1 re-expression in UPF1-deficient T cells with wild-type and helicase-dead alleles. This is realistic in primary T cells using mRNA electroporation or virus-based systems. Approximate time 2 to 3 weeks, including guide design check and expansion. Reagents and sequencing about 2 to 4k USD depending on donor numbers. This would help separate viability or stress effects from a direct role in ZFP36L1 mRNA handling.

The inference that ZFP36L1 proximity to decapping and deadenylation complexes reflects pathway engagement is reasonable and, frankly, expected. Still, where the manuscript moves from proximity to function, the narrative works best when supported by orthogonal validation. Two compact additions would raise confidence without opening new lines of work. First, a small set of reciprocal co-IPs for PATL1 or DDX6 at endogenous levels in activated T cells, run with and without RNase, would tie the RNase-class assignments to biochemistry. Second, a short-pulse proximity experiment using a reduced biotin dose and shorter labeling window in activated cells would address whether long incubations drive non-specific labeling. Both are feasible in 2 to 3 weeks with minimal extra cost for antibodies and MS runs if the facility is in-house.

Reproducibility is helped by donor pooling, repeated T-cell screens, Jurkat confirmation, and detailed methods including MaxQuant, LIMMA, and supervised patterning. Deposition of MS data is listed. The authors should consider adding a brief, stand-alone analysis notebook in SI or on GitHub with exact filtering thresholds and "shape" definitions, since the supervised profiles are central to claims. This would let others reproduce figures from raw tables with the same code and workflows.

Replication and statistics are mostly adequate for discovery proteomics. The thresholds are clear, and PCA and correlation frameworks are appropriate. For functional readouts in edited T cells, please make the number of donors and independent experiments explicit in figure legends, and indicate whether statistics are paired by donor. Where viability differs (UPF1), note any gating strategies used to avoid bias in puromycin or activation marker measurements. These clarifications are quick to add.

Minor comments

The UltraID optimization in primary T cells is useful, but the long 16-hour labeling and high biotin should be framed as a compromise rather than a standard. A short statement about potential off-target labeling during extended incubations would set expectations and justify the RNase and time-course controls.

The overlap across T-cell screens and with HEK293T APEX datasets is discussed, but a compact quantitative reconciliation would help. A table that lists shared versus cell-type-specific interactors with brief notes on known expression patterns would make this point concrete.

Figures are generally clear. Where proximity and total proteome PCA are shown, consider adding sample-wise annotations for donor pools and activation time to help readers link variance to biology. Ensure all volcano plots and heatmaps display the exact cutoffs used in text.

Prior work on ZFP36 family roles in decay, deadenylation via CCR4-NOT, granules, and translational control is cited within the manuscript. In a few places, recent proximity and interactome papers could be more explicitly integrated when comparing overlap, especially where conclusions differ by cell type. A concise paragraph in Discussion that lays out what is truly new in primary T cells would help clarify the contribution of this work to the field.

Significance

Nature and type of advance. The study is a technical and contextual advance in mapping ZFP36L1 proximity partners directly in human primary T cells during activation. The combination of time-resolved labeling and RNase-class assignments is informative. The CRISPR perturbations provide an initial functional bridge from proximity to phenotype, especially for UPF1.

Context in the literature. ZFP36 family proteins have long been linked to ARE-mediated decay, CCR4-NOT recruitment, and granule localization. The present work confirms those cores and extends them to include decapping and GIGYF1/2-4EHP scaffolds in primary T cells with temporal resolution. The UPF1 link to ZFP36L1 expression adds a plausible surveillance angle that merits follow-up. The cell-type specificity analysis versus HEK293T underscores that proximity networks vary with context.

Audience. Readers in RNA biology, T-cell biology, and proteomics will find the dataset valuable. Groups studying post-transcriptional regulation in immunity can use the resource to prioritize candidate nodes for mechanistic work.

Expertise and scope. I work on post-transcriptional regulation, RNA-protein complexes, and T-cell effector biology. I am comfortable evaluating the conceptual claims, experimental design, and statistical treatment. I am not a mass spectrometry specialist, so I rely on the presented parameters and deposited data for MS acquisition specifics.

To conclude, the manuscript delivers a substantive proximity map of ZFP36L1 in human T cells, with useful temporal and RNA-class information. The UPF1 observations are promising and would benefit from a compact rescue to secure causality. A few minor additions for biochemical validation and transparency in replication would further strengthen the paper.

This page lifts quite a bit out of my descriptions of networked agency, a term I coined 2016, without attribution. It reads like a generated text. Unclear too what the point of the site is at all.

eLife Assessment

This paper presents the fundamental discovery that lipid metabolic imbalance induced by Snail, an EMT-related transcription factor, contributes to the acquisition of chemoresistance in cancer cells. The evidence, supported by a wide range of methods and adequate quantification, provides a convincing mechanistic explanation of how Snail drives ectopic expression of the cholesterol- and drug-efflux transporter ABCA1. This work, which introduces a novel therapeutic concept targeting invasive cancer, will be of broad interest to researchers in cancer biology, lipid metabolism, and cell biology.

Reviewer #1 (Public review):

The authors focus on the molecular mechanisms by which EMT cells confer resistance to cancer cells. The authors use a wide range of methods to reveal that overexpression of Snail in EMT cells induces cholesterol/sphingomyelin imbalance via transcriptional repression of biosynthetic enzymes involved in sphingomyelin synthesis. The study also revealed that ABCA1 is important for cholesterol efflux and thus for counterbalancing the excess of intracellular free cholesterol in these snail-EMT cells. Inhibition of ACAT, an enzyme catalyzing cholesterol esterification, also seems essential to inhibit the growth of snail-expressing cancer cells.

Overall, the provided data are convincing and enhance our knowledge on cancer biology.

Reviewer #2 (Public review):

Summary:

This revised study provides a clearer and more mechanistically grounded explanation of how lipid metabolic imbalance contributes to EMT-associated chemoresistance in renal cancer. In this study, the authors discovered that chemoresistance in RCC cell lines correlates with the expression levels of ABCA1 and the EMT-related transcription factor Snail. They demonstrate that Snail induces ABCA1 expression and chemoresistance, and that inhibition of ABCA1-associated pathways can counteract this resistance. The study also suggests that Snail disrupts the cholesterol-sphingomyelin balance by repressing enzymes involved in VLCFA-sphingomyelin synthesis, leading to excess free cholesterol and activation of the LXR-ABCA1 axis. Importantly, inhibiting cholesterol esterification, which renders free cholesterol inert, selectively suppresses growth of a xenograft model of Snail-positive kidney cancer. These findings provide potential lipid metabolism-targeting strategies for cancer therapy. The revised version includes additional quantitative analyses and new experiments addressing lipid balance and ABCA1 localization, further strengthening the overall mechanistic model.

Strengths:

This revised manuscript provides a more comprehensive and convincing mechanistic explanation for how Snail-driven EMT induces chemoresistance through altered lipid homeostasis. The study presents a novel concept in which the Chol/SM balance, rather than individual lipid levels, shapes therapeutic vulnerability. The potential for targeting cholesterol detoxification pathways in Snail-positive cancer cells remains a significant therapeutic implication. In the revised version, the authors provide additional quantitative analyses and complementary experiments - including ABCA1 localization, restoration of VLCFA-SM levels by supplementation with C22:0 ceramide, and membrane-order assays - which further strengthen the mechanistic interpretation and address key concerns raised in earlier reviews.

Weaknesses:

The revised version includes new experiments showing that restoring sphingomyelin levels suppresses ABCA1 expression, thereby strengthening the causal link between altered lipid balance and ABCA1 induction. However, the evidence that ABCA1 is directly required for chemoresistance remains somewhat limited, as the phenotype was not reproduced by ABCA1 knockout or knockdown, and CsA may affect additional targets beyond ABCA1.

Author response:

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

Reviewer #1 (Public review):

The authors focus on the molecular mechanisms by which EMT cells confer resistance to cancer cells. The authors use a wide range of methods to reveal that overexpression of Snail in EMT cells induces cholesterol/sphingomyelin imbalance via transcriptional repression of biosynthetic enzymes involved in sphingomyelin synthesis. The study also revealed that ABCA1 is important for cholesterol efflux and thus for counterbalancing the excess of intracellular free cholesterol in these snail-EMT cells. Inhibition of ACAT, an enzyme catalyzing cholesterol esterification, also seems essential to inhibit the growth of snail-expressing cancer cells.

However, It seems important to analyze the localization of ABCA1, as it is possible that in the event of cholesterol/sphingomyelin imbalance, for example, the intracellular trafficking of the pump may be altered.

The authors should also analyze ACAT levels and/or activity in snail-EMT cells that should be increased. Overall, the provided data are important to better understand cancer biology.

We thank the reviewer for recognizing the significance of our study. Consistent with the hypothesis that ABCA1 contributes to chemoresistance in hybrid E/M cells, we agree that demonstrating the localization of ABCA1 at the plasma membrane is important, and we have included additional experiments to address this point.

We also examined the expression of the major ACAT isoform in the kidney, SOAT1, across RCC cell lines. However, its expression did not correlate with that of Snail (Figure 4B), suggesting that SOAT1 is constitutively expressed at a certain level regardless of Snail expression. The details of these additional experiments are provided in the point-by-point responses below.

Reviewer #2 (Public review):

Summary:

In this study, the authors discovered that the chemoresistance in RCC cell lines correlates with the expression levels of the drug transporter ABCA1 and the EMT-related transcription factor Snail. They demonstrate that Snail induces ABCA1 expression and chemoresistance, and that ABCA1 inhibitors can counteract this resistance. The study also suggests that Snail disrupts the cholesterol-sphingomyelin (Chol/SM) balance by repressing the expression of enzymes involved in very long-chain fatty acid-sphingomyelin synthesis, leading to excess free cholesterol. This imbalance activates the cholesterol-LXR pathway, inducing ABCA1 expression. Moreover, inhibiting cholesterol esterification suppresses Snail-positive cancer cell growth, providing potential lipid-targeting strategies for invasive cancer therapy.

Strengths:

This research presents a novel mechanism by which the EMT-related transcription factor Snail confers drug resistance by altering the Chol/SM balance, introducing a previously unrecognized role of lipid metabolism in the chemoresistance of cancer cells. The focus on lipid balance, rather than individual lipid levels, is a particularly insightful approach. The potential for targeting cholesterol detoxification pathways in Snail-positive cancer cells is also a significant therapeutic implication.

Weaknesses:

The study's claim that Snail-induced ABCA1 is crucial for chemoresistance relies only on pharmacological inhibition of ABCA1, lacking additional validation. The causal relationship between the disrupted Chol/SM balance and ABCA1 expression or chemoresistance is not directly supported by data. Some data lack quantitative analysis.

We thank the reviewer for his/her insightful and constructive comments. In response, we have performed additional experiments using complementary approaches to further substantiate the contribution of Snail-induced ABCA1 expression to chemoresistance. Furthermore, to clarify the causal relationship between reduced sphingomyelin biosynthesis and ABCA1 expression, we conducted new experiments showing that supplementation with sphingolipids attenuates ABCA1 upregulation (Figure 3H). The details of these additional experiments are described in the point-by-point responses below.

Reviewer #1 (Recommendations for the authors):

In this paper, the authors reveal that snail expression in EMT-cells leads to an imbalance between cholesterol and sphingomyelin via a transcriptional repression of enzymes involved in the biosynthesis of sphingomyelin.

This paper is interesting and highlights how the imbalance of lipids would impact chemotherapy resistance. However, I have a few comments.

In Figure 2 in Eph4 cells, while filipin staining appears exclusively at the plasma membrane in the case of EpH4-snail cells filipin staining is also intracellular. It seems plausible that all filipin-positive intracellular staining is not exclusively in LDs, authors should therefore try to colocalize filipin with other intracellular markers. To this aim, authors might want to use topfluocholesterol-probe for instance.

We examined the distribution of TopFluor-cholesterol in hybrid E/M cells (Figure 2H) and found that TopFluor-cholesterol colocalizes with lipid droplets. In addition, we analyzed the colocalization between intracellular filipin signals and organelle-specific proteins, ADRP (lipid droplets) and LAMP1 (lysosomes) (Figure 2I). Since filipin binds exclusively to unesterified cholesterol, filipin signals did not colocalize with ADRP. Instead, we observed colocalization of filipin with LAMP1, suggesting that cholesterol accumulates in hybrid E/M cells in both esterified and unesterified forms.

In Figure 3, the authors reveal that the exogenous expression of the snail alters the ratio of cholesterol to sphingomyelin. The authors should reveal where is found the intracellular cholesterol and intracellular sphingomyelin within these cells Eph4-snail.

To investigate the lipid composition of the plasma membrane, we utilized lipid-binding protein probes, D4 (for cholesterol) and lysenin (for sphingomyelin) (Figures 2L and 2M). We found that the plasma membrane cholesterol content was not affected by EMT, whereas sphingomyelin levels were markedly decreased. In addition, intracellular cholesterol was visualized (Comment 1-1; Figures 2E–2K). On the other hand, because visualization of intracellular sphingomyelin is technically challenging, we were unable to include this analysis in the present study. We consider this an important direction for future investigation.

Regarding the model described in panel K of Figure 3. I would expect that the changes in lipid-membrane organization depicted in panel K should affect the pattern of GM1 toxin for instance or the motility of raft-associated proteins for instance. The authors could perform these experiments in order to sustain the change of lipid plasma membrane organization.

We attempted staining with FITC–cholera toxin to visualize GM1, but both EpH4 and EpH4–Snail cells exhibited very low levels of GM1, resulting in minimal or no detectable staining (data not shown). Instead, to assess the impact of decreased sphingomyelin on the overall biophysical properties of the plasma membrane, we used a plasma membrane–specific lipid-order probe, FπCM–SO₃ (Figures 2N–2P and Figure 2—figure supplement 3). We found that the plasma membrane of EpH4–Snail cells was more disordered (fluidized), suggesting that the overall properties of the plasma membrane are altered by ectopic expression of Snail.

Another issue is the intracellular localization of ABCA1 in Eph4-Snail cells. Knowing that a change in the cholesterol/sphingomyelin ratio can also modify intracellular protein trafficking, it seems important to analyze the intracellular localization of ABCA1 in EPh4-Snail cells.

We performed immunofluorescence microscopy for ABCA1 and found that ABCA1 was mainly localized at the plasma membrane in EpH4–Snail cells (Figure 1M).

As for the data on ACAT inhibition, we expect an increase in ACAT activity and protein levels in EMT cells overexpressing Snail. The authors should also investigate this point.

As noted in our response to the public review, we examined the expression of the major ACAT isoform in the kidney, SOAT1, across RCC cell lines. However, its expression did not correlate with Snail (Figure 4B), suggesting that SOAT1 is expressed at sufficient levels even in cells with low Snail expression. We agree that measuring ACAT activity would be important, as ACATs are regulated at multiple levels. However, we consider this to be beyond the scope of the present study and plan to address it in future work.

Minor comments

I do not understand why in the text, Figure S1 appears after Figure S2. The authors might want to change the numbering of these two figures.

We thank the reviewer for pointing this out. We have corrected the numbering of the supplementary figures so that Figure S1 now appears before Figure S2 in both the text and the revised figure legends.

Page 5, lane 20 Figure 1I instead of 1H.

Page 6, lane 2, Figure 1J instead of 1I, and lane 9 Figure 1H instead of 1I.

We thank the reviewer for carefully checking the figure references. We have corrected the figure numbering errors in the text as suggested.

Reviewer #2 (Recommendations for the authors):

For Figures 1B, 1H, 1J, 2B, 2C, 3G, S3A, and S3B, to enhance data reliability, it is necessary to conduct a quantitative analysis of the Western blot data. The average values from at least three biological replicates should be calculated, with statistical significance assessed.

We have conducted quantitative analyses of the Western blot data for Figures 1B, 1H, 1J, 2B, 2C, 3G, S3A, and S3B. Band intensities from at least three independent biological replicates were quantified, and the mean values with statistical significance are now presented in the revised figures.

For Figures 1D, 2A, 2D, and S2, the images of cells or tissues should not rely solely on selected fields. Quantitative analysis is required, and the mean values from at least three biological replicates should be provided with statistical significance testing.

We have performed quantitative analyses for Figures 1D, 2A, 2D, and S2. The quantification was based on data from at least three independent biological replicates, and the mean values with statistical significance are now included in the revised figures.

For Figures 1A, 1G, 4, and S5, evaluating ABCA1's involvement in drug resistance based solely on CsA treatment is insufficient. Demonstrating the loss of drug resistance through ABCA1 knockdown or knockout is necessary.

We generated ABCA1 knockout EpH4–Snail cells and examined their resistance to nitidine chloride. However, knockout of ABCA1 alone did not affect resistance to the compound (Figure 2 - figure supplement 2). This may be due to secondary metabolic alterations induced by ABCA1 loss or compensatory upregulation of other LXR-induced cholesterol efflux transporters. Instead, we demonstrated that treatment with the LXR inhibitor GSK2033 reduced the nitidine chloride resistance of EpH4–Snail cells (Figure 2C), supporting the idea that enhanced efflux of antitumor agents through the LXR–ABCA1–mediated cholesterol efflux pathway contributes to nitidine chloride resistance.

For Figure 3, to establish a causal relationship between changes in the Chol/SM balance and ABCA1 expression, it is important to test whether modifying cholesterol and SM levels to disrupt this balance affects ABCA1 expression.

Regarding causality, as shown in Figure 2, we have already demonstrated that reducing cholesterol levels in EpH4–Snail cells decreases ABCA1 expression. To further explore this relationship, we examined whether increasing sphingomyelin levels by adding ceramide to the culture medium—thereby restoring the sphingomyelin-to-cholesterol ratio—would reduce ABCA1 expression (Figure 3H). Indeed, supplementation with C22:0 ceramide decreased ABCA1 expression, suggesting that downregulation of the VLCFA-sphingomyelin biosynthetic pathway triggers ABCA1 upregulation. Collectively, these findings support a causal relationship between the Chol/SM balance and ABCA1 expression.

In Figure 3, if there is any information on differences in cholesterol affinity between LCFA-SM and VLCFA-SM, it would be beneficial to include it in the manuscript.

Differences in cholesterol affinity between LCFA-SM and VLCFA-SM in cellular membranes remain controversial and have yet to be fully elucidated. The decrease in cell surface sphingomyelin content, evaluated by lysenin staining (Figure 2L), was more pronounced than that of total sphingomyelin (Figure 3A). Given that VLCFA-SMs have been suggested to undergo distinct trafficking during recycling from endosomes to the plasma membrane (Koivusalo et al. Mol Biol Cell 2007), their reduction may lead to decreased plasma membrane sphingomyelin content by altering its intracellular distribution. We have added this discussion to the revised manuscript.

In Figure 3F, it is recommended to assess housekeeping gene expression as a control. Quantitative real-time PCR should be performed, and the average values from at least three biological replicates should be presented.

We have performed quantitative RT-PCR analysis. The average values from at least three independent biological replicates are presented in Figure 3G.

For Figure 3F, to show whether the reduction of CERS3 or ELOVL7 affects the Chol/SM balance and ABCA1 expression, it is necessary to investigate the phenotypes following the knockdown or knockout of these enzymes.

We fully agree that phenotypic analyses of epithelial cells lacking CerS3 or ELOVL7 would provide valuable insights. However, we consider such investigations to be beyond the scope of the present study and plan to pursue them in future work.

Clarifying whether similar phenotypes are induced by other EMT-related transcription factors, or if they are specific to Snail, would be beneficial.

We agree that examining whether similar phenotypes are induced by other EMT-related transcription factors would be highly valuable for understanding the broader EMT network. However, as the focus of the present study is on lipid metabolic alterations associated with EMT—particularly the imbalance between sphingomyelin and cholesterol—we consider this investigation to be beyond the scope of the current work and plan to address it in future studies.

There are errors in figure citations within the text that need correction:

p.9 l.18 Fig. 3D → Fig. 3G

p.9 l.22 Fig. 3I → Fig. 3H

p.9 l.23 Fig. S2 → Fig. S4

p.10 l.6 Fig. 3J → Fig. 1J

p.10 l.8 Fig. 3J → Fig. 1J

p.10 l.9 Fig. 3K → Fig. 3I

p.10 l.12 Fig. 3H → Fig. 3J

p.10 l.14 Fig. 2D and Fig. S4 → Fig. 2G and Fig. S4D

We thank the reviewer for carefully pointing out these citation errors. We have corrected all figure references in the text as suggested.

eLife Assessment

This study reports the important development and characterization of next-generation analogs of the molecule AA263, which was previously identified for its ability to promote adaptive ER proteostasis remodeling. The evidence supporting the conclusions is convincing, with rigorous assays used to benchmark the changes in potency and efficacy of the AA263 analogs as well as AA263 targets. The ability of AA263 analogs to restore the loss of function associated with disease-associated proteins prone to misfolding will be of interest to pharmacologists, chemical biologists, and cell biologists, as well as those working on protein misfolding disorders.

Reviewer #1 (Public review):

Summary:

This study builds off prior work that focused on the molecule AA147 and its role as an activator of the ATF6 arm of the unfolded protein response. In prior manuscripts, AA147 was shown to enter the ER, covalently modify a subset of protein disulfide isomerases (PDIs), and improve ER quality control for the disease-associated mutants of AAT and GABAA. Unsuccessful attempts to improve the potency of AA147 have led the authors to characterize a second hit from the screen in this study: the phenylhydrazone compound AA263. The focus of this study on enhancing biological activity of the AA147 molecule is compelling, and overcomes a hurdle of the prior AA147 drug that proved difficult to modify. The study successfully identifies PDIs as a shared cellular target of AA263 and its analogs. The authors infer, based on the similar target hits previously characterized for AA147, that PDI modification likely accounts for a mechanism of action for AA263.

Strengths:

The work establishes the ability to modify the AA263 molecule to create analogs with more potency and efficacy for ATF6 activation. The "next generation" analogs are able to enhance the levels of functional AAT and GABAA receptors in cellular models expressing the Z-variant of AAT or an epilepsy-associated variant of the GABAA receptor, outlining the therapeutic potential for this molecule and laying the foundation for future organism-based studies.

The authors are able to establish that like AA147, AA263 covalently targets ER PDIs. While it is a likely mechanism that AA263 works through the PDIs, the authors are careful to discuss that this is a potential mechanism that remains to be explicitly proven. The study provides the foundation for future work to further define a role for the PDIs in the actions of AA263.

Reviewer #2 (Public review):

Modulating the UPR by pharmacological targeting of its sensors (or regulators) provides mostly uncharted opportunities in diseases associated with protein misfolding in the secretory pathway. Spearheaded by the Kelly and Wiseman labs, ATF6 modulators were developed in previous years that act on ER PDIs as regulators of ATF6. However, hurdles in their medicinal chemistry have hampered further developments. In this study, the authors provide evidence that the small molecule AA263 also targets and covalently modifies ER PDIs with the effect of activating ATF6. Importantly, AA263 turned out to be amenable to chemical optimization while maintaining its desired activity. Building on this, the authors show that AA263 derivatives can improve aggregation, trafficking and function of two disease-associated mutants of secretory pathway proteins. Together, this study provides compelling evidence for AA263 (and its derivatives) being interesting modulators of ER proteostasis. Mechanistic details of its mode of action will need more attention in future studies that can now build on this.

In detail, the authors provide strong evidence that AA263 covalently binds to ER PDIs, which will inhibit the protein disulfide isomerase activity. ER PDIs regulate ATF6, and thus their finding provides a mechanistic interpretation of AA263 activating the UPR. It should be noted, however, that AA263 shows broad protein labeling (Fig. 1G) which may suggest additional targets, beyond the ones defined as MS hits in this study. Also, a further direct analysis of the IRE1 and PERK pathways (activated or not by AA263) may be an interesting future directions, as e.g. PDIA1, a target of AA263, directly regulates IRE1 (Yu et al., EMBOJ, 2020) and other PDIs also act on PERK and IRE1. The authors interpret modest activation of IRE1/PERK target genes (Fig. 2C) as an effect on target gene overlap, indeed the most likely explanation based on their selective analyses on IRE1 (ERdj4) and PERK (CHOP) downstream genes, but direct activation due to the targeting of their PDI regulators is also a possible explanation. Further key findings of this paper are the observed improvement of AAT behavior and GABAA trafficking and function. Further strength to the mechanistic conclusion that ATF6 activation causes this could be obtained by using ATF6 inhibitors/knockouts in the presence of AA263 (as the target PDIs may directly modulate behavior of AAT and/or GABAA). Along the same line, it also warrants further investigation in future studies why the different compounds, even if all were used at concentrations above their EC50, had different rescuing capacities on the clients.

Together, the study now provides a strong basis for such in-depth mechanistic analyses.

Reviewer #3 (Public review):

Summary:

This study aims to develop and characterize phenylhydrazone-based small molecules that selectively activate the ATF6 arm of the unfolded protein response by covalently modifying a subset of ER-resident PDIs. The authors identify AA263 as a lead scaffold and optimize its structure to generate analogs with improved potency and ATF6 selectivity, notably AA263-20. These compounds are shown to restore proteostasis and functional expression of disease-associated misfolded proteins in cellular models involving both secretory (AAT-Z) and membrane (GABAA receptor) proteins. The findings provide valuable chemical tools for modulating ER proteostasis and may serve as promising leads for therapeutic development targeting protein misfolding diseases.

Strengths:

The study presents a well-defined chemical biology framework integrating proteomics, transcriptomics, and disease-relevant functional assays.

Identification and optimization of a new electrophilic scaffold (AA263) that selectively activates ATF6 represents a valuable advance in UPR-targeted pharmacology.

SAR studies are comprehensive and logically drive the development of more potent and selective analogs such as AA263-20.

Functional rescue is demonstrated in two mechanistically distinct disease models of protein misfolding-one involving a secretory protein and the other a membrane protein-underscoring the translational relevance of the approach.

Weaknesses:

ATF6 activation is primarily inferred from reporter assays and transcriptional profiling; direct biochemical evidence of ATF6 cleavage or nuclear translocation remains missing. However, the authors have added supporting data showing that co-treatment with the ATF6 inhibitor CP7 suppresses target gene induction, which partially strengthens the evidence for ATF6-dependent activity.

Although the proposed mechanism involving PDI modification and ATF6 activation is plausible, it is still not experimentally demonstrated and remains incompletely characterized.

In vivo validation is absent, and thus the pharmacological feasibility, selectivity, and bioavailability of these compounds in physiological systems remain untested.

Comments on revisions:

The authors have generally addressed my comments.

Author response:

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

Reviewer #1 (Public review):

Summary: 

This study builds off prior work that focused on the molecule AA147 and its role as an activator of the ATF6 arm of the unfolded protein response. In prior manuscripts, AA147 was shown to enter the ER, covalently modify a subset of protein disulfide isomerases (PDIs), and improve ER quality control for the disease-associated mutants of AAT and GABAA. Unsuccessful attempts to improve the potency of AA147 have led the authors to characterize a second hit from the screen in this study: the phenylhydrazone compound AA263. The focus of this study on enhancing the biological activity of the AA147 molecule is compelling, and overcomes a hurdle of the prior AA147 drug that proved difficult to modify. The study successfully identifies PDIs as a shared cellular target of AA263 and its analogs. The authors infer, based on the similar target hits previously characterized for AA147, that PDI modification accounts for a mechanism of action for AA263. 

Strengths: 

The authors are able to establish that, like AA147, AA263 covalently targets ER PDIs. The work establishes the ability to modify the AA263 molecule to create analogs with more potency and efficacy for ATF6 activation. The "next generation" analogs are able to enhance the levels of functional AAT and GABAA receptors in cellular models expressing the Z-variant of AAT or an epilepsy-associated variant of the GABAA receptor, outlining the therapeutic potential for this molecule and laying the foundation for future organism-based studies. 

We thank the reviewer for the positive comments on our manuscript. We address the reviewers remaining comments on our work, as described below.

Weaknesses: 

Arguably, the work does not fully support the statement provided in the abstract that the study "reveals a molecular mechanism for the activation of ATF6". The identification of targets of AA263 and its analogs is clear. However, it is a presumption that the overlap in PDIs as targets of both AA263 and AA147 means that AA263 works through the PDIs. While a likely mechanism, this conclusion would be bolstered by establishing that knockdown of the PDIs lessens drug impact with respect to ATF6 activation. 

We thank the reviewer for this comment. We previously showed that genetic depletion of different PDIs modestly impacts ATF6 activation afforded by ATF6 activating compound such as AA147 (see Paxman et al (2018) ELIFE). However, as discussed in this manuscript, the ability for AA147 and AA263 to activate ATF6 signaling is mediated through polypharmacologic targeting of multiple different PDIs involved in regulating the redox state of ATF6. Thus, individual knockdowns are predicted to only minimally impact the ability for AA263 and its analogs to activate ATF6 signaling. 

