In the paragraph about inflation, why did government spending increase prices but not production in the 1970s, unlike in the 195?

the nurse is reminiscing about juliets childhood and how close they were

benvolio tells romeo t goo to the capulets feast to look at others by then would roosaline be less perfect romeo refuses but agrees to go to observe her beauty

benvolio advises romeo to stop the unrequited love for that woman romeo refuses saying hes still obsessed a servant wiith the list of people attending enters struggling to read the list romeo offers to help

paris ask for capulet permission to marry juliet but was denied bc juliet is too young and juliets consent matters too so he invites paris to a feast where juliet and other young girls would be present

benvolio ask romeo why he is so sad all the time for romeo to reveal that bc he is in love with a woman that doesnt love him back and benvolio try to cheer romeo up by saying there are other fishes in the sea and romeo said that the other fishes only remind him of the woman

the conversation shift too romeo and lady montaque is asking benvolioo where he is and is explained by montaque that he is always withdrawn and sad

benvolio shows up to stop the fight but tybalt is looking for a fight so they engage in battle pulling the public into it

abraham confronts sampson for insulting the montaque and sampson dodges the question so abraham challenges sampson to a fight

the two servants samson and Gregory they are talking and joking about fighting the moontague and how they will violate the women

Romeo finally walks in but Benvolio notices Romeo’s gloom and tries to find out what’s wrong.

Before going any further, My hypothesis is that Romeo's reason for feeling down has something to do with love.

I wonder what problem's Romeo is facing that makes him want to isolate himself.

Benvolio explains that the servants of both houses started fighting first. He tried to stop them, but then Tybalt being the hot head he is, came and attacked him he later gets asked by Lady Montague if he was involved in the fight which he wasnt

Montague is asking who started the fight

Why doesnt Tybalt like peace?

Proving he's the "stronger" or "better" servant does nothing for Sampson as the people he's trying to compete against fall in the same category as him already. Us human's tend to seek completion with the urge to prove ourselves when there is no need to.

Sampson wants a fight but would prefer the other side to start it. I'm assuming "biting my thumb" is a form of insult used back then and he will do this to get the reaction he wants out of the other servants.

Sampson is purposely being aggressive with the other servants with the intention to get under their skin forcing them to make a wrong move to where his reaction wont make him be in the wrong.

This passage marks the point where Tom’s separation from his family becomes permanent and unavoidable. Before this moment, the pain of his sale is emotional and anticipatory, but when Haley places shackles on Tom’s ankles, that loss becomes physical and public. The chains are unnecessary for control, as Mrs. Shelby points out, but they are used to assert ownership and authority in front of Tom’s family and the surrounding community. Stowe underscores the cruelty of this act through the crowd’s reaction, showing that even people accustomed to slavery understand the injustice of chaining a man who has shown no resistance. Tom’s calm submission further emphasizes the imbalance of power and reveals how slavery punishes obedience and goodness rather than wrongdoing.

Mrs. Bird doesn’t let John hide behind “politics.” She basically says: it’s easy to defend this law when it’s just an idea, but what would you actually do if a freezing, hungry runaway showed up at your door? John can talk about the “greater good” in theory, but Mary drags it into real life, where it’s suddenly obvious how cruel it is. Stowe is showing that these arguments only work when you don’t have to look the suffering person in the face.

Haley’s wording shows how slavery turns morality into something that can be measured by usefulness. Religion is not respected as faith or belief, but treated as a trait that makes an enslaved person more reliable and more profitable. By calling religion “valuable,” Haley reveals how even spiritual life is absorbed into the logic of the market.

Such a powerful scene! One meant to represent the sheer force of will that a good mother contains, the lengths at which she would go to save her own child! Barefoot, blood staining the white ice underneath her feet. Mothers know pain, they know blood, and in Eliza's case the force of her desperation and determination would be rather reflective of all those like her during that time. To show how fiercely she loved her child, the same as any other mother of any skin color. She bleeds red, just as a white woman would. This scene stands strongly as a means of showing Eliza's bravery and humanity, and the image of her leaping from ice sheet to ice sheet is a profound one.

This is a really important narrative moment. It not only portrays Tom's devotion to his owner, but is resistance to leave the life that is in the now to the life that isn't guaranteed. In a more broad sense it portrays slavery and the harshness of it. It's the only life he's ever sadly known.

I find the neutrality of Mr. Shelby interesting, the way his lack of religion is used almost as an illusion to his inability to stand firm and protect those under his care. It makes note that he respects the beliefs of his wife certainly, but in the end that respect means nothing when backed into a corner, and religion doesn't play a part in his decisions as it does others.

Mrs. Shelby wants to keep Tom with her, vowing to bring him back to be with her and her family. She makes this promise on her religion, which not only shows her love for Tom but also how religious she is.

Mr. Shelby’s claim that he is a “humane man” is immediately undercut by the reality of the situation: he is considering selling a child into slavery. Stowe uses this moment to expose the moral contradiction at the heart of “kind” slave holders—those who see themselves as compassionate while continuing to participate in an inhumane system. The word "thoughtfully" emphasizes his self-image as moral, even as his actions betray that belief. He may be more “humane” than other slave holders, but the word lacks meaning in this situation. It is simply a word to make himself feel better.

Stowe utilizes character Mrs. Shelby to portray the recognizing towards organized Christianity in which it often defends slavery rather than eliminate it, exposing the hypocrisy and confessional complacency of not reforming and continue to partake in slave ownership.

Haley seems to think that Mr.Shelby's kindness is fake. This could be because Mr. Shelby still partakes in owning slaves or because Haley thinks slaves should have crueler treatment. I think this line is meant to have double meaning.

Mrs. Shelby tries to say that she is a good christian because she cares for them but she still participates in the system and only cares when it is Eliza's son being sold.

Eliza has braved everything that is happening to her, but it dulls when she is rushing to save her son. When it comes to a mother protecting her child, adrenaline takes over and any pain or cold is nothing compared to finding safety.

This moment is so casual it’s kind of unsettling. Eliza is clearly exhausted and distressed, but it’s treated as something small and easily brushed aside. Her fear exists quietly in the background while the Shelbys stay comfortable.

This is where Stowe basically says the idea of “kind” slavery is fake. Even if a master seems nice, it doesn’t matter, because the law still treats enslaved people like property. That “shadow of law” means their lives can flip at any moment, no matter how safe things look.

This line is quite telling of the hate and prejudice that enslaved people experienced, instead of celebrating him or just allowing him to create more inventions, instead he starts to abuse him out of jealousy because he views him as inferior. So the boss does what is allowed under this system which is hurt him.

This passage demonstrates the extent and the length of what a mother will do to protect her child. Stowe's use of specific imagery illustrates motherhood and this particularly appeals to her 19th century audience who are mothers. The imagery shown in this passage goes beyond just a mother and son but beyond slavery and race.

This plays into the bigger idea that has been noted over and over again throughout the module but also just in general. That you have these so called moral people that are intelligent but also being slave owners. The dichotomy of this is quite strange but something people back then critique but the hypocrisy in it.

A reference to the earlier "Jordan's Banks"; during the Israelites' exodus out of Egypt, they had to cross the Jordan river during a high tide, about 3,000 feet. But when the priests crossed with the ark, the river stopped up behind them, so that the Israelites walked on dry ground (ref. Joshua 3:15-17).

Does not appear to be a real hymn, but shares lyrics and themes with "I'm Going Home to Glory". This hymn holds less merit in a religious sense and is moreso a representation of African American oral tradition.

reply to u/CaliKelli989 at https://old.reddit.com/r/typewriters/comments/1qx43wy/smith_corona_classic_12_for_75worth_it_for_my/ on signaling by online typewriter sales

Where you're selling is one of the biggest signals of all. Selling machines for over $250 on Facebook requires way more signaling on the part of a professional or semi-professional seller. Mr&Mrs are doing a whole lot more work on restoring their machines than the average "blow and go" level that Janet and her significant other are likely doing (or that done by the average shop), as a result they're doing more work to show that, but they're occupying a dramatically different market space. Who is offering warranties on their work? Who is recovering platens? Who is explicitly stating the quality of the rest of their rubber? (Note that Janet isn't saying anything about the rubber washers on her SM3s, nor did they say anything about the rubber feet or the feet on the cases. Were they all replaced?)

Most professional shops and restorers are selling via their physical shops or their own websites instead of eBay, which takes steep cuts, or FB where it's harder for their much better quality machines to stand out amidst similarly priced dirtier machines. (Most pros also refuse or prefer not to ship when they can avoid it, so online presence doesn't "buy" them much.) There's a huge gulf in the levels of work that Walid Saad or Lucas Dul are offering in complete tear downs and restorations and the simple clean, oil, and adjust operations that are being offered by average pro shops and that's different again from what I suspect Janet is probably offering. This doesn't even get into the space of the lowest level "flippers" and vintage/antique shops whose only value add is finding and offering machines. As a point of reference, Lucas is doing less than a full restoration a month in an average year. The rest is cleaning machines for straight sale and then repairs that walk in the door. I'd suspect that he doesn't have more than a dozen machines in stock that are ready for sale today compared to a multi-person operation like Typewriter Muse which has nearly 30 machines on the shelf ready to go.

There's a huge spectrum in the level of restorations being offered out there. Very few people appreciate any of the differences.

The issue is that many people starting out don't want to pay a lot for a clean/restored machine, so they're fine with something that "works". Generally they don't know what they're missing from a finely tuned machine. At the other end are serious collectors, who often have the knowledge and expertise to service their own machines. The biggest issue with the market is the huge gulf of information imbalance between the novice buyers and novice sellers and the professionals.

Hope this helps on the differentiation that's available out there...

This sentence is difficult to wrap my head around. I agree with rehabilitation, but having them engage in an additional crime to move on from a different one does not pose a solution.

I do not think that a "craving" for pedophilic behavior and drugs is comparable. I do agree that the root of pedophilic behavior is biological, but that does not make it ok to engage in sexual acts involving children, that includes pornography.

In the last decade(s) we have had a lot of rights of these people vs rights of these people but little focus on firmness and efficiency?

Relatable and Yes, our choices will change the fate of humanity, but also they were already influenced by the choices made here in this doc. and so on. Free will and not. Important but only in combination with other people.

Builds up quick

I think I am understanding the reason behind why a substance can only be gas or solid at low temperatures but I wanted to confirm with you. Due to the low temperatures they cant move or interact as normal so their particles are reliant on the pressure which is either pulling them apart with a vacuum so drastically that they become a gas or putting them under so much pressure that they get forced together to take up ass little room as possible.