To address this comment, we have tempered our language regarding the mechanism of AA263-dependent ATF6 activation through PDI targeting described herein to better reflect the fact that we have not explicitly proven that PDI targeting is responsible for this activity, as highlighted below:

“Page 7, Line 158: “Intriguingly, 12 proteins were shared between these two conditions, including 7 different ER-localized PDIs (Fig. 1H). This includes PDIs previously shown to regulate ATF6 activation including TXNDC12/ERP18.[45,46] These results are similar to those observed when comparing proteins modified by the selective ATF6 activating compound AA147^yne and AA132^yne.[38] Further, we found that the extent of labeling for PDIs including PDIA1, PDIA4, PDIA6, and TMX1, but not TXNDC12, showed greater modification by AA132^yne, as compared to AA263^yne (Fig. 1I). Similar results were observed for AA147^yne.[38] This suggests that, like AA147, the selective activation of ATF6 afforded by AA263 is likely attributed to the modifications of a subset of multiple different ER-localized PDIs by this compound.”

Alternatively, it has previously been suggested that the cell-type dependent activity of AA263 may be traced to the presence of cell-type specific P450s that allow for the metabolic activation of AA263 or cell-type specific PDIs (Plate et al 2016; Paxman et al 2018). If the PDI target profile is distinct in different cell types, and these target difference correlates with ATF6-induced activity by AA263, that would also bolster the authors' conclusion. 

As highlighted by the reviewer, different ER oxidases (e.g., P450s) could differentially influence activation of compounds such as AA263 to promote PDI modification and subsequent ATF6 activation. The specific ER oxidases responsible for AA263 activation are currently unknown; however, we anticipate that multiple different enzymes can promote this activity making it difficult to discern the specific contributions of any one oxidase. We have made this point clearer in the revised submission, as below:

Page 7, Line 169: “This specificity for ER proteins instead suggests the localized generation of AA263 quinone methides at the ER membrane, likely through metabolic activation by different ER localized oxidases, which has been previously been shown to contribute to the selective modification of ER proteins afforded by other compounds such as AA147 [49]”   

Reviewer #2 (Public review):

Modulating the UPR by pharmacological targeting of its sensors (or regulators) provides mostly uncharted opportunities in diseases associated with protein misfolding in the secretory pathway. Spearheaded by the Kelly and Wiseman labs, ATF6 modulators were developed in previous years that act on ER PDIs as regulators of ATF6. However, hurdles in their medicinal chemistry have hampered further development. In this study, the authors provide evidence that the small molecule AA263 also targets and covalently modifies ER PDIs, with the effect of activating ATF6. Importantly, AA263 turned out to be amenable to chemical optimization while maintaining its desired activity. Building on this, the authors show that AA263 derivatives can improve the aggregation, trafficking, and function of two disease-associated mutants of secretory pathway proteins. Together, this study provides compelling evidence for AA263 (and its derivatives) being interesting modulators of ER proteostasis. Mechanistic details of its mode of action will need more attention in future studies that can now build on this.

We thank the reviewer for their positive comments on our manuscript. We address the reviewer’s specific queries on our work, as outlined below. 

In detail, the authors provide strong evidence that AA263 covalently binds to ER PDIs, which will inhibit the protein disulfide isomerase activity. ER PDIs regulate ATF6, and thus their finding provides a mechanistic interpretation of AA263 activating the UPR. It should be noted, however, that AA263 shows broad protein labeling (Figure 1G), which may suggest additional targets, beyond the ones defined as MS hits in this study. 

This is true. We do show broad proteome-wide labeling with AA263^yne, which are largely reflected in the hits identified by MS beyond PDI family members. It is possible that other observed engaged targets, in addition to PDIs, may contribute to the activation of ATF6 signaling. Regardless, our MS analysis clearly shows that the compounds modified by AA263 are enriched for PDIs, further supporting our model whereby AA263-dependent PDI modification is likely responsible for ATF6 activation. 

Also, a further direct analysis of the IRE1 and PERK pathways (activated or not by AA263) would have been a benefit, as e.g., PDIA1, a target of AA263, directly regulates IRE1 (Yu et al., EMBOJ, 2020), and other PDIs also act on PERK and IRE1. The authors interpret modest activation of IRE1/PERK target genes (Figure 2C) as an effect on target gene overlap, indeed the most likely explanation based on their selective analyses on IRE1 (ERdj4) and PERK (CHOP) downstream genes, but direct activation due to the targeting of their PDI regulators is also a possible explanation. 

While we do observe mild increases in IRE1/XBP1s target genes, we do not observe significant increases in PERK/ISR target genes in cells treated with optimized AA263 analogs (see Fig. 2C). We previously showed that genetic ATF6 activation leads to a modest increase in IRE1/XBP1s target genes, reflecting the overlap in target genes of the IRE1/XBP1s and ATF6 pathways (see Shoulders et al (2013) Cell Reports). However, with our data, we cannot explicitly rule out the possibility that the mild increase in IRE1/XBP1s target genes reflects direct IRE1/XBP1s activation, as suggested by the reviewer. To address this, we have adapted the text to highlight this point, now specifically referring to preferential ATF6 activation afforded by these compounds, as below:

Page 5, Line 100: “In addition to finding AA147, our original high-throughput screen also identified the phenylhydrazone compound AA263 as a compound that preferentially activates the ATF6 arm of the UPR [26]”  

Further key findings of this paper are the observed improvement of AAT behavior and GABAA trafficking and function. Further strength to the mechanistic conclusion that ATF6 activation causes this could be obtained by using ATF6 inhibitors/knockouts in the presence of AA263 (as the target PDIs may directly modulate the behavior of AAT and/or GABAA). 

AA263 and related compounds could influence ER proteostasis of destabilized proteins through multiple mechanisms including ATF6 activation or direct modification of a subset of PDIs. We previously showed that AA263-dependent enhancement of A1AT-Z secretion and activity can be largely attributed to ATF6 activation (see Sun et al (2023) Cell Chem Biol). In the revised submission, we now show that increased levels of g2(R177G) afforded by treatment with AA263^yne are partially blocked by co-treatment with the ATF6 inhibitor Ceapin-A7 (CP7), highlighting the contributions of ATF6 activation for this phenotype (Fig. S5B,C). Intriguingly, this result also demonstrates the benefit for targeting ER proteostasis using compounds such as our optimized AA263 analogs, as this approach allows us to enhance ER proteostasis of destabilized proteins through multiple mechanisms. We further expand on this specific point in the revised manuscript as below:

Page 14, Line 375: “AA263 and its related analogs can influence ER proteostasis in these models through different mechanisms including ATF6-dependent remodeling of ER proteostasis and direct alterations to the activity of specific PDIs.(*) Consistent with this, we show that pharmacologic inhibition of ATF6 only partially blocks increases of g2(R177G) afforded by treatment with AA263^yne, highlighting the benefit for targeting multiple aspects of ER proteostasis to enhance ER proteostasis of this diseaserelevant GABA_A variant. While additional studies are required to further deconvolute the relative contributions of these two mechanisms on the protection afforded by our optimized compounds, our results demonstrate the potential for these compounds to enhance ER proteostasis in the context of different protein misfolding diseases.”  

Along the same line, it also warrants further investigation why the different compounds, even if all were used at concentrations above their EC50, had different rescuing capacities on the clients.

This is an interesting question that we are continuing to study. While in general, we observe fairly good correlation between ATF6 activation and correction of diseases of ER proteostasis linked to proteins such as A1AT-Z or GABA_A receptors, as the reviewer points out, we do find some compounds are more efficient at correcting proteostasis than others activate ATF6 to similar levels. We attribute this to differences in either labeling efficiency of PDIs or differential regulation of various ER proteostasis factors, although that remains to be further defined. As we continue working with these (and other) compounds, we will focus on defining a more molecular basis for these findings. 

Together, the study now provides a strong basis for such in-depth mechanistic analyses.

We agree and we are continuing to pursue the mechanistic basis of ER proteostasis remodeling afforded by these and related compounds. 

Reviewer #3 (Public review):

Summary: 

This study aims to develop and characterize phenylhydrazone-based small molecules that selectively activate the ATF6 arm of the unfolded protein response by covalently modifying a subset of ER-resident PDIs. The authors identify AA263 as a lead scaffold and optimize its structure to generate analogs with improved potency and ATF6 selectivity, notably AA263-20. These compounds are shown to restore proteostasis and functional expression of disease-associated misfolded proteins in cellular models involving both secretory (AAT-Z) and membrane (GABAA receptor) proteins. The findings provide valuable chemical tools for modulating ER proteostasis and may serve as promising leads for therapeutic development targeting protein misfolding diseases.

Strengths: 

(1) The study presents a well-defined chemical biology framework integrating proteomics, transcriptomics, and disease-relevant functional assays. 

(2) Identification and optimization of a new electrophilic scaffold (AA263) that selectively activates ATF6 represents a valuable advance in UPR-targeted pharmacology.

(3) SAR studies are comprehensive and logically drive the development of more potent and selective analogs such as AA263-20.

(4) Functional rescue is demonstrated in two mechanistically distinct disease models of protein misfolding-one involving a secretory protein and the other a membrane protein-underscoring the translational relevance of the approach. 

We thank the reviewer for their positive comments related to our work. We address specific weaknesses highlighted by the reviewer, as outlined below. 

Weaknesses: 

(1) ATF6 activation is primarily inferred from reporter assays and transcriptional profiling; however, direct evidence of ATF6 cleavage is lacking.

While ATF6 trafficking and processing can be visualized in cell culture models following severe ER insults (e.g., Tg, Tm), we showed previously that the more modest activation afforded by pharmacologic activators such as AA147 and AA263 cannot be easily visualized by monitoring ATF6 processing (see Plate et al (2016) ELIFE). As we have shown in numerous other manuscripts, we have established a transcriptional profiling approach that accurately defines ATF6 activation. We use that approach to confirm preferential ATF6 activation in this manuscript. We feel that this is sufficient for confirming ATF6 activation. However, we also now include data showing that co-treatment with ATF6 inhibitors (e.g., CP7) blocks increased expression of ATF6 target genes induced by our prioritized compound AA263^yne (Fig. S1B). This further supports our assertion that this compound activates ATF6 signaling.  

(2) While the mechanism involving PDI modification and ATF6 activation is plausible, it remains incompletely characterized. 

We thank the reviewer for this comment. We previously showed that genetic depletion of different PDIs modestly impacts ATF6 activation afforded by ATF6 activating compound such as AA147. However, as discussed in this manuscript, the ability for AA147 and AA263 to activate ATF6 signaling is mediated through polypharmacologic targeting of multiple different PDIs involved in regulating ATF6 redox. Thus, individual knockdowns are predicted to only minimally impact the ability for AA263 and its analogs to activate ATF6 signaling. 

To address this comment, we have tempered out language regarding the mechanism of AA263-dependent ATF6 activation through PDI targeting described herein to better reflect the fact that we have not explicitly proven that PDI targeting is responsible for this activity, as highlighted below:

Page 7, Line 158: “Intriguingly, 12 proteins were shared between these two conditions, including 7 different ER-localized PDIs (Fig. 1H). This includes PDIs previously shown to regulate ATF6 activation including TXNDC12/ERP18.[45,46] These results are similar to those observed when comparing proteins modified by the selective ATF6 activating compound AA147^yne and AA132^yne.[38] Further, we found that the extent of labeling for PDIs including PDIA1, PDIA4, PDIA6, and TMX1, but not TXNDC12, showed greater modification by AA132^yne, as compared to AA263^yne (Fig. 1I). Similar results were observed for AA147^yne[38] This suggests that, like AA147, the selective activation of ATF6 afforded by AA263 is likely attributed to the modifications of a subset of multiple different ER-localized PDIs by this compound.”

(3) No in vivo data are provided, leaving the pharmacological feasibility and bioavailability of these compounds in physiological systems unaddressed.

We are continuing to test the in vivo activity of these compounds in work outside the scope of this initial study. 

Reviewer #1 (Recommendations for the authors): 

(1) First page of the discussion, last sentence. "We previously showed the relatively labeling of PDI modification directly impacts..." should be reworded.

Thank you. We have corrected this in the revised manuscript. 

(2) What is the rationale for measuring ERSE-Fluc activity at 18 h but RNAseq at 6 h? What is known about the timing of action for AA263?

Compound-dependent activation of luciferase reporters requires the translation and accumulation of the luciferase protein for sufficient signal, while qPCR does not. We normally use longer incubations for reporter assays to ensure that we have sufficient quantity of reporter protein to accurately monitor activation. We have found that AA263 can rapidly increase ATF6 activity, with gene expression increases being observed after only a few hours of treatment. This is consistent with the proposed mechanism of ATF6 activation discussed herein involving metabolic activation and subsequent PDI modification.   

(3) Figure 1 panel E and Figure S2 panel B. Are these the same data for AA263 and AA263yne, with the AA2635 added to the plot for Figure S2? If so, it would be nice to note that panel B represents data from 3 of the replicates that are shown in Figure 1 (n=6).

Yes. The AA263 and AA263^yne data shown in Fig. 1E and Fig. S2B are the same data, as these experiments were performed at the same time. We apologize for this oversight, which has now been corrected in the revised version. Note that there were n=3 replicates for the dose response shown in Fig. 1E, which we corrected in the figure legend as below:

Fig. S2B Figure Legend: “B. Activation of the ERSE-FLuc ATF6 reporter in HEK293T cells treated for 18 h with the indicated concentration of AA263, AA263^yne, or AA263-5. Error bars show SEM for n= 3 replicates. The data for AA263 and AA263^yne is the same as that shown in Fig. 1E and are shown for comparison.” 

(4) Figure S3. The legend notes 5 µM AA263-yne and 20 µM analog, whereas the figure itself outlines the same ratio but different concentrations: 10 µM and 40 µM.

We apologize for this mistake in the legend, which has been corrected. The information in the figure is correct. 

Reviewer #2 (Recommendations for the authors): 

(1) The activation mechanism of ATF6 is still debated (really trafficking as a monomer?); the authors may want to word more carefully here. 

We agree. We have corrected this in the revised manuscript to indicate that increased populations of reduced ATF6 traffic for proteolytic processing. 

(2) In Figure 1B, below the figure, mM is written for BME, but micromolar is meant.

Thank you. This has been corrected in the revised manuscript. 

(3) The authors may want to make clearer, why BME does not completely inhibit AA263 and does not cause ER stress itself under the conditions tested.

The addition of BME in our experiments is designed to shift the redox potential of the cell to increase intracellular thiol reagents, such as glutathione, that can quench ‘activated’ AA263 and its analogs. However, BME is actively being oxidized upon addition and the intracellular redox environment can rapidly equilibrate following BME addition. Thus, we do not expect that AA263 or other metabolically activated compounds will be fully quenched using this approach, as is observed. This is consistent with other experiments where we show that the use of these types of reducing agents do not fully suppress the activity of reactive molecules, instead shifting their dosedependent activation of specific pathways.  

(4) The data in Figure 4C seems to disagree with the other data on the tested compounds; this should be clarified. 

It is unclear to what the reviewer is referring. The data in 4C shows that treatment with our optimized AA263 analogs improved elastase inhibition afforded by secreted A1AT, as would be predicted. 

(5) PDIs that have been shown to regulate ATF6 should be discussed in more detail in the light of the presented data/interactome (e.g., ERp18).

Thank you for the suggestion. We now explicitly note that AA263^yne covalent modifies TXNDC12/ERP18 in our proteomic dataset. However, we also note that there is no difference in labeling of this specific PDI between AA263^yne and AA132^yne. This may indicate that the targeting of this protein is responsible for the larger levels of ATF6 activation afforded by both these compounds relative to AA147, with the activation of other UPR pathways afforded by AA132 resulting from increased labeling of other PDIs. We are now exploring this possibility in work outside the scope of this current manuscript. 

Page 7 Line 158: “Intriguingly, 12 proteins were shared between these two conditions, including 7 different ER-localized PDIs (Fig. 1H). This includes PDIs previously shown to regulate ATF6 activation including TXNDC12/ERP18.[45,46] These results are similar to those observed when comparing proteins modified by the selective ATF6 activating compound AA147^yne and AA132^yne.[38] Further, we found that the extent of labeling for PDIs including PDIA1, PDIA4, PDIA6, and TMX1, but not TXNDC12, showed greater modification by AA132^yne, as compared to AA263^yne (Fig. 1I). Similar results were observed for AA147^yne [38] This suggests that, like AA147, the selective activation of ATF6 afforded by AA263 is likely attributed to the modifications of a subset of multiple different ER-localized PDIs by this compound.”

Reviewer #3 (Recommendations for the authors):

(1) Please consider adding detection of ATF6 cleavage by Western blot as direct evidence of AA263-induced ATF6 activation, to substantiate the central mechanistic claim.

While ATF6 trafficking and processing can be visualized in cell culture models following severe ER insults (e.g., Tg, Tm), we showed previously that the more modest activation afforded by pharmacologic activators such as AA147 and AA263 cannot be easily visualized through monitoring ATF6 proteolytic processing by western blotting (see Plate et al (2016) ELIFE). As we have shown in numerous other manuscripts, we have established a transcriptional profiling approach that accurately defines ATF6 activation. We use that approach to confirm preferential ATF6 activation in this manuscript. We feel that this is sufficient for confirming ATF6 activation. However, we also now include qPCR data showing that co-treatment with ATF6 inhibitors (e.g., CP7) blocks increased expression of ATF6 target genes induced by our prioritized compounds. 

(2) To strengthen causal inference, loss-of-function experiments such as PDI knockdown, cysteine mutant inactivation, or reconstitution studies may be informative.

We thank the reviewer for this comment. We previously showed that genetic depletion of different PDIs modestly impacts ATF6 activation afforded by ATF6 activating compound such as AA147. However, as discussed in this manuscript, the ability for AA147 and AA263 to activate ATF6 signaling is mediated through polypharmacologic targeting of multiple different PDIs involved in regulating ATF6 redox state rather than a single PDI family member. Thus, individual knockdowns are predicted to only minimally impact the ability for AA263 and its analogs to activate ATF6 signaling. 

To address this comment, we have tempered out language regarding the mechanism of AA263-dependent ATF6 activation through PDI targeting described herein to better reflect the fact that we have not explicitly proven that PDI targeting is responsible for this activity.

(3) Since β-mercaptoethanol inhibits ATF6 activation, it would be helpful to examine whether DTT also suppresses the activity of AA263 or its analogs, to clarify the redox sensitivity of the mechanism.

The use of reducing agents stronger than BME, such as DTT, globally activates the UPR, including the ATF6 arm of the UPR. Thus, we are unable to perform the requested experiments. We specifically use BME because it is a sufficiently mild reducing agent that can quench reactive metabolites (e.g., activated AA263 analogs) through alterations in cellular glutathione levels without globally activating the UPR.  

(4) Given the electrophilic nature of AA263, which may allow it to react with endogenous thiols (e.g., glutathione or cysteine), a brief discussion or experimental validation of this potential liability would enhance the interpretation of in vivo applicability.

Metabolically activated AA263, like AA147, can be quenched by endogenous thiols such as glutathione. However, treatment with our metabolically activatable electrophiles AA147 and AA263 , either in vitro or in vivo, does not seem to induce activation of the NRF2-regulated oxidative stress response (OSR) in the cell lines used in this manuscript (e.g., Fig. S2C). This suggests that treatment with these compounds does not globally disrupt the intracellular redox state, at least in the tested cell lines. While AA147 has been shown to activate NRF2 in specifical neuronal cell lines and in primary neurons, AA147 does not activate NRF2 signaling in other nonneuronal cell lines or other tissues (see Rosarda et al (2021) ACS Chem Bio). We are currently testing the potential for AA263 to similarly activate adaptive NRF2 signaling in neuronal cells. Regardless, AA147, which functions through a similar mechanism to that proposed for AA263, has been shown to be beneficial in multiple models of disease both in vitro and in vivo. This indicates that this mechanism of action is suitable for continued translational development to mitigate pathologic ER proteostasis disruption observed in diverse types of human disease.  

(5) Evaluation of in vivo activity, such as BiP induction in the liver following intraperitoneal administration of AA263-20 or related analogs, could substantially increase the translational impact of the work.

We are continuing to probe the activity of our optimized AA263 analogs in vivo in work outside the scope of this current manuscript. We thank the reviewer for this suggestion. 

(6) The degree of BiP induction may also be contextualized by comparison with known ER stress inducers such as thapsigargin or tunicamycin, ideally by providing relative dose-equivalent responses.

We are not sure to what the reviewer is referring. We show comparative activation of ATF6 in cells treated with the ER stressor Tg and our compounds by both reporter assay (e.g., Fig. 2B) and qPCR of the ATF6 target gene BiP (HSPA5) (Fig. S2A). We feel that this provides context for the more physiologic levels of ATF6 activation afforded by these compounds.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf141), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Nicolas Gaggion

The manuscript "pyRootHair: Machine Learning Accelerated Software for High-Throughput Phenotyping of Plant Root Hair Traits" presents a valuable tool for plant phenotyping in microscopy images. As it stands, my recommendation is to accept after minor revisions.

I found the GitHub repository provides detailed instructions and was straightforward to install and run. The whole process took only a few minutes to execute, which speaks well to the software's accessibility.

To further enhance the clarity, precision, and accessibility of the manuscript, I have several comments.

On the random forest classifier training process, the manuscript states "For this comparison, the RFC was trained on a single input image, and used to perform inference on all subsequent images." However, the repository documentation indicates: "To train a random forest model, you will need to train the model on a single representative example of an image, and a corresponding binary mask of the image." The repository further notes that "You will need to ensure that all the images are relatively consistent in terms of lighting, appearance, root hair morphology, and have the same input dimensions. Should your images vary for these traits, you will need to train separate random forest models for different batches of images."

The manuscript needs clarification on this training process. Please specify whether users are expected to manually segment one of their own images, use the nnUNet model to generate binary segmentation and refine it using annotation tools (such as ilastik), or select one of the images from ones provided by the authors of the manuscript that best matches their new data.

Regarding nnUNet performance, I support the decision not to compare with other models, as nnUNet represents state-of-the-art performance and enables easy training for non-expert users. However, I have several questions: Do you plan to release the training dataset so users can retrain the model by incorporating new manually annotated data? The manuscript would benefit from quantifying segmentation performance by crop type. Measuring performance solely by computing time is insufficient, and quantitative metrics such as Dice scores on test holdout sets or cross-validation results (as performed by the nnUNet model) should be reported.

The current abstract describes pyRootHair as an "AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates." This description needs to clarify that images were obtained via microscopy. Do you have insights on how the trained model performs across different microscope systems?

The manuscript requires additional clarification on the root straightening process using piecewise transformation, as this represents an important step in the measurement procedure. Please specify how this is performed and whether a specific algorithm or function from a library is used for the piecewise affine transformation. For readers who are not computer vision specialists, a figure illustrating the measurement steps (segmentation → skeletonization → straightening → measurement) would be valuable.

Really minor comments: It would be helpful if the demo generated all plots by default, and Random Forest Classifier (RFC) is not included in the abbreviation list.

Overall, this represents solid work that addresses an important need in plant phenotyping research. The suggested clarifications will enhance both the scientific rigor and practical utility of the contribution.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf141), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Wanneng Yang

This paper introduces an artificial intelligence-driven software named pyRootHair, which enables high-throughput automated extraction of root hair traits from plant root images, thereby facilitating rapid analysis of root hair morphological variations in various plants, including wheat. However, the following issues remain: 1）Compared to previously published work, the contributions and innovations of this study are not sufficiently highlighted. For instance, the work by Lu, Wei, Xiaochan Wang, and Wei Jia, titled "Root hair image processing based on deep learning and prior knowledge" (Comput. Electron. Agric. 202, 2022: 107397), should be explicitly referenced to clarify the advancements presented here. 2） Although the study demonstrates that pyRootHair can be applied to multiple plant species, including Arabidopsis, Brachypodium, rice, and tomato, the primary validation and analysis are conducted on wheat. For other species, only segmentation results and trait extraction figures are presented, lacking detailed comparative validation with manual measurements as thoroughly as for wheat. 3）The process of "straightening" curved roots is implemented, but the potential introduction of new errors by this procedure is not discussed. 4） In the trait validation section, the correlation analysis between automated and manual measurements shows strong agreement for root hair length and root length, but weaker correlation for elongation zone length. The study should provide a more in-depth discussion on the possible reasons for this lower correlation. 5）The details of the core algorithms (CNN architecture, random forest classifier) are insufficiently described. Key aspects such as parameter selection, optimization, training procedures, and the division ratios of the training/validation/test sets are not clearly specified. Additionally, the specific strategies for data augmentation are not mentioned. 6） No quantitative comparisons with similar tools (e.g., in terms of speed and accuracy) are provided.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf133), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Yang Yang

The manuscript describes RNA-SeqEZPZ, an automated RNA-Seq analysis pipeline with a user-friendly point-and-click interface. It aims to make comprehensive transcriptomics analyses more accessible to researchers who lack extensive bioinformatics skills by addressing common issues with standardization and usability that arise from using in-house scripts. The pipeline's main features are the use of a Singularity container to simplify software installation and a Nextflow version to support scalability across different computing environments like clouds and clusters. However, I'm not sure if this manuscript fits the journal's scope in its current form. It seems to be just an integration of existing tools without offering new methods or findings.

Major comments:

1. The manuscript mentions several existing RNA-Seq pipelines, such as ENCODE, nf-core, ROGUE, Shiny-Seq, bulkAnalyseR, Partek™ flow, RaNA-Seq, and RASflow. A more detailed comparison of RNA-SeqEZPZ with these tools is needed, especially regarding specific features, performance metrics, and ease of use. For example, it would be helpful to compare the computational resources required by each pipeline or the statistical methods used for differential expression analysis.

2. The manuscript emphasizes reproducibility through Singularity containers and Nextflow. However, it would be stronger if it included a more rigorous demonstration of reproducibility. This could involve running the pipeline on multiple datasets and comparing the results, or providing a detailed protocol for other researchers to reproduce the findings.

3. The manuscript highlights the scalability and portability of RNA-SeqEZPZ due to its Nextflow version. It would be useful to include specific examples of how the pipeline has been used in different computing environments (e.g., cloud, cluster) and to provide performance data to demonstrate its scalability.

4. The point-and-click interface is a key feature, but the manuscript could benefit from a more detailed description of the interface and its functionalities. Including screenshots or a video demonstration would be valuable for potential users.

5. The manuscript shows the effects of batch adjustment using a public dataset. It would be beneficial to expand this section with a discussion of the limitations of batch adjustment methods and to provide guidance on when and how to apply them.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf133), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Unitsa Sangket

This research presents a well-designed and powerful program for comprehensive transcriptomics analysis with interactive visualizations. The tool is conceptually strong and user-friendly, requiring only raw reads in FASTQ format to initiate the analysis, with no need for manual quality checks. However, a limitation is that the software must be installed manually, which typically requires access to a high-performance computing (HPC) system and support from a system administrator for installation and server maintenance. As such, non-technical users may find it difficult to install and operate the program independently.

With appropriate revisions based on the comments below, the manuscript has the potential to be significantly improved.

• Page 8, line 158-160 "DESeq2 was selected based on findings by Rapaport et al. (2013)40, which demonstrated its superior specificity and sensitivity as well as good control of false positive errors." The findings in the paper titled "bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data" (https://peerj.com/articles/14344) show that DESeq2 achieves higher sensitivity than other tools when applied to newer human RNA-Seq datasets. This finding should be included in the manuscript. For example, DESeq2 was selected based on findings by Rapaport et al. (2013)⁴⁰, which demonstrated its superior specificity and sensitivity as well as good control of false positive errors. Additionally, recent findings from the bestDEG study (cite bestDEG) further support the higher sensitivity of DESeq2 than other tools when applied to newer human RNA-Seq datasets.

• Page 6, line 124-125 "Raw reads quality control are then performed using 125 FASTQC18 and QC reports are compiled using MultiQC19." The quality of the trimmed reads can be assessed using FastQC, as demonstrated and summarized in the paper titled "VOE: automated analysis of variant epitopes of SARS-CoV-2 for the development of diagnostic tests or vaccines for COVID-19." (https://peerj.com/articles/17504/) (Page 4, in last paragraph ""(1) Per base sequence quality (median value of each base greater than 25), (2) per sequence quality (median quality greater than 27), (3) perbase N content (N base less than 5% at each read position) and (4) adapter content (adapter sequences at each position less than 5% of all reads)". This point should be mentioned in the manuscript, including the cutoff values for each FastQC metrics used in RNA-SeqEZPZ, as these thresholds may vary. For example, the quality of the trimmed FASTQ reads was assessed based on the four FastQC metrics, as summarized by Lee et al. (2024). The cutoffs for RNA-SeqEZPZ were set as follows: the median value of each base must be greater than [x], the median quality score must be above [y], the percentage of N bases at each read position must be less than [z]%, and the proportion of adapter sequences at each position must be below [xx]% of all reads.

• The programs used for counts table creation and alignment process should be mentioned in the manuscript.

• The default cutoffs for FDR and log₂ fold change, as well as instructions on how to modify these thresholds, should be clearly stated in the manuscript.

Playing "at ninepins" refers to playing a version of the game of bowling that uses nine pins instead of ten.

[https://en.wikipedia.org/wiki/Nine-pin_bowling]

The "village worthy" is a person who is popular and respected in their community but lacks true moral character.

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

The Catskill Mountains, or the Catskills, are a group of mountains in southeastern New York and are part of the larger Appalachian Mountain range.

[https://en.wikipedia.org/wiki/Catskill_Mountains]

"to stand in the shade of sth" means to position oneself in the area of darkness created by a large object, like a tree.

eLife Assessment

This useful study develops an individual-based model to investigate the evolution of division of labor in vertebrates, comparing the contributions of group augmentation and kin selection. The model incorporates several biologically relevant features, including age-dependent task switching and separate manipulation of relatedness and group-size benefits. However, the evidence remains incomplete to support the authors' central claim that group augmentation is the primary driver of vertebrate division of labor. Key modelling assumptions, such as limited opportunities for task synergy, the structure of helper and floater dynamics, and the relatively narrow parameter space explored, continue to restrict the potential for kin selection to produce division of labor, thereby limiting the generality of the conclusions.

Reviewer #2 (Public review):

Summary:

This paper formulates an individual-based model to understand the evolution of division of labor in vertebrates. The model considers a population subdivided in groups, each group has a single asexually-reproducing breeder, other group members (subordinates) can perform two types of tasks called "work" or "defense", individuals have different ages, individuals can disperse between groups, each individual has a dominance rank that increases with age, and upon death of the breeder a new breeder is chosen among group members depending on their dominance. "Workers" pay a reproduction cost by having their dominance decreased, and "defenders" pay a survival cost. Every group member receives a survival benefit with increasing group size. There are 6 genetic traits, each controlled by a single locus, that control propensities to help and disperse, and how task choice and dispersal relate to dominance. To study the effect of group augmentation without kin selection, the authors cross-foster individuals to eliminate relatedness. The paper allows for the evolution of the 6 genetic traits under some different parameter values to study the conditions under which division of labour evolves, defined as the occurrence of different subordinates performing "work" and "defense" tasks. The authors envision the model as one of vertebrate division of labor.

The main conclusion of the paper is that group augmentation is the primary factor causing the evolution of vertebrate division of labor, rather than kin selection. This conclusion is drawn because, for the parameter values considered, when the benefit of group augmentation is set to zero, no division of labor evolves and all subordinates perform "work" tasks but no "defense" tasks.

Strengths:

The model incorporates various biologically realistic details, including the possibility to evolve age polytheism where individuals switch from "work" to "defence" tasks as they age or vice versa, as well as the possibility of comparing the action of group augmentation alone with that of kin selection alone.

Weaknesses:

The model and its analysis are limited, which in my view makes the results insufficient to reach the main conclusion that group augmentation and not kin selection is the primary cause of the evolution of vertebrate division of labour. There are several reasons.

First, although the main claim that group augmentation drives the evolution of division of labour in vertebrates, the model is rather conceptual in that it doesn't use quantitative empirical data that applies to all/most vertebrates and vertebrates only. So, I think the approach has a conceptual reach rather than being able to achieve such a conclusion about a real taxon.

Second, I think that the model strongly restricts the possibility that kin selection is relevant. The two tasks considered essentially differ only by whether they are costly for reproduction or survival. "Work" tasks are those costly for reproduction and "defense" tasks are those costly for survival. The two tasks provide the same benefits for reproduction (eqs. 4, 5) and survival (through group augmentation, eq. 3.1). So, whether one, the other, or both helper types evolve presumably only depends on which task is less costly, not really on which benefits it provides. As the two tasks give the same benefits, there is no possibility that the two tasks act synergistically, where performing one task increases a benefit (e.g., increasing someone's survival) that is going to be compounded by someone else performing the other task (e.g., increasing that someone's reproduction). So, there is very little scope for kin selection to cause the evolution of labour in this model. Note synergy between tasks is not something unusual in division of labour models, but is in fact a basic element in them, so excluding it from the start in the model and then making general claims about division of labour is unwarranted. In their reply, the authors point out that they only consider fertility benefits as this, according to them, is what happens in cooperative breeders with alloparental care; however, alloparental care entails that workers can increase other's survival *without group augmentation*, such as via workers feeding young or defenders reducing predator-caused mortality, as a mentioned in my previous review but these potentially kin-selected benefits are not allowed here.

Third, the parameter space is understandably little explored. This is necessarily an issue when trying to make general claims from an individual-based model where only a very narrow parameter region of a necessarily particular model can be feasibly explored. As in this model the two tasks ultimately only differ by their costs, the parameter values specifying their costs should be varied to determine their effects. In the main results, the model sets a very low survival cost for work (yh=0.1) and a very high survival cost for defense (xh=3), the latter of which can be compensated by the benefit of group augmentation (xn=3). Some limited variation of xh and xn is explored, always for very high values, effectively making defense unevolvable except if there is group augmentation. In this revision, additional runs have been included varying yh and keeping xh and xn constant (Fig. S6), so without addressing my comment as xn remains very high. Consequently, the main conclusion that "division of labor" needs group augmentation seems essentially enforced by the limited parameter exploration, in addition to the second reason above.

Fourth, my view is that what is called "division of labor" here is an overinterpretation. When the two helper types evolve, what exists in the model is some individuals that do reproduction-costly tasks (so-called "work") and survival-costly tasks (so-called "defense"). However, there are really no two tasks that are being completed, in the sense that completing both tasks (e.g., work and defense) is not necessary to achieve a goal (e.g., reproduction). In this model there is only one task (reproduction, equation 4,5) to which both helper types contribute equally and so one task doesn't need to be completed if completing the other task compensates for it; instead, it seems more fitting to say that there are two types of helpers, one that pays a fertility cost and another one a survival cost, for doing the same task. So, this model does not actually consider division of labor but the evolution of different helper types where both helper types are just as good at doing the single task but perhaps do it differently and so pay different types of costs. In this revision, the authors introduced a modified model where "work" and "defense" must be performed to a similar extent. Although I appreciate their effort, this model modification is rather unnatural and forces the evolution of different helper types if any help is to evolve.

I should end by saying that these comments don't aim to discourage the authors, who have worked hard to put together a worthwhile model and have patiently attended to my reviews. My hope is that these comments can be helpful to build upon what has been done to address the question posed.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This paper presents a computational model of the evolution of two different kinds of helping ("work," presumably denoting provisioning, and defense tasks) in a model inspired by cooperatively breeding vertebrates. The helpers in this model are a mix of previous offspring of the breeder and floaters that might have joined the group, and can either transition between the tasks as they age or not. The two types of help have differential costs: "work" reduces "dominance value," (DV), a measure of competitiveness for breeding spots, which otherwise goes up linearly with age, but defense reduces survival probability. Both eventually might preclude the helper from becoming a breeder and reproducing. How much the helpers help, and which tasks (and whether they transition or not), as well as their propensity to disperse, are all evolving quantities. The authors consider three main scenarios: one where relatedness emerges from the model, but there is no benefit to living in groups, one where there is no relatedness, but living in larger groups gives a survival benefit (group augmentation, GA), and one where both effects operate. The main claim is that evolving defensive help or division of labor requires the group augmentation; it doesn't evolve through kin selection alone in the authors' simulations.

This is an interesting model, and there is much to like about the complexity that is built in. Individual-based simulations like this can be a valuable tool to explore the complex interaction of life history and social traits. Yet, models like this also have to take care of both being very clear on their construction and exploring how some of the ancillary but potentially consequential assumptions affect the results, including robust exploration of the parameter space. I think the current manuscript falls short in these areas, and therefore, I am not yet convinced of the results. In this round, the authors provided some clarity, but some questions still remain, and I remain unconvinced by a main assumption that was not addressed.

Based on the authors' response, if I understand the life history correctly, dispersers either immediately join another group (with 1-the probability of dispersing), or remain floaters until they successfully compete for a breeder spot or die? Is that correct? I honestly cannot decide because this seems implicit in the first response but the response to my second point raises the possibility of not working while floating but can work if they later join a group as a subordinate. If it is the case that floaters can have multiple opportunities to join groups as subordinates (not as breeders; I assume that this is the case for breeding competition), this should be stated, and more details about how. So there is still some clarification to be done, and more to the point, the clarification that happened only happened in the response. The authors should add these details to the main text. Currently, the main text only says vaguely that joining a group after dispersing " is also controlled by the same genetic dispersal predisposition" without saying how.

In each breeding cycle, individuals have the opportunity to become a breeder, a helper, or a floater. Social role is really just a state, and that state can change in each breeding cycle (see Figure 1). Therefore, floaters may join a group as subordinates at any point in time depending on their dispersal propensity, and subordinates may also disperse from their natal group any given time. In the “Dominance-dependent dispersal propensities” section in the SI, this dispersal or philopatric tendency varies with dominance rank.

We have added: “In each breeding cycle” (L415) to clarify this further.

In response to my query about the reasonableness of the assumption that floaters are in better condition (in the KS treatment) because they don't do any work, the authors have done some additional modeling but I fail to see how that addresses my point. The additional simulations do not touch the feature I was commenting on, and arguably make it stronger (since assuming a positive beta_r -which btw is listed as 0 in Table 1- would make floaters on average be even more stronger than subordinates). It also again confuses me with regard to the previous point, since it implies that now dispersal is also potentially a lifetime event. Is that true?

We are not quite sure where the reviewer gets this idea because we have never assumed a competitive advantage of floaters versus helpers. As stated in the previous revision, floaters can potentially outcompete subordinates of the same age if they attempt to breed without first queuing as a subordinate (step 5 in Figure 1) if subordinates are engaged in work tasks. However, floaters also have higher mortality rates than group members, which makes them have lower age averages. In addition, helpers have the advantage of always competing for an open breeding position in the group, while floaters do not have this preferential access (in Figure S2 we reduce even further the likelihood of a floater to try to compete for a breeding position).

Moreover, in the previous revision (section: “Dominance-dependent dispersal propensities” in the SI) we specifically addressed this concern by adding the possibility that individuals, either floaters or subordinate group members, react to their rank or dominance value to decide whether to disperse (if subordinate) or join a group (if floater). Hence, individuals may choose to disperse when low ranked and then remain on the territory they dispersed to as helpers, OR they may remain as helpers in their natal territory as low ranked individuals and then disperse later when they attain a higher dominance value. The new implementation, therefore, allows individuals to choose when to become floaters or helpers depending on their dominance value. This change to the model affects the relative competitiveness between floaters and helpers, which avoids the assumption that either low- or high-quality individuals are the dispersing phenotype and, instead, allows rank-based dispersal as an emergent trait. As shown in Figure S5, this change had no qualitative impact on the results.

To make this all clearer, we have now added to all of the relevant SI tables a new row with the relative rank of helpers vs floaters. As shown, floaters do not consistently outrank helpers. Rather, which role is most dominant depends on the environment and fitness trade-offs that shape their dispersing and helping decisions.

Some further clarifications: beta_r is a gene that may evolve either positive or negative values, 0 (no reaction norm of dispersal to dominance rank) is the initial value in the simulations before evolution takes place. Therefore, this value may evolve to positive or negative values depending on evolutionary trade-offs. Also, and as clarified in the previous comment, the decision to disperse or not occurs at each breeding cycle, so becoming a floater, for example, is not a lifetime event unless they evolve a fixed strategy (dispersal = 0 or 1). 

Meanwhile, the simplest and most convincing robustness check, which I had suggested last round, is not done: simply reduce the increase in the R of the floater by age relative to subordinates. I suspect this will actually change the results. It seems fairly transparent to me that an average floater in the KS scenario will have R about 15-20% higher than the subordinates (given no defense evolves, y_h=0.1 and H_work evolves to be around 5, and the average lifespan for both floaters and subordinates are in the range of 3.7-2.5 roughly, depending on m). That could be a substantial advantage in competition for breeding spots, depending on how that scramble competition actually works. I asked about this function in the last round (how non-linear is it?) but the authors seem to have neglected to answer.

As we mentioned in the previous comment above, we have now added the relative rank between helpers and floaters to all the relevant SI tables, to provide a better idea of the relative competitiveness of residents versus dispersers for each parameter combination. As seen in Table S1, the competitive advantage of floaters is only marginally in the favor for floaters in the “Only kin selection” implementation. This advantage only becomes more pronounced when individuals can choose whether to disperse or remain philopatric depending on their rank. In this case, the difference in rank between helpers and floaters is driven by the high levels of dispersal, with only a few newborns (low rank) remaining briefly in the natal territory (Table S6). Instead, the high dispersal rates observed under the “Only kin selection” scenario appear to result from the low incentives to remain in the group when direct fitness benefits are absent, unless indirect fitness benefits are substantially increased. This effect is reinforced by the need for task partitioning to occur in an all-or-nothing manner (see the new implementation added to the “Kin selection and the evolution of division of labor” in the Supplementary materials; more details in following comments).

In addition, we specifically chose not to impose this constraint of forcing floaters to be lower rank than helpers because doing so would require strong assumptions on how the floaters rank is determined. These assumptions are unlikely to be universally valid across natural populations (and probably not commonly met in most species) and could vary considerably among species. Therefore, it would add complexity to the model while reducing generalizability.

As stated in the previous revision, no scramble competition takes place, this was an implementation not included in the final version of the manuscript in which age did not have an influence in dominance. Results were equivalent and we decided to remove it for simplicity prior to the original submission, as the model is already very complex in the current stage; we simply forgot to remove it from Table 1, something we explained in the previous round of revisions.

More generally, I find that the assumption (and it is an assumption) floaters are better off than subordinates in a territory to be still questionable. There is no attempt to justify this with any data, and any data I can find points the other way (though typically they compare breeders and floaters, e.g.: https://bioone.org/journals/ardeola/volume-63/issue-1/arla.63.1.2016.rp3/The-Unknown-Life-of-Floaters--The-Hidden-Face-of/10.13157/arla.63.1.2016.rp3.full concludes "the current preliminary consensus is that floaters are 'making the best of a bad job'."). I think if the authors really want to assume that floaters have higher dominance than subordinates, they should justify it. This is driving at least one and possibly most of the key results, since it affects the reproductive value of subordinates (and therefore the costs of helping).

We explicitly addressed this in the previous revision in a long response about resource holding potential (RHP). Once again, we do NOT assume that dispersers are at a competitive advantage to anyone else. Floaters lack access to a territory unless they either disperse into an established group or colonize an unoccupied territory. Therefore, floaters endure higher mortalities due to the lack of access to territories and group living benefits in the model, and are not always able to try to compete for a breeding position.

The literature reports mixed evidence regarding the quality of dispersing individuals, with some studies identifying them as low-quality and others as high-quality, attributing this to them experiencing fewer constraints when dispersing that their counterparts (e.g. Stiver et al. 2007 Molecular Ecology; Torrents‐Ticó, et al. 2018 Journal of Zoology). Additionally, dispersal can provide end-of-queue individuals in their natal group an opportunity to join a queue elsewhere that offers better prospects, outcompeting current group members (Nelson‐Flower et al. 2018 Journal of Animal Ecology). Moreover, in our model floaters do not consistently have lower dominance values or ranks than helpers, and dominance value is often only marginally different.

In short, we previously addressed the concern regarding the relative competitiveness of floaters compared to subordinate group members. To further clarify this point here, we have now included additional data on relative rank in all of the relevant SI tables. We hope that these additions will help alleviate any remaining concerns on this matter.

Regarding division of labor, I think I was not clear so will try again. The authors assume that the group reproduction is 1+H_total/(1+H_total), where H_total is the sum of all the defense and work help, but with the proviso that if one of the totals is higher than "H_max", the average of the two totals (plus k_m, but that's set to a low value, so we can ignore it), it is replaced by that. That means, for example, if total "work" help is 10 and "defense" help is 0, total help is given by 5 (well, 5.1 but will ignore k_m). That's what I meant by "marginal benefit of help is only reduced by a half" last round, since in this scenario, adding 1 to work help would make total help go to 5.5 vs. adding 1 to defense help which would make it go to 6. That is a pretty weak form of modeling "both types of tasks are necessary to successfully produce offspring" as the newly added passage says (which I agree with), since if you were getting no defense by a lot of food, adding more food should plausibly have no effect on your production whatsoever (not just half of adding a little defense). This probably explains why often the "division of labor" condition isn't that different than the no DoL condition.

The model incorporates division of labor as the optimal strategy for maximizing breeder productivity, while penalizing helping efforts that are limited to either work or defense alone. Because the model does not intend to force the evolution of help as an obligatory trait (breeders may still reproduce in the absence of help; k₀ ≠ 0), we assume that the performance of both types of task by the helpers is a non-obligatory trait that complements parental care.

That said, we recognize the reviewer’s concern that the selective forces modeled for division of labor might not be sufficient in the current simulations. To address this, we have now introduced a new implementation, as discussed in the “Kin selection and the evolution of division of labor” section in the SI. In this implementation, division of labor becomes obligatory for breeders to gain a productivity boost from the help of subordinate group members. The new implementation tests whether division of labor can arise solely from kin selection benefits. Under these premises, philopatry and division of labor do emerge through kin selection, but only when there is a tenfold increase in productivity per unit of help compared to the default implementation. Thus, even if such increases are biologically plausible, they are more likely to reflect the magnitudes characteristic of eusocial insects rather than of cooperatively breeding vertebrates (the primary focus of this model). Such extreme requirements for productivity gains and need for coordination further suggest that group augmentation, and not kin selection, is probably the primary driving force particularly in harsh environments. This is now discussed in L210-213.

Reviewer #2 (Public review):

Summary:

This paper formulates an individual-based model to understand the evolution of division of labor in vertebrates. The model considers a population subdivided in groups, each group has a single asexually-reproducing breeder, other group members (subordinates) can perform two types of tasks called "work" or "defense", individuals have different ages, individuals can disperse between groups, each individual has a dominance rank that increases with age, and upon death of the breeder a new breeder is chosen among group members depending on their dominance. "Workers" pay a reproduction cost by having their dominance decreased, and "defenders" pay a survival cost. Every group member receives a survival benefit with increasing group size. There are 6 genetic traits, each controlled by a single locus, that control propensities to help and disperse, and how task choice and dispersal relate to dominance. To study the effect of group augmentation without kin selection, the authors cross-foster individuals to eliminate relatedness. The paper allows for the evolution of the 6 genetic traits under some different parameter values to study the conditions under which division of labour evolves, defined as the occurrence of different subordinates performing "work" and "defense" tasks. The authors envision the model as one of vertebrate division of labor.

The main conclusion of the paper is that group augmentation is the primary factor causing the evolution of vertebrate division of labor, rather than kin selection. This conclusion is drawn because, for the parameter values considered, when the benefit of group augmentation is set to zero, no division of labor evolves and all subordinates perform "work" tasks but no "defense" tasks.

Strengths:

The model incorporates various biologically realistic details, including the possibility to evolve age polytheism where individuals switch from "work" to "defence" tasks as they age or vice versa, as well as the possibility of comparing the action of group augmentation alone with that of kin selection alone.

Weaknesses:

The model and its analysis is limited, which makes the results insufficient to reach the main conclusion that group augmentation and not kin selection is the primary cause of the evolution of vertebrate division of labor. There are several reasons.

First, the model strongly restricts the possibility that kin selection is relevant. The two tasks considered essentially differ only by whether they are costly for reproduction or survival. "Work" tasks are those costly for reproduction and "defense" tasks are those costly for survival. The two tasks provide the same benefits for reproduction (eqs. 4, 5) and survival (through group augmentation, eq. 3.1). So, whether one, the other, or both tasks evolve presumably only depends on which task is less costly, not really on which benefits it provides. As the two tasks give the same benefits, there is no possibility that the two tasks act synergistically, where performing one task increases a benefit (e.g., increasing someone's survival) that is going to be compounded by someone else performing the other task (e.g., increasing that someone's reproduction). So, there is very little scope for kin selection to cause the evolution of labour in this model. Note synergy between tasks is not something unusual in division of labour models, but is in fact a basic element in them, so excluding it from the start in the model and then making general claims about division of labour is unwarranted. I made this same point in my first review, although phrased differently, but it was left unaddressed.

The scope of this paper was to study division of labor in cooperatively breeding species with fertile workers, in which help is exclusively directed towards breeders to enhance offspring production (i.e., alloparental care), as we stated in the previous review. Therefore, in this context, helpers may only obtain fitness benefits directly or indirectly by increasing the productivity of the breeders. This benefit is maximized when division of labor occurs between group members as there is a higher return for the least amount of effort per capita. Our focus is in line with previous work in most other social animals, including eusocial insects and humans, which emphasizes how division of labor maximizes group productivity. This is not to suggest that the model does not favor synergy, as engaging in two distinct tasks enhances the breeders' productivity more than if group members were to perform only one type of alloparental care task. We have expanded on the need for division of labor by making the performance of each type of task a requirement to boost the breeders productivity, see more details in a following comment.

Second, the parameter space is very little explored. This is generally an issue when trying to make general claims from an individual-based model where only a very narrow parameter region has been explored of a necessarily particular model. However, in this paper, the issue is more evident. As in this model the two tasks ultimately only differ by their costs, the parameter values specifying their costs should be varied to determine their effects. Instead, the model sets a very low survival cost for work (yh=0.1) and a very high survival cost for defense (xh=3), the latter of which can be compensated by the benefit of group augmentation (xn=3). Some very limited variation of xh and xn is explored, always for very high values, effectively making defense unevolvable except if there is group augmentation. Hence, as I stated in my previous review, a more extensive parameter exploration addressing this should be included, but this has not been done. Consequently, the main conclusion that "division of labor" needs group augmentation is essentially enforced by the limited parameter exploration, in addition to the first reason above.

We systematically explored the parameter landscape and report in the body of the paper only those ranges that lead to changes in the reaction norms of interest (other ranges are explored in the SI). When looking into the relative magnitude of cost of work and defense tasks, it is important to note that cost values are not directly comparable because they affect different traits. However, the ranges of values capture changes in the reaction norms that lead to rank-depending task specialization.

To illustrate this more clearly, we have added a new section in the SI (Variation in the cost of work tasks instead of defense tasks section) showing variation in y_h, which highlights how individuals trade off the relative costs of different tasks. As shown, the results remain consistent with everything we showed previously: a higher cost of work (high y_h) shifts investment toward defense tasks, while a higher cost of defense (high x_h) shifts investment toward work tasks.

Importantly, additional parameter values were already included in the SI of the previous revision, specifically to favor the evolution of division of labor under only kin selection. Basically, division of labor under only kin selection does happen, but only under conditions that are very restrictive, as discussed in the “Kin selection and the evolution of division of labor” section in the SI. We have tried to make this point clearer now (see comments to previous reviewer above, and to this reviewer right below).

Third, what is called "division of labor" here is an overinterpretation. When the two tasks evolve, what exists in the model is some individuals that do reproduction-costly tasks (so-called "work") and survival-costly tasks (so-called "defense"). However, there are really no two tasks that are being completed, in the sense that completing both tasks (e.g., work and defense) is not necessary to achieve a goal (e.g., reproduction). In this model there is only one task (reproduction, equation 4,5) to which both "tasks" contribute equally and so one task doesn't need to be completed if the other task compensates for it. So, this model does not actually consider division of labor.

Although it is true that we did not make the evolution of help obligatory and, therefore, did not impose division of labor by definition, the assumptions of the model nonetheless create conditions that favor the emergence of division of labor. This is evident when comparing the equilibria between scenarios where division of labor was favored versus not favored (Figure 2 triangles vs circles).

That said, we acknowledge the reviewer’s concern that the selective forces modeled in our simulations may not, on their own, be sufficient to drive the evolution of division of labor under only kin selection. Therefore, we have now added a section where we restrict the evolution of help to instances in which division of labor is necessary to have an impact on the dominant breeder productivity. Under this scenario, we do find division of labor (as well as philopatry) evolving under only kin selection. However, this behavior only evolves when help highly increases the breeders’ productivity (by a factor of 10 what is needed for the evolution of division of labor under group augmentation). Therefore, group augmentation still appears to be the primary driver of division of labor, while kin selection facilitates it and may, under certain restrictive circumstances, also promote division of labor independently (discussed in L210-213).

Reviewer #1 (Recommendations for the authors):

I really think you should do the simulations where floaters do not come out ahead by floating. That will likely change the result, but if it doesn't, you will have a more robust finding. If it does, then you will have understood the problem better.

As we outlined in the previous round of revisions, implementing this change would be challenging without substantially increasing model complexity and reducing its general applicability, as it would require strong assumptions that could heavily influence dispersal decisions. For instance, by how much should helpers outcompete floaters? Would a floater be less competitive than a helper regardless of age, or only if age is equal? If competitiveness depends on equal age, what is the impact of performing work tasks given that workers always outcompete immigrants? Conversely, if floaters are less competitive regardless of age, is it realistic that a young individual would outcompete all immigrants? If a disperser finds a group immediately after dispersal versus floating for a while, is the dominance value reduced less (as would happen to individuals doing prospections before dispersal)? 

Clearly it is not as simple as the referee suggests because there are many scenarios that would need to be considered and many assumptions made in doing this. As we explained to the points above, we think our treatment of floaters is consistent with the definition of floaters in the literature, and our model takes a general approach without making too many assumptions.

Reviewer #2 (Recommendations for the authors):

The paper's presentation is still unclear. A few instances include the following. It is unclear what is plotted in the vertical axes of Figure 2, which is T but T is a function of age t, so this T is presumably being plotted at a specific t but which one it is not said.

The values graphed are the averages of the phenotypically expressed tasks, not the reaction norms per se. We have now rewritten the the axis to “Expressed task allocation T (0 = work, 1 = defense)” to increase clarity across the manuscript.

The section titled "The need for division of labor" in the methods is still very unclear.

We have rephased this whole section to improve clarity.

eLife Assessment

The authors identify the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in the Drosophila wing disc. These valuable findings with potential biomedical relevance are, however, supported by incomplete evidence based largely on overexpression studies that lack quantification, limited molecular support for their model, and issues with Bearded family protein specificity. The work could be of interest to researchers in the fields of cell signaling and developmental biology.

Reviewer #1 (Public review):

Summary:

The authors investigate how the Drosophila TNF receptor-associated factor Traf4 - a multifunctional adaptor protein with potential E3 ubiquitin ligase activity - regulates JNK signaling and adherens junctions (AJs) in wing disc epithelium. When they overexpress Traf4 in the posterior compartment of the wing disc, many posterior cells express the JNK target gene puckered (puc), apoptose, and are basally extruded from the epithelium. The authors term this process "delamination", but I think that this is an inaccurate description, especially since they can suppress the "delamination" by blocking programmed cell death (by concomitantly overexpressing p35). Through Y2H assays using Traf4 as a bait, they identified the Bearded family proteins E(spl)m4 (and to a lesser extent E(spl)m2), as Traf4 interactors. They use Alphafold to model computationally the interaction between Traf4 and E(spl)m4. They show that co-overexpression of Traf4 with E(spl)m4 in the posterior domain of the wing disc reduces death of posterior cells. They generate a new, weaker hypomorphic allele of Traf4 that is viable (as opposed to the homozygous lethality of null Traf4 alleles). There is some effect of these mutations on wing margin bristles; fewer wing margin bristle defects are seen when E(spl)m4 is overexpressed, suggesting opposite effects of Traf4 and E(spl)m4. Finally, they use the Minute model of cell competition to show that Rp/+ loser clones have greater clone area (indicating increased survival) when they are depleted for Traf4 or when they overexpress E(spl)m4. Only the cell competition results are quantified. Because most of the data in the preprint are not quantified, it is impossible to know how penetrant the phenotypes are. The authors conclude that E(spl)m4 binds the Traf4 MATH/TRAF domain, disrupts Traf4 trimerization, and selectively suppresses Traf4-mediated JNK and caspase activation without affecting its role in AJ destabilization. However, I believe that this is an overstatement. First, there is no biochemical evidence showing that Traf4 binds E(spl)m4 and that E(spl)m4 disrupts Traf4 trimerization. Second, the data on AJs is weak and not quantified; additionally, cells that are being basally extruded lose contact with neighboring cells, hence changes in adhesion proteins. Related to this, the authors, in my opinion, inaccurately describe basal extrusion of dying cells from the wing disc epithelium as delamination.

Strengths:

(1) The authors use multiple approaches to test the model that overexpressed E(spl)m4 inhibits Traf4, including genetics, cell biological imaging, yeast two-hybrid assays, and molecular modeling.

(2) The authors generate a new Traf4 hypomorphic mutant and use this mutant in cell competition studies, which supports the concept that E(spl)m4 (when overexpressed) can antagonize Traf4.

Weaknesses:

(1) Conflation of "delamination" with "basal extrusion of apoptotic cells": Over-expression of Traf4 causes apoptosis in wing disc cells, and this is a distinct process from delamination of viable cells from an epithelium. However, the two processes are conflated by the authors, and this weakens the premise of the paper.

(2) Dependence on overexpression: The conclusions rely heavily on ectopic expression of Traf4 and E(spl)m4. Thus, the physiological relevance of the interaction remains inferred rather than demonstrated.

(3) Lack of quantitative rigor: Except for the cell competition studies, phenotypic descriptions (e.g., number of apoptotic cells, puc-LacZ intensity) are qualitative; additional quantification, inclusion of sample size, and statistical testing would strengthen the conclusions.

(4) Limited biochemical validation: The Traf4-E(spl)m4 binding is inferred from Y2H and in silico models, but no co-immunoprecipitation or in vitro binding assays confirm direct interaction or the predicted disruption of trimerization.

(5) Specificity within the Bearded family: While E(spl)m2 shows partial binding and Tom shows none, the mechanistic basis for this selectivity is not deeply explored experimentally, leaving questions about motif-context contributions unresolved.