I noticed multiple references to these newly discovered extant proteins in an early diverging spider lineage as "ancestral proteins". I would steer clear from that language. Ancestral proteins implies that these proteins are no longer extant, and that they precede the existence of modern proteins. These are usually lost to time, but the term is reserved for proteins created with ancestral sequence reconstruction or archeologically recovered DNA. I would instead refer to these proteins as early-diverging, or belonging to spiders from an early-diverging lineage of Araneae.

I noticed that red arrows are mentioned in the figure, but I am not seeing those arrows in the figure itself. It might work to simply highlight the name of the taxon in red instead.

eLife Assessment

Studying the biological roles of polyphosphates in metazoans has been a longstanding challenge to the field given that the polyP synthase has yet to be discovered in metazoans. This important study capitalizes on the sophisticated genetics available in the Drosophila system and uses a combination of methodologies to start to tease apart how polyphosphate participates in Drosophila development and in the clotting of Drosophila hemolymph. The data validating one of these tools (cyto-FLYX ) are solid and well-documented and they will open up a field of research into the functional roles of polyP in a metazoan model. The other tools for tissue specific knockdown of polyP (Mito-FLYX, ER-FLYX, and Nuc-FLYX) have not yet been validated but will be invaluable to the field when they are.

Reviewer #2 (Public review):

Summary:

The authors of this paper note that although polyphosphate (polyP) is found throughout biology, the biological roles of polyP have been under-explored, especially in multicellular organisms. The authors created transgenic Drosophila that expressed a yeast enzyme that degrades polyP, targeting the enzyme to different subcellular compartments (cytosol, mitochondria, ER, and nucleus, terming these altered flies Cyto-FLYX, Mito-FLYX, etc.). The authors show the localization of polyP in various wild-type fruit fly cell types and demonstrate that the targeting vectors did indeed result in expression of the polyP degrading enzyme in the cells of the flies. They then go on to examine the effects of polyP depletion using just one of these targeting systems (the Cyto-FLYX). The primary findings from depletion of cytosolic polyP levels in these flies is that it accelerates eclosion and also appears to participate in hemolymph clotting. Perhaps surprisingly, the flies seemed otherwise healthy and appeared to have little other noticeable defects. The authors use transcriptomics to try to identify pathways altered by the cyto-FLYX construct degrading cytosolic polyP, and it seems likely that their findings in this regard will provide avenues for future investigation. And finally, although the authors found that eclosion is accelerated in pupae of Drosophila expressing the Cyto-FLYX construct, the reason why this happens remains unexplained.

Strengths:

The authors capitalize on the work of other investigators who had previously shown that expression of recombinant yeast exopolyphosphatase could be targeted to specific subcellular compartments to locally deplete polyP, and they also use a recombinant polyP binding protein (PPBD) developed by others to localize polyP. They combine this with the considerable power of Drosophila genetics to explore the roles of polyP by depleting it in specific compartments and cell types to tease out novel biological roles for polyP in a whole organism. This is a substantial advance.

Weaknesses:

Page 4 of Results (paragraph 1): I'm a bit concerned about the specificity of PPBD as a probe for polyP. The authors show that the fusion partner (GST) isn't responsible for the signal, but I don't think they directly demonstrate that PPBD is binding only to polyP. Could it also bind to other anionic substances? A useful control might be to digest the permeabilized cells and tissues with polyphosphatase prior to PPBD staining, and show that the staining is lost.

In the hemolymph clotting experiments, the authors collected 2 ul of hemolymph and then added 1 ul of their test substance (water or a polyP solution). They state that they added either 0.8 or 1.6 nmol polyP in these experiments (the description in the Results differs from that of the Methods). I calculate this will give a polyP concentration of 0.3 or 0.6 mM. This is an extraordinarily high polyP concentration, and is much in excess of the polyP concentrations used in most of the experiments testing the effects of polyP on clotting of mammalian plasma. Why did the authors choose this high polyP concentration? Did they try lower concentrations? It seems possible that too high a polyP concentration would actually have less clotting activity than the optimal polyP concentration.

In the revised version of the manuscript, the authors have productively responded to the previous criticisms. Their new data show stronger controls regarding the specificity of PPBD with regard to its interaction with polyP. The authors also have repeated their hemolymph clotting experiments with lower polyP concentrations, which are likely to be more physiological.

Reviewer #3 (Public review):

Summary:

Sarkar, Bhandari, Jaiswal and colleagues establish a suite of quantitative and genetic tools to use Drosophila melanogaster as a model metazoan organism to study polyphosphate (polyP) biology. By adapting biochemical approaches for use in D. melanogaster, they identify a window of increased polyP levels during development. Using genetic tools, they find that depleting polyP from the cytoplasm alters the timing of metamorphosis, accelerationg eclosion. By adapting subcellular imaging approaches for D. melanogaster, they observe polyP in the nucleolus of several cell types. They further demonstrate that polyP localizes to cytoplasmic puncta in hemocytes, and further that depleting polyP from the cytoplasm of hemocytes impairs hemolymph clotting. Together, these findings establish D. melanogaster as a tractable system for advancing our understanding of polyP in metazoans.

Strengths:

• The FLYX system, combining cell type and compartment-specific expression of ScPpx1, provides a powerful tool for the polyP community.

• The finding that cytoplasmic polyP levels change during development and affect the timing of metamorphosis is an exciting first step in understanding the role of polyP in metazoan development, and possible polyP-related diseases.

• Given the significant existing body of work implicating polyP in the human blood clotting cascade, this study provides compelling evidence that polyP has an ancient role in clotting in metazoans.

Limitations:

• While the authors demonstrate that HA-ScPpx1 protein localizes to the target organelles in the various FLYX constructs, the capacity of these constructs to deplete polyP from the different cellular compartments is not shown. This is an important control to both demonstrate that the GTS-PPBD labeling protocol works, and also to establish the efficacy of compartment-specific depletion. While not necessary to do for all the constructs, it would be helpful to do this for the cyto-FLYX and nuc-FLYX.

• The cell biological data in this study clearly indicates that polyP is enriched in the nucleolus in multiple cell types, consistent with recent findings from other labs, and also that polyP affects gene expression during development. Given that the authors also generate the Nuc-FLYX construct to deplete polyP from the nucleus, it is surprising that they test how depleting cytoplasmic but not nuclear polyP affects development. However, providing these tools is a service to the community, and testing the phenotypic consequences of all the FLYX constructs may arguably be beyond the scope of this first study.

Editors' note: The authors have satisfactorily responded to our most major concerns related to the specificity of PPDB and the physiological levels of polyPs in the clotting experiments. We also recognise the limitations related to the depletion of polyP in other tissues and hope that these data will be made available soon.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Polymers of orthophosphate of varying lengths are abundant in prokaryotes and some eukaryotes, where they regulate many cellular functions. Though they exist in metazoans, few tools exist to study their function. This study documents the development of tools to extract, measure, and deplete inorganic polyphosphates in *Drosophila*. Using these tools, the authors show:

(1) That polyP levels are negligible in embryos and larvae of all stages while they are feeding. They remain high in pupae but their levels drop in adults.

(2) That many cells in tissues such as the salivary glands, oocytes, haemocytes, imaginal discs, optic lobe, muscle, and crop, have polyP that is either cytoplasmic or nuclear (within the nucleolus).

(3) That polyP is necessary in plasmatocytes for blood clotting in Drosophila.

(4) That ployP controls the timing of eclosion.

The tools developed in the study are innovative, well-designed, tested, and well-documented. I enjoyed reading about them and I appreciate that the authors have gone looking for the functional role of polyP in flies, which hasn't been demonstrated before. The documentation of polyP in cells is convincing as its role in plasmatocytes in clotting.

We sincerely thank the reviewer for their encouraging assessment and for recognizing both the innovation of the FLYX toolkit and the functional insights it enables. Their remarks underscore the importance of establishing Drosophila as a tractable model for polyP biology, and we are grateful for their constructive feedback, which further strengthened the manuscript.

Its control of eclosion timing, however, could result from non-specific effects of expressing an exogenous protein in all cells of an animal.

We now explicitly state this limitation in the revised manuscript (p.16, l.347–349). The issue is that no catalytic-dead ScPpX1 is available as a control in the field. We plan to generate such mutants through systematic structural and functional studies and will update the FLYX toolkit once they are developed and validated. Importantly, the accelerated eclosion phenotype is reproducible and correlates with endogenous polyP dynamics.

The RNAseq experiments and their associated analyses on polyP-depleted animals and controls have not been discussed in sufficient detail.  In its current form, the data look to be extremely variable between replicates and I'm therefore unsure of how the differentially regulated genes were identified.

We thank the reviewer for pointing out the lack of clarity. We have expanded our RNAseq analysis in the revised manuscript (p.20, l.430–434). Because of inter-sample variation (PC2 = 19.10%, Fig. S7B), we employed Gene Set Enrichment Analysis (GSEA) rather than strict DEG cutoffs. This method is widely used when the goal is to capture pathway-level changes under variability (1). We now also highlight this limitation explicitly (p.20, l.430–432) and provide an additional table with gene-specific fold change (See Supplementary Table for RNA Sequencing Sheet 1). Please note that we have moved RNAseq data to Supplementary Fig. 7 and 8 as suggested in the review.

It is interesting that no kinases and phosphatases have been identified in flies. Is it possible that flies are utilising the polyP from their gut microbiota? It would be interesting to see if these signatures go away in axenic animals.

This is an interesting possibility. Several observations argue that polyP is synthesized by fly tissues: (i) polyP levels remain very low during feeding stages but build up in wandering third instar larvae after feeding ceases; (ii) PPBD staining is absent from the gut except the crop (Fig. S3O–P); (ii) In C. elegans, intestinal polyP was unaffected when worms were fed polyP-deficient bacteria (2); (iv) depletion of polyP from plasmatocytes alone impairs hemolymph clotting, which would not be expected if gut-derived polyP were the major source and may have contributed to polyP in hemolymph. Nevertheless, we agree that microbiota-derived polyP may contribute, and we plan systematic testing in axenic flies in future work.

Reviewer #2 (Public review):

Summary:

The authors of this paper note that although polyphosphate (polyP) is found throughout biology, the biological roles of polyP have been under-explored, especially in multicellular organisms. The authors created transgenic Drosophila that expressed a yeast enzyme that degrades polyP, targeting the enzyme to different subcellular compartments (cytosol, mitochondria, ER, and nucleus, terming these altered flies Cyto-FLYX, Mito-FLYX, etc.). The authors show the localization of polyP in various wild-type fruit fly cell types and demonstrate that the targeting vectors did indeed result in the expression of the polyP degrading enzyme in the cells of the flies. They then go on to examine the effects of polyP depletion using just one of these targeting systems (the Cyto-FLYX). The primary findings from the depletion of cytosolic polyP levels in these flies are that it accelerates eclosion and also appears to participate in hemolymph clotting. Perhaps surprisingly, the flies seemed otherwise healthy and appeared to have little other noticeable defects. The authors use transcriptomics to try to identify pathways altered by the cyto-FLYX construct degrading cytosolic polyP, and it seems likely that their findings in this regard will provide avenues for future investigation. And finally, although the authors found that eclosion is accelerated in the pupae of Drosophila expressing the Cyto-FLYX construct, the reason why this happens remains unexplained.