Reviewer #2 (Public review):

Summary:

This manuscript analyzes the contribution of Traf4 to the fate of epithelial cells in the developing wing imaginal disc tissue. The manuscript is direct and concise and suggests an interesting and valuable hypothesis with dual functions of Traf4 in JNK pathway activation and cell delamination. However, the text is partially speculative, and the evidence is incomplete as the main claims are only partially supported. Some results require validation to support the conclusions.

Strengths:

(1) The manuscript is direct and concise, with a well-written and precise introduction.

(2) It presents an interesting and valuable hypothesis regarding the dual role of Traf4 in JNK pathway activation and cell delamination.

(3) The study addresses a relevant biological question in epithelial tissue development using a genetically tractable model.

(4) The use of newly generated Traf4 mutants adds novelty to the experimental approach.

(5) The manuscript includes multiple experimental strategies, such as genetic manipulation and imaging, to explore Traf4 function.

Weaknesses:

(1) The evidence supporting key claims is incomplete, and some conclusions are speculative.

(2) The use of GFP-tagged Traf4 lacks validation regarding its functional integrity.

(3) Orthogonal views and additional imaging data are needed to confirm changes in apicobasal localization and cell delamination.

(4) Experimental conditions and additional methods should be further detailed.

(5) The interaction between Traf4 and E(spl)m4 remains speculative in Drosophila.

(6) New mutants require deeper analysis and validation.

(7) The elimination of Traf4 mutant clones may be due to cell competition, which requires further experimental clarification.

(8) The role of Traf4 in cell competition is contradictory and needs to be resolved.

Reviewer #3 (Public review):

Summary:

This is an important and well-conceived study that identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila. By combining classical genetics, yeast two-hybrid assays, and AlphaFold in silico modeling, the authors convincingly demonstrate that E(spl)m4 acts as an inhibitor of TRAF4-mediated induction of JNK-driven apoptosis in developing larval imaginal wing discs, while not affecting TRAF4's role in adherence junction remodeling.

Based primarily on modeling, the authors propose that the specificity of E(spl)m4 towards TRAF4-mediated signaling arises from its interference with TRAF4 trimerization, which is likely required for the activation of the JNK signaling arm but not for the maintenance of adherence junctions and stability of E-cadherin/β-catenin complex.

Overall, this study is of broad interest to cell and developmental biologists. It also holds potential biomedical relevance, particularly for strategies aimed at modulating TRAF protein activities to dissect and modulate canonical versus non-canonical signaling functions.

Strengths:

(1) The work identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila, extending the understanding of E(spl)m4 beyond its established functions in Notch signaling.

(2) The study is experimentally solid, well-executed, and written, combining classical genetics with protein-protein interaction assays and modeling to reveal E(spl)m4 as a new regulator of TRAF4 signaling.

(3) The genetic and biochemical data convincingly show the ability of E(spl)m4 overexpression to inhibit TRAF4-induced JNK-dependent apoptosis, while leaving the TRAF4 role in adherens junction remodeling unaffected.

(4) The findings have important implications for the regulation of cell signaling and apoptosis and may guide pharmacological targeting of TRAF proteins.

Weaknesses:

The study is overall strong; however, several aspects could be clarified or expanded to strengthen the proposed mechanism and data presentation:

(1) The proposed mechanism that E(spl)m4 inhibits TRAF4 activation of JNK signaling by affecting TRAF4 trimerization relies mainly on modeling. Experimental evidence would strengthen this claim. For example, a native or non-denaturing SDS-PAGE could be used to assess TRAF4 oligomerization states in the absence or presence of E(spl)m4 overexpression, testing whether E(spl)m4 interferes with high-molecular-weight TRAF4 assemblies.

(2) The study depends largely on E(spl)m4 overexpression, which may not reflect physiological conditions. It would be valuable to test, or at least discuss, whether loss-of-function or knockdown of E(spl)m4 modulates the strength or duration of JNK-mediated signaling, potentially accelerating apoptosis. Such data would reinforce the model that E(spl)m4 acts as a physiological modulator of TRAF4-JNK signaling in vivo.

(3) The authors initially identify both E(spl)m4 and E(spl)m2 as TRAF4 interactions, but subsequently focus on E(spl)m4. It would be helpful to clarify or discuss the rationale for prioritizing E(spl)m4 for detailed functional analysis.

(4) E(spl)m4 overexpression appears to protect RpS3 loser clones (Figure 6H-K), yet caspase-3-positive cells are still visible in mosaic wing discs. Please comment on the nature of these Caspase 3-positive cells, whether they are cell-autonomous to the clone or non-autonomous (Figure 6K)?

(5) This is a clear, well-executed, and conceptually strong study that significantly advances understanding of TRAF4 signaling specificity and its modulation by the Bearded-type protein E(spl)m4.

eLife Assessment

This important study applies an innovative multi-model strategy to implicate the ribosomal protein (RP) encoding genes as candidates causing Hypoplastic Left Heart Syndrome. The evidence from the screen in stem cell-derived cardiomyocytes and whole genome sequencing of human patients, followed by functional analyses of RP genes in fly and fish models, is convincing and supports the authors' claims. This work and methodology applied would be of broad interest to medical biologists working on congenital heart diseases.

Reviewer #1 (Public review):

Nielsen et al have identified a new disease mechanism underlying hypoplastic left heart syndrome due to variants in ribosomal protein genes that lead to impaired cardiomyocyte proliferation. This detailed study starts with an elegant screen in stem cell derived cardiomyocytes and whole genome sequencing of human patients and extends to careful functional analysis of RP gene variants in fly and fish models. Striking phenotypic rescue is seen by modulating known regulators of proliferation including the p53 and Hippo pathways. Additional experiments suggest that cell type specificity of the variants in these ubiquitously expressed genes may result from genetic interactions with cardiac transcription factors. This work positions RPs as important regulators of cardiomyocyte proliferation and differentiation involved in the etiology of HLHS, and point to potential downstream mechanisms.

The revised manuscript has been extended, facilitating interpretation and reinforcing the authors' conclusions.

Reviewer #2 (Public review):

Tanja Nielsen et al. presents a novel strategy for identification of candidate genes in Congenital Heart Disease (CHD). Their methodology, which is based on comprehensive experiments across cell models, drosophila and zebrafish models, represents an innovative, refreshing and very useful set of tools for identification of disease genes, in a field which are struggling with exactly this problem.

The authors have applied their methodology to investigate the pathomechanisms of Hypoplastic Left Heart Syndrome (HLHS) - a severe and rare subphenotype in the large spectrum of CHD malformations. Their data convincingly implicates ribosomal proteins (RPs) in growth and proliferation defects of cardiomyocytes, a mechanism which is suspected to be associated with HLHS.

By whole genome sequencing analysis of a small cohort of trios (25 HLHS patients and their parents) the authors investigated a possible association between RP encoding genes and HLHS.

Although the possible association between defective RPs and HLHS needs to be verified, the results suggest a novel disease mechanism in HLHS, which is a potentially substantial advance in our understanding of HLHS and CHD. The conclusions of the paper are based on solid experimental evidence from appropriate high- to medium-throughput models, while additional genetic results from an independent patient cohort is needed to verify an association between RP encoding genes and HLHS in patients.

Author response:

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

Reviewer #1 (Public review):

Nielsen et al have identified a new disease mechanism underlying hypoplastic left heart syndrome due to variants in ribosomal protein genes that lead to impaired cardiomyocyte proliferation. This detailed study starts with an elegant screen in stemcell-derived cardiomyocytes and whole genome sequencing of human patients and extends to careful functional analysis of RP gene variants in fly and fish models. Striking phenotypic rescue is seen by modulating known regulators of proliferation, including the p53 and Hippo pathways. Additional experiments suggest that the cell type specificity of the variants in these ubiquitously expressed genes may result from genetic interactions with cardiac transcription factors. This work positions RPs as important regulators of cardiomyocyte proliferation and differentiation involved in the etiology of HLHS, although the downstream mechanisms are unclear.

We thank Reviewer 1 for the thoughtful assessment of our manuscript. Our point-bypoint responses to the recommendations are provided (Reviewer 1, “Recommendations for the authors”).

Reviewer #2 (Public review):

Tanja Nielsen et al. present a novel strategy for the identification of candidate genes in Congenital Heart Disease (CHD). Their methodology, which is based on comprehensive experiments across cell models, Drosophila and zebrafish models, represents an innovative, refreshing and very useful set of tools for the identification of disease genes, in a field which are struggling with exactly this problem. The authors have applied their methodology to investigate the pathomechanisms of Hypoplastic Left Heart Syndrome (HLHS) - a severe and rare subphenotype in the large spectrum of CHD malformations. Their data convincingly implicates ribosomal proteins (RPs) in growth and proliferation defects of cardiomyocytes, a mechanism which is suspected to be associated with HLHS.

By whole genome sequencing analysis of a small cohort of trios (25 HLHS patients and their parents), the authors investigated a possible association between RP encoding genes and HLHS. Although the possible association between defective RPs and HLHS needs to be verified, the results suggest a novel disease mechanism in HLHS, which is a potentially substantial advance in our understanding of HLHS and CHD. The conclusions of the paper are based on solid experimental evidence from appropriate high- to medium-throughput models, while additional genetic results from an independent patient cohort are needed to verify an association between RP encoding genes and HLHS in patients.

We thank Reviewer 2 for the thoughtful assessment of our manuscript. Our point-by-point responses to the recommendations are provided (Reviewer 2, “Recommendations for the authors”).

Reviewer #1 (Recommendations for the authors): 

(1) Despite an interesting surveillance model, the disease-causing mechanisms directly downstream of the RP variants remain unclear. Can the authors provide any evidence for abnormal ribosomes or defects in translation in cells harboring such variants? The possibility that reduced translation of cardiac transcription factors such as TBX5 and NKX2-5 may contribute to the functional interactions observed should be considered. How do the authors consider that the RP variants are affecting transcript levels as observed in the study?

Our model implies that cell cycle arrest does not require abnormal ribosomes or translational defects but instead relies on the sensing of RP levels or mutations as a fitness-sensing mechanism that activates TP53/CDKN1A-dependent arrest. Supporting this framework, we observed no significant changes in TBX5 or NKX2-5 expression (data not shown), but rather an upregulation of CDKN1A levels upon RP KD.

(2) The authors suggest that a nucleolar stress program is activated in cells harboring RP gene variants. Can they provide additional evidence for this beyond p53 activation? 

We added additional data to support nucleolar stress (Suppl. Fig. 6) and text (lines 52635):

To determine whether cardiac KD of RpS15Aa causes nucleolar stress in the Drosophila heart, we stained larval hearts for Fibrillarin, a marker for nucleoli and nucleolar integrity.  We found that RpS15Aa KD causes expansion of nucleolar Fibrillarin staining in cardiomyocyte, which is a hallmark of nucleolar stress (Suppl. Fig. 6A-C). As a control, we also performed cardiac KD of Nopp140, which is known to cause nucleolar stress upon loss-of-function. We found a similar expansion of Fibrillarin staining in larval cardiomyocyte nuclei (Suppl. Fig. 6C,D). This suggests that RpS15Aa KD indeed causes nucleolar stress in the Drosophila heart, that likely contributes to the dramatic heart loss in adults.

Other recommendations: 

(3) Concerning the cell type specificity, in the proliferation screen, were similar effects seen on the actinin negative as actinin positive EdU+ cells? It would be helpful to refer to the fibroblast result shown in Supplementary Figure 1C in the results section. 

As suggested by reviewer #1, we have added a reference to Supplementary Fig. 1C, D and noted that RP knockdown exerts a non–CM-specific effect on proliferation.

(4) The authors refer to HLHS patients with atrial septal defects and reduced right ventricular ejection fraction. Please clarify the specificity of the new findings to HLHS versus other forms of CHD, as implied in several places in the manuscript, including the abstract.

This study focused on a cohort of 25 HLHS proband-parent trios selected for poor clinical outcome, including restrictive atrial septal defect and reduced right ventricular ejection fraction.  We have revised the following sentence  in response to the Reviewer’s comment (lines 567-571): “While our study highlights the potential of this approach for gene prioritization, additional research is needed to directly demonstrate the functional consequence of the identified genetic variants, verify an association between RP encoding genes and HLHS in other patient cohorts with and without poor outcome, and determine if RP variants have a broader role in CHD susceptibility.

(5) The multi-model approach taken by the authors is clearly a good system for characterizing disease-causing variants. Did the authors score for cardiomyocyte proliferation or the time of phenotypic onset in the zebrafish model? 

We used an antibody against phosphohistone 3 to identify proliferating cells and DAPI to identify all cardiac cells in control injected, rps15a morphants, and rps15a crispants. We found that  cell numbers and proliferating cells were significantly reduced at 24 and 48 hpf. By 72 hpf cardiac cell proliferation is greatly diminished even in controls, where proliferation typically declines. 

Reduced ventricular cardiomyocyte numbers could potentially result from impaired addition of LTPB3-expressing progenitors. In experiments where altered cardiac rhythm is observed, please comment on the possible links to proliferation.

Heart function data showed that heart period (R-R interval) was unaffected in morphants and crispants at 72 hpf where we also observed significant reductions in cell numbers. This suggests that the bradycardia observed in the rps15a + nkx2.5 or tbx5a double KD (Sup. Fig. 5D & E) was not due to the reduction in cell numbers alone. 

Author response image 1.

Finally, the use of the mouse to model HLHS in potential follow-up studies should be discussed. 

We have added a mouse model comment to the discussion (lines 571-74): “In conclusion, we propose that the approach outlined in this study provides a novel framework for rapidly prioritizing candidate genes and systematically testing them, individually or in combination, using a CRISPR/Cas9 genome-editing strategy in mouse embryos (PMID: 28794185)”.

(6) When the authors scored proliferation in cells from the proband in family 75H, did they validate that RPS15A expression is reduced, consistent with a regulatory region defect? 

Good point. We examined RPS15A expression in these cells and found no significant reduction in gene expression in day 25 cardiomyocytes (data not shown). One possible explanation is that this variant may regulate RPS15A expression in a stage-specific manner during differentiation or under additional stress conditions.

(7) Minor point. Typo on line 494: comma should be placed after KD, not before.

Thank you, this has now been corrected (new line 490)

Reviewer #2 (Recommendations for the authors):  

(1) The authors are invited to revise the part of the manuscript that describes the genetic analysis and provide a more balanced discussion of the WGS data, with a conclusion that aligns with the strength of the human genetic data. 

We disagree with reviewer #2’s assessment. The goal of our study is not to apply a classical genetic approach to establish variant pathogenicity, but rather to employ a multidisciplinary framework to prioritize candidate genes and variants and to examine their roles in heart development using model systems. In this context, genetic analysis serves primarily as a filtering tool rather than as a means of definitively establishing causality.

(2) The genetic analysis of patients does not appear to provide strong evidence for an association between RP gene variants and HLHS. More information regarding methodology and the identified variants is needed. 

HLHS is widely recognized as an oligogenic and heterogeneous genetic disease in which traditional genetic analyses have consistently failed to prioritize any specific gene class as reviewer#2 is pointing out. Therefore, relying solely on genetic analysis is unlikely to yield strong evidence for association with a given gene class. This limitation provides the rationale for our multidisciplinary gene prioritization strategy, which leverages model systems to interrogate candidate gene function. Ultimately, definitive validation of this approach will require studies in relevant in vivo models to establish causality within the context of a four-chambered heart (see also Discussion).

In Table S2, it would be appropriate to provide information on sequence, MAF, and CADD. Please note the source of MAF% (GnomAD version?, which population?).  

As summarized in Figure 2A, the 292 genes from the families with the 25 proband with poor outcome displayed in Supplemental Table 2 fulfilled a comprehensive candidate gene prioritization algorithm based on the variant, gene, inheritance, and enrichment, which required all of the following: 1) variants identified by whole genome sequencing with minor allele frequency <1%; 2) missense, loss-of-function, canonical splice, or promoter variants; 3) upper quartile fetal heart expression; and 4)De novo or recessive inheritance. Unbiased network analysis of these 292 genes, which are displayed in Supplemental Table 2 for completeness, identified statistically significant enrichment of ribosomal proteins. The details about MAF, CADD score, and sequence highlighted by the Reviewer are provided for the RP genes in Table 1, which are central to the focus and findings of the manuscript.    

It would also be helpful for the reader if genome coordinates (e.g., 16-11851493-G-A for RSL1D1 p.A7V) were provided for each variant in both Table 1 and S2.

Genome coordinates have been added to Table 1.

(3) The dataset from the hPSC-CM screen could be of high value for the community. It would be appropriate if the complete dataset were made available in a usable format. 

The dataset from the hPSC-CM screen has been added to the manuscript as Supp Table 1

(4) The "rare predicted-damaging promoter variant in RPS15A" (c.-95G>A) does not appear so rare. Considering the MAF of 0,00662, the frequency of heterozygous carriers of this variant is 1 out of 76 individuals in the general population. Thus, considering the frequency of HLHS in the population (2-3 out of 10,000) and the small size of family 75H, the data do not appear to indicate any association between this particular variant and HLHS. The variants in Table 1 also appear to have relatively mild effects on the gene product, judging from the MAF and CADD scores. The authors are invited to discuss why they find these variants disease-causing in HLHS. 

Our study design is based on the widely held premise that HLHS is an oligogenic disorder. Our multi-model systems platform centered on comprehensive filtering of coding and regulatory variants identified by whole genome sequencing of HLHS probands to identify candidate genes associated with susceptibility to this rare developmental phenotype. 75H proved to be a high-value family for generating a relatively short list of candidate genes for left-sided CHD. Given the rarity of both left-sided CHD and the RPS15A variant identified in the HLHS proband and his 5th degree relative, with a frequency consistent with a risk allele for an oligogenic disorder, we made the reasonable assumption that this was a bona fide genotype-phenotype association rather than a chance occurrence. Moreover, incomplete penetrance and variable expression is consistent with a genetically complex basis of disease whereby the shared variant is risk-conferring and acts in conjunction with additional genetic, epigenetic, and/or environmental factors that lead to a left-sided CHD phenotype. In sum, we do not claim these variants are definitively disease causing, but rather potentially contributing risk factors.

(5) Information is lacking on how clustering of RP genes was demonstrated using STRING (with P-values that support the conclusions). What is meant by "when the highest stringency filter was applied"? Does this refer to the STRING interaction score or something else? The authors could also explain which genes were used to search STRING (e.g., all 292 candidate genes) and provide information on the STRING interaction score used in the analysis, the number of nodes and edges in the network.

To determine whether certain gene networks were over-represented, two online bioinformatics tools were used. First, genes were inputted into STRING (Author response table 2 below) to investigate experimental and predicted protein-protein and genetic interactions. Clustering of ribosomal protein genes was demonstrated when applying the highest stringency filter. Next, genes were analyzed for potential enrichment of genes by ontology classification using PANTHER .Applying Fisher’s exact test and false discovery rate corrections, ribosomal proteins were the most enriched class when compared to the reference proteome, including data annotated by molecular function (4.84-fold, p=0.02), protein class (6.45-fold, p=0.00001), and cellular component (9.50fold, p=0.001). A majority of the identified RP candidate genes harbored variants that fit a recessive inheritance disease model.

Author response image 2.

eLife Assessment

This valuable work substantially advances our understanding of prognostic value of total gfDNA in gastric cancer. The evidence supporting the conclusions is solid, supported by a large, well-classified patient cohort and controlled clinical variables. The work will be of broad interest to scientists and clinical pathologist working in the field of gastric cancer.

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn.

Comments on revisions:

The authors have addressed all concerns in the revision.

Reviewer #2 (Public review):

Summary

The authors aimed to evaluate whether total DNA concentration in gastric fluid (gfDNA) collected during routine endoscopy could serve as a diagnostic and prognostic biomarker for gastric cancer. Using a large cohort (n=941), they reported elevated gfDNA in gastric cancer patients, an unexpected association with improved survival, and a positive correlation with immune cell infiltration.

Strengths

The study benefits from a substantial sample size, clear patient stratification, and control of key clinical confounders. The method is simple and clinically feasible, with preliminary evidence linking gfDNA to immune infiltration.

Weaknesses

(1) While the study identifies gfDNA as a potential prognostic tool, the evidence remains preliminary. Unexplained survival associations and methodological gaps weaken support for the conclusions.

(2) The paradoxical association between high gfDNA and better survival lacks mechanistic validation. The authors acknowledge but do not experimentally distinguish tumor vs. immune-derived DNA, leaving the biological basis speculative.

(3) Pre-analytical variables were noted but not systematically analyzed for their impact on gfDNA stability.

Comments on revisions:

To enhance the completeness and credibility of this research, it is essential to clarify the biological origin of gastric fluid DNA and validate these preliminary findings through a prospective, longitudinal study design.

Author response:

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

Reviewer #1 (Public review): 

“The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:” 

(1) “This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.”

We are grateful for this astute remark. A comparison of gfDNA concentration among the diagnostic groups indicates a trend of increasing values as the diagnosis progresses toward malignancy. The observed values for the diagnostic groups are as follows:

Author response table 1.

The chart below presents the statistical analyses of the same diagnostic/tumor-stage groups (One-Way ANOVA followed by Tukey’s multiple comparison tests). It shows that gastric fluid gfDNA concentrations gradually increase with malignant progression. We observed that the initial tumor stages (T0 to T2) exhibit intermediate gfDNA levels, which in this group is significantly lower than in advanced disease (p = 0.0036), but not statistically different from non-neoplastic disease (p = 0.74).

Author response image 1.

(2) “The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.”

We appreciate the attention to detail regarding the numbers analyzed in the manuscript. Importantly, the results are meaningful because the number of subjects in each group is comparable (T0-T2, N = 65; T3, N = 91; T4, N = 63). The mean gastric fluid gfDNA values (ng/µL) increase with disease stage (T0-T2: 15.12; T3-T4: 30.75), and both are higher than the mean gfDNA values observed in non-neoplastic disease (10.81 ng/µL for N+PD and 10.10 ng/µL for PN). These subject numbers in each diagnostic group accurately reflect real-world data from a tertiary cancer center.

(3) “The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.”

Histopathological analyses were performed throughout the study not only for the initial diagnosis of tissue biopsies, but also for the classification of Lauren’s subtypes, tumor staging, and the assessment of the presence and extent of immune cell infiltrates. Regarding the time of disease onset, this variable is inherently unknown--by definition--at the time of a diagnostic EGD. While the prognosis definition is indeed straightforward, we believe that a simple, cost-effective, and practical approach is advantageous for patients across diverse clinical settings and is more likely to be effectively integrated into routine EGD practice.

(4) “The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort. “

We wish to reinforce that EGD, along with conventional histopathology, remains the gold standard for gastric cancer evaluation. EGD under sedation is routinely performed for diagnosis, and the collection of gastric fluids for gfDNA evaluation does not affect patient comfort. Thus, while gfDNA analysis was evidently not intended as a diagnostic EGD and biopsy replacement, it may provide added prognostic value to this exam.

(5) “There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc. “

We are grateful for these comments and apologize for the clerical oversight. All figures, tables, titles and figure legends have now been double-checked.

(6) “The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn. “

We assume that the unusual wording remark regarding “overall logicality” pertains to the rationale and/or reasoning of this investigational study. Our working hypothesis was that during neoplastic disease progression, tumor cells continuously proliferate and, depending on various factors, attract immune cell infiltrates. Consequently, both tumor cells and immune cells (as well as tumor-derived DNA) are released into the fluids surrounding the tumor at its various locations, including blood, urine, saliva, gastric fluids, and others. Thus, increases in DNA levels within some of these fluids have been documented and are clinically meaningful. The concurrent observation of elevated gastric fluid gfDNA levels and immune cell infiltration supports the hypothesis that increased gfDNA—which may originate not only from tumor cells but also from immune cells—could be associated with better prognosis, as suggested by this study of a large real-world patient cohort.

In summary, we thank Reviewer #1 for his time and effort in a constructive critique of our work.

Reviewer #2 (Public review):

Summary: 

“The authors investigated whether the total DNA concentration in gastric fluid (gfDNA), collected via routine esophagogastroduodenoscopy (EGD), could serve as a diagnostic and prognostic biomarker for gastric cancer. In a large patient cohort (initial n=1,056; analyzed n=941), they found that gfDNA levels were significantly higher in gastric cancer patients compared to non-cancer, gastritis, and precancerous lesion groups. Unexpectedly, higher gfDNA concentrations were also significantly associated with better survival prognosis and positively correlated with immune cell infiltration. The authors proposed that gfDNA may reflect both tumor burden and immune activity, potentially serving as a cost-effective and convenient liquid biopsy tool to assist in gastric cancer diagnosis, staging, and follow-up.”

Strengths: 

“This study is supported by a robust sample size (n=941) with clear patient classification, enabling reliable statistical analysis. It employs a simple, low-threshold method for measuring total gfDNA, making it suitable for large-scale clinical use. Clinical confounders, including age, sex, BMI, gastric fluid pH, and PPI use, were systematically controlled. The findings demonstrate both diagnostic and prognostic value of gfDNA, as its concentration can help distinguish gastric cancer patients and correlates with tumor progression and survival. Additionally, preliminary mechanistic data reveal a significant association between elevated gfDNA levels and increased immune cell infiltration in tumors (p=0.001).”

Reviewer #2 has conceptually grasped the overall rationale of the study quite well, and we are grateful for their assessment and comprehensive summary of our findings.

Weaknesses: 

(1) “The study has several notable weaknesses. The association between high gfDNA levels and better survival contradicts conventional expectations and raises concerns about the biological interpretation of the findings.“

We agree that this would be the case if the gfDNA was derived solely from tumor cells. However, the findings presented here suggest that a fraction of this DNA would be indeed derived from infiltrating immune cells. The precise determination of the origin of this increased gfDNA remains to be achieved in future follow-up studies, and these are planned to be evaluated soon, by applying DNA- and RNA-sequencing methodologies and deconvolution analyses.

(2) “The diagnostic performance of gfDNA alone was only moderate, and the study did not explore potential improvements through combination with established biomarkers. Methodological limitations include a lack of control for pre-analytical variables, the absence of longitudinal data, and imbalanced group sizes, which may affect the robustness and generalizability of the results.“

Reviewer #2 is correct that this investigational study was not designed to assess the diagnostic potential of gfDNA. Instead, its primary contribution is to provide useful prognostic information. In this regard, we have not yet explored combining gfDNA with other clinically well-established diagnostic biomarkers. We do acknowledge this current limitation as a logical follow-up that must be investigated in the near future.

Moreover, we collected a substantial number of pre-analytical variables within the limitations of a study involving over 1,000 subjects. Longitudinal samples and data were not analyzed here, as our aim was to evaluate prognostic value at diagnosis. Although the groups are imbalanced, this accurately reflects the real-world population of a large endoscopy center within a dedicated cancer facility. Subjects were invited to participate and enter the study before sedation for the diagnostic EGD procedure; thus, samples were collected prospectively from all consenting individuals.

Finally, to maintain a large, unbiased cohort, we did not attempt to balance the groups, allowing analysis of samples and data from all patients with compatible diagnoses (please see Results: Patient groups and diagnoses).

(3) “Additionally, key methodological details were insufficiently reported, and the ROC analysis lacked comprehensive performance metrics, limiting the study's clinical applicability.“

We are grateful for this useful suggestion. In the current version, each ROC curve (Supplementary Figures 1A and 1B) now includes the top 10 gfDNA thresholds, along with their corresponding sensitivity and specificity values (please see Suppl. Table 1). The thresholds are ordered from-best-to-worst based on the classic Youden’s J statistic, as follows:

Youden Index = specificity + sensitivity – 1 [Youden WJ. Index for rating diagnostic tests. Cancer 3:32-35, 1950. PMID: 15405679]. We have made an effort to provide all the key methodological details requested, but we would be glad to add further information upon specific request.

Reviewer #1 (Recommendations for the authors):

The authors should pay attention to ensuring uniformity in the format of all cited references, such as the number of authors for each reference, the journal names, publication years, volume numbers, and page number formats, to the best extent possible. 

Thank you for pointing this inconsistency. All cited references have now been revisited and adjusted properly. We apologize for this clerical oversight.

Reviewer #2 (Recommendations for the authors):

(1) “High gfDNA levels were surprisingly linked to better survival, which conflicts with the conventional understanding of cfDNA as a tumor burden marker. Was any qualitative analysis performed to distinguish DNA derived from immune cells versus tumor cells?“

Tumor-derived DNA is certainly present in gfDNA, as our group has unequivocally demonstrated in a previous publication [Pizzi M. P., et al. (2019) Identification of DNA mutations in gastric washes from gastric adenocarcinoma patients: Possible implications for liquid biopsies and patient follow-up Int J Cancer 145:1090–1097. DOI: 10.1002/ijc.32114]. However, in the present manuscript, our data suggest that gfDNA may also contain DNA derived from infiltrating immune cells. This may also be the case for other malignancies, and qualitative deconvolution studies could provide more informative information. To achieve this, DNA sequencing and RNA-Seq analyses may offer relevant evidence. Our study should be viewed as an original and preliminary analysis that may encourage such quantitative and qualitative studies in biofluids from cancer patients. Currently, this is a simple approach (which might be its essential beauty), but we hope to investigate this aspect further in future studies.

(2) “The ROC curve AUC was 0.66, indicating only moderate discrimination ability. Did the authors consider combining gfDNA with markers such as CEA or CA19-9 to improve diagnostic accuracy?“

This is indeed a logical idea, which shall certainly be explored in planned follow-up studies.

(3) “DNA concentration could be influenced by non-biological factors, including gastric fluid pH, sampling location, time delay, or freeze-thaw cycles. Were these operational variables assessed for their effect on data stability?“

We appreciate the rigor of the evaluation. Yes, information regarding gastric fluid pH was collected. All samples were collected from the stomach during EGD procedure. Samples were divided in aliquots and were thawed only once. This information is now provided in the updated manuscript text.

(4) “This cross-sectional study lacks data on gfDNA changes over time, limiting conclusions on its utility for monitoring treatment response or predicting recurrence.“

Again, temporal evaluation is another excellent point, and it will be the subject of future analyses. In this exploratory study, samples were collected at diagnosis, at a single point. We have not obtained serial samples, as participants received appropriate therapy soon following diagnosis.

(5) The normal endoscopy group included only 10 patients, the precancerous lesion group 99 patients, while the gastritis group had 596 patients. Such uneven sample sizes may affect statistical reliability and generalizability. Has weighted analysis or optimized sampling been considered for future studies?“

Yes, in future studies this analysis will be considered, probably by employing stratified random sampling with relevant patient attributes recorded.

(6) “The SciScore was only 2 points, indicating that key methodological details such as inclusion/exclusion criteria, randomization, sex variables, and power calculation were not clearly described. It is recommended that these basic research elements be supplemented in the Methods section. “

This was an exploratory research, the first of its kind, to evaluate prognostic potential of gfDNA in the context of gastric cancer. Patients were not included if they did not sign the informed consent or excluded if they withdrew after consenting. Other exclusion criteria included diagnoses of conditions such as previous gastrectomy or esophagectomy, or the presence of non-gastric malignancies. Randomization and power analyses were not applicable, as no prior data were available regarding gfDNA concentration values or its diagnostic/prognostic potential. All subjects, regardless of sex, were invited to participate without discrimination or selection.

(7) “Although a ROC curve was provided in the supplementary materials (Supplementary Figure 1), only the curve and AUC value were shown without sensitivity, specificity, predictive values, or cutoff thresholds. The authors are advised to provide a full ROC performance assessment to strengthen the study's clinical relevance.

These data are now given alongside the ROC curves in the Supplementary Information section, specifically in Supplementary Figure 1 and in the newly added Supplementary Table 1.

We thank Reviewer #2 for an insightful and positive overall assessment of our work.

Hoe can z-eef decrease as going down?

The entry \(2\) should here be \(1\).

This should be

$$ \alpha = \lambda_1 y_1 v_1 + \cdots + \lambda_m y_m v_m $$

Notice that \(\beta\) can be found of the binding condition in the primal problem for some \(\lambda_i > 0\).

OBSERVATION: Poinsett explains that despite the amicable relations between the US and the Latin American nations, there was no obligation on the part of the US to interfere in Latin American skirmishes such as the attempted invasion of Cuba by France.

INTERPRETATION: Poinsett is relaying clarifications on the part of the administration at the time, who were not keen on having demands made by other countries whom they had no intention of involving themselves officially with.

CONNECTION: The tertiary source notes that although the Monroe Doctrine made a strong declaration that invasion of any of the Americas by European nations will not be tolerated by the US, the people behind it (particularly Henry Clay) quickly changed their stance on US interference with other nations' affairs, choosing to take a more neutral stance and deny any obligation to help anybody beyond their borders.

CHANGE OVER TIME: The US went from boldly positioning themselves as a guiding figure for other nations and a powerful force against European invasion to recoiling and attempting to distance themselves from foreign affairs.

OBSERVATION: Upon having his authority questioned, Poinsett strongly affirms to Clay that his declaration of the US not to permit Europe to interfere with any Latin American governmental affairs was entirely in reference to declarations made by President Monroe in the Monroe Doctrine.

INTERPRETATION: Only three years after the doctrine was issued, miscommunication occurred between the Monroe administration and personnel such as Poinsett, with Poinsett being chastised simply for informing Mexican diplomats of official statements made by the President.

CONNECTION: The tertiary source stated that the authors behind the Monroe Doctrine began to have cold feet not too long after publishing it due to the unintended implication that Spanish-American territories would receive military aid as a part of efforts to keep Europe out of their hair, and that by 1824 Clay's position was that the US would not concern itself with affairs outside of its own borders. It seems that a side effect of this was a discrepancy in understanding between different levels of authority.

CHANGE OVER TIME: The Monroe administration published the Monroe Doctrine as a bold, authoritative stance on international affairs, reflective of the attitude of the War Hawks who were coming into power at the time, but by 1826, they were beginning to backpedal on their assertions due to a fear of misconception on the part of their neighbors down South.

OBSERVATION: Poinsett explains that the government of Mexico entirely understands the nature of the Monroe Doctrine, which is merely a statement made by the Executive branch and not binding in any way.

INTERPRETATION: The US administration saw Poinsett's statements to the government of Mexico as an affirmation of actions which they weren't intent on performing. As such, Poinsett felt the need to not only clarify that he had zero intent to do so, but also that Mexico was fully cognizant of the unbinding nature of the doctrine.

CONNECTION: The implication that Mexico fully understood the true implications of the Monroe Doctrine seems to contradict the tertiary sources, which state that many South American nations thought they would be receiving aid from the US in order to fend off European invasion, hence why Clay needed to clarify that South American nations would be expected to defend themselves entirely.

COMPLEXITY: This suggests that although much of South America misunderstood the implications of the Monroe Doctrine, some nations such as Mexico fully understood the non-committal nature of what was expressed. It could also suggest CHANGE OVER TIME, as perhaps these countries only came to understand this by the time Poinsett had made his statements to the Mexican plenipotentiaries.

Editors Assessment:

Coded and written up as part of the African Society for Bioinformatics and Computational Biology (ASBCB) Omicscodeathons, EMImR is a novel Shiny application for transcriptomic and epigenomic change identification and correlation wrapped up using a combination of Bioconductor and CRAN packages. Case studies are on publicly available GEO data corresponding to sequencing data of human blood cell samples of multiple sclerosis patients to demonstrate how the tool works. And a documentation and videos are provided. Peer review and the study highlighting the usefulness of the developed tool for analyzing transcriptomic and epigenomic data.

This evaluation refers to version 1 of the preprint

This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.168), and has published the reviews under the same license.

Reviewer 1. Haikuo Li

Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is? No. Should be made more clear.

Comments: The authors developed EMImR as an R toolkit and open-sourced software for analysis of bulk RNA-seq as well as epigenomic sequencing data including DNA methylation seq and non-coding RNA profiling. This work is very interesting and should be of interest to people interested in transcriptomic and epigenomic data analysis but without computational background. I have two major comments: 1. Results presented in this manuscript were only from microarray datasets and are kind of “old” data. Although these data types and sequencing platforms are still very valuable, I don’t think they are widely used as of today, and therefore, it may be less compelling to the audience. It is suggested to validate EMImR using additional more recently published datasets. 2. The authors studied bulk transcriptomic and epigenomic sequencing data. In fact, single-cell and spatially resolved profiling of these modalities are becoming the mainstream of biomedical research since those methods offer much better resolution and biological insights. The authors are encouraged to discuss some key references of this field (for example, PMIDs: 34062119 and 38513647 for single-cell multiomics; PMID: 40119005 for spatial multiomics sequencing), potentially as the future direction of package development. Re-review: The authors have answered my questions and added new content in the Discussion section as suggested.

Reviewer 2. Weiming He

Dear Editor-in-Chief, The EMImR developed by the author is a Shiny application designed for the identification of transcriptomic and epigenomic changes and data association. This program is mainly targeted at Windows UI users who do not possess extensive computational skills. Its core function is to identify the intersections between genetic and epigenetic modifications

Review Recommendation I recommend that after making appropriate revisions to the current “Minor Revision”, the article can be accepted. However, the author needs to address the following issues.

Major Issue The article does not provide specific information on the resource consumption (memory and time) of the program. This is crucial for new users. Although we assume that the resource consumption is minimal, users need to know the machine configuration required to run the program. Therefore, I suggest adding two columns for “Time” and “Memory” in Table 1.

Minor Issues 1. GitHub Page The Table of Contents on the GitHub page provides a Demonstration Video. However, due to restricted access to YouTube in some regions, it is recommended to also upload a manual in PDF format named “EMImR_manual.pdf” on GitHub. In step 4 of the Installation Guide, it states that “All dependencies will be installed automaticly”. It is advisable to add a step: if the installation fails, prompt the user about the specific error location and guide the user to install the dependent packages manually first to ensure successful installation. Currently, the command “source(‘Dependencies_emimr.R’)” does not return any error messages, which is extremely inconvenient for novice users. The author can provide the maintainer's email address so that users can seek timely solutions when encountering problems

1. R Version The author recommends using R - 4.2.1 (2022), which was released three years ago. The current latest version is R 4.5.1. It is suggested that the author test the program with the latest version to ensure its adaptability to future developments.

2. Flowchart Suggestion It is recommended to add a flowchart to illustrate the sequential relationships among packages such as DESeq2 for differential analysis, clusterProfiler for clustering, enrichplot for plotting, and miRNA - related packages (this is optional).

4.Function Addition Currently, the program seems to lack a button for saving PDFs, as well as functions for batch uploading, saving sessions, and one - click exporting of PDF/PNG files. It is recommended to add the “shinysaver” and “downloadHandler” functions to fulfill these requirements.

1. Personalized Features and Upgrade Plan To attract more users, more personalized features should be added. The author can mention the future upgrade plan in the discussion section. For example, currently, DESeq2 is used for differential analysis, and in future upgrades, more methods such as PossionDis, NOIseq, and EBseq could be provided for users to choose from.

2. Text Polishing Suggestions 6.1 Unify the usage of “down - regulated” and “downregulated”, preferably using the latter. 6.2 “R - studio version” ---》 “RStudio” 6.3 Lumian, ---》 Lumian 6.4 no login wall ---》 does not require user registration 6.5 Rewrite “genes were simultaneously differentially expressed and methylated” as “genes that were both differentially expressed and differentially methylated”. 6.6 Ensure that Latin names of species are in italics 6.7 make corresponding modifications to other sentences to improve the accuracy and professionalism of the language in the article.

The above are my detailed review comments on this article. I hope they can provide a reference for your decision - making.

/hyperpost/🌐/🧊/0/

Origo Folder - hyperpost web directory on IPFS

https://k51qzi5uqu5div1auuxm59ygav4p7gdg9z4e9iggtu6m43rmc3xw75mczx2b7x.ipns.dweb.link/📝/?isPathWritable=true&path=/♖/hyperpost/🌐/🧊/0/index.html

cut the middle bit out and have access to the document to read and share

📝/?isPathWritable=true&path=

root of the ipfs indy web

Know your genre when making your concept map

this is a helpful and realistic explanation of the writing process

This section argues that more research is needed to understand effective teaching for historically underserved students, especially studies that include the voices of families and communities. It also encourages teachers to study their own practice, valuing their lived experiences. Overall, it challenges both researchers and educators to rethink what “good teaching” means and to ensure it is accessible to all students.

This text explains that culturally relevant pedagogy goes beyond finding “correct” teaching strategies. Instead, it emphasizes a humanizing approach that values students’ histories, realities, and perspectives. It builds on earlier ideas about pedagogical knowledge (Shulman) and critiques the limits of expert teaching models (Berliner), arguing that teaching must connect deeply to students’ lived experiences.

• Companies aggressively push app downloads, especially in places like Taiwan, offering discounts but often installing without full consent, leading to spam and unwanted data collection.
• Avoid handing over your phone to staff and never download apps, as they provide minimal benefits compared to the risks involved.
• Primary risks include surveillance capitalism: apps enable extensive data tracking for targeted ads and "surveillance pricing," where prices vary based on inferred financial status (e.g., charging more after payday).
• This undermines fair pricing, giving corporations power over individual costs beyond market forces.
• Apps enforce binding arbitration clauses in Terms of Service, waiving rights to court, jury trials, or oversight; examples include Disney attempting to force arbitration in a wrongful death case linked to a Disney+ trial.
• Predictions highlight future abuses, like arbitration forced via unrelated services (e.g., Uber Eats leading to self-driving car disputes).
• Recommendation: Use websites or PWAs instead to preserve privacy and rights.

Hacker News Discussion

• Users debate apps vs. websites/PWAs: many praise PWAs (e.g., Mastodon, Photoprism) for performance when implemented well, criticizing poor web apps and noting apps often wrap webviews with extra tracking.
• Privacy concerns dominate: native apps access more device data (contacts, SMS, biometrics, etc.) even with permissions, unlike sandboxed PWAs; tools like NetGuard suggested for blocking app internet access.
• Loyalty discounts viewed as modern coupons by some, saving money despite data sharing, but others warn of surveillance pricing via purchase patterns and arbitration risks.
• Experiences shared: retailers reject Apple Pay to force accounts; global pushiness for apps noted; arbitrage limits price discrimination viability.
• Calls for better OS controls, open-source apps without tracking, and skepticism of app store security.

Then you could move this to below the research sites sections and rewrite this as the short introductory paragraph to your main methods called "Data-processing and analysis" or something similar. Essentially describe your general workflow and then go into detail in the following sections. You could take a similar approach to this paper, although there may be other examples: https://www.mdpi.com/1999-4907/15/6/1043

It would end with your existing sentences

A slightly reworded version of this may be better at the end of your intro as part of an aims/objectives and methodological approach paragraph (without the figure reference at this stage).

Add how you tagged each tree. E.g., did you tag in the centre of each crown, at the base of the trunk, within a set distance from the trunk?

I would probably add a statement about the final accuracy if you have that data, especially if it was much better than 30cm. E.g., "..confirmed accuracy of <30 cm with the final mean location accuracy being XX ± XX cm (SD).

Maybe add env./weather conditions here and remove the conservation groups unless you need to refer to this later. You can add them all to your acknowledgements. Also maybe add the size of each site.

I think it is important to give an indication/description here of the type of bush you are working in (plant types, size of areas, what the surrounding areas are).

For example, mixed native and give some examples of plants. You could even include an example image/snip of the ortho for each to give people some perspective on the sites. They key message being that it is a complex matrix of different but similar trees, the bush is dense but they relatively small sites nestled in urban areas close to houses. All things you will come back to in your discussion.

For auckland sites, you could use the codes found in the "ecosystem extent" layer on the council GIS and described here: https://knowledgeauckland.org.nz/media/1399/indigenous-terrestrial-and-wetland-ecosystems-of-auckland-web-print-mar-2017.pdf

You would have to look into if there is something equivalent for Hamilton

add: "H1 is unmanaged and the management status of H2 is unknown". If your observations suggest it is also not managed you could add "but assumed to be unmanaged based on field observations."

I would just include this information in the text. If anything, a summary table of the flight parameters etc. per site may be more useful. If this was for a paper, I would probably put the site-based parameters table in an appendix though, so could go either way for the traditional thesis-style

I would split this into two sub-headings "UAV-MSI data capture" and "UAV-LiDAR data capture" or similar names,

Key information to get across: - Drone used - Sensor used (incl. band wavelengths, and maybe the accuracy levels for the LiDAR vertical and horizontal measurements) - Flight parameters for multispec (resolution (height + GSD), overlap, flight speed) - Flight parameters for LiDAR (no. of returns, flight height, speed, overlap), maybe also resolution, Graham might have more thoughts on the LiDAR parameters. LiDAR/MSI pre-processing - You could take the approach of this paper and include the pre-processing steps in these sections too since these steps are not the point of your work - https://www.mdpi.com/1999-4907/15/6/1043

But essentially, you should include anything someone would need to repeat your flights in another location. So maybe also state that all other camera settings were set to default settings. You can always include a summary table in the appendix too if there is too much going on.

Such a phrasing, seem like two independent contradict statements at first but they are just the same thing twice after reading the main text. The RR numbers are not even showed in the main text.

Copied from a previous page

Find the specific name for the chapter, and spell out.

Make sure the font size is consistent on this page.

Spell out. Also, find out the precise name for the chapter..

The darker background makes it really difficult for me to read...use a lighter background if possible, to increase the contrast with the print color.

CHallenges to brics financing systems

important

domination of the US dollar exposes them to western sanctions - because the moeny is ofrced to go through US systems and they can easily block or snactio them

recreating old systems. Their absence undermines te institutions

it clearly defines both the porpuse and the basic structure of this type of essay

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

Manuscript number: RC -2025-03175

Corresponding author(s): Gernot Längst

[Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

• *

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

We thank the reviewers for their efforts and detailed evaluation of our manuscript. We think that the comments of the reviewers allowed us to significantly improve the manuscript.

With best regards

The authors of the manuscript

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary: Holzinger et al. present a new automated pipeline, nucDetective, designed to provide accurate nucleosome positioning, fuzziness, and regularity from MNase-seq data. The pipeline is structured around two main workflows-Profiler and Inspector-and can also be applied to time-series datasets. To demonstrate its utility, the authors re-analyzed a Plasmodium falciparum MNase-seq time-series dataset (Kensche et al., 2016), aiming to show that nucDetective can reliably characterize nucleosomes in challenging AT-rich genomes. By integrating additional datasets (ATAC-seq, RNA-seq, ChIP-seq), they argue that the nucleosome positioning results from their pipeline have biological relevance.

Major Comments:

Despite being a useful pipeline, the authors draw conclusions directly from the pipeline's output without integrating necessary quality controls. Some claims either contradict existing literature or rely on misinterpretation or insufficient statistical support. In some instances, the pipeline output does not align with known aspects of Plasmodium biology. I outline below the key concerns and suggested improvements to strengthen the manuscript and validate the pipeline:

Clarification of +1 Nucleosome Positioning in P. falciparum The authors should acknowledge that +1 nucleosomes have been previously reported in P. falciparum. For example, Kensche et al. (2016) used MNase-seq to map ~2,278 TSSs (based on enriched 5′-end RNA data) and found that the +1 nucleosome is positioned directly over the TSS in most genes:

"Analysis of 2278 start sites uncovered positioning of a +1 nucleosome right over the TSS in almost all analysed regions" (Figure 3A).

They also described a nucleosome-depleted region (NDR) upstream of the TSS, which varies in size, while the +1 nucleosome frequently overlaps the TSS. The authors should nuance their claims accordingly. Nevertheless, I do agree that the +1 positioning in P. falciparum may be fuzzier as compared to yeast or mammals. Moreover, the correlation between +1 nucleosome occupancy and gene expression is often weak and that several genes show similar nucleosome profiles regardless of expression level. This raises my question: did the authors observe any of these patterns in their new data?

We appreciate the reviewer’s insightful comment and agree that +1 nucleosomes and nucleosome depleted promoter regions have been previously reported in P. falciparum, notably by the Bartfai and Le Roch groups, including Kensche et al. (PMID: 26578577). Our study advances this understanding by providing, for the first time, a comprehensive view of the entirety of a canonical eukaryotic promoter architecture in P. falciparum—encompassing the NDR, the well-positioned +1 nucleosome, and the downstream phased nucleosome array. This downstream nucleosome array structure has not been characterized before, as prior studies noted a “lack of downstream nucleosomal arrays” (PMID: 26578577) or “relatively random” nucleosome organization within gene bodies (PMID: 24885191). We have revised the manuscript to more clearly acknowledge previous work and highlight our contributions. The changes we applied in the manuscript are highlighted in yellow and shown as well below.

In the Abstract L26-L230: Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf.

Introduction L156-L159: For example, we identify a phased nucleosome array downstream of the TSS. Together with a well-positioned +1 nucleosome and an upstream nucleosome-free region. These findings support a promoter architecture in Pf that resembles classical eukaryotic promoters (Bunnik et al. 2014, Kensche et al. 2016).

Results L181-L183: These new Pf nucleosome maps reveal a nucleosome organisation at transcription start sites (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a reported well-positioned +1 nucleosome , an upstream nucleosome-free region (NFR, Bunnik et al. 2014, Kensche et al. 2016), and shown for the first time in Pf, a phased nucleosome array downstream of the TSS.

Discussion L414-L419: Previous analyses of Pf chromatin have identified +1 nucleosomes and NFRs (Bunnik et al 2014, Kensche et al. 2016). Here we extend this understanding by demonstrating phased nucleosome array structures throughout the genome. This finding provides evidence for a spatial regulation of nucleosome positioning in Pf, challenging the notion that nucleosome positioning is relatively random in gene bodies (Bunnik et al. 2014, Kensche et al. 2016). Consequently our results contribute to the understanding that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays (Schones et al. 2008; Yuan et al. 2005).

Regarding the reviewer’s question on +1 nucleosome dynamics. Our data agrees with the reviewer and other studies (e.g. PMID: 31694866), that the +1 nucleosome position is robust and does not correlate with gene expression strength. In the manuscript we show that dynamic nucleosomes are preferentially detected at the –1 nucleosome position (Figure 2C). In line with that we show that the +1 nucleosome position does not markedly change during transcription initiation of a subset of late transcribed genes (Figure 5A). However, we observe an opening of the NDR and within the gene body increased fuzziness and decreased nucleosome array regularity (Figure S4A). To illustrate the relationship between the +1 nucleosome positioning and expression strength, we have included a heatmap showing nucleosome occupancy at the TSS, ordered according to expression strength (NEW Figure S4C):

We included a sentence describing the relationship of +1 nucleosome position with gene expression in L257-L258: Furthermore, the +1 nucleosome positioning is unaffected by the strength of gene expression (Figure S2C).

__ Lack of Quality Control in the Pipeline __

The authors claim (lines 152-153) that QC is performed at every stage, but this is not supported by the implementation. On the GitHub page (GitHub - uschwartz/nucDetective), QC steps are only marked at the Profiler stage using standard tools (FastQC, MultiQC). The Inspector stage, which is crucial for validating nucleosome detection, lacks QC entirely. The authors should implement additional steps to assess the quality of nucleosome calls. For example, how are false positives managed? ROC curves should be used to evaluate true positive vs. false positive rates when defining dynamic nucleosomes. How sequencing biases are adressed?

The workflow overview chart on GitHub was not properly color coded. Therefore, we changed the graphics and highlighted the QC steps in the overview charts accordingly:

Based on our long standing expertise of analysing MNase-seq data (PMID: 38959309, PMID: 37641864, PMID: 30496478, PMID: 25608606), the best quality metrics to assess the performance of the challenging MNase experiment are the fragment size distributions revealing the typical nucleosomal DNA lengths and the TSS plots showing a positioned +1 nucleosome and regularly phased nucleosome arrays downstream of the +1 nucleosome. Additionally, visual inspection of the nucleosome profiles in a genome browser is advisable. We make those quality metrics easily available in the nucDetective Profiler workflow (Insertsize Histogram, TSS plot and provide nucleosome profile bigwig files). Furthermore, the PC and correlation analysis based on the nucleosome occupancy in the inspector workflow allows to evaluate replicate reproducibility or integrity of time series data, as shown for data evaluated in this manuscript.

The inspector workflow uses the well-established DANPOS toolkit to call nucleosome positions. Based on our experience, this step is particularly robust and well-established in the DANPOS toolkit (PMID: 23193179), so there is no need to reinvent it. Nevertheless, appropriate pre-processing of the data as done in the nucDetective pipeline is crucial to obtain highly resolved nucleosome positions. Using the final nucleosome profiles (bigwig) and the nucleosome reference positions (bed) as output of the Inspector workflow allows visual inspection of the called nucleosomes in a genome viewer. Furthermore, to avoid using false positive nucleosome positions for dynamic nucleosome analysis, we take only the 20% best positioned nucleosomes of each sample, as determined by the fuzziness score.

We understand the value of a gold standard of dynamic nucleosomes to test performance using ROC curves. However, we are not aware that such a gold standard exists in the nucleosome analysis field, especially not when using multi-sample settings, such as time series data. One alternative would be to use simulated data; however, this has several limitations:

• __Lack of biological complexity: __simulated data often fails to capture the full complexity of biological systems including the heterogeneity, variability, and subtle dependencies present in real-world data. Simplifications and omissions in simulation models can result in test datasets that are more tractable but less realistic, causing software to appear robust or accurate under idealized conditions, while underperforming on actual experimental data.
• __Risks of Overfitting: __Software may be tuned to perform well on simulated datasets leading to overfitting and falsely inflated performance metrics. This undermines the predictive or diagnostic value of the results for real biological data

• Poor Model Fidelity and Hidden Assumptions: The authenticity of simulated data is bounded by the fidelity of the underlying models. If those models are inaccurate or make untested assumptions, the generated data may not reflect real experimental or clinical scenarios. This can mask software shortcomings or bias validation toward specific, perhaps irrelevant, scenarios. Therefore, we decided to validate the performance of the pipeline in the biological context of the analyzed data:

• PCA analysis of the individual nucleosome features shows a cyclic structure as expected for the IDC (Fig. 1D-G).

• Nucleosome occupancy changes anti-correlate with chromatin accessibility (Fig. 3B) as expected.
• Dynamic nucleosome features correlate with expression changes (Fig. 5C) We are aware that MNase-seq experiments might have sequence bias caused by the enzyme's endonuclease sequence preference (PMID: 30496478). However, the main aim of the nucDetective pipeline is to identify dynamic nucleosome features genome wide. Therefore, we are comparing the nucleosome features across multiple samples to find the positions in the genome with the highest variability. Comparisons are performed between the same nucleosome positions at the same genomic sites across multiple conditions, so the sequence context is constant and does not confound the analysis. This is like the differential expression analysis of RNA-seq data, where the gene counts are not normalized by gene length. Introducing a sequence normalization step might distort and bias the results of dynamic nucleosomes.

We included a paragraph describing the limitations to the discussion (L447-457):

Depending on the degree of MNase digestion, preferentially nucleosomes from GC rich regions are revealed in MNase-seq experiments (Schwartz et al. 2019). However, no sequence or gDNA normalisation step was included in the nucDetective pipeline. To identify dynamic nucleosomes, comparisons are performed between the same nucleosome positions at the same genomic sites across multiple samples. Hence, the sequence context is constant and does not confound the analysis. Introducing a sequence normalization step might even distort and bias the results. Nevertheless, it is highly advisable to use low MNase concentrations in chromatin digestions to reduce the sequence bias in nucleosome extractions. This turned out to be a crucial condition to obtain a homogenous nucleosome distribution in the AT-rich intergenic regions of eukaryotic genomes and especially in the AT-rich genome of Pf (Schwartz et al. 2019, Kensche et al. 2016).

__ Use of Mono-nucleosomes Only __

The authors re-analyze the Kensche et al. (2016) dataset using only mono-nucleosomes and claim improved nucleosome profiles, including identification of tandem arrays previously unreported in P. falciparum. Two key issues arise: 1. Is the apparent improvement due simply to focusing on mono-nucleosomes (as implied in lines 342-346)?

The default setting in nucDetective is to use fragment sizes of 140 – 200 bp, which corresponds to the main mono-nucleosome fraction in standard MNase-seq experiments. However, the correct selection of fragment sizes may vary depending on the organism and the variations in MNase-seq protocols. Therefore, the pipeline offers the option of changing the cutoff parameter (--minLen; --maxLen), accordingly. Kensche et al thoroughly tested and established the best parameters for the data set. We agree with their selected parameters and used the same cutoffs (75-175 bp) in this manuscript. For this particular data set, the fragment size selection is not the reason why we obtain a better resolution. MNase-seq analysis is a multistep process which is optimized in the nucDetective pipeline. Differences in the analysis to Kensche et al are at the pre-processing stage and alignment step:

Kensche et al. : “Paired-end reads were clipped to 72 bp and all data was mapped with BWA sample (Version 0.6.2-r126)”

nucDetective:

• Trimming using TrimGalore --paired -q 10 --stringency 2
• Mapping using bowtie2 --very-sensitive –dovetail --no-discordant
• MAPQ >= 20 filtering of aligned read-pairs (samtools). The manuscript text L379 was changed to

This is achieved using MNase-seq optimized alignment settings, and proper selection of the fragment sizes corresponding to mono-nucleosomal DNA to obtain high resolution nucleosome profiles.

How does the pipeline perform with di- or tri-nucleosomes, which are also biologically relevant (Kensche et al., 2016 and others)? Furthermore, the limitation to mono-nucleosomes is only mentioned in the methods, not in the results or discussion, which could mislead readers.

The pipeline is optimized for mono-nucleosome analysis. However, the cutoffs for fragment size selection can be adjusted to analyse other fragment populations in MNase-seq data (--minLen; --maxLen). For example we know from previous studies that the settings in the pipeline could be used for sub-nucleosome analysis as well (PMID: 38959309). Di- or Tri-nucleosome analysis we have not explicitly tested. However, in a previous study (PMID: 30496478) we observed that the inherited MNase sequence bias is more pronounced in di-nucleosomes, which are preferentially isolated from GC-rich regions. This is in line with the depletion of di-nucleosomes in AT-rich intergenic regions in Pf, as was already described by Kensche et al.