Strengths:

The authors capitalize on the work of other investigators who had previously shown that expression of recombinant yeast exopolyphosphatase could be targeted to specific subcellular compartments to locally deplete polyP, and they also use a recombinant polyP-binding protein (PPBD) developed by others to localize polyP. They combine this with the considerable power of Drosophila genetics to explore the roles of polyP by depleting it in specific compartments and cell types to tease out novel biological roles for polyP in a whole organism. This is a substantial advance.

We are grateful to the reviewer for their thorough and thoughtful evaluation. Their balanced summary of our work, recognition of the strengths of our genetic tools, and constructive suggestions have been invaluable in clarifying our experiments and strengthening the conclusions.

Weaknesses:

Page 4 of the Results (paragraph 1): I'm a bit concerned about the specificity of PPBD as a probe for polyP. The authors show that the fusion partner (GST) isn't responsible for the signal, but I don't think they directly demonstrate that PPBD is binding only to polyP. Could it also bind to other anionic substances? A useful control might be to digest the permeabilized cells and tissues with polyphosphatase prior to PPBD staining and show that the staining is lost.

To address this concern, we have done two sets of experiments:

(1) We generated a PPBD mutant (GST-PPBD^Mut). We establish that GST-PPBD binds to polyP-2X FITC, whereas GST-PPBD^Mut and GST do not bind polyP₁₀₀-2X FITC using Microscale Thermophoresis. We found that, unlike the punctate staining pattern of GST-PPBD (wild-type), GST-PPBD^Mut does not stain hemocytes. This data has been added to the revised manuscript (Fig. 2B-D, p.8, l.151–165).

(2) A study in C.elegans by Quarles et.al has performed a similar experiment, suggested by the reviewer. In that study, treating permeabilized tissues with polyphosphatase prior to PPBD staining resulted in a decrease of PPBD-GFP signal from the tissues (2). We also performed the same experiment where we subjected hemocytes to GST-PPBD staining with prior incubation of fixed and permeabilised hemocytes with ScPpX1 and heat-inactivated ScPpX1 protein. We find that both staining intensity and the number of punctae are higher in hemocytes left untreated and in those treated with heat-inactivated ScPpX1. The hemocytes pre-treated with ScPpX1 showed reduced staining intensity and number of punctae. This data has been added to the revised manuscript (Fig. 2E-G, p.8, l.166-172).

Further, Saito et al. reported that PPBD binds to polyP in vitro, as well as in yeast and mammalian cells, with a high affinity of ~45µM for longer polyP chains (35 mer and above) (3). They also show that the affinity of PPBD with RNA and DNA is very low. Furthermore, PPBD could detect differences in polyP labeling in yeasts grown under different physiological conditions that alter polyP levels (3). Taken together, published work and our results suggest that PPBD specifically labels polyP.

In the hemolymph clotting experiments, the authors collected 2 ul of hemolymph and then added 1 ul of their test substance (water or a polyP solution). They state that they added either 0.8 or 1.6 nmol polyP in these experiments (the description in the Results differs from that of the Methods). I calculate this will give a polyP concentration of 0.3 or 0.6 mM. This is an extraordinarily high polyP concentration and is much in excess of the polyP concentrations used in most of the experiments testing the effects of polyP on clotting of mammalian plasma. Why did the authors choose this high polyP concentration? Did they try lower concentrations? It seems possible that too high a polyP concentration would actually have less clotting activity than the optimal polyP concentration.

We repeated the assays using 125 µM polyP, consistent with concentrations employed in mammalian plasma studies (4,5). Even at this lower, physiologically relevant concentration, polyP significantly enhanced clot fibre formation (Included as Fig. S5F–I, p.12, l.241–243). This reconfirms the conclusion that polyP promotes hemolymph clotting.

Author response image 1.

Reviewer #3 (Public review):

Summary:

Sarkar, Bhandari, Jaiswal, and colleagues establish a suite of quantitative and genetic tools to use Drosophila melanogaster as a model metazoan organism to study polyphosphate (polyP) biology. By adapting biochemical approaches for use in D. melanogaster, they identify a window of increased polyP levels during development. Using genetic tools, they find that depleting polyP from the cytoplasm alters the timing of metamorphosis, accelerating eclosion. By adapting subcellular imaging approaches for D. melanogaster, they observe polyP in the nucleolus of several cell types. They further demonstrate that polyP localizes to cytoplasmic puncta in hemocytes, and further that depleting polyP from the cytoplasm of hemocytes impairs hemolymph clotting. Together, these findings establish D. melanogaster as a tractable system for advancing our understanding of polyP in metazoans.

Strengths:

(1) The FLYX system, combining cell type and compartment-specific expression of ScPpx1, provides a powerful tool for the polyP community.

(2) The finding that cytoplasmic polyP levels change during development and affect the timing of metamorphosis is an exciting first step in understanding the role of polyP in metazoan development, and possible polyP-related diseases.

(3) Given the significant existing body of work implicating polyP in the human blood clotting cascade, this study provides compelling evidence that polyP has an ancient role in clotting in metazoans.

We sincerely thank the reviewer for their generous and insightful comments. Their recognition of both the technical strengths of the FLYX system and the broader biological implications reinforces our confidence that this work will serve as a useful foundation for the community.

Limitations:

(1) While the authors demonstrate that HA-ScPpx1 protein localizes to the target organelles in the various FLYX constructs, the capacity of these constructs to deplete polyP from the different cellular compartments is not shown. This is an important control to both demonstrate that the GTS-PPBD labeling protocol works, and also to establish the efficacy of compartment-specific depletion. While not necessary to do this for all the constructs, it would be helpful to do this for the cyto-FLYX and nuc-FLYX.

We confirmed polyP depletion in Cyto-FLYX using the malachite green assay (Fig. 3D, p.10, l.212–214). The efficacy of ScPpX1 has also been earlier demonstrated in mammalian mitochondria (6). Our preliminary data from Mito-ScPpX1 expressed ubiquitously with Tubulin-Gal4 showed a reduction in polyP levels when estimated from whole flies (See Author response image 2 below, ongoing investigation). In an independent study focusing on mitochondrial polyP depletion, we are characterizing these lines in detail  and plan to check the amount of polyP contributed to the cellular pool by mitochondria using subcellular fractionation. Direct phenotypic and polyP depletion analyses of Nuc-FLYX and ER-FLYX are also being carried out, but are in preliminary stages. That there is a difference in levels of polyP in various tissues and that we get a very little subscellular fraction for polyP analysis have been a few challenging issues. This analysis requires detailed, independent, and careful analysis, and thus, we refrain from adding this data to the current manuscript.

Author response image 2.

Regarding the specificity, Saito et.al. reported that PPBD binds to polyP in vitro, as well as in yeast and mammalian cells with a high affinity of ~45µM for longer polyP chains (35 mer and above) (3). They also show that the affinity of PPBD with RNA and DNA is very low. Further, PPBD could reveal differences in polyP labeling with yeasts grown in different physiological conditions that can alter polyP levels. Now in the manuscript, we included following data to show specificity of PPBD:

To address this concern we have done two sets of experiments:

We generated a PPBD mutant (GST-PPBD^Mut). Using Microscale Thermophoresis, we establish that GST-PPBD binds to polyP₁₀₀-2X-FITC, whereas, GST-PPBD^Mut and GST do not bind polyP₁₀₀-2X-FITC at all. We found that unlike the punctate staining pattern of GST-PPBD (wild-type), GST-PPBD^Mut does not stain hemocytes. This data has been added to the revised manuscript (Fig. 2B-D, p.8, l.151–165).

A study in C.elegans by Quarles et.al has performed a similar experiment suggested by the reviewer. In that study, treating permeabilized tissues with polyphosphatase prior to PPBD staining resulted in decrease of PPBD-GFP signal from the tissues (2). We also performed the same experiment where we subjected hemocytes to GST-PPBD staining with prior incubation of fixed and permeabilised hemocytes with ScPpX1 and heat inactivated ScPpX1 protein. We find that both intensity of staining and number of punctae are higher in hemocytes that were left untreated and the one where heat inactivated ScPpX1 was added. The hemocytes pre-treated with ScPpX1 showed reduced staining intensity and number of punctae. This data has been added to the revised manuscript (Fig. 2E-G, p.8, l.166-172).

(2) The cell biological data in this study clearly indicates that polyP is enriched in the nucleolus in multiple cell types, consistent with recent findings from other labs, and also that polyP affects gene expression during development. Given that the authors also generate the Nuc-FLYX construct to deplete polyP from the nucleus, it is surprising that they test how depleting cytoplasmic but not nuclear polyP affects development. However, providing these tools is a service to the community, and testing the phenotypic consequences of all the FLYX constructs may arguably be beyond the scope of this first study.

We agree this is an important avenue. In this first study, we focused on establishing the toolkit and reporting phenotypes with Cyto-FLYX. We are systematically assaying phenotypes from all FLYX constructs, including Nuc-FLYX, in ongoing studies

Recommendations for the authors:

Reviewing Editor Comment:

The reviewers appreciated the general quality of the rigour and work presented in this manuscript. We also had a few recommendations for the authors. These are listed here and the details related to them can be found in the individual reviews below.

(1) We suggest including an appropriate control to show that PPBD binds polyP specifically.

We have updated the response section as follows:

(a) Highlighted previous literature that showed the specificity of PPBD.

(b) We show that the punctate staining observed by PPBD is not demonstrated by the mutant PPBD (PPBD^Mut) in which amino acids that are responsible for polyP binding are mutated.

(c) We show that PPBD^Mut does not bind to polyP using Microscale Thermophoresis.

(d) We show that treatment of fixed and permeabilised hemocytes with ScPpX1 reduces the PPBD staining intensity and number of punctae, as compared to tissues left untreated or treated with heat-inactivated ScPpX1.

We have included these in our updated revised manuscript (Fig. 2B-G, p.8, l.151–157)

(2) The high concentration of PolyP in the clotting assay might be impeding clotting. The authors may want to consider lowering this in their assays.

We have addressed this concern in our revised manuscript. We have performed the clotting assays with lower polyP concentrations (concentrations previously used in clotting experiments with human blood and polyP). Data is included in Fig. S5F–I, p.12, l.241–243.

(3) The RNAseq study: can the authors please describe this better and possibly mine it for the regulation of genes that affect eclosion?

In our revised manuscript, we have included a broader discussion about the RNAseq analysis done in the article in both the ‘results’ and the ‘discussion’ sections, where we have rewritten the narrative from the perspective of accelerated eclosion. (p.15 l.310-335, p. 20, l.431-446).

(4) Have the authors considered the possibility that the gut microbiota might be contributing to some of their measurements and assays? It would be good to address this upfront - either experimentally, in the discussion, or (ideally) both.

This is an exciting possibility. Several observations argue that fly tissues synthesize polyP: (i) polyP levels remain very low during feeding stages but build up in wandering third instar larvae after feeding ceases; (ii) PPBD staining is absent from the gut except the crop (Fig. S3O–P); (iii) in C. elegans, intestinal polyP was unaffected when worms were fed polyP-deficient bacteria (2); (iv) depletion of polyP from plasmatocytes alone impairs hemolymph clotting, which would not be expected if gut-derived polyP were the major source and may have contributed to polyP in hemolymph. Nevertheless, microbiota-derived polyP may contribute, and we plan systematic testing in axenic flies in future work.

Reviewer #1 (Recommendations for the authors):

(1) While the authors have shown that the depletion tool results in a general reduction of polyP levels in Figure 3D, it would have been nice to show this via IHC. Particularly since the depletion depends on the strength of the Gal4, it is possible that the phenotypes are being under-estimated because the depletions are weak.

We agree that different Gal4 lines have different strengths and will therefore affect polyP levels and the strength of the phenotype differently.

We performed PPBD staining on hemocytes expressing ScPPX; however, we observed very intense, uniform staining throughout the cells, which was unexpected. It seems like PPBD is recognizing overexpressed ScPpX1. Indeed, in an unpublished study by Manisha Mallick (Bhandari lab), it was found that His-ScPpX1 specifically interacts with GST-PPBD in a protein interaction assay (See Author response image 3). Due to these issues, we refrained from IHC/PPBD-based validation.

Author response image 3.

(2) The subcellular tools for depletion are neat! I wonder why the authors didn't test them. For example in the salivary gland for nuclear depletion?

We have addressed this question in the reviewer responses. We are systematically assaying phenotypes from all FLYX constructs, including Mito-FLYX, and Nuc-FLYX, in ongoing independent investigations. As discussed in #1, a possible interaction of ScPpX and PPBD is making this test a bit more challenging, and hence, they each require a detailed investigation.

(a) Does the absence of clotting defects using Lz-gal4 suggest that PolyP is more crucial in the plasmatocytoes and for the initial clotting process? And that it is dispensible/less important in the crystal cells and for the later clotting process. Or is it that the crystal cells just don't have as much polyP? The image (2E-H) certainly looks like it.

In hemolymph, the primary clot formation is a result of the clotting factors secreted from the fat bodies and the plasmatocytes. The crystal cells are responsible for the release of factors aiding in successfully hardening the soft clot initially formed. Reports suggest that clotting and melanization of the clot are independent of each other (7). Since Crystal cells do not contribute to clot fibre formation, the absence of clotting defects using LzGAL4-CytoFLYX is not surprising. Alternatively, PolyP may be secreted from all hemocytes and contribute to clotting; however, the crystal cells make up only 5% hemocytes, and hence polyP depletion in those cells may have a negligible effect on blood clotting.

Crystal cells do show PPBD staining. Whether polyP is significantly lower in levels in the crystal cells as compared to the plasmatocytes needs more systematic investigation. Image (2E-H) is a representative image of the presence of polyP in crystal cells and can not be considered to compare polyP levels in the crystal cells vs Plasmatocytes.

(b) The RNAseq analyses and data could be better presented. If the data are indeed variable and the differentially expressed genes of low confidence, I might remove that data entirely. I don't think it'll take away from the rest of the work.

We understand this concern and, therefore, in the revised manuscript, we have included a broader discussion about the RNAseq analysis done in the article in both the ‘results’ and the ‘discussion’ sections, where we have rewritten the narrative from the perspective of accelerated eclosion. (p.15 l.310-335, p. 20, l.431-446). We have also stated the limitations of such studies.

(c) I would re-phrase the first sentence of the results section.

We have re-phrased it in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) The authors created several different versions of the FLYX system that would be targeted to different subcellular compartments. They mostly report on the effects of cytosolic targeting, but some of the constructs targeted the polyphosphatase to mitochondria or the nucleus.

They report that the targeting worked, but I didn't see any results on the effects of those constructs on fly viability, development, etc.

There is a growing literature of investigators targeting polyphosphatase to mitochondria and showing how depleting mitochondrial polyP alters mitochondrial function. What was the effect of the Nuc-FLYX and Mito-FLYX constructs on the flies?

Also, the authors should probably cite the papers of others on the effects of depleting mitochondrial polyP in other eukaryotic cells in the context of discussing their findings in flies.

We have addressed this question in the reviewer responses. We did not see any obvious developmental or viability defects with any of the FLYX lines, and only after careful investigation did we come across the clotting defects in the CytoFLYX. We are currently systematically assaying phenotypes from all FLYX constructs, including Mito-FLYX and Nuc-FLYX, in independent ongoing investigations.

We have discussed the heterologous expression of mitochondrial polyphosphatase in mammalian cells to justify the need for developing Mito-FLYX (p. 10, l. 197-200). In the discussion section, we also discuss the presence and roles of polyP in the nucleus and how Nuc-FLYX can help study such phenomena (p. 19, l. 399-407).

(2) The authors should number the pages of their manuscript to make it easier for reviewers to refer to specific pages.

We have numbered our lines and pages in the revised manuscript.

(3) Abstract: the abbreviation, "polyP", is not defined in the abstract. The first word in the abstract is "polyphosphate", so it should be defined there.

We have corrected it in the revised version.

(4) The authors repeatedly use the phrase, "orange hot", to describe one of the colors in their micrographs, but I don't know how this differs from "orange".

‘OrangeHot’ is the name of the LUT used in the ImageJ analysis and hence referred to as the colour

(5) First page of the Introduction: the phrase, "feeding polyP to αβ expression Alzheimer's model of Caenorhabditis elegans" is awkward (it literally means feeding polyP to the model instead of the worms).

We have revised it. (p.3, l.55-57).

(6) Page 2 of the Introduction: The authors should cite this paper when they state that NUDT3 is a polyphosphatase: https://pubmed.ncbi.nlm.nih.gov/34788624/

We have cited the paper in the revised version of the manuscript. (p.4, l. 68-70)

(7) Page 2 of Results: The authors report the polyP content in the third instar larva (misspelled as "larval") to five significant digits ("419.30"). Their data do not support more than three significant digits, though.

We have corrected it in the revised manuscript.

(8) Page 3 of Results (paragraph 1): When discussing the polyP levels in various larval stages, the authors are extracting total polyP from the larvae. It seems that at least some of the polyP may come from gut microbes. This should probably be mentioned.

This is an interesting possibility. Several observations argue that polyP is synthesized by fly tissues: (i) polyP levels remain very low during feeding stages but build up in wandering third instar larvae after feeding ceases; (ii) PPBD staining is absent from the gut except the crop (Fig. S3O–P); (ii) In C. elegans, intestinal polyP was unaffected when worms were fed polyP-deficient bacteria (2); (iv) depletion of polyP from plasmatocytes alone impairs hemolymph clotting, which would not be expected if gut-derived polyP were the major source and may have contributed to polyP in hemolymph. We mention this limitation in the revised manuscript (p.19-20, l. 425-433).

(9) Page 3 of Results (paragraph 2): stating that the 4% paraformaldehyde works "best" is imprecise. What do the authors mean by "best"?

We have addressed this comment in the revised manuscript and corrected it as 4% paraformaldehyde being better among the three methods we used to fix tissues, which also included methanol and Bouin’s fixative  (p.8, l. 152-154).

(10) Page 4 of Results (paragraph 2, last line of the page): The scientific literature is vast, so one can never be sure that one knows of all the papers out there, even on a topic as relatively limited as polyP. Therefore, I would recommend qualifying the statement "...this is the first comprehensive tissue staining report...". It would be more accurate (and safer) to say something like, "to our knowledge, this is the first..." There is a similar statement with the word "first" on the next page regarding the FLYX library.

We have addressed this concern and corrected it accordingly in the revised version of the manuscript (p.9, l. 192-193)

Reviewer #3 (Recommendations for the authors):

(1) The authors should include in their discussion a comparison of cell biological observations using the polyP binding domain of E. coli Ppx (GST-PPBD) to fluorescently label polyP in cells and tissues with recent work using a similar approach in C. elegans (Quarles et al., PMID:39413779).

In the revised manuscript, we have cited the work of Quarles et al. and have added a comparison of observations (p.19,l.408-410). In the discussion, we have also focused on multiple other studies about how polyP presence in different subcellular compartments, like the nucleus, can be assayed and studied with the tools developed in this study.

(2) The gene expression studies of time-matched Cyto-FLYX vs WT larvae is very intriguing. Given the authors' findings that non-feeding third instar Cyto-FLYX larvae are developmentally ahead of WT larvae, can the observed trends be explained by known changes in gene expression that occur during eclosion? This is mentioned in the results section in the context of genes linked to neurons, but a broader discussion of which pathway changes observed can be explained by the developmental stage difference between the WT and FLYX larvae would be helpful in the discussion.

We have included a broader discussion about the RNAseq analysis done in the article in both the ‘results’ and the ‘discussion’ sections, where we have rewritten the narrative from the perspective of accelerated eclosion. (p.15 l.310-335, p. 20, l.431-446). We have also stated the limitations of such studies.

(3) The sentence describing NUDT3 is not referenced.

We have addressed this comment and have cited the paper of NUDT3 in the revised version of the manuscript.(p.4, l. 68-70)

(4) In the first sentence of the results section, the meaning/validity of the statement "The polyP levels have decreased as evolution progressed" is not clear. It might be more straightforward to give an estimate of the total pmoles polyP/mg protein difference between bacteria/yeast and metazoans.

In the revised manuscript, we have given an estimate of the polyP content across various species across evolution to uphold the statement that polyP levels have decreased as evolution progressed (p. 5, l. 87-91).

(5) The description of the malachite green assay in the results section describes it as "calorimetric" but this should read "colorimetric?"

We have corrected it in the revised manuscript.

References

(1) Chicco D, Agapito G. Nine quick tips for pathway enrichment analysis. PLoS Comput Biol. 2022 Aug 11;18(8):e1010348.

(2) Quarles E, Petreanu L, Narain A, Jain A, Rai A, Wang J, et al. Cryosectioning and immunofluorescence of C. elegans reveals endogenous polyphosphate in intestinal endo-lysosomal organelles. Cell Rep Methods. 2024 Oct 8;100879.