Changes to the manuscript text: We included a paragraph describing the limitations to the discussion (L428-434):

The nucDetective pipeline has been optimized for the analysis of mono-nucleosomes. However, the selection of fragment sizes can be adjusted manually, enabling the pipeline to be used for other nucleosome categories. The pipeline is suitable to map and annotate sub-nucleosomal particles (

__ Reference Nucleosome Numbers __

The authors identify 49,999 reference nucleosome positions. How does this compare to previous analyses of similar datasets? This should be explicitly addressed.

We thank the reviewer for this suggestion. In order to put our results in perspective, it is important to distinguish between reference nucleosome positions (what we reported in the manuscript) and all detectable nucleosomes. The reference positions are our attempt to build a set of nucleosome positions with strong evidence, allowing confident further analysis across timepoints. The selection of a well positioned subset of nucleosomes for downstream analysis has been done previously (PMID: 26578577) and the merging algorithm we used across timepoints is also used by DANPOS to decide if a MNase-Seq peak is a new nucleosome position or belongs to an existing position (PMID: 23193179).

To be able to address the reviewer suggestion we prepared and added a table to the supplementary data, including the total number of all nucleosomes detected by our pipeline at each timepoint. We adjusted the results to the following (L223-226):

“The pipeline identified a total of 127370 ± 1151 (mean ± SD) nucleosomes at each timepoint (Supplementary Data X). To exclude false positive positions in our analysis, we conservatively selected 49,999 reference nucleosome positions, representing sites with a well-positioned nucleosome at least at one time point (see Methods). Among these 1192 nucleosomes exhibited […]”

Several groups reported nucleosome positioning data for P. falciparum (PMID: 20015349, PMID: 20054063, PMID: 24885191, PMID: 26578577), however only Ponts et al (2010) reported resolved numbers (~45000-90000 nucleosomes depending in development stage) and Bunnik et al reported ~ 75000 nucleosomes in a graph. Although we do not know the reason of why the other studies did not include specific numbers, we speculate that the data quality did not allow them to confidently report a number. In fact, nucleosomal reads are severely depleted in AT-rich intergenic regions in the Ponts and Bunnik datasets. In contrast, Kensche et al (and our analysis) shows that nucleosomes can be identified throughout the genome of Pf. Therefore, the nucleosome numbers reported by Ponts et al and Bunnik et al are very likely underestimated.

We included the following text in the discussion, addressing previously published datasets (L404 – 405):

“For example, our pipeline was able to identify a total of ~127,000 nucleosomes per timepoint (=5.4 per kb) in range with observed nucleosome densities in other eukaryotes (typically 5 to 6 per kb). From these, we extracted 49,999 reference nucleosome positions with strong positioning evidence across all timepoints, which we used to characterize nucleosome dynamics of Pf longitudinally. Previous studies of P. falciparum chromatin organization, did not report a total number of nucleosomes (Westenberger et al. 2009, Kensche et al. 2016), or estimated approximately ~45000-90000 nucleosomes across the genome at different developmental stages (Bunnik et al. 2014, Ponts et al. 2010). However, this value likely represents an underestimation due to the depletion of nucleosomal reads in AT-rich intergenic regions observed in their datasets.”

__ Figure 1B and Nucleosome Spacing __

The authors claim that Figure 1B shows developmental stage-specific variation in nucleosome spacing. However, only T35 shows a visible upstream change at position 0. In A4, A6, and A8 (Figure S4), no major change is apparent. Statistical tests are needed to validate whether the observed differences are significant and should be described in the figure legends and main text.

We would like to thank the reviewer for bringing this issue to our attention. We apologize for an error we made, wrongly labelling the figure numbers. The differences in nucleosome spacing across time are visible in Figure 1C. Figure 1B shows the precise array structure of the Pf nucleosomes, when centered on the +1 nucleosome, and is mentioned before. The mistake is now corrected.

In Figure 1C the mean NRL and 95% confidence interval are depicted, allowing a visual assessment of data significance (non-overlapping 95% CI-Intervals correspond to p Taken together we corrected this mistake and edited the text as follows (L194 – 199):

“With this +1 nucleosome annotation, regularly spaced nucleosome arrays downstream of the TSS were detected, revealing a precise nucleosome organization in Pf (Figure 1B). Due to the high resolution maps of nucleosomes we can now observe significantvariations in nucleosome spacing depending on the developmental stage (Figure 1C, ANOVA on bootstrapped values (3 per timepoint) F₇,₇₂ = 35.10, p

__ Genome-wide Occupancy Claims __

The claim that nucleosomes are "evenly distributed throughout the genome" (Figure S2A) is questionable. Chromosomes 3 and 11 show strong peaks mid-chromosome, and chromosome 14 shows little to no signal at the ends. This should be discussed. Subtelomeric regions, such as those containing var genes, are known to have unique chromatin features. For instance, Lopez-Rubio et al. (2009) show that subtelomeric regions are enriched for H3K9me3 and HP1, correlating with gene silencing. Should these regions not display different nucleosome distributions? Do you expect the Plasmodium genome (or any genome) to have uniform nucleosome distribution?

On global scale (> 10 kb) we would expect a homogenous distribution of nucleosomes genome wide, regardless of euchromatin or heterochromatin. We have shown this in a previous study for human cells (PMID: 30496478), which was later confirmed for drosophila melongaster (PMID: 31519205,PMID: 30496478) and yeast (PMID: 39587299).

However, Figure S2A shows the distribution of the dynamic nucleosome features during the IDC, called with our pipeline. We agree with the reviewer, that there are a few exceptions of the uniform distribution, which we address now in the manuscript.

Furthermore, we agree with the reviewer that the H3K9me3 / HP1 subtelomeric regions are special. Those regions are depleted of dynamic nucleosomes in the IDC as shown in Fig. 2D and now mentioned in L280 - L282.

We included an additional genome browser snapshot in Supplemental Figure S2B and changed the text accordingly (L245-249):

We observed a few exceptions to the even distribution of the nucleosomes in the center of chromosome 3, 11 and 12, where nucleosome occupancy changes accumulated at centromeric regions (Figure S2B). Furthermore, the ends of the chromosomes are rather depleted of dynamic nucleosome features.

Genome browser snapshot illustrating accumulation of nucleosome occupancy changes at a centromeric site. Centered nucleosome coverage tracks (T5-T40 colored coverage tracks), nucleosomes occupancy changes (yellow bar) and annotated centromers (grey bar) taken from (Hoeijmakers et al., 2012)

Dependence on DANPOS

The authors criticize the DANPOS pipeline for its limitations but use it extensively within nucDetective. This contradiction confuses the reader. Is nucDetective an original pipeline, or a wrapper built on existing tools?

One unique feature of the nucDetective pipeline is to identify dynamic nucleosomes (occupancy, fuzziness, regularity, shifts) in complex experimental designs, such as time series data (Inspector workflow). To our knowledge, there is no other tool for MNase-seq data which allows multi-condition/time-series comparisons (PMID: 35061087). For example, DANPOS allows only pair-wise comparisons, which cannot be used for time-series data. For the analysis of dynamic nucleosome features we require nucleosome profiles and positions at high resolution. For this purpose, several tools do already exist (PMID: 35061087). However, researchers without experience in MNase-seq analysis often find the plethora of available tools overwhelming, which makes it challenging to select the most appropriate ones. Here we share our experience and provide the user an automated workflow (Profiler), which builds on existing tools.

In summary the Profiler workflow is a wrapper built on existing tools and the Inspector workflow is partly a wrapper (uses DANPOS to normalize nucleosome profiles and call nucleosome positions) and implements our original algorithm to detect dynamic nucleosome features in multiple conditions / time-series data.

__ Control Data Usage __

The authors should clarify whether gDNA controls were used throughout the analysis, as done in Kensche et al. (2016). Currently, this is mentioned only in the figure legend for Figure 5, not in the methods or results.

We used the gDNA normalisation to optimize the visualization of the nucleosome depleted region upstream of the TSS in Fig 5A. Otherwise, we did not normalize the data by the gDNA control. The reason is the same as we did not include sequence normalization in the pipeline (see comment above)

We included a paragraph describing the limitations to the discussion (L447-457):

Depending on the degree of MNase digestion, preferentially nucleosomes from GC rich regions are revealed in MNase-seq experiments (Schwartz et al. 2019). However, no sequence or gDNA normalisation step was included in the nucDetective pipeline. To identify dynamic nucleosomes, comparisons are performed between the same nucleosome positions at the same genomic sites across multiple samples. Hence, the sequence context is constant and does not confound the analysis. Introducing a sequence normalization step might even distort and bias the results. Nevertheless, it is highly advisable to use low MNase concentrations in chromatin digestions to reduce the sequence bias in nucleosome extractions. This turned out to be a crucial condition to obtain a homogenous nucleosome distribution in the AT-rich intergenic regions of eukaryotic genomes and especially in the AT-rich genome of Pf (Schwartz et al. 2019, Kensche et al. 2016).

We added following statement to the methods part: Additionally, the TSS profile shown in Figure 5A was normalized by the gDNA control for better NDR visualization.

__ Lack of Statistical Power for Time-Series Analyses __

Although the pipeline is presented as suitable for time-series data, it lacks statistical tools to determine whether differences in nucleosome positioning or fuzziness are significant across conditions. Visual interpretation alone is insufficient. Statistical support is essential for any differential analysis.

We understand the value of statistical support in such an analysis. However, in biology we often face the limitations in terms of the appropriate sample sizes needed to accurately estimate the variance parameters required for statistical modeling. As MNase-seq experiments require a large amount of input material and high sequencing depth, the number of samples in most experiments is low, often with only two replicates (PMID: 23193179). Therefore, we decided that the nucDetective pipeline should be rather handled as a screening method to identify nucleosome features with high variance across all conditions. This prevents misuse of p-values. A common misinterpretation we observed is the use of non-significant p-values to conclude that no biological change exists, despite inadequate statistical power to detect such changes. We included a paragraph in the limitations section discussing the limitations of statistical analysis of MNase-Seq data.

Changes to the manuscript text: We included a paragraph describing the limitations to the discussion (L435-446).

As MNase-seq experiments require a large amount of input material and high sequencing depths, most published MNase-seq experiments do not provide the appropriate sample sizes required to accurately estimate the variance parameters necessary for statistical modelling (Chen et al. 2013). Therefore, dynamic nucleosomes are not identified through statistical testing but rather by ranking nucleosome features according to their variance across all samples and applying a variance threshold to distinguish them. This concept is well established to identify super-enhancers (Whyte et al. 2013). In this study we set the variance cutoff to a slope of 3, resulting in a high data confidence. However, other data sets might require further adjustment of the variance cutoff, depending on data quality or sequencing depth. The nucDetective identification of dynamic nucleosomes can be seen as a screening approach to provide a holistic overview of nucleosome dynamics in the system, which provides a basis for further research.

Reproducibility of Methods

The Methods section is not sufficient to reproduce the results. The GitHub repository lacks the necessary code to generate the paper's figures and focuses on an exemplary yeast dataset. The authors should either: o Update the repository with relevant scripts and examples, o Clearly state the repository's purpose, or o Remove the link entirely. Readers must understand that nucDetective is dedicated to assessing nucleosome fuzziness, occupancy, shift, and regularity dynamics-not downstream analyses presented in the paper.

We thank the reviewer for this helpful comment. In addition to the main nucDetective repository, a second GitHub link is provided in the Data Availability section, which contains the scripts used to generate the figures presented in the paper. This separation was intentional to distinguish the general-purpose nucDetective tool from the project-specific analyses performed for this study. We acknowledge that this may not have been sufficiently clear.

To have all resources available at a single citable permanent location we included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The Zenodo repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

Changes in the manuscript text (L518-519):

The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

__ Supplementary Tables __

Including supplementary tables showing pipeline outputs (e.g., nucleosome scores, heatmaps, TSS extraction) would help readers understand the input-output structure and support figure interpretations.

See comments above.

We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Minor Comments:

The authors should moderate claims such as "no studies have reported a well-positioned +1 nucleosome" in P. falciparum, as this contradicts existing literature. Similarly, avoid statements like "poorly understood chromatin architecture of Pf," which undervalue extensive prior work (e.g., discovery of histone lactylation in Plasmodium, Merrick et al., 2023).

We would like to clarify that we neither wrote that ““no studies have reported a well-positioned +1 nucleosome”” in P. falciparum nor did we intend to imply such thing. However, we acknowledge that our original wording may have been unclear. To address this, we have revised the manuscript to explicitly acknowledge prior studies on chromatin organization and highlight our contribution.

In the Abstract L26-L30: Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf.

Introduction L156-L159: For example, we identify a phased nucleosome array downstream of the TSS. Together with a well-positioned +1 nucleosome and an upstream nucleosome-free region. These findings support a promoter architecture in Pf that resembles classical eukaryotic promoters (Bunnik et al. 2014, Kensche et al. 2016).

Results L180-L183: These new Pf nucleosome maps reveal a nucleosome organisation at transcription start sites (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a reported well-positioned +1 nucleosome , an upstream nucleosome-free region (NFR, Bunnik et al. 2014, Kensche et al. 2016), and shown for the first time in Pf, a phased nucleosome array downstream of the TSS.

Discussion L412-L421: Previous analyses of Pf chromatin have identified +1 nucleosomes and NFRs (Bunnik et al 2014, Kensche et al. 2016). Here we extend this understanding by demonstrating phased nucleosome array structures throughout the genome. This finding provides evidence for a spatial regulation of nucleosome positioning in Pf, challenging the notion that nucleosome positioning is relatively random in gene bodies (Bunnik et al. 2014, Kensche et al. 2016). Consequently our results contribute to the understanding that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays (Schones et al. 2008; Yuan et al. 2005).

The phrase “poorly understood chromatin architecture” has been modified to “underexplored chromatin architecture” in order to more accurately reflect the potential for further analyses and contributions to the field, while avoiding any potential misinterpretation of an attempt to undervalue previous work.

Track labels in figures (e.g., Figure 5B) are too small to be legible.

We made the labels bigger.

Several figures (e.g., Figure 5B, S4B) lack statistical significance tests. Are the differences marked with stars statistically significant or just visually different?

We added statistics to S4B.

Differences in 5B were identified by visual inspection. To clarify this, we exchanged the asterisks to arrows in Fig.5B and changed the text in the legend:

Arrows mark descriptive visual differences in nucleosome occupancy.

Figure S3 includes a small black line on top of the table. Is this an accidental crop?

We checked the figure carefully; however, the black line does not appear in our PDF viewer or on the printed paper

The authors should state the weaknesses and limitations of this pipeline.

We added a limitation section in discussion, see comments above

Reviewer #1 (Significance (Required)):

The proposed pipeline is useful and timely. It can benefit research groups willing to analyse MNase-Seq data of complex genomes such as P. falciparum. The tool requires users to have extensive experience in coding as the authors didn't include any clear and explicit codes on how to start processing the data from raw files. Nevertheless, there are multiple tool that can detect nucleosome occupancy and that are not cited by the authors not mention. I have included for the authors a link where a large list of tools for analysis of nucleosome positioning experiments tools/pipelines were developed for (Software to analyse nucleosome positioning experiments - Gene Regulation - Teif Lab). I think it would be useful for the authors to direct the reference this.

We appreciate the reviewer’s valuable suggestion. We included a citation to the comprehensive database of nucleosome analysis tools curated by the Teif lab (Shtumpf et al., 2022). We chose to reference only selected tools in addition to this resource rather than listing all individual tools to maintain clarity and avoid overloading the manuscript with numerous citations.

Despite valid, I still believe that controlling their pipeline by filtering out false positives and including more QC steps at the Inspector stage is strongly needed. That would boost the significance of this pipeline.

We thank the reviewer for the assessment of our study and for recognizing that our MNase-seq analysis pipeline nucDetective can be a useful tool for the chromatin community utilizing MNase-Seq in complex settings.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Holzinger and colleagues have developed a new pipeline to assess chromatin organization in linear space and time. They used this pipeline to reevaluate nucleosome organization in the malaria parasite, P. falciparum. Their analysis revealed typical arrangement of nucleosomes around the transcriptional start site. Furthermore, it further strengthened and refined the connection between specific nucleosome dynamics and epigenetic marks, transcription factor binding sites or transcriptional activity.

Major comments

• I am wondering what is the main selling point of this manuscript is. If it is the development of the nucDetective pipeline, perhaps it would be best to first benchmark it and directly compare it to existing tools on a dataset where nucleosome fussiness, shifting and regularity has been analyzed before. If on the other hand, new insights into Plasmodium chromatin biology is the primary target validation of some of the novel findings would be advantageous (e.g. refinement of TSS positions, relevance of novel motifs, etc).

NucDetective presents a novel pipeline to identify dynamic nucleosome properties within different datasets, like time series or developmental stages, as analysed for the erythrocytic cycle in this manuscript. As such kind of a pipeline, allowing direct comparisons, does not exist for MNase-Seq data, we used the existing analysis and high quality dataset of Kensche et al., to visualize the strong improvements of this kind of analysis. Accordingly, we combined the pipeline development and the reasearch of chromatin structure analysis, being able to showcase the utility of this new pipeline.

• The authors identify a strong positioning of +1 nucleosome by searching for a positioned nucleosomes in the vicinity of the assigned TSS. Given the ill-defined nature of TSSs, this approach sounds logic at first glance. However, given the rather broad search space from -100 till +300bp, I am wondering whether it is a sort of "self-fulfilling prophecy". Conversely, it would be good to validate that this approach indeed helps to refine TSS positions.

We thank the reviewer for raising this important point. We would like to clarify that we do not claim to redefine or precisely determine TSS positions in our study. Instead, we use annotated TSS coordinates as a reference to identify nucleosomes that correspond to the +1 nucleosome, based on their proximity to the TSS.

We selected the search window from -100 to +300 bp to account for known variability in Pf TSS annotation. For example, dominant transcription start sites identified by 5'UTR-seq tag clusters can differ by several hundred base pairs within a single time point (Chappell et al., 2020). The broad window thus allows us to capture the principal nucleosome positions near a TSS, even when the TSS itself is imprecise or heterogeneous. Based on the TSS centered plots (Figure 2C and Figure S1B), we reasoned that a window of -100 to +300 is sufficient to capture the majority of the +1 nucleosomes, which would have been missed by using smaller window sizes. This strategy aligns with well-established conventions in yeast chromatin biology, where the +1 nucleosome is defined relative to the TSS (Jiang and Pugh, 2009; Zhang et al. 2011) and commonly used as an anchor point to visualize downstream phased nucleosome arrays and upstream nucleosome-depleted regions (Rossi et al., 2021; Oberbeckmann et al., 2019; Krietenstein et al., 2016 and many more). Accordingly, our approach leverages these accepted standards to interpret nucleosome positioning without re-defining TSS annotations.

• Figure 1C: I am wondering how should the reader interpret the changes in nucleosomal repeat length changes throughout the cycle. Is linker DNA on average 10 nucleotides shorter at T30 compared to T5 timepoint? If so how could such "dramatic reorganization" be achieved at the molecular level in absence of a known linker DNA-binding protein. More importantly is this observation supported by additional evidence (e.g. dinucleosomal fragment length) or could it be due to slightly different digestion of the chromatin at the different stages or other technical variables?

We thank the reviewer for this insightful question regarding the interpretation of NRL changes across the cell cycle. The reviewer is right in her or his interpretation – linker DNA is on average ~10 bp shorter at T30 than at T5.

To address concerns about additional evidence and potential MNase digestion variability, we now analyzed MNase-seq fragment sizes by shifting mononucleosome peaks of each time point to the canonical 147 bp length, to correct for MNase digestion differences. After this normalisation, dinucleosome fragment length distributions revealed the shortest linker lengths at T30 and T35, whereas T5 and T10 showed longer DNA linkers. These results confirm our previous NRL measurements based on mononucleosomal read distances while controlling for MNase digestion bias.

The molecular basis of this reorganization, is still unclear. While linker histone H1 is considered absent in Plasmodium falciparum, presence of an uncharacterized linker DNA–binding protein or alternative factors fulfilling a similar role can not be excluded (Gill et al. 2010). However, H1 absence across all developmental stages, fails to explain stage-specific chromatin changes. We hypothesize that Apicomplexans evolved specialized chromatin remodelers to compensate for the missing H1, which may also drive the dynamic NRL changes observed. The low NRL coincides with high transcriptional activity in Pf during trophozoite stage is consistent with previous reports linking elevated transcription to reduced NRL in other eukaryotes (Baldi et al. 2018). In addition, the schizont stage involves multiple rounds of DNA replication requiring large histone supplies being produced during that time. It may well be that a high level of histone synthesis and DNA amplification, results in a short time period with increased nucleosome density and shorter NRL, until the system reaches again equilibrium (Beshnova et al. 2014). Although speculative we suggest a model wherein increased transcription promotes elevated nucleosome turnover and re-assembly by specialized remodeling enzymes, combined with high abundance of histones, resulting in higher nucleosome density and decreased NRL. Unfortunately, absolute quantification of nucleosome levels from this MNase-seq dataset is not possible without spike-in controls, which makes it infeasible to test the hypothesis with the available data set (Chen et al. 2016).

Minor comments

• I am wondering whether fuzziness and occupancy changes are truly independent categories. I am asking as both could lead to reduction of the signal at the nucleosome dyad and because they show markedly similar distribution in relation to the TSS and associate with identical epigenetic features (Figure 2B-D). Figure 2A indicates minimal overlap between them, but this could be due to the fact that the criteria to define these subtypes is defined such to place nucleosomes to one or the other category, but at the end they represent two flavors of the same thing.

Indeed, changes in occupancy and fuzziness can appear related because both features may reduce signal intensity at the nucleosome dyad and both are connected to “poor nucleosome positioning”. However, their definitions and measurements are clearly distinct and technically independent. Occupancy reflects the peak height at the nucleosome dyad, while fuzziness quantifies the spread of reads around the peak, measured as the standard deviation of read positions within each nucleosome peak (Jiang and Pugh, 2009; Chen et al., 2013). Although a reduction in occupancy can contribute to increased fuzziness by diminishing the dyad axis signal, fuzziness primarily arises from increased variability in the flanking regions around the nucleosome position center. While this distinction is established in the field, it is also often confused by the concept of well (high occupancy, low fuzziness) and poorly (high fuzziness, low occupancy) positioned nucleosomes, where both of these features are considered.

• Do the authors detect spatial relationship between fuzzy and repositioned/evicted nucleosomes at the level of individual nucleosomes pairs. With other words, can fuzziness be the consequence of repositioning/eviction of the neighboring nucleosome?

In Figure 2A we analyse the spatial overlap of all features to each other. The analysis clearly shows that fuzziness, occupancy changes and position changes occur mostly at distinct spatial sites (overlaps between 3 and 10%, Fig. 2A). Therefore, we suggest that the features correspond to independent processes. Likewise, we do observe an overlap between occupancy and ATAC-seq peaks, but not nucleosome positioning shifts, clearly discriminating different processes.

• Figure 4: enrichment values and measure of statistical significance for the different motifs are missing. Also have there been any other motifs identified.

This information is present in Supplemental Figure S3. Here we show the top 3 hits in each cluster. In the figure legend of Figure 4 we reference to Fig. S3:

L1054 –1055:

“Additional enriched motifs along with the significance of motif enrichment and the fraction of motifs at the respective nucleosome positions are shown in Figure S3”

• The M&M would benefit from some more details, e.g. settings in the piepline, or which fragment sizes were used to map the MNase-seq data?

We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

Changes in the manuscript (L518-519):

The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

which fragment sizes were used to map the MNase-seq data?

The default setting in nucDetective is to use fragment sizes of 140 – 200 bp, which corresponds to the main mono-nucleosome fraction in standard MNase-seq experiments. However, the correct selection of fragment sizes may vary depending on the organism and the variations in MNase-seq protocols. Therefore, the pipeline offers the option of changing the cutoff parameter (--minLen; --maxLen), accordingly. Kensche et al thoroughly tested the best selection of the fragment sizes for the data set, which is used in this manuscript. We agree with their selection and used the same cutoffs (75-175 bp).

This is stated in line 535-536:

The fragments are further filtered to mono-nucleosome sized fragments (here we used 75 – 175 bp)

We changed the text:

The fragments are further filtered to mono-nucleosome sized fragments (default setting 140-200 bp; changed in this study to 75 – 175 bp)

We highlighted other parameters used in this study in the material and methods part.

Reviewer #2 (Significance (Required)):

Overall, the manuscript is well written and findings are clearly and elegantly presented. The manuscript describes a new pipeline to map and analyze MNase-seq data across different stages or conditions, though the broader applicability of the pipeline and advancements over existing tools could be better demonstrated. Importantly, the manuscript make use of this pipeline to provide a refined and likely more accurate view on (the dynamics of) nucleosome positioning over the AT-rich genome of P. falciparum. While these observations make sense they remain rather descriptive/associative and lack further experimental validation. Overall, this manuscript could be interest to both researchers working on chromatin biology and Plasmodium gene-regulation.

We thank the reviewer for the assessment of our study and for recognizing that the results of our MNase-seq analysis pipeline nucDetective contribute to a better understanding of Pf chromatin biology.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The manuscript "Deciphering chromatin architecture and dynamics in Plasmodium 2 falciparum using the nucDetective pipeline" describes computational analysis of previously published data of P falciparum chromatin. This work corrects the prevailing view that this parasitic organism has an unusually disorganized chromatin organization, which had been attributed to its high genomic AT content, lack of histone H1, and ancient derivation. The authors show that instead P falciparum has a very typical chromatin organization. Part of the refinement is due to aligning data on +1 nucleosome positions instead of TSSs, which have been poorly mapped. The computational tools corral some useful features, for querying epigenomic structure that make visualization straightforward, especially for fuzzy nucleosomes.

Reviewer #3 (Significance (Required)):

As a computational package this is a nice presentation of fairly central questions. The assessment and display of fuzzy nucleosomes is a nice feature.

We thank the reviewer for the assessment of our study and are pleased that the reviewer acknowledges the value and usability of our pipeline.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The manuscript "Deciphering chromatin architecture and dynamics in Plasmodium 2 falciparum using the nucDetective pipeline" describes computational analysis of previously published data of P falciparum chromatin. This work corrects the prevailing view that this parasitic organism has an unusually disorganized chromatin organization, which had been attributed to its high genomic AT content, lack of histone H1, and ancient derivation. The authors show that instead P falciparum has a very typical chromatin organization. Part of the refinement is due to aligning data on +1 nucleosome positions instead of TSSs, which have been poorly mapped. The computational tools corral some useful features, for querying epigenomic structure that make visualization straightforward, especially for fuzzy nucleosomes.

Significance

As a computational package this is a nice presentation of fairly central questions. The assessment and display of fuzzy nucleosomes is a nice feature.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In this manuscript, Holzinger and colleagues have developed a new pipeline to assess chromatin organization in linear space and time. They used this pipeline to reevaluate nucleosome organization in the malaria parasite, P. falciparum. Their analysis revealed typical arrangement of nucleosomes around the transcriptional start site. Furthermore, it further strengthened and refined the connection between specific nucleosome dynamics and epigenetic marks, transcription factor binding sites or transcriptional activity.

Major comments

• I am wondering what is the main selling point of this manuscript is. If it is the development of the nucDetective pipeline, perhaps it would be best to first benchmark it and directly compare it to existing tools on a dataset where nucleosome fussiness, shifting and regularity has been analyzed before. If on the other hand, new insights into Plasmodium chromatin biology is the primary target validation of some of the novel findings would be advantageous (e.g. refinement of TSS positions, relevance of novel motifs, etc).
• The authors identify a strong positioning of +1 nucleosome by searching for a positioned nucleosomes in the vicinity of the assigned TSS. Given the ill-defined nature of TSSs, this approach sounds logic at first glance. However, given the rather broad search space from -100 till +300bp, I am wondering whether it is a sort of "self-fulfilling prophecy". Conversely, it would be good to validate that this approach indeed helps to refine TSS positions.
• Figure 1C: I am wondering how should the reader interpret the changes in nucleosomal repeat length changes throughout the cycle. Is linker DNA on average 10 nucleotides shorter at T30 compared to T5 timepoint? If so how could such "dramatic reorganization" be achieved at the molecular level in absence of a known linker DNA-binding protein. More importantly is this observation supported by additional evidence (e.g. dinucleosomal fragment length) or could it be due to slightly different digestion of the chromatin at the different stages or other technical variables?

Minor comments

• I am wondering whether fuzziness and occupancy changes are truly independent categories. I am asking as both could lead to reduction of the signal at the nucleosome dyad and because they show markedly similar distribution in relation to the TSS and associate with identical epigenetic features (Figure 2B-D). Figure 2A indicates minimal overlap between them, but this could be due to the fact that the criteria to define these subtypes is defined such to place nucleosomes to one or the other category, but at the end they represent two flavors of the same thing.
• Do the authors detect spatial relationship between fuzzy and repositioned/evicted nucleosomes at the level of individual nucleosomes pairs. With other words, can fuzziness be the consequence of repositioning/eviction of the neighboring nucleosome?
• Figure 4: enrichment values and measure of statistical significance for the different motifs are missing. Also have there been any other motifs identified.
• The M&M would benefit from some more details, e.g. settings in the piepline, or which fragment sizes were used to map the MNase-seq data?