(3) Saito K, Ohtomo R, Kuga-Uetake Y, Aono T, Saito M. Direct labeling of polyphosphate at the ultrastructural level in Saccharomyces cerevisiae by using the affinity of the polyphosphate binding domain of Escherichia coli exopolyphosphatase. Appl Environ Microbiol. 2005 Oct;71(10):5692–701.

(4) Smith SA, Mutch NJ, Baskar D, Rohloff P, Docampo R, Morrissey JH. Polyphosphate modulates blood coagulation and fibrinolysis. Proc Natl Acad Sci USA. 2006 Jan 24;103(4):903–8.

(5) Smith SA, Choi SH, Davis-Harrison R, Huyck J, Boettcher J, Rienstra CM, et al. Polyphosphate exerts differential effects on blood clotting, depending on polymer size. Blood. 2010 Nov 18;116(20):4353–9.

(6) Abramov AY, Fraley C, Diao CT, Winkfein R, Colicos MA, Duchen MR, et al. Targeted polyphosphatase expression alters mitochondrial metabolism and inhibits calcium-dependent cell death. Proc Natl Acad Sci USA. 2007 Nov 13;104(46):18091–6.

(7) Schmid MR, Dziedziech A, Arefin B, Kienzle T, Wang Z, Akhter M, et al. Insect hemolymph coagulation: Kinetics of classically and non-classically secreted clotting factors. Insect Biochem Mol Biol. 2019 Jun;109:63–71.

(8) Jian Guan, Rebecca Lee Hurto, Akash Rai, Christopher A. Azaldegui, Luis A. Ortiz-Rodríguez, Julie S. Biteen, Lydia Freddolino, Ursula Jakob. HP-Bodies – Ancestral Condensates that Regulate RNA Turnover and Protein Translation in Bacteria. bioRxiv 2025.02.06.636932; doi: https://doi.org/10.1101/2025.02.06.636932.

(9) Lonetti A, Szijgyarto Z, Bosch D, Loss O, Azevedo C, Saiardi A. Identification of an evolutionarily conserved family of inorganic polyphosphate endopolyphosphatases. J Biol Chem. 2011 Sep 16;286(37):31966–74.

I think this is a really important point, and I like that you showed conservation of binding thermodynamics for PfBDP1 with RMM23 as my biggest question throughout the manuscript was how you ensure that protein function is preserved when introducing this many sequence changes. These constraints seem like a strong step, as well as that control, but maybe still an important thing to keep in mind. For example, even if the binding pocket geometry is structurally conserved, mutations outside the pocket could still influence binding (e.g., through altered dynamics, stability of the fold, or long-range effects on binding energetics).

This is really powerful as it's allowing you to study proteins that otherwise were not able to be studied in this way. But related to this idea that AF2 isn't capturing conformational ensembles, are you thinking about the effect that stabilizing the protein might have on your ability to study biologically relevant conformational dynamics of the protein?

Why did you select the most sequence-diverse candidates? I could be wrong, but if the goal is just to stabilize the proteins while maintaining their other biological functions wouldn't you want to minimize changes? Also curious how many candidates you considered per protein and how many met the criteria before you selected the candidates you moved forward with.

madonna whore complex. i wonder how he views his mother?

k, but why not Oberon (with curly braces, of course)? It's both small and real/complete.

Right. Okay.

Good.

I like this! Our world runs based on what we pay attention to, and what is attention and a willingness to do something about what is noticed but care? If we don't care about something, only the sensational or head turning is revealed to us-- it seems like we lose the everyday

It's interesting that the definition (or the impression) of the innovation/repair dichotomy leaves little room for maintenance. It's almost like we never see a plateau, or little course corrections visibly indicated for a technology simply because they might not be interesting. We're constantly looking for new pieces of information: things to be proud of and things to solve, but we don't quite let ourselves stay in that intermediary stage without feeling stagnant or getting bored.

It's interesting how this thinking is so dichotomous-- we oscillate between both the pain and beauty of what's broken and feel torn about what to believe (or more specifically, what's more helpful to believe). You can protect your peace al you want, but is it worth it to not see problems in the world without solutions and without an inherently positive twist on them?

the definition of who is elite radically changes the problematic potential.

I still don't think I fully understand this, but it's a bit easier to think about after doing the Kinetics Discussion worksheet.

If possible, can someone explain this in other words?

This is also the same sort of passive aggressive tone as from before, since he is coupling negotiations from a position of power and basically drawing a hard line that he will not come back unless the Colonel Anderson can guarantee his daughters' safety. But he couples it with a sort of innocent question about schooling as well. It's surprisingly educated in its delivery, since everytime it takes something or delivers a devastating statement, it also balances it with either an outright compliment or a mundane question to dilute the previous blow. It as a whole seems to be establishing a sort of idea that, yes, he's willing to return, but he's actually not, as he's demanding many things that the Colonel can't provide. It's basically a soft rejection of his offer by making it look instead of as a personal decision off of just a refusal to work for him, but by disguising it as a logical decision based on just common working conditions and employment, such as differing wages and benefits. Again, it seems surprisingly well put together and educated, which likely is also intentionally done to undermine the Colonel's position.

I feel like this is almost an attempt at flattery, trying to get a good feeling for the Colonel Anderson's reaction. Seeing as it would be a more positive comment, since he's blatantly saying that he was proud to have been owned by a Colonel, but it also shows a level of passive aggressive education as well. This is because he's asking to hear about the wages, basically challenging Colonel Anderson to actually offer better than anybody else can, as he had said. It makes the situation interesting simply because It's a subservient tone that then shifts into this demanding and negotiating tone, since he's basically acting as if he is still a slave before moving on to discuss wages and other benefits.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary

This paper introduces a dual-pathway model for reconstructing naturalistic speech from intracranial ECoG data. It integrates an acoustic pathway (LSTM + HiFi-GAN for spectral detail) and a linguistic pathway (Transformer + Parler-TTS for linguistic content). Output from the two components is later merged via CosyVoice2.0 voice cloning. Using only 20 minutes of ECoG data per participant, the model achieves high acoustic fidelity and linguistic intelligibility.

Strengths

(1) The proposed dual-pathway framework effectively integrates the strengths of neural-to-acoustic and neural-to-text decoding and aligns well with established neurobiological models of dual-stream processing in speech and language.

(2) The integrated approach achieves robust speech reconstruction using only 20 minutes of ECoG data per subject, demonstrating the efficiency of the proposed method.

(3) The use of multiple evaluation metrics (MOS, mel-spectrogram R², WER, PER) spanning acoustic, linguistic (phoneme and word), and perceptual dimensions, together with comparisons against noisedegraded baselines, adds strong quantitative rigor to the study.

We thank Reviewer #1 for the supportive comments. In addition, we appreciate Reviewer #1’s thoughtful comments and feedback. By addressing these comments, we believe we have greatly improved the clarity of our claims and methodology. Below we list our point-to-point responses addressing concerns raised by Reviewer #1.

Weaknesses:

(1) It is unclear how much the acoustic pathway contributes to the final reconstruction results, based on Figures 3B-E and 4E. Including results from Baseline 2 + CosyVoice and Baseline 3 + CosyVoice could help clarify this contribution.

We sincerely appreciate the inquiry from Reviewer 1. We thank the reviewer for this suggestion. However, we believe that directly applying CosyVoice to the outputs of Baseline 2 or Baseline 3 in isolation is not methodologically feasible and would not correctly elucidate the contribution of the auditory pathway and might lead to misinterpretation.

The role of CosyVoice 2.0 in our framework is specifically voice cloning and fusion, not standalone enhancement. It is designed to integrate information from two pathways. Its operation requires two key inputs:

(1) A voice reference speech that provides the target speaker's timbre and prosodic characteristics. In our final pipeline, this is provided by the denoised output of the acoustic pathway (Baseline 2).

(2) A target word sequence that specifies the linguistic content to be spoken. This is obtained by transcribing the output of the linguistic pathway (Baseline 3) using Whisper ASR. Therefore, the standalone outputs of Baseline 2 and Baseline 3 are the purest demonstrations of what each pathway contributes before fusion. The significant improvement in WER/PER and MOS in the final output (compared to Baseline 2) and the significant improvement in melspectrogram R² (compared to Baseline 3) together demonstrate the complementary contributions of the two pathways. The fusion via CosyVoice is the mechanism that allows these contributions to be combined. We have added a clearer explanation of CosyVoice's role and the rationale for not testing it on individual baselines in the revised manuscript (Results section: "The fine-tuned voice cloner further enhances...").

Edits:

Page 11, Lines 277-282:

“ Voice cloning is used to bridge the gap between acoustic fidelity and linguistic intelligibility in speech reconstruction. This approach strategically combines the strengths of complementary pathways: the acoustic pathway preserves speaker-specific spectral characteristics while the linguistic pathway maintains lexical and phonetic precision. By integrating these components through neural voice cloning, we achieve balanced reconstruction that overcomes the limitations inherent in isolated systems. CosyVoice 2.0, the voice cloner module serves specifically as a voice cloning and fusion engine, requiring two inputs: (1) a voice reference speech (provided by the denoised output of the acoustic pathway) to specify the target speaker's identity, and (2) a target word sequence (transcribed from the output of the linguistic pathway) to specify the linguistic content. The standalone baseline outputs of the two pathways can be integrated in this way.”

(2) As noted in the limitations, the reconstruction results heavily rely on pre-trained generative models. However, no comparison is provided with state-of-the-art multimodal LLMs such as Qwen3-Omni, which can process auditory and textual information simultaneously. The rationale for using separate models (Wav2Vec for speech and TTS for text) instead of a single unified generative framework should be clearly justified. In addition, the adaptor employs an LSTM architecture for speech but a Transformer for text, which may introduce confounds in the performance comparison. Is there any theoretical or empirical motivation for adopting recurrent networks for auditory processing and Transformer-based models for textual processing?

We thank the reviewer for the insightful suggestion regarding multimodal large language models (LLMs) such as Qwen3-Omni. It is important to clarify the distinction between general-purpose interactive multimodal models and models specifically designed for high-fidelity voice cloning and speech synthesis.

As for the comparison with the state-of-the-art multimodal LLMs:

Qwen3-Omni and GLM-4-Voice are powerful conversational agents capable of processing multiple modalities including text, speech, image, and video, as described in its documentation (see: https://help.aliyun.com/zh/model-studio/qwen-tts-realtime and https://docs.bigmodel.cn/cn/guide/models/sound-and-video/glm-4-voice). However, it is primarily optimized for interactive dialogue and multimodal understanding rather than for precise, speaker-adaptive speech reconstruction from neural signals. In contrast, CosyVoice 2.0, developed by the same team at Alibaba, is specifically designed for voice cloning and text-to-speech synthesis (see: https://help.aliyun.com/zh/model-studio/text-to-speech). It incorporates advanced speaker adaptation and acoustic modeling capabilities that are essential for reconstructing naturalistic speech from limited neural data. Therefore, our choice of CosyVoice for the final synthesis stage aligns with the goal of integrating acoustic fidelity and linguistic intelligibility, which is central to our study.

For the selection of LSTM and Transformer in the two pathways:

The goal of the acoustic adaptor is to reconstruct fine-grained spectrotemporal details (formants, harmonic structures, prosodic contours) with millisecond-to-centisecond precision. These features rely heavily on local temporal dynamics and short-to-medium range dependencies (e.g., within and between phonemes/syllables). In our ablation studies (to be added in the supplementary), we found that Transformer-based adaptors, which inherently emphasize global sentence-level context through self-attention, tended to oversmooth the reconstructed acoustic features, losing critical fine-temporal details essential for naturalness. In contrast, the recurrent nature of LSTMs, with their inherent temporal state propagation, proved more effective at modeling these local sequential dependencies without excessive smoothing, leading to higher mel-spectrogram fidelity. This aligns with the neurobiological observation that early auditory cortex processes sound with precise temporal fidelity. Moreover, from an engineering perspective, LSTM-based decoders have been empirically shown to perform well in sequential prediction tasks with limited data, as evidenced in prior work on sequence modeling and neural decoding (1).

The goal of the linguistic adaptor is to decode abstract, discrete word tokens. This task benefits from modeling long-range contextual dependencies across a sentence to resolve lexical ambiguity and syntactic structure (e.g., subject-verb agreement). The self-attention mechanism of Transformers is exceptionally well-suited for capturing these global relationships, as evidenced by their dominance in NLP. Our experiments confirmed that a Transformer adaptor outperformed an LSTM-based one in word token prediction accuracy.

While a unified multimodal LLM could in principle handle both modalities, such models often face challenges in modality imbalance and task specialization. Audio and text modalities have distinct temporal scales, feature distributions, and learning dynamics. By decoupling them into separate pathways with specialized adaptors, we ensure that each modality is processed by an architecture optimized for its inherent structure. This divide-and-conquer strategy avoids the risk of one modality dominating or interfering with the learning of the other, leading to more stable training and better final performance, especially important when adapting to limited neural data.

Edits:

Page 9, Lines 214-223:

“The acoustic pathway, implemented through a bi-directional LSTM neural adaptor architecture (Fig. 1B), specializes in reconstructing fundamental acoustic properties of speech. This module directly processes neural recordings to generate precise time-frequency representations, focusing on preserving speaker-specific spectral characteristics like formant structures, harmonic patterns, and spectral envelope details. Quantitative evaluation confirms its core competency: achieving a mel-spectrogram R² of 0.793 ± 0.016 (Fig. 3B) demonstrates remarkable fidelity in reconstructing acoustic microstructure. This performance level is statistically indistinguishable from original speech degraded by 0dB additive noise (0.771 ± 0.014, p = 0.242, one-sided t-test). We chose a bidirectional LSTM architecture for this adaptor because its recurrent nature is particularly suited to modeling the fine-grained, short- to medium-range temporal dependencies (e.g., within and between phonemes and syllables) that are critical for acoustic fidelity. An ablation study comparing LSTM against Transformerbased adaptors for this task confirmed that LSTMs yielded superior mel-spectrogram reconstruction fidelity (higher R²), as detailed in Table S1, likely by avoiding the oversmoothing of spectrotemporal details sometimes induced by the strong global context modeling of Transformers”.

“To confirm that the acoustic pathway’s output is causally dependent on the neural signal rather than the generative prior of the HiFi-GAN, we performed a control analysis in which portions of the input ECoG recording were replaced with Gaussian noise. When either the first half, second half, or the entirety of the neural input was replaced by noise, the melspectrogram R² of the reconstructed speech dropped markedly, corresponding to the corrupted segment (Fig. S5). This demonstrates that the reconstruction is temporally locked to the specific neural input and that the model does not ‘hallucinate’ spectrotemporal structure from noise. These results validate that the acoustic pathway performs genuine, input-sensitive neural decoding”.

Edits:

Page 10, Lines 272-277:

“We employed a Transformer-based Seq2Seq architecture for this adaptor to effectively capture the long-range contextual dependencies across a sentence, which are essential for resolving lexical ambiguity and syntactic structure during word token decoding. This choice was validated by an ablation study (Table S2), indicating that the Transformer adaptor outperformed an LSTM-based counterpart in word prediction accuracy”

(3) The model is trained on approximately 20 minutes of data per participant, which raises concerns about potential overfitting. It would be helpful if the authors could analyze whether test sentences with higher or lower reconstruction performance include words that were also present in the training set.

Thank you for raising the important concern regarding potential overfitting given the limited size of our training dataset (~20 minutes per participant). To address this point directly, we performed a detailed lexical overlap analysis between the training and test sets.

The test set contains 219 unique words. Among these:

127 words (58.0%) appeared in the training set (primarily high-frequency, common words).

92 words (42.0%) were entirely novel and did not appear in the training set. We further examined whether trials with the best reconstruction (WER = 0) relied more on training vocabulary. Among these top-performing trials, 55.0% of words appeared in the training set. In contrast, the worst-performing trials showed 51.9% overlap in words in the training set. No significant difference was observed, suggesting that performance is not driven by simple lexical memorization.

The presence of a substantial proportion of novel words (42%) in the test set, combined with the lack of performance advantage for overlapping content, provides strong evidence that our model is generalizing linguistic and acoustic patterns rather than merely memorizing the training vocabulary. High reconstruction performance on unseen words would be improbable under severe overfitting.

Therefore, we conclude that while some lexical overlap exists (as expected in natural language), the model’s performance is driven by its ability to decode generalized neural representations, effectively mitigating the overfitting risk highlighted by the reviewer.

(4) The phoneme confusion matrix in Figure 4A does not appear to align with human phoneme confusion patterns. For instance, /s/ and /z/ differ only in voicing, yet the model does not seem to confuse these phonemes. Does this imply that the model and the human brain operate differently at the mechanistic level?

We thank the reviewer for this detailed observation regarding the difference between our model's phoneme confusion patterns and typical human perceptual confusions (e.g., the lack of /s/-/z/ confusion).

The reviewer is correct in inferring a mechanistic difference. This divergence is primarily attributable to the Parler-TTS model acting as a powerful linguistic prior. Our linguistic pathway decodes word tokens, which Parler-TTS then converts to speech. Trained on massive corpora to produce canonical pronunciations, Parler-TTS effectively performs an implicit "error correction." For instance, if the neural decoding is ambiguous between the words "sip" and "zip," the TTS model's strong prior for lexical and syntactic context will likely resolve it to the correct word, thereby suppressing purely acoustic confusions like voicing.

This has important implications for interpreting our model's errors and its relationship to brain function. The phoneme errors in our final output reflect a combination of neural decoding errors and the generative biases of the TTS model, which is optimized for intelligibility rather than mimicking raw human misperception. This does imply our model operates differently from the human auditory periphery. The human brain may first generate a percept with acoustic confusions, which higher-level language regions then disambiguate. Our model effectively bypasses the "confused percept" stage by directly leveraging a pre-trained, high-level language model for disambiguation. This is a design feature contributing to its high intelligibility, not necessarily a flaw. This observation raises a fascinating question: Could a model that more faithfully simulates the hierarchical processing of the human brain (including early acoustic confusions) provide a better fit to neural data at different processing stages? Future work could further address this question.

Edits:

add another paragraph in Discussion (Page 14, Lines 397-398):

“The phoneme confusion pattern observed in our model output (Fig. 4A) differs from classic human auditory confusion matrices. We attribute this divergence primarily to the influence of the Parler-TTS model, which serves as a strong linguistic prior in our pipeline. This component is trained to generate canonical speech from text tokens. When the upstream neural decoding produces an ambiguous or erroneous token sequence, the TTS model’s internal language model likely performs an implicit ‘error correction,’ favoring linguistically probable words and pronunciations. This underscores that our model’s errors arise from a complex interaction between neural decoding fidelity and the generative biases of the synthesis stage”

(5) In general, is the motivation for adopting the dual-pathway model to better align with the organization of the human brain, or to achieve improved engineering performance? If the goal is primarily engineeringoriented, the authors should compare their approach with a pretrained multimodal LLM rather than relying on the dual-pathway architecture. Conversely, if the design aims to mirror human brain function, additional analysis, such as detailed comparisons of phoneme confusion matrices, should be included to demonstrate that the model exhibits brain-like performance patterns.

Our primary motivation is engineering improvement, to overcome the fundamental trade-off between acoustic fidelity and linguistic intelligibility that has limited previous neural speech decoding work. The design is inspired by the related works of the convergent representation of speech and language perception (2). However, we do not claim that our LSTM and Transformer adaptors precisely simulate the specific neural computations of the human ventral and dorsal streams. The goal was to build a high-performance, data-efficient decoder. We will clarify this point in the Introduction and Discussion, stating that while the architecture is loosely inspired by previous neuroscience results, its primary validation is its engineering performance in achieving state-of-the-art reconstruction quality with minimal data.

Edits:

Page 14, Line 358-373:

“In this study, we present a dual-path framework that synergistically decodes both acoustic and linguistic speech representations from ECoG signals, followed by a fine-tuned zero-shot text-to-speech network to re-synthesize natural speech with unprecedented fidelity and intelligibility. Crucially, by integrating large pre-trained generative models into our acoustic reconstruction pipeline and applying voice cloning technology, our approach preserves acoustic richness while significantly enhancing linguistic intelligibility beyond conventional methods. Our dual-pathway architecture, while inspired by converging neuroscience insights on speech and language perception, was principally designed and validated as an engineering solution. The primary goal to build a practical decoder that achieves state-of-theart reconstruction quality with minimal data. The framework's success is therefore ultimately judged by its performance metrics, high intelligibility (WER, PER), acoustic fidelity (melspectrogram R²), and perceptual quality (MOS), which directly address the core engineering challenge we set out to solve. Using merely 20 minutes of ECoG recordings, our model achieved superior performance with a WER of 18.9% ± 3.3% and PER of 12.0% ± 2.5% (Fig. 2D, E). This integrated architecture, combining pre-trained acoustic (Wav2Vec2.0 and HiFiGAN) and linguistic (Parler-TTS) models through lightweight neural adaptors, enables efficient mapping of ECoG signals to dual latent spaces. Such methodology substantially reduces the need for extensive neural training data while achieving breakthrough word clarity under severe data constraints. The results demonstrate the feasibility of transferring the knowledge embedded in speech-data pre-trained artificial intelligence (AI) models into neural signal decoding, paving the way for more advanced brain-computer interfaces and neuroprosthetics”.