Significance

Overall, the manuscript is well written and findings are clearly and elegantly presented. The manuscript describes a new pipeline to map and analyze MNase-seq data across different stages or conditions, though the broader applicability of the pipeline and advancements over existing tools could be better demonstrated. Importantly, the manuscript make use of this pipeline to provide a refined and likely more accurate view on (the dynamics of) nucleosome positioning over the AT-rich genome of P. falciparum. While these observations make sense they remain rather descriptive/associative and lack further experimental validation. Overall, this manuscript could be interest to both researchers working on chromatin biology and Plasmodium gene-regulation.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary:

Holzinger et al. present a new automated pipeline, nucDetective, designed to provide accurate nucleosome positioning, fuzziness, and regularity from MNase-seq data. The pipeline is structured around two main workflows-Profiler and Inspector-and can also be applied to time-series datasets. To demonstrate its utility, the authors re-analyzed a Plasmodium falciparum MNase-seq time-series dataset (Kensche et al., 2016), aiming to show that nucDetective can reliably characterize nucleosomes in challenging AT-rich genomes. By integrating additional datasets (ATAC-seq, RNA-seq, ChIP-seq), they argue that the nucleosome positioning results from their pipeline have biological relevance.

Major Comments:

Despite being a useful pipeline, the authors draw conclusions directly from the pipeline's output without integrating necessary quality controls. Some claims either contradict existing literature or rely on misinterpretation or insufficient statistical support. In some instances, the pipeline output does not align with known aspects of Plasmodium biology. I outline below the key concerns and suggested improvements to strengthen the manuscript and validate the pipeline:

• Clarification of +1 Nucleosome Positioning in P. falciparum The authors should acknowledge that +1 nucleosomes have been previously reported in P. falciparum. For example, Kensche et al. (2016) used MNase-seq to map ~2,278 TSSs (based on enriched 5′-end RNA data) and found that the +1 nucleosome is positioned directly over the TSS in most genes: "Analysis of 2278 start sites uncovered positioning of a +1 nucleosome right over the TSS in almost all analysed regions" (Figure 3A). They also described a nucleosome-depleted region (NDR) upstream of the TSS, which varies in size, while the +1 nucleosome frequently overlaps the TSS. The authors should nuance their claims accordingly. Nevertheless, I do agree that the +1 positioning in P. falciparum may be fuzzier as compared to yeast or mammals. Moreover, the correlation between +1 nucleosome occupancy and gene expression is often weak and that several genes show similar nucleosome profiles regardless of expression level. This raises my question: did the authors observe any of these patterns in their new data?
• Lack of Quality Control in the Pipeline The authors claim (lines 152-153) that QC is performed at every stage, but this is not supported by the implementation. On the GitHub page (GitHub - uschwartz/nucDetective), QC steps are only marked at the Profiler stage using standard tools (FastQC, MultiQC). The Inspector stage, which is crucial for validating nucleosome detection, lacks QC entirely. The authors should implement additional steps to assess the quality of nucleosome calls. For example, how are false positives managed? ROC curves should be used to evaluate true positive vs. false positive rates when defining dynamic nucleosomes. How sequencing biases are addressed?
• Use of Mono-nucleosomes Only The authors re-analyze the Kensche et al. (2016) dataset using only mono-nucleosomes and claim improved nucleosome profiles, including identification of tandem arrays previously unreported in P. falciparum. Two key issues arise:
• Is the apparent improvement due simply to focusing on mono-nucleosomes (as implied in lines 342-346)?
• How does the pipeline perform with di- or tri-nucleosomes, which are also biologically relevant (Kensche et al., 2016 and others)? Furthermore, the limitation to mono-nucleosomes is only mentioned in the methods, not in the results or discussion, which could mislead readers.
• Reference Nucleosome Numbers The authors identify 49,999 reference nucleosome positions. How does this compare to previous analyses of similar datasets? This should be explicitly addressed.
• Figure 1B and Nucleosome Spacing The authors claim that Figure 1B shows developmental stage-specific variation in nucleosome spacing. However, only T35 shows a visible upstream change at position 0. In A4, A6, and A8 (Figure S4), no major change is apparent. Statistical tests are needed to validate whether the observed differences are significant and should be described in the figure legends and main text.
• Genome-wide Occupancy Claims The claim that nucleosomes are "evenly distributed throughout the genome" (Figure S2A) is questionable. Chromosomes 3 and 11 show strong peaks mid-chromosome, and chromosome 14 shows little to no signal at the ends. This should be discussed. Subtelomeric regions, such as those containing var genes, are known to have unique chromatin features. For instance, Lopez-Rubio et al. (2009) show that subtelomeric regions are enriched for H3K9me3 and HP1, correlating with gene silencing. Should these regions not display different nucleosome distributions? Do you expect the Plasmodium genome (or any genome) to have uniform nucleosome distribution?
• Dependence on DANPOS The authors criticize the DANPOS pipeline for its limitations but use it extensively within nucDetective. This contradiction confuses the reader. Is nucDetective an original pipeline, or a wrapper built on existing tools?
• Control Data Usage The authors should clarify whether gDNA controls were used throughout the analysis, as done in Kensche et al. (2016). Currently, this is mentioned only in the figure legend for Figure 5, not in the methods or results.
• Lack of Statistical Power for Time-Series Analyses Although the pipeline is presented as suitable for time-series data, it lacks statistical tools to determine whether differences in nucleosome positioning or fuzziness are significant across conditions. Visual interpretation alone is insufficient. Statistical support is essential for any differential analysis.
• Reproducibility of Methods The Methods section is not sufficient to reproduce the results. The GitHub repository lacks the necessary code to generate the paper's figures and focuses on an exemplary yeast dataset. The authors should either:
  • Update the repository with relevant scripts and examples,
  • Clearly state the repository's purpose, or
  • Remove the link entirely. Readers must understand that nucDetective is dedicated to assessing nucleosome fuzziness, occupancy, shift, and regularity dynamics-not downstream analyses presented in the paper.
• Supplementary Tables Including supplementary tables showing pipeline outputs (e.g., nucleosome scores, heatmaps, TSS extraction) would help readers understand the input-output structure and support figure interpretations.

Minor Comments:

• The authors should moderate claims such as "no studies have reported a well-positioned +1 nucleosome" in P. falciparum, as this contradicts existing literature. Similarly, avoid statements like "poorly understood chromatin architecture of Pf," which undervalue extensive prior work (e.g., discovery of histone lactylation in Plasmodium, Merrick et al., 2023).
• Track labels in figures (e.g., Figure 5B) are too small to be legible.
• Several figures (e.g., Figure 5B, S4B) lack statistical significance tests. Are the differences marked with stars statistically significant or just visually different?
• Figure S3 includes a small black line on top of the table. Is this an accidental crop?
• The authors should state the weaknesses and limitations of this pipeline.

Significance

• The proposed pipeline is useful and timely. It can benefit research groups willing to analyse MNase-Seq data of complex genomes such as P. falciparum. The tool requires users to have extensive experience in coding as the authors didn't include any clear and explicit codes on how to start processing the data from raw files. Nevertheless, there are multiple tool that can detect nucleosome occupancy and that are not cited by the authors not mention. I have included for the authors a link where a large list of tools for analysis of nucleosome positioning experiments tools/pipelines were developed for (Software to analyse nucleosome positioning experiments - Gene Regulation - Teif Lab). I think it would be useful for the authors to direct the reference this.
• Despite valid, I still believe that controlling their pipeline by filtering out false positives and including more QC steps at the Inspector stage is strongly needed. That would boost the significance of this pipeline.

eLife Assessment

This study presents a valuable finding on whether executive resources mediate the impact of language predictability in reading in the context of aging. The evidence is solid in the investigation of prediction in reading, with one caveat that the text materials used could be biased against the aging population. The work will be of interest to cognitive neuroscientists working on reading, language comprehension, and executive control.

Reviewer #1 (Public review):

The authors of this study set out to address a central question in the psycholinguistics literature: does the human brain's ability to predict upcoming language come at a cognitive cost, or is it an automatic, "free" process? To investigate this, they employed a dual-task paradigm where participants read texts word-by-word while simultaneously performing a secondary task (an n-back task on font color) designed to manipulate cognitive load. The study examines how this external cognitive load, along with the effects of aging, modulates the impact of word predictability (measured by surprisal and entropy) on reading times. The central finding is that increased cognitive load diminishes the effects of word predictability, supporting the conclusion that language prediction is a resource-dependent process.

A major strength of the revised manuscript is its comprehensive and parallel analysis of both word surprisal and entropy. The initial submission focused almost exclusively on surprisal, which primarily reflects the cost of integrating a word into its context after it has been perceived. The new analysis now thoroughly investigates entropy as well, which reflects the uncertainty and cognitive effort involved in predicting the next word before it appears. This addition provides a much more complete and theoretically nuanced picture, allowing the authors to address how cognitive load affects both predictive and integrative stages of language processing. This is a significant improvement and substantially increases the paper's contribution to the field.

Furthermore, the authors have commendably addressed the initial concerns regarding the robustness of their replication findings. The first version of the manuscript presented replication results that were inconsistent, particularly for key interaction effects. In the revision, the authors have adopted a more focused and appropriately powered modeling approach for the replication analysis. This revised analysis now demonstrates a consistent effect of cognitive load on the processing of predictable words across both the original and replication datasets. This strengthens the evidence for the paper's primary claim.

The initial review also raised concerns that the results could be explained by general cognitive factors, such as task-switching costs, rather than the specific demands on the language prediction system. While the complexity of cognitive load in a dual-task paradigm remains a challenge, the authors have provided sufficient justification in their revisions and rebuttal to support their interpretation that the observed effects are genuinely tied to the process of language prediction.

Reviewer #2 (Public review):

Summary:

This paper considers the effects of cognitive load (using an n-back task related to font color), predictability, and age on reading times in two experiments. There were main effects of all predictors, but more interesting effects of load and age on predictability. The effect of load is very interesting, but the manipulation of age is problematic, because we don't know what is predictable for different participants (in relation to their age). There are some theoretical concerns about prediction and predictability, and a need to address literature (reading time, visual world, ERP studies).

There is a major concern about the effects of age. See the results (155-190): this depends what is meant by word predictability. It's correct if it means the predictability in the corpus. But it may or may not be correct if it refers to how predictable a word is to an individual participant. The texts are unlikely to be equally predictable to different participants, and in particular to younger vs. older participants, because of their different experience. To put it informally, the newspaper articles may be more geared to the expectations of younger people. But there is also another problem: the LLM may have learned on the basis of language that has largely been produced by young people and so its predictions are based on what young people are likely to say. Both of these possibilities strike me as extremely likely. So it may be that older adults are affected more by words that they find surprising, but it is also possible that the texts are not what they expect, or the LLM predictions from the text are not the ones that they would make. In sum, I am not convinced that the authors can say anything about the effects of age unless they can determine what is predictable for different ages of participants. I suspect that this failure to control is an endemic problem in the literature on aging and language processing and needs to be systematically addressed.

Overall, I think the paper makes enough of a contribution with respect to load to be useful to the literature. But for discussion of age, we would need something like evidence of how younger and older adults would complete these texts (on a word-by-word basis) and that they were equally predictable for different ages. I assume there are ways to get LLMs to emulate different participant groups, but I doubt if we could be confident about their accuracy without a lot of testing. But without something like this, I think making claims about age would be quite misleading.

The authors respond to my summary comment by saying that prediction is individual and that they account for age-related effects in their models. But these aren't my concerns. Rather:

(1) The texts (these edited newspaper articles) could be more predictable for younger than older adults. If so, effects with older adults could simply be because people are less likely to predict less than more predictable words.

(2) The GPT-2 generated surprisal scores may correspond more closely to younger than older adult responses -- that is, its next word predictions may be more younger- than older-adult-like.

In my view, the authors have two choices: they could remove the discussion of age-related effects, or they could try to address BOTH (1) and (2).

As an aside, consider what we would conclude if we drew similar conclusions from a study in which children and adults read the same (children's) texts, but we didn't test what was predictable to each of them separately.

The paper is really strong in other respects and if my concern is not addressed, the conclusions about age might be generally accepted.

Author response:

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

Reviewer #1 (Public review):

This manuscript reports a dual-task experiment intended to test whether language prediction relies on executive resources, using surprisal-based measures of predictability and an n-back task to manipulate cognitive load. While the study addresses a question under debate, the current design and modeling framework fall short of supporting the central claims. Key components of cognitive load, such as task switching, word prediction vs integration, are not adequately modeled. Moreover, the weak consistency in replication undermines the robustness of the reported findings. Below unpacks each point. 

Cognitive load is a broad term. In the present study, it can be at least decomposed into the following components: 

(1)  Working memory (WM) load: news, color, and rank. 

(2)  Task switching load: domain of attention (color vs semantics), sensorimotor rules (c/m vs space).

(3)  Word comprehension load (hypothesized against): prediction, integration. 

The components of task switching load should be directly included in the statistical models. Switching of sensorimotor rules may be captured by the "n-back reaction" (binary) predictor. However, the switching of attended domains and the interaction between domain switching and rule complexity (1-back or 2-back) were not included. The attention control experiment (1) avoided useful statistical variation from the Read Only task, and (2) did not address interactions. More fundamentally, task-switching components should be directly modeled in both performance and full RT models to minimize selection bias. This principle also applies to other confounding factors, such as education level. While missing these important predictors, the current models have an abundance of predictors that are not so well motivated (see later comments). In sum, with the current models, one cannot determine whether the reduced performance or prolonged RT was due to affecting word prediction load (if it exists) or merely affecting the task switching load. 

The entropy and surprisal need to be more clearly interpreted and modeled in the context of the word comprehension process. The entropy concerns the "prediction" part of the word comprehension (before seeing the next word), whereas surprisal concerns the "integration" part as a posterior. This interpretation is similar to the authors writing in the Introduction that "Graded language predictions necessitate the active generation of hypotheses on upcoming words as well as the integration of prediction errors to inform future predictions [1,5]." However, the Results of this study largely ignored entropy (treating it as a fixed effect) and only focus on surprisal without clear justification. 

In Table S3, with original and replicated model fitting results, the only consistent interaction is surprisal x age x cognitive load [2-back vs. Reading Only]. None of the two-way interactions can be replicated. This is puzzling and undermines the robustness of the main claims of this paper. 

Reviewer #2 (Public review):

Summary

This paper considers the effects of cognitive load (using an n-back task related to font color), predictability, and age on reading times in two experiments. There were main effects of all predictors, but more interesting effects of load and age on predictability. The effect of load is very interesting, but the manipulation of age is problematic, because we don't know what is predictable for different participants (in relation to their age). There are some theoretical concerns about prediction and predictability, and a need to address literature (reading time, visual world, ERP studies). 

Strengths/weaknesses 

It is important to be clear that predictability is not the same as prediction. A predictable word is processed faster than an unpredictable word (something that has been known since the 1970/80s), e.g., Rayner, Schwanenfluegel, etc. But this could be due to ease of integration. I think this issue can probably be dealt with by careful writing (see point on line 18 below). To be clear, I do not believe that the effects reported here are due to integration alone (i.e., that nothing happens before the target word), but the evidence for this claim must come from actual demonstrations of prediction. 

The effect of load on the effects of predictability is very interesting (and also, I note that the fairly novel way of assessing load is itself valuable). Assuming that the experiments do measure prediction, it suggests that they are not cost-free, as is sometimes assumed. I think the researchers need to look closely at the visual world literature, most particularly the work of Huettig. (There is an isolated reference to Ito et al., but this is one of a large and highly relevant set of papers.) 

There is a major concern about the effects of age. See the Results (161-5): this depends on what is meant by word predictability. It's correct if it means the predictability in the corpus. But it may or may not be correct if it refers to how predictable a word is to an individual participant. The texts are unlikely to be equally predictable to different participants, and in particular to younger vs. older participants, because of their different experiences. To put it informally, the newspaper articles may be more geared to the expectations of younger people. But there is also another problem: the LLM may have learned on the basis of language that has largely been produced by young people, and so its predictions are based on what young people are likely to say. Both of these possibilities strike me as extremely likely. So it may be that older adults are affected more by words that they find surprising, but it is also possible that the texts are not what they expect, or the LLM predictions from the text are not the ones that they would make. In sum, I am not convinced that the authors can say anything about the effects of age unless they can determine what is predictable for different ages of participants. I suspect that this failure to control is an endemic problem in the literature on aging and language processing and needs to be systematically addressed. 

Overall, I think the paper makes enough of a contribution with respect to load to be useful to the literature. But for discussion of age, we would need something like evidence of how younger and older adults would complete these texts (on a word-by-word basis) and that they were equally predictable for different ages. I assume there are ways to get LLMs to emulate different participant groups, but I doubt that we could be confident about their accuracy without a lot of testing. But without something like this, I think making claims about age would be quite misleading. 

We thank both reviewers for their constructive feedback and for highlighting areas where our theoretical framing and analyses could be clarified and strengthened. We have carefully considered each of the points raised and made substantial additions and revisions.

As a summary, we have directly addressed the concerns raised by the reviewers by incorporating task-switching predictors into the statistical models, paralleling our focus on surprisal with a full analysis and interpretation of entropy, clarifying the robustness (and limitations) of the replicated findings, and addressing potential limitations in our Discussion.

We believe these revisions substantially strengthen the manuscript and improve the reading flow, while also clarifying the scope of our conclusions. We will not illustrate these changes in more detail:

(1) Cognitive load and task-switching components.

We agree that cognitive load is a multifaceted construct, particularly since our secondary task broadly targets executive functioning. In response to Reviewer 1, we therefore examined task-switching demands more closely by adding the interaction term n-back reaction × cognitive load to a model restricted to 1-back and 2-back Dual Task blocks (as there were no n-back reactions in the Reading Only condition). This analysis showed significantly longer reading times in the 2-back than in the 1back condition, both for trials with and without an n-back reaction. Interestingly, the difference between reaction and no-reaction trials was smaller in the 2-back condition (β = -0.132, t(188066.09) = -34.269, p < 0.001), which may simply reflect the general increase in reading time for all trials so that the effect of the button press time decreases in comparison to the 1-back. In that sense, these findings are not unexpected and largely mirror the main effect of cognitive load. Crucially, however, the three-way interaction of cognitive load, age, and surprisal remained robust (β = 0.00004, t(188198.86) = 3.540, p < 0.001), indicating that our effects cannot be explained by differences in taskswitching costs across load conditions. To maintain a streamlined presentation, we opted not to include this supplementary analysis in the manuscript.

(2) Entropy analyses.

Reviewer 1 pointed out that our initial manuscript placed more emphasis on surprisal. In the revised manuscript, we now report a full set of entropy analyses in the supplementary material. In brief, these analyses show that participants generally benefit from lower entropy across cognitive load conditions, with one notable exception: young adults in the Reading Only condition, where higher entropy was associated with faster reading times. We have added these results to the manuscript to provide a more complete picture of the prediction versus integration distinction highlighted in the review (see sections “Control Analysis: Disentangling the Effect of Cognitive Load on Pre- and PostStimulus Predictive Processing” in the Methods and “Disentangling the Effect of Cognitive Load on Pre- and Post-Stimulus Predictive Processing“ in the Results).

(3) Replication consistency.

Reviewer 1 noted that the results of the replication analysis were somewhat puzzling. We take this point seriously and agree that the original model was likely underpowered to detect the effect of interest. To address this, we excluded the higher-level three-way interaction of age, cognitive load, and surprisal, focusing instead on the primary effect examined in this paper: the modulatory influence of cognitive load on surprisal. Using this approach, we observed highly consistent results between the original online subsample and the online replication sample.

(4) Potential age bias in GPT-2.  

We thank Reviewer 2 for their thoughtful and constructive feedback and agree that a potential age bias in GPT-2’s next-token predictions warrants caution. We thus added a section in the Discussion explicitly considering this limitation, and explain why it should not affect the implications of our study.

Reviewer #1 (Recommendations for the authors):

The d-prime model operates at the block level. How many observation goes into the fitting (about 175*8=1050)? How can the degrees of freedom of a certain variable go up to 188435? 

We thank the reviewer for spotting this issue. Indeed, there was an error in our initial calculations, which we have now corrected in the manuscript. Importantly, the correction does not meaningfully affect the results for the analysis of d-primes or the conclusions of the study (see line 102).  

“A linear mixed-effects model revealed n-back performance declined with cognitive load (β = -1.636, t(173.13) = -26.120, p < 0.001), with more pronounced effects with advancing age (β = -0.014, t(169.77) = -3.931, p > 0.001; Fig. 3b, Table S1)”.

Consider spelling out all the "simple coding schemes" explicitly. 

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have now included the modelled contrasts in brackets after each predictor variable.

“Example from line 527: In both models, we included recording location (online vs. lab), cognitive load (1-back and 2back Dual Task vs. Reading Only as the reference level) and continuously measured age (centred) in both models as well as the interaction of age and cognitive load as fixed effects”.

The relationship between comprehension accuracy and strategies for color judgement is unclear or not intuitive. 

We thank the reviewer for this helpful comment. The n-back task, which required participants to judge colours, was administered at the single-trial level, with colours pseudorandomised to prevent any specific colour - or sequence of colours - from occurring more frequently than others. In contrast, comprehension questions were presented at the end of each block, meaning that trial-level stimulus colour was unrelated to accuracy on the block-level comprehension questions. However, we agree that this distinction may not have been entirely clear, and we have now added a brief clarification in the Methods section to address this point (see line 534):  

“Please note that we did not control for trial-level stimulus colour here. The n-back task, which required participants to judge colours, was administered at the single-trial level, with colours pseudorandomised to prevent any specific colour - or sequence of colours - from occurring more frequently than others. In contrast, comprehension questions were presented at the end of each block, meaning that trial-level stimulus colour was unrelated to accuracy on the blocklevel comprehension questions”.

Could you explain why comprehension accuracy is not modeled in the same way as d-prime, i.e., with a similar set of predictors? 

This is a very good point. After each block, participants answered three comprehension questions that were intentionally designed to be easy: they could all be answered correctly after having read the corresponding text, but not by common knowledge alone. The purpose of these questions was primarily to ensure participants paid attention to the texts and to allow exclusion of participants who failed to understand the material even under minimal cognitive load. As comprehension accuracy was modelled at the block level with 3 questions per block, participants could achieve only discrete scores of 0%, 33.3%, 66.7%, or 100%. Most participants showed uniformly high accuracy across blocks, as expected if the comprehension task fulfilled its purpose. However, this limited variance in performance caused convergence issues when fitting a comprehension-accuracy model at the same level of complexity as the d′ model. To model comprehension accuracy nonetheless, we therefore opted for a reduced model complexity in this analysis.

RT of previous word: The motivations described in the Methods, such as post-error-slowing and sequential modulation effects, lack supporting evidence. The actual scope of what this variable may account for is unclear.  

We are happy to elaborate further regarding the inclusion of this predictor. Reading times, like many sequential behavioral measures, exhibit strong autocorrelation (Schuckart et al., 2025, doi: 10.1101/2025.08.19.670092). That is, the reading time of a given word is partially predictable from the reading time of the previous word(s). Such spillover effects can confound attempts to isolate trialspecific cognitive processes. As our primary goal was to model single-word prediction, we explicitly accounted for this autocorrelation by including the log reading time of the preceding trial as a covariate. This approach removes variance attributable to prior behavior, ensuring that the estimated effects reflect the influence of surprisal and cognitive load on the current word, rather than residual effects of preceding trials. We now added this explanation to the manuscript (see line 553):

“Additionally, it is important to consider that reading times, like many sequential behavioural measures, exhibit strong autocorrelation (Schuckart et al., 2025), meaning that the reading time of a given word is partially predictable from the reading time of the previous word. Such spillover effects can confound attempts to isolate trial-specific cognitive processes. As our primary goal was to model single-word prediction, we explicitly accounted for this autocorrelation by including the reading time of the preceding trial as a covariate”.  

Block-level d-prime: It was shown with the d-prime performance model that block-level d-prime is a function of many of the reading-related variables. Therefore, it is not justified to use them here as "a proxy of each participant's working memory capacity."

We thank the reviewer for their comment. We would like to clarify that the d-prime performance model indeed included only dual-task d-primes (i.e., d-primes obtained while participants were simultaneously performing the reading task). In contrast, the predictor in question is based on singletask d-primes, which are derived from the n-back task performed in isolation. While dual- and singletask d-primes may be correlated, they capture different sources of variance, justifying the use of single-task d-primes here as a measure of each participant’s working memory capacity.

Word frequency is entangled with entropy and surprisal. Suggest removal.

We appreciate the reviewer’s comment. While word frequency is correlated with word surprisal, its inclusion does not affect the interpretation of the other predictors and does not introduce any bias. Moreover, it is a theoretically important control variable in reading research. Since we are interested in the effects of surprisal and entropy beyond potential biases through word length and frequency, we believe these are important control variables in our model. Moreover, checks for collinearity confirmed that word frequency was neither strongly correlated with surprisal nor entropy. In this sense, including it is largely pro forma: it neither harms the model nor materially changes the results, but it ensures that the analysis appropriately accounts for a well-established influence on word processing.

Entropy reflects the cognitive load of word prediction. It should be investigated in parallel and with similar depth as surprisal (which reflects the load of integration).

This is an excellent point that warrants further investigation, especially since the previous literature on the effects of entropy on reading time is scarce and somewhat contradictory. We have thus added additional analyses and now report the effects of cognitive load, entropy, and age on reading time (see sections “Disentangling the Effect of Cognitive Load on Pre- and Post-Stimulus Predictive Processing” in the Results, “Control Analysis: Disentangling the Effect of Cognitive Load on Pre- and Post-Stimulus Predictive Processing” in the Methods as well as Fig. S7 and Table S6 in the Supplements for full results). In brief, we observe a significant three-way interaction among age, cognitive load, and entropy. Specifically, while all participants benefit from low entropy under high cognitive load, reflected by shorter reading times, in the baseline condition this benefit is observed only in older adults. Interestingly, in the baseline condition with minimal cognitive load, younger adults even show a benefit from high entropy. Thus, although the overall pattern for entropy partly mirrors that for surprisal – older adults showing increased reading times when word entropy is high and generally greater sensitivity to entropy variations – the effects differ in one important respect. Unlike for surprisal, the detrimental impact of increased word entropy is more pronounced under high cognitive load across all participants.

Reviewer #2 (Recommendations for the authors):

I agree in relation to prediction/load, but I am concerned (actually very concerned) that prediction needs to be assessed with respect to age. I suspect this is one reason why there is so much inconsistency in the effects of age in prediction and, indeed, comprehension more generally. I think the authors should either deal with it appropriately or drop it from the manuscript.

Thank you for raising this important concern. It is true that prediction is a highly individual, complex process as it depends upon the experiences a person has made with language over their lifespan. As such, one-size-fits-all approaches are not sufficient to model predictive processing. In our study, we thus took particular care to ensure that our analyses captured both age-related and other interindividual variability in predictive processing.

First, in our statistical models, we included age not only as a nuisance regressor, but also assessed age-related effects in the interplay of surprisal and cognitive load. By doing so, we explicitly model potential age-related differences in how individuals of different ages predict language under different levels of cognitive load.

Second, we hypothesised that predictive processing might also be influenced by a range of interindividual factors beyond age, including language exposure, cognitive ability, and more transient states such as fatigue. To capture such variability, all models included by-subject random intercepts and slopes, ensuring that unmodelled individual differences were statistically accommodated.

Together, these steps allow us to account for both systematic age-related differences and residual individual variability in predictive processing. We are therefore confident that our findings are not confounded by unmodelled age-related variability.

Line 18, do not confuse prediction (or pre-activation) with predictability. Predictability effects can be due to integration difficulty. See Pickering and Gambi 2018 for discussion. The discussion then focuses on graded parallel predictions, but there is also a literature concerned with the prediction of one word, typically using the "visual world" paradigm (which is barely cited - Reference 60 is an exception). In the next paragraph, I would recommend discussing the N400 literature (particularly Federmeier). There are a number of reading time studies that investigate whether there is a cost to a disconfirmed prediction - often finding no cost (e.g., Frisson, 2017, JML), though there is some controversy and apparent differences between ERP and eye-tracking studies (e.g., Staub). This literature should be addressed. In general, I appreciate the value of a short introduction, but it does seem too focused on neuroscience rather than the very long tradition of behavioural work on prediction and predictability.

We thank the reviewer for this suggestion. In the revised manuscript, we have clarified the relevant section of the introduction to avoid confusion between predictability and predictive processing, thereby improving conceptual clarity (see line 16).

“Instead, linguistic features are thought to be pre-activated broadly rather than following an all-or-nothing principle, as there is evidence for predictive processing even for moderately- or low-restraint contexts (Boston et al., 2008; Roland et al., 2012; Schmitt et al., 2021; Smith & Levy, 2013)”.  

We also appreciate the reviewer’s comment regarding the introduction. While our study is behavioural, we frame it in a neuroscience context because our findings have direct implications for understanding neural mechanisms of predictive processing and cognitive load. We believe that this framing is important for situating our results within the broader literature and highlighting their relevance for future neuroscience research.

I don't think 2 two-word context is enough to get good indicators of predictability. Obviously, almost anything can follow "in the", but the larger context about parrots presumably gives a lot more information. This seems to me to be a serious concern - or am I misinterpreting what was done? 

This is a very important point and we thank the reviewer for raising it. Our goal was to generate word surprisal scores that closely approximate human language predictions. In the manuscript, we report analyses using a 2-word context window, following recommendations by Kuribayashi et al. (2022).