Reviewer #2 (Public review):

Summary:

The study by Li et al. proposes a dual-path framework that concurrently decodes acoustic and linguistic representations from ECoG recordings. By integrating advanced pre-trained AI models, the approach preserves both acoustic richness and linguistic intelligibility, and achieves a WER of 18.9% with a short (~20-minute) recording.

Overall, the study offers an advanced and promising framework for speech decoding. The method appears sound, and the results are clear and convincing. My main concerns are the need for additional control analyses and for more comparisons with existing models.

Strengths:

(1) This speech-decoding framework employs several advanced pre-trained DNN models, reaching superior performance (WER of 18.9%) with relatively short (~20-minute) neural recording.

(2) The dual-pathway design is elegant, and the study clearly demonstrates its necessity: The acoustic pathway enhances spectral fidelity while the linguistic pathway improves linguistic intelligibility.

We thank Reviewer #2 for supportive comments. In addition, we appreciate Reviewer #2’s thoughtful comments and feedback. By addressing these comments, we believe we have greatly improved the clarity of our claims and methodology. Below we list our point-to-point responses addressing concerns raised by Reviewer #2.

Weaknesses:

The DNNs used were pre-trained on large corpora, including TIMIT, which is also the source of the experimental stimuli. More generally, as DNNs are powerful at generating speech, additional evidence is needed to show that decoding performance is driven by neural signals rather than by the DNNs' generative capacity.

Thank you for raising this crucial point regarding the potential for pre-trained DNNs to generate speech independently of the neural input. We fully agree that it is essential to disentangle the contribution of the neural signals from the generative priors of the models. To address this directly, we have conducted two targeted control analyses, as you suggested, and have integrated the results into the revised manuscript (see Fig. S5 and the corresponding description in the Results section):

(1) Random noise input: We fed Gaussian noise (matched in dimensionality and temporal structure to real ECoG recordings) into the trained adaptors. The outputs were acoustically unstructured and linguistically incoherent, confirming that the generative models alone cannot produce meaningful speech without valid neural input.

(2) Partial sentence input (real + noise): For the acoustic pathway, we systematically replaced portions of the ECoG input with noise. The reconstruction quality (mel-spectrogram R²) dropped significantly in the corrupted segments, demonstrating that the decoding is temporally locked to the neural signal and does not “hallucinate” speech from noise.

These results provide strong evidence that our model’s performance is causally dependent on and sensitive to the specific neural input, validating that it performs genuine neural decoding rather than merely leveraging the generative capacity of the pre-trained DNNs.

The detailed edits are in the “recommendations” below. (See recommendations (1) and (2))

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Clarify the results shown in Figure 4E. The integrated approach appears to perform comparably to Baseline 3 in phoneme class clarity. However, Baseline 3 represents the output of the linguistic pathway alone, which is expected to encode information primarily at the word level.

We appreciate the reviewer's observation and agree that clarification is needed. The phoneme class clarity (PCC) metric shown in Figure 4E measures whether mis-decoded phonemes are more likely to be confused within their own class (vowel-vowel or consonantconsonant) rather than across classes (vowel-consonant). A higher PCC indicates that the model's errors tend to be phonologically similar sounds (e.g., one vowel mistaken for another), which is a reasonable property for intelligibility.

We would like to clarify the nature of Baseline 3. As stated in the manuscript (Results section: "The linguistic pathway reconstructs high-intelligibility, higher-level linguistic information"), Baseline 3 is the output of our linguistic pathway. This pathway operates as follows: the ECoG signals are mapped to word tokens via the Transformer adaptor, and these tokens are then synthesized into speech by the frozen Parler-TTS model. Crucially, the input to Parler-TTS is a sequence of word tokens.

It is important to distinguish between the levels of performance measured: Word Error Rate (WER) reflects accuracy at the lexical level (whole words). The linguistic pathway achieves a low WER by design, as it directly decodes word sequences. Phoneme Error Rate (PER) reflects accuracy at the sublexical phonetic level (phonemes). A low WER generally implies a low PER, because robust word recognition requires reliable phoneme-level representations within the TTS model's prior. This explains why Baseline 3 also exhibits a low PER. However, acoustic fidelity (captured by metrics like mel-spectrogram R²) requires the preservation of fine-grained spectrotemporal details such as pitch, timbre, prosody, and formant structures, information that is not directly encoded at the lexical level and is therefore not a strength of the purely linguistic pathway.

While Parler-TTS internally models sub-word/phonetic information to generate the acoustic waveform, the primary linguistic information driving the synthesis is at the lexical (word) level. The generated speech from Baseline 3 therefore contains reconstructed phonemic sequences derived from the decoded word tokens, not from direct phoneme-level decoding of ECoG.

Therefore, the comparable PCC between our final integrated model and Baseline 3 (linguistic pathway) suggests that the phoneme-level error patterns (i.e., the tendency to confuse within-class phonemes) in our final output are largely inherited from the high-quality linguistic prior embedded in the pre-trained TTS model (Parler-TTS). The integrated framework successfully preserves this desirable property from the linguistic pathway while augmenting it with speaker-specific acoustic details from the acoustic pathway, thereby achieving both high intelligibility (low WER/PER) and high acoustic fidelity (high melspectrogram R²).

We will revise the caption of Figure 4E and the corresponding text in the Results section to make this interpretation explicit.

Edits:

Page 12, Lines 317-322:

“In addition to the confusion matrices, we categorized the phonemes into vowels and consonants to assess the phoneme class clarity. We defined "phoneme class clarity" (PCC) as the proportion of errors where a phoneme was misclassified within the same class versus being misclassified into a different class. The purpose of introducing PCC is to demonstrate that most of the misidentified phonemes belong to the same category (confusion between vowels or consonants), rather than directly comparing the absolute accuracy of phoneme recognition. For instance, a vowel being mistaken for another vowel would be considered a within-class error, whereas a vowel being mistaken for a consonant would be classified as a between-class error” 

(2) Add results from Baseline 2 + CosyVoice and Baseline 3 + CosyVoice to clarify the contribution of the auditory pathway.

Thank you for the suggestion. We appreciate the opportunity to clarify the role of CosyVoice in our framework.

As explained in our response to point (1), CosyVoice 2.0 is designed as a fusion module that requires two inputs: 1) a voice reference (from the acoustic pathway) to specify speaker identity, and 2) a word sequence (from the linguistic pathway) to specify linguistic content. Because it is not a standalone enhancer, applying CosyVoice to a single pathway output (e.g., Baseline 2 or 3 alone) is not quite feasible and would not reflect its intended function and could lead to misinterpretation of each pathway’s contribution.

Instead, we have evaluated the contribution of each pathway by comparing the final integrated output against each standalone pathway output (Baseline 2 and 3). The significant improvements in both acoustic fidelity and linguistic intelligibility demonstrate the complementary roles of the two pathways, which are effectively fused through CosyVoice.

(3) Justify your choice of using LSTM and Transformer architecture for the auditory and linguistic neural adaptors, respectively, and how your methods could compare to using a unified generative multimodal LLM for both pathways.

Thank you for revisiting this important point. We appreciate your interest in the architectural choices and their relationship to state-of-the-art multimodal models.

As detailed in our response to point (2), our choice of LSTM for the acoustic pathway and Transformer for the linguistic pathway is driven by task-specific requirements, supported by ablation studies (Supplementary Tables 1–2). The acoustic pathway benefits from LSTM’s ability to model fine-grained, local temporal dependencies without over-smoothing. The linguistic pathway benefits from Transformer’s ability to capture long-range semantic and syntactic context.

Regarding comparison with unified multimodal LLMs (e.g., Qwen3-Omni), we clarified that such models are optimized for interactive dialogue and multimodal understanding, while our framework relies on specialist models (CosyVoice 2.0, Parler-TTS) that are explicitly designed for high-fidelity, speaker-adaptive speech synthesis, a requirement central to our decoding task.

We have incorporated these justifications into the revised manuscript (Results and Discussion sections) and appreciate the opportunity to further emphasize these points.

Edits:

Page 9, Lines 214-223:

“The acoustic pathway, implemented through a bi-directional LSTM neural adaptor architecture (Fig. 1B), specializes in reconstructing fundamental acoustic properties of speech. This module directly processes neural recordings to generate precise time-frequency representations, focusing on preserving speaker-specific spectral characteristics like formant structures, harmonic patterns, and spectral envelope details. Quantitative evaluation confirms its core competency: achieving a mel-spectrogram R² of 0.793 ± 0.016 (Fig. 3B) demonstrates remarkable fidelity in reconstructing acoustic microstructure. This performance level is statistically indistinguishable from original speech degraded by 0dB additive noise (0.771 ± 0.014, p = 0.242, one-sided t-test). We chose a bidirectional LSTM architecture for this adaptor because its recurrent nature is particularly suited to modeling the fine-grained, short- to medium-range temporal dependencies (e.g., within and between phonemes and syllables) that are critical for acoustic fidelity. An ablation study comparing LSTM against Transformerbased adaptors for this task confirmed that LSTMs yielded superior mel-spectrogram reconstruction fidelity (higher R²), as detailed in Table S1, likely by avoiding the oversmoothing of spectrotemporal details sometimes induced by the strong global context modeling of Transformers”.

“To confirm that the acoustic pathway’s output is causally dependent on the neural signal rather than the generative prior of the HiFi-GAN, we performed a control analysis in which portions of the input ECoG recording were replaced with Gaussian noise. When either the first half, second half, or the entirety of the neural input was replaced by noise, the melspectrogram R² of the reconstructed speech dropped markedly, corresponding to the corrupted segment (Fig. S5). This demonstrates that the reconstruction is temporally locked to the specific neural input and that the model does not ‘hallucinate’ spectrotemporal structure from noise. These results validate that the acoustic pathway performs genuine, input-sensitive neural decoding”.

Page 10, Lines 272-277:

“We employed a Transformer-based Seq2Seq architecture for this adaptor to effectively capture the long-range contextual dependencies across a sentence, which are essential for resolving lexical ambiguity and syntactic structure during word token decoding. This choice was validated by an ablation study (Table S2), indicating that the Transformer adaptor outperformed an LSTM-based counterpart in word prediction accuracy”.

(4) Discuss the differences between the model's phoneme confusion matrix in Figure 4A and human phoneme confusion patterns. In addition, please clarify whether the adoption of the dual-pathway architecture is primarily intended to simulate the organization of the human brain or to achieve engineering improvements.

The observed difference between our model's phoneme confusion matrix and typical human perceptual confusion patterns (e.g., the noted lack of confusion between /s/ and /z/) is, as the reviewer astutely infers, likely attributable to the TTS model (Parler-TTS) acting as a powerful linguistic prior. The linguistic pathway decodes word tokens, and Parler-TTS converts these tokens into speech. Parler-TTS is trained on massive text and speech corpora to produce canonical, clean pronunciations. It effectively performs a form of "error correction" or "canonicalization" based on its internal language model. For example, if the neural decoding is ambiguous between "sip" and "zip", the TTS model's strong prior for lexical and syntactic context may robustly resolve it to the correct word, suppressing purely acoustic confusions like voicing. Therefore, the phoneme errors in our final output reflect a combination of neural decoding errors and the TTS model's generation biases, which are optimized for intelligibility rather than mimicking human misperception. We will add this explanation to the paragraph discussing Figure 4A.

Our primary motivation is engineering improvement, to overcome the fundamental tradeoff between acoustic fidelity and linguistic intelligibility that has limited previous neural speech decoding work. The design is inspired by the convergent representation of speech and language perception (1). However, we do not claim that our LSTM and Transformer adaptors precisely simulate the specific neural computations of the human ventral and dorsal streams. The goal was to build a high-performance, data-efficient decoder. We will clarify this point in the Introduction and Discussion, stating that while the architecture is loosely inspired by previous neuroscience results, its primary validation is its engineering performance in achieving state-of-the-art reconstruction quality with minimal data.

Edits:

Pages 2-3, Lines 74-85:

“Here, we propose a unified and efficient dual-pathway decoding framework that integrates the complementary strengths of both paradigms to enhance the performance of re-synthesized natural speech from the engineering performance. Our method maps intracranial electrocorticography (ECoG) signals into the latent spaces of pre-trained speech and language models via two lightweight neural adaptors: an acoustic pathway, which captures low-level spectral features for naturalistic speech synthesis, and a linguistic pathway, which extracts high-level linguistic tokens for semantic intelligibility. These pathways are fused using a finetuned text-to-speech (TTS) generator with voice cloning, producing re-synthesized speech that retains both the acoustic spectrotemporal details, such as the speaker’s timbre and prosody, and the message linguistic content. The adaptors rely on near-linear mappings and require only 20 minutes of neural data per participant for training, while the generative modules are pre-trained on large unlabeled corpora and require no neural supervision”.

Page 14, Lines 358-373:

“In this study, we present a dual-path framework that synergistically decodes both acoustic and linguistic speech representations from ECoG signals, followed by a fine-tuned zero-shot text-to-speech network to re-synthesize natural speech with unprecedented fidelity and intelligibility. Crucially, by integrating large pre-trained generative models into our acoustic reconstruction pipeline and applying voice cloning technology, our approach preserves acoustic richness while significantly enhancing linguistic intelligibility beyond conventional methods. Our dual-pathway architecture, while inspired by converging neuroscience insights on speech and language perception, was principally designed and validated as an engineering solution. The primary goal to build a practical decoder that achieves state-of-the-art reconstruction quality with minimal data. The framework's success is therefore ultimately judged by its performance metrics, high intelligibility (WER, PER), acoustic fidelity (mel-spectrogram R²), and perceptual quality (MOS), which directly address the core engineering challenge we set out to solve. Using merely 20 minutes of ECoG recordings, our model achieved superior performance with a WER of 18.9% ± 3.3% and PER of 12.0% ± 2.5% (Fig. 2D, E). This integrated architecture, combining pre-trained acoustic (Wav2Vec2.0 and HiFi-GAN) and linguistic (Parler-TTS) models through lightweight neural adaptors, enables efficient mapping of ECoG signals to dual latent spaces. Such methodology substantially reduces the need for extensive neural training data while achieving breakthrough word clarity under severe data constraints. The results demonstrate the feasibility of transferring the knowledge embedded in speech-data pre-trained artificial intelligence (AI) models into neural signal decoding, paving the way for more advanced brain-computer interfaces and neuroprosthetics”.

Reviewer #2 (Recommendations for the authors):

(1) My main question is whether any experimental stimuli overlap with the data used to pre-train the models. The authors might consider using pre-trained models trained on other corpora and training their own model without the TIMIT corpus. Additionally, as pretrained models were used, it might be helpful to evaluate to what extent the decoding is sensitive to the input neural recording or whether the model always outputs meaningful speech. The authors might consider two control analyses: a) whether the model still generates speech-like output if the input is random noise; b) whether the model can decode a complete sentence if the first half recording of a sentence is real but the second half is replaced with noise.

We thank the reviewer for raising this crucial point regarding potential data leakage and the sensitivity of decoding to neural input.

We confirm that the pre-training phase of our core models (Wav2Vec2.0 encoder, HiFiGAN decoder) was conducted exclusively on the LibriSpeech corpus (960 hours), which is entirely separate from the TIMIT corpus used for our ECoG experiments. The subsequent fine-tuning of the CosyVoice 2.0 voice cloner for speaker adaptation was performed on the training set portion of the entire TIMIT corpus. Importantly, the test set for all neural decoding evaluations was strictly held out and never used during any fine-tuning stage. This data separation is now explicitly stated in the " Methods" sections for the Speech Autoencoder and the CosyVoice fine-tuning.

Regarding the potential of training on other corpora, we agree it is a valuable robustness check. Previous work has demonstrated that self-supervised speech models like Wav2Vec2.0 learn generalizable representations that transfer well across domains (e.g., Millet et al., NeurIPS 2022). We believe our use of LibriSpeech, a large and diverse corpus, provides a strong, general-purpose acoustic prior.

We agree with the reviewer that control analyses are essential to demonstrate that the decoded output is driven by neural signals and not merely the generative prior of the models. We have conducted the following analyses and will include them in the revised manuscript (likely in a new Supplementary Figure or Results subsection):

(a) Random Noise Input: We fed Gaussian noise (matched in dimensionality and temporal length to the real ECoG input) into the trained acoustic and linguistic adaptors. The outputs were evaluated. The acoustic pathway generated unstructured, noisy spectrograms with no discernible phonetic structure, and the linguistic pathway produced either highly incoherent word sequences or failed to generate meaningful tokens. The fusion via CosyVoice produced unintelligible babble. This confirms that the generative models alone cannot produce structured speech without meaningful neural input.

(b) Partial Sentence Input (Real + Noise): In the acoustic pathway, we replaced the first half, the second half, and all the ECoG recording for test sentences with Gaussian noise. The melspectrogram R² showed a clear degradation in the reconstructed speech corresponding to the noisy segment. We did not do similar experiments in the linguistic pathway because the TTS generator is pre-trained by HuggingFace. We did not train any parameters of Parler-TTS. These results strongly indicate that our model's performance is contingent on and sensitive to the specific neural input, validating that it is performing genuine neural decoding.

Edits:

Page 19, Lines 533-538:

“The parameters in Wav2Vec2.0 were frozen within this training phase. The parameters in HiFi-GAN were optimized using the Adam optimizer with a fixed learning rate of 10_-5, 𝛽_! = 0.9, 𝛽₂ = 0.999. We trained this Autoencoder in LibriSpeech, a 960-hour English speech corpus with a sampling rate of 16kHz, which is entirely separate from the TIMIT corpus used for our ECoG experiments. We spent 12 days in parallel training on 6 Nvidia GeForce RTX3090 GPUs. The maximum training epoch was 2000. The optimization did not stop until the validation loss no longer decreased”.

Edits:

Page9, Lines214-223:

“The acoustic pathway, implemented through a bi-directional LSTM neural adaptor architecture (Fig. 1B), specializes in reconstructing fundamental acoustic properties of speech. This module directly processes neural recordings to generate precise time-frequency representations, focusing on preserving speaker-specific spectral characteristics like formant structures, harmonic patterns, and spectral envelope details. Quantitative evaluation confirms its core competency: achieving a mel-spectrogram R² of 0.793 ± 0.016 (Fig. 3B) demonstrates remarkable fidelity in reconstructing acoustic microstructure. This performance level is statistically indistinguishable from original speech degraded by 0dB additive noise (0.771 ± 0.014, p = 0.242, one-sided t-test). We chose a bidirectional LSTM architecture for this adaptor because its recurrent nature is particularly suited to modeling the fine-grained, short- to medium-range temporal dependencies (e.g., within and between phonemes and syllables) that are critical for acoustic fidelity. An ablation study comparing LSTM against Transformer-based adaptors for this task confirmed that LSTMs yielded superior mel-spectrogram reconstruction fidelity (higher R²), as detailed in Table S1, likely by avoiding the oversmoothing of spectrotemporal details sometimes induced by the strong global context modeling of Transformers”.

“To confirm that the acoustic pathway’s output is causally dependent on the neural signal rather than the generative prior of the HiFi-GAN, we performed a control analysis in which portions of the input ECoG recording were replaced with Gaussian noise. When either the first half, second half, or the entirety of the neural input was replaced by noise, the melspectrogram R² of the reconstructed speech dropped markedly, corresponding to the corrupted segment (Fig. S5). This demonstrates that the reconstruction is temporally locked to the specific neural input and that the model does not ‘hallucinate’ spectrotemporal structure from noise. These results validate that the acoustic pathway performs genuine, input-sensitive neural decoding”

(2) For BCI applications, the decoding speed matters. Please report the model's inference speed. Additionally, the authors might also consider reporting cross-participant generalization and how the accuracy changes with recording duration.

We thank the reviewer for these practical and important suggestions. 

Inference Speed: You are absolutely right. On our hardware (single NVIDIA GeForce RTX 3090 GPU), the current pipeline has an inference time that is longer than the duration of the target speech segment. The primary bottlenecks are the sequential processing in the autoregressive linguistic adaptor and the high-resolution waveform generation in CosyVoice 2.0. This latency currently limits real-time application. We have now added this in the Discussion acknowledging this limitation and stating that future work must focus on architectural optimizations (e.g., non-autoregressive models, lighter vocoders) and potential hardware acceleration to achieve real-time performance, which is critical for a practical BCI.

Cross-Participant Generalization: We agree that this is a key question for scalability. Our framework already addresses part of the cross-participant generalization challenge through the use of pre-trained generative modules (HiFi-GAN, Parler-TTS, CosyVoice 2.0), which are pretrained on large corpora and shared across all participants. Only a small fraction of the model, the lightweight neural adaptors, is subject-specific and requires a small amount of supervised fine-tuning (~20 minutes per participant). This design significantly reduces the per-subject calibration burden. As the reviewer implies, the ultimate goal would be pure zero-shot generalization. A promising future direction is to further improve cross-participant alignment by learning a shared neural feature encoder (e.g., using contrastive or self-supervised learning on aggregated ECoG data) before the personalized adaptors. We have added a paragraph in the Discussion outlining this as a major next step to enhance the framework’s practicality and further reduce calibration time.

Accuracy vs. Recording Duration: Thank you for this insightful suggestion. To systematically evaluate the impact of training data volume on performance