To evaluate the impact of context length, we also tested longer windows of up to 60 words (not reported). While previous work (Goldstein et al., 2022) shows that GPT-2 predictions can become more human-like with longer context windows, we found that in our stimuli – short newspaper articles of only 300 words – surprisal scores from longer contexts were highly correlated with the 2word context, and the overall pattern of results remained unchanged. To illustrate, surprisal scores generated with a 10-word context window and surprisal scores generated with the 2-word context window we used in our analyses correlated with Spearman’s ρ = 0.976.

Additionally, on a more technical note, using longer context windows reduces the number of analysable trials, since surprisal cannot be computed for the first k words of a text with a k-word context window (e.g., a 50-word context would exclude ~17% of the data).  

Importantly, while a short 2-word context window may introduce additional noise in the surprisal estimates, this would only bias effects toward zero, making our analyses conservative rather than inflating them. Critically, the observed effects remain robust despite this conservative estimate, supporting the validity of our findings.

However, we agree that this is a particularly important and sensitive point, and have now added a discussion of it to the manuscript (see line 476).

“Entropy and surprisal scores were estimated using a two-word context window. While short contexts have been shown to enhance GPT-2’s psychometric alignment with human predictions, making next-word predictions more human-like (Kuribayashi et al., 2022), other work suggests that longer contexts can also increase model–human similarity (Goldstein et al., 2022). To reconcile these findings in our stimuli and guide the choice of context length, we tested longer windows and found surprisal scores were highly correlated with the 2-word context (e.g., 10-word vs. 2-word context: Spearman’s ρ = 0.976), with the overall pattern of results unchanged. Additionally, employing longer context windows would have also reduced the number of analysable trials, since surprisal cannot be computed for the first k words of a text with a k-word context window. Crucially, any additional noise introduced by the short context biases effect estimates toward zero, making our analyses conservative rather than inflating them”.

Line 92, task performance, are there interactions? Interactions would fit with the experimental hypotheses. 

Yes, we did include an interaction term of age and cognitive load and found significant effects on nback task performance (d-primes; b = -0.014, t(169.8) = -3.913, p < 0.001), but not on comprehension question accuracy (see table S1 and Fig. S2 in the supplementary material).

Line 149, what were these values?

We found surprisal values ranged between 3.56 and 72.19. We added this information in the manuscript (see line 143).

• Background
• Show off a bit.
• Skills I bring and interests, come and see!

Is energy consumption the best way to measure compute power? What about things like flops per second?

eLife Assessment

M proteins are essential group A streptococci virulence factors that bind to numerous human proteins; a small subset of M proteins, such as M3, have been reported to bind collagen, which is thought to promote tissue adherence. In this important paper, the authors provide a solid characterization of M3 interactions with collagen. The work raises significant questions regarding the specificity of the structure and its interactions with different collagens, with implications for the variable actions of M protein collagen interactions on biofilm formation.

Reviewer #1 (Public review):

Summary:

Wojnowska et al. report structural and functional studies of the interaction of Streptococcus pyogenes M3 protein with collagen. They show through X-ray crystallographic studies that the N-terminal hypervariable region of M3 protein forms a T-like structure, and that the T-like structure binds a three-stranded collagen-mimetic peptide. They indicate that the T-like structure is predicted by AlphaFold3 with moderate confidence level in other M proteins that have sequence similarity to M3 protein and M-like proteins from group C and G streptococci. For some, but not all, of these related M and M-like proteins, AlphaFold3 predicts, with moderate confidence level, complexes similar to the one observed for M3-collagen. Functionally, the authors show that emm3 strains form biofilms with more mass when surfaces are coated with collagen, and this effect can be blocked by an M3 protein fragment that contains the T-structure. They also show the co-occurrence of emm3 strains and collagen in patient biopsies and a skin tissue organoid. Puzzlingly, M1 protein has been reported to bind collagen, but collagen inhibits biofilm in a particular emm1 strain but that same emm1 strain colocalizes with collagen in a patient biopsy sample. The implications of the variable actions of collagen on biofilm formation are not clear.

Strengths:

The paper is well written and the results are presented in a logical fashion.

Weaknesses:

A major limitation of the paper is that it is almost entirely observational and lacks detailed molecular investigation. Insufficient details or controls are provided to establish the robustness of the data.

Comments on revisions:

The authors' response to this reviewer's Major issue #1 is inadequate. Their argument is essentially that if they denature the protein, then there is no activity. This does not address the specificity of the structure or its interactions.

They went only part way to addressing this reviewer's Major issue #2. While Figure 8 - supplement 3 shows 1D NMR spectra for M3 protein (what temperature?), it does not establish that stability is unaltered (to a significant degree).

This reviewer's Major issue #3 is one of the major reasons for considering this study to be observational. This reviewer agrees that structural biology is by its nature observational, but modern standards require validation of structural observations. The authors' response is that a mechanistic investigation involving mutant bacterial strains and validation involving mutated proteins is beyond their scope. Therefore, the study remains observational.

Major issue 4 was addressed suitably, but brings up the problematic point that the emm1 2006 strain colocalizes quite well with collagen in a patient biopsy sample but not in other assays. This calls into question the overall interpretability of the patient biopsy data.

The authors have not provided a point-by-point response. Issues that were indicated to be minor previously were deemed to be minor because this reviewer thought that they could easily be addressed in a revision. It appears that the authors have ignored many of these comments, and these issues are therefore now considered to be major issues. For example, no errors are given for Kd measurements, Table 2 is sloppy and lacks the requested information, negative controls are missing (Figure 10 - figure supplement 1), and there is no indication of how many independent times each experiment was done.

And "C4-binding protein" should be corrected to "C4b-binding protein."

Reviewer #2 (Public review):

Streptococcus pyogenes, or group A streptococci (GAS) can cause diseases ranging skin and mucosal infections, plasma invasion, and post-infection autoimmune syndromes. M proteins are essential GAS virulence factors that include an N-terminal hypervariable region (HVR). M proteins are known to bind to numerous human proteins; a small subset of M proteins were reported to bind collagen, which is thought to promote tissue adherence. In this paper, authors characterize M3 interactions with collagen and its role in biofilm formation. Specifically, they screened different collagen type II and III variants for full-length M3 protein binding using an ELISA-like method, detecting anti-GST antibody signal. By statistical analysis, hydrophobic amino acids and hydroxyproline found to positively support binding, whereas acidic residues and proline negatively impacted binding. The authors applied X-ray crystallography to determine the structure of the N-terminal domain (42-151 amino acids) of M3 protein (M3-NTD). M3-NTD dimmer (PDB 8P6K) forms a T-shaped structure with three helices (H1, H2, H3), which are stabilized by a hydrophobic core, inter-chain salt bridges and hydrogen bonds on H1, H2 helices, and H3 coiled coil. The conserved Gly113 serves as the turning point between H2 and H3. The M3-NTD is co-crystalized with a 24-residue peptide, JDM238, to determine the structure of M3-collagen binding. The structure (PDB 8P6J) shows that two copies of collagen in parallel bind to H1 and H2 of M3-NTD. Among the residues involved binding, conserved Try96 is shown to play a critical role supported by structure and isothermal titration calorimetry (ITC). The authors also apply a crystal-violet assay and fluorescence microscopy to determine that M3 is involved in collagen type I binding, but not M1 or M28. Tissue biopsy staining indicates that M3 strains co-localize with collagen IV-containing tissue, while M1 strains do not. The authors provide generally compelling evidence to show that GAS M3 protein binds to collagen, and plays a critical role in forming biofilms, which contribute to disease pathology. This is a very well-executed study and a well-written report relevant to understanding GAS pathogenesis and approaches to combatting disease; data are also applicable to emerging human pathogen Streptococcus dysgalactiae. One caveat that was not entirely resolved is if/how different collagen types might impact M3 binding and function. Due to the technical constrains, the in vitro structure and other binding assays use type II collagen whereas in vivo, biofilm formation assays and tissue biopsy staining use type I and IV collagen; it was unclear if this difference is significant. One possibility is that M3 has an unbiased binding to all types of collagens, only the distribution of collagens leads to the finding that M3 binds to type IV (basement membrane) and type I (varies of tissue including skin), rather than type II (cartilage).

Comments on revisions:

We are glad to see that the authors addressed our prior comments on M3 binding to different types of collagens in discussion section; adding a prediction of M3 binding to type I collagen (Figure 8-figure supplement 1B and 1C) is helpful to fill in the gap. Although it would be nice to experimentally fill in the gap by putting all types of collagens into one experiment (For example, like Figure 9A, use different types of human collagens to test biofilm formation; or Figure 10, use different types of human collagens to compete for biofilm formation), this appears to be beyond the scope of this paper. Meanwhile, the changes they have made are constructive.

The authors have addressed the majority of our prior comments.

Author response:

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

We thank the reviewers for their comments on the initial submission, which helped us improve and extend the paper. We would like to respond specifically to reviewer #1.

We disagree with the broad criticism of this study as being “almost entirely observational” and lacking “detailed molecular investigation”. We report structures and binding data, show mechanistic detail, identify critical residues and structural features underlying biological activity, and present biologically meaningful data demonstrating a role of the interaction of the M3 protein with collagens. We disagree that insufficient details or controls are included. We agree that our report has limitations, such as an understanding of potential emm1 strain binding to collagen, which might play a role in host tissue colonization, but not in biofilm.

In response to issues raised in the initial review, we conducted several new experiments for the revised manuscript. We believe these strengthen what we report. Firstly, as the reviewer suggested, we conducted a binding experiment where the tertiary fold of M3-NTD was disrupted to confirm the T-shaped fold is indeed required for binding to collagen, as might be expected based on the crystal structure of the complex. To achieve this, we did not, as the reviewer states, use denatured protein in the ITC binding experiment. Instead, we used a monomeric form of M3-NTD, which does not adopt a well-defined tertiary structure, but retains all residues in the context of alpha helices. Secondly, we added more evidence for the importance of structural features (amino acid side chains defining the collagen binding site) by analysing the role of Trp103. Together, we provide clear evidence for the specific role of the T-shaped fold of M3-NTD for collagen binding.

Responding to a constructive criticism by reviewer #1 we characterised M3-NTD mutants to demonstrate conservation of overall structure. NMR is an exquisite tool for this as it is highly sensitive to structural changes. It is not clear why the reviewer suggested we should have measured the stability of the proteins, which is irrelevant here. What matters is that the fold is conserved between mutated variants at the chosen experimental temperature (now added to the Methods section), which NMR demonstrates.

We added errors for the ITC-derived dissociation constants.

In the submitted versions of the paper we did not include the negative control requested by reviewer #1 for experiments shown in Figure 10 - figure supplement 1B. In our view this does not add information supporting our findings. However, we have now added two negative controls, staining of emm1 and emm28 strains. As expected, no reactivity was found with the type-specific M3 HVR antiserum while the M3 BCW antiserum showed weak reactivity, in line with some sequence similarity of the C-terminal regions of M proteins.

Table 2 contains essential information, in line with what generally is shown in crystallographic tables in this journal. All other information can be found in the depositions of our data at the PDB. The structures have been scrutinised and checked by the PDB and passed all quality tests.

We stated how many times experiments were done where appropriate. We now added this information for CLC assays (as given in the previously published protocol, refs. 45, 47). ITC was carried out more than once for optimization but the results of single experiments are shown (as is common practice).

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

Many thanks for assessing our submission. We are grateful for the reviews that have informed a revised version of the paper, which includes additional data and modified text to take into account the reviewers’ comments. 

We addressed the major limitation identified by Reviewer #1 by including data to demonstrate that collagen binding is indeed dependent on the T-shaped fold (major issue 1). Reviewer #1 suggested this needs to be done through extensive mutational work. This in our view was neither feasible nor necessary. Instead, we used ITC to measure collagen peptide binding using a monomeric form of M3, which preserves all residues including the ones involved in binding, but cannot form the T-shaped structure. This achieves the same as unravelling the T fold through mutations, but without the risk of aJecting binding through altering residues that are involved in both binding and definition of the T fold. The experiment shows a very weak interaction, confirming the fold of the M3-NTD is required for binding activity.

Reviewer #1 finds the study limited for being “almost entirely observational”. Structural biology is by its nature observational, which is not a limitation but the very purpose of this approach. Our study goes beyond observing structures. In the first version of our paper, we identified a critical residue within a previously mapped binding site, and demonstrated through mutagenesis a causal link between presence of this residue on a tertiary fold and collagen binding activity. However, we agree this analysis could have been strengthened by additional mutagenesis, which we carried out and describe in the revised manuscript. This identifies a second residue that is critical for collagen binding. We firmed up these mutational experiments with a characterisation of mutated forms of M3 by NMR spectroscopy to confirm that these mutations did not aJect the overall fold, addressing major issue no. 2 of reviewer #1. We further demonstrate that the interaction between M3 and collagen is the cause of greatly enhanced biofilm formation as observed in patient biopsies and a tissue model of infection. We show that other streptococci that do not possess a surface protein presenting collagen binding sites like M3 do not form collagen-dependent biofilm. We therefore do not think that criticising our study for being almost entirely observational is valid. 

Major issue 3:

We agree with the reviewer that it would be useful to carry out experiments with k.o. and complemented strains. Such experiments go beyond the scope of our study, but might be carried out by us or others in the future. We disagree that emm1 is used “as a negative”. Instead, we established that, in contrast to emm3 strains, emm1 strain biofilm formation is not enhanced by collagen. 

We addressed major issue 4 by quantifying colocalizations in the patient biopsies and 3D tissue model experiments.

We thank Reviewer #2 for the thorough analysis of our reported findings. The main criticism here (issue 1) concerns the question of whether binding of emm3 streptococci would diJer to diJerent types of collagen. Our collagen peptide binding assays together with the structural data identify the collagen triple helix as the binding site for M3. While collagen types diJer in their distribution, functions and morphology in diJerent tissues, they all have in common triple-helical (COL) regions with high sequence similarity that are non-specifically recognised by M3. Therefore, our data in conjunction with the body of published work showing binding to M3 to collagens I, II, III and IV suggest it is highly likely that emm3 streptococci will indeed bind to all types of collagen in the same manner. We added a statement to the manuscript to make this point more clearly. We also added a prediction of a complex between M3 and a collagen I triple-helical peptide, which supports the idea of conserved binding mechanism for all collagen types. Whether this means all collagen types in the various tissues where they occur are targeted by emm3 streptococci is a very interesting question, however one that goes beyond the scope of our study.

Minor issues identified by the reviewers were addressed through changes in the text and addition of figures.

Summary of changes:

(1) Two new authors have been added due to inclusion of additional data and analysis.

(2) New experimental data included in section "M3-NTD harbors the collagen binding site".

(3) Figure 3 panels A and B assigned and swapped.

(4) Figure 4 changed to include new data and move mutant M3-NTD ITC graphs to supplement.

(5) Table 2 corrected and amended.

(6) AlphaFold3 quality parameters ipTM and pTM added to all figures showing predicted structures.

(7) New supplementary figure added showing crystal packing of M3-NTD/collagen peptide complex.

(8) Figure supplement of predicted M-protein/collagen peptide complexes includes new panel for a type I collagen peptide bound to M3.

(9) New figure supplement showing mutant M3-NTD ITC data.

(10) New figure supplement showing 1D ¹H NMR spectra of M3-NTD mutants.

(11) Included data for additional M3-NTD mutants assessing role of Trp103 in collagen binding. Text extended to describe and place into context findings from ITC binding studies using these mutants.

(12) Added quantitative analysis of biopsy and tissue model data (Mander's overlap coeJicient).

(13) Corrected and extended table 3 to take into account new primers.

(14) Added experimental details for new NMR and ITC experiments as well as new quantitative image analysis.

(15) Minor adjustments to the text to improve clarity and correct errors.

• Zorin OS 18 has surpassed 1 million downloads in just over five weeks since its release.
• Over 78% of these downloads came from Windows users, reflecting many switching from Windows 10 after its support ended.
• Zorin OS 18 features a redesigned interface merging Windows 11 and MacOS styles, improved Windows software compatibility, and new features like Progressive Web App installer and integrated OneDrive support.
• The OS offers long-term support until 2029, with quality-of-life improvements such as better search in file manager, integrated RDP support, and improved Bluetooth audio via PipeWire.
• The release timing coincides with the end of Windows 10 support and user dissatisfaction with Windows 11, driving increased interest in alternatives like Zorin OS.

• New "Prima" eye implant is a breakthrough for the blind – allows regaining the ability to read.
• Involves implanting a microprocessor under the retina, with patients wearing glasses with a camera that transmits the image to the implant.
• Of 32 people implanted, 27 could read using central vision; after a year, they improved by 5 lines on the vision test chart.
• 70-year-old patient Sheila Irvine, who lost her sight over 30 years ago and has had the implant for 3 years, now solves crosswords.
• Rights to PRIMA acquired in 2024 by U.S. company Science Corporation. (video in source)

Is this really valuable to me?

eLife Assessment

This important study introduces the Life Identification Number (LIN) coding system as a powerful and versatile approach for classifying Neisseria gonorrhoeae lineages. The authors show that LIN codes capture both previously defined lineages and their relationships in a way that aligns with the species' phylogenetic structure. The compelling evidence presented, together with its integration into the PubMLST platform, underscores its strong potential to enhance epidemiological surveillance and advance our understanding of gonococcal population biology.

Reviewer #3 (Public review):

Summary:

In this well-written manuscript, Unitt and colleagues propose a new, hierarchical nomenclature system for the pathogen Neisseria gonorrhoeae. The proposed nomenclature addresses a longstanding problem in N. gonorrhoeae genomics, namely that the highly recombinant population complicates typing schemes based on only a few loci and that previous typing systems, even those based on the core genome, group strains at only one level of genomic divergence without a system for clustering sequence types together. In this work, the authors have revised the core genome MLST scheme for N. gonorrhoeae and devised life identification numbers (LIN) codes to describe the N. gonorrhoeae population structure.

Strengths:

The LIN codes proposed in this manuscript are congruent with previous typing methods for Neisseria gonorrhoeae like cgMLST groups, Ng-STAR, and NG-MAST. Importantly, they improve upon many of these methods as the LIN codes are also congruent with the phylogeny and represent monophyletic lineages/sublineages. Additionally, LIN code cluster assignment is fixed, and clusters are not fused as is common in other typing schemes.

The LIN code assignment has been implemented in PubMLST allowing other researchers to assign LIN codes to new assemblies and put genomes of interest in context with global datasets, including in private datasets.

Weaknesses:

The authors have defined higher resolution thresholds for the LIN code scheme. However, they do not investigate how these levels correspond to previously identified transmission clusters from genomic epidemiology studies. This will be an important focus of future work, but it may be beyond the scope of the current manuscript.

Comments on revisions:

The authors have addressed my previous comments. I have no additional recommendations.

Author response:

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

Reviewer #1 (Public review):

Summary:

Bacterial species that frequently undergo horizontal gene transfer events tend to have genomes that approach linkage equilibrium, making it challenging to analyze population structure and establish the relationships between isolates. To overcome this problem, researchers have established several effective schemes for analyzing N. gonorrhoeae isolates, including MLST and NG-STAR. This report shows that Life Identification Number (LIN) Codes provide for a robust and improved discrimination between different N. gonorrhoeae isolates.

Strengths:

The description of the system is clear, the analysis is convincing, and the comparisons to other methods show the improvements offered by LIN Codes.

Weaknesses:

No major weaknesses were identified by this reviewer.

We thank the reviewer for their assessment of our paper.

Reviewer #2 (Public review):

Summary:

This paper describes a new approach for analyzing genome sequences.

Strengths:

The work was performed with great rigor and provides much greater insights than earlier classification systems.

Weaknesses:

A minor weakness is that the clinical application of LIN coding could be articulated in a more in-depth way. The LIN coding system is very impressive and is certainly superior to other protocols. My recommendation, although not necessary for this paper, is that the authors expand their analysis to noncoding sequences, especially those upstream of open reading frames. In this respect, important cis-acting regulatory mutations that might help to further distinguish strains could be identified.

We thank the reviewer for their comments. LIN code could be applied clinically, for example in the analysis of antibiotic resistant isolates, or to investigate outbreaks associated with a particular lineage. We have updated the text to note this, starting at line 432.

In regards to non-coding sequences: unfortunately, intergenic regions are generally unsuitable for use in typing systems as (i) they are subject to phase variation, which can occlude relationships based on descent; (ii) they are inherently difficult to assemble and therefore can introduce variation due to the sequencing procedure rather than biology. For the type of variant typing that LIN code represents, which aims to replicate phylogenetic clustering, protein encoding sequences are the best choice for convenience, stability, and accuracy. This is not to say that it is not a valid object to base a nomenclature on intergenic regions, which might be especially suitable for predicting some phenotypic characters, but this will still be subject to problem (ii), depending on the sequencing technology used.  Such a nomenclature system should stand beside, rather than be combined with or used in place of, phylogenetic typing. However, we could certainly investigate the relationship between an isolates LIN code and regulatory mutations in the future.

Reviewer #3 (Public review):

Summary:

In this well-written manuscript, Unitt and colleagues propose a new, hierarchical nomenclature system for the pathogen Neisseria gonorrhoeae. The proposed nomenclature addresses a longstanding problem in N. gonorrhoeae genomics, namely that the highly recombinant population complicates typing schemes based on only a few loci and that previous typing systems, even those based on the core genome, group strains at only one level of genomic divergence without a system for clustering sequence types together. In this work, the authors have revised the core genome MLST scheme for N. gonorrhoeae and devised life identification numbers (LIN) codes to describe the N. gonorrhoeae population structure.

Strengths:

The LIN codes proposed in this manuscript are congruent with previous typing methods for Neisseria gonorrhea, like cgMLST groups, Ng-STAR, and NG-MAST. Importantly, they improve upon many of these methods as the LIN codes are also congruent with the phylogeny and represent monophyletic lineages/sublineages.

The LIN code assignment has been implemented in PubMLST, allowing other researchers to assign LIN codes to new assemblies and put genomes of interest in context with global datasets.

Weaknesses:

The authors correctly highlight that cgMLST-based clusters can be fused due n to "intermediate isolates" generated through processes like horizontal gene transfer. However, the LIN codes proposed here are also based on single linkage clustering of cgMLST at multiple levels. It is unclear if future recombination or sequencing of previously unsampled diversity within N. gonorrhoeae merges together higher-level clusters, and if so, how this will impact the stability of the nomenclature.

The authors have defined higher resolution thresholds for the LIN code scheme. However, they do not investigate how these levels correspond to previously identified transmission clusters from genomic epidemiology studies. It would be useful for future users of the scheme to know the relevant LIN code thresholds for these investigations.

We thank the reviewer for their insightful comments. LIN codes do use multi-level single linkage clustering to define the cluster number of isolates. However, unlike previous applications of simple single linkage clustering such as N. gonorrhoeae core genome groups (Harrison et al., 2020), once assigned in LIN code, these cluster numbers are fixed within an unchanging barcode assigned to each isolate. Therefore, the nomenclature is stable, as the addition of new isolates cannot change previously established LIN codes.

Cluster stability was considered during the selection of allelic mismatch thresholds. By choosing thresholds based on natural breaks in population structure (Figure 3), applying clustering statistics such as the silhouette score, and by assessing where cluster stability has been maintained within the previous core genome groups nomenclature, we can have confidence that the thresholds which we have selected will form stable clusters. For example, with core genome groups there has been significant group fusion with clusters formed at a threshold of 400 allelic differences, while clustering at a threshold of 300 allelic differences has remained cohesive over time (supported by a high silhouette score) and so was selected as an important threshold in the gonococcal LIN code. LIN codes have now been applied to >27000 isolates in PubMLST, and the nomenclature has remained effective despite the continual addition of new isolates to this collection. The manuscript emphasises these points at line 96 and 346.

Work is in progress to explore what LIN code thresholds are generally associated with transmission chains. These will likely be the last 7 thresholds (25, 10, 7, 5, 3, 1, and 0 allelic differences), as previous work has suggested that isolates linked by transmission within one year are associated with <14 single nucleotide polymorphism differences (De Silva et al., 2016). The results of this analysis will be described in a future article, currently in preparation.

Harrison, O.B., et al. Neisseria gonorrhoeae Population Genomics: Use of the Gonococcal Core Genome to Improve Surveillance of Antimicrobial Resistance. The Journal of Infectious Diseases 2020.

De Silva, D., et al. Whole-genome sequencing to determine transmission of Neisseria gonorrhoeae: an observational study. The Lancet Infectious Diseases 2016;16(11):1295-1303.

Reviewer #3 (Recommendations for the authors):

(1) Data/code availability: While the genomic data and LIN codes are available in PubMLST and new isolates uploaded to PubMLST can be assigned a LIN code, it is also important to have software version numbers reported in the methods section and code/commands associated with the analysis in this manuscript (e.g. generation of core genome, statistical analysis, comparison with other typing methods) documented in a repository like GitHub.

Software version numbers have been added to the manuscript. Scripts used to run the software have been compiled and documented on protocols.io, DOI: dx.doi.org/10.17504/protocols.io.4r3l21beqg1y/v1

(2) Line 37: Missing "a" before "multi-drug resistant pathogen".

This has been corrected in the text.

(3) Line 60: Typo in geoBURST.

The text refers to a tool called goeBURST (global optimal eBURST) as described in Francisco, A.P. et al., 2009. DOI: 10.1186/1471-2105-10-152. Therefore, “geoBURST” would be incorrect.

(4) Line 136-138: It might be helpful to discuss how premature stop codons are treated in this scheme. Often in isolates with alleles containing early premature stop codons, annotation software like prokka will annotate two separate ORFs, which are then clustered with pangenome software like PIRATE. How does the cgMLST scheme proposed here treat premature stop codons? Are sequences truncated at the first stop codon, or is the nucleotide sequence for the entire gene used even if it is out of frame?

In PubMLST, alleles with premature stop codons are flagged, but otherwise annotated from the typical start to the usual stop codon, if still present. This also applies to frameshift mutations – a new unique allele will be annotated, but flagged as frameshift. In both cases, each new allele with a premature stop codon or frameshift will require human curator involvement to be assigned, to ensure rigorous allele assignment. As the Ng cgMLST v2 scheme prioritised readily auto-annotated genes, loci which are prone to internal stop codons or frameshifts with inconsistent start/end codons are excluded from the scheme. The text has been updated at line 128 to mention this.

(5) Line 213-214: What were the versions of software and parameters used for phylogenetic tree construction?

Version numbers have been added to the text between lines 214-219. Parameters have been included with the scripts documented at protocols.io DOI: dx.doi.org/10.17504/protocols.io.4r3l21beqg1y/v1

(6) Line 249: K. pneumoniae may also be a more diverse/older species than N. gonorrhoeae.

The text has been updated at line 252-253 to emphasize the difference in diversity. The age of N. gonorrhoeae as a species is a matter of scientific debate, and out of the scope of this paper to discuss.

(7) Line 278-279: Were some isolates unable to be typed, or have they just been added since the LIN code assignment occurred?

Some genomes cannot be assigned a LIN code due to poor genome quality. A minimum of 1405/1430 core genes must have an allele designated for a LIN code to be assigned. Genomes with large numbers of contigs may not meet this requirement. LIN code assignment is an ongoing process that occurs on a weekly basis in PubMLST, performed in batches starting at 23:00 (UK local time) on Sundays. The text has been updated to describe this at lines 196 and 282-283.

(8) Line 314-315: Was BAPS rerun on the dataset used in this manuscript, or is this based on previously assigned BAPS groups?

This was based on previously assigned BAPs groups, as described between lines 315-320.

(9) Line 421-423: Are there options for assigning LIN codes that do not require uploading genomes to PubMLST? I can imagine that there may be situations where researchers or public health institutions cannot share genomic data prior to publication.

Isolate data does not need to be shared to be uploaded and assigned a LIN code in PubMLST. data owners can create a private dataset within PubMLST viewable only to them, on which automated assignment will be performed. LIN code requires a central repository of genomes for new codes to be assigned in relation to. The text has been updated to emphasize this at line 197 and 427.

(10) Figure 6: How is this tree rooted? Additionally, do isolates that have unannotated LIN codes represent uncommon LIN codes or were those isolates not typed?

The tree has been left unrooted, as it is being used to visualise the relationships between the isolates rather than to explore ancestry. Detail on what LIN codes have been annotated can be found in the figure legend, which describes that the 21 most common LIN code lineages in this 1000 isolate dataset have been labelled. All 1000 isolates used in the tree had a LIN code assigned, but to ensure good legibility not all lineages were annotated on the tree. The legend has been updated to improve clarity.

Amend the meta description to the condensed 150 characters

GFS simplifies last-mile delivery with smart tech, proactive support and multi-carrier choice, giving retailers seamless, scalable UK and international delivery.

We would add a section that relates directly to the technology as there is lots of search keywords about last mile software, solutions, platforms

We would advise a section after here that speaks directly to what the services are (this is very common on the top ranking pages)

It may mean we want to split this section into what you do and why choose you

This would be good if you could cite the customer name and company as text below the quote

We would move this below the benefits section

Can we highlights a couple of the blogs or resources related to this page to stay consistent with the rest of the website landing pages

The word count on this page is very low (under 400) - we would advise looking to double that word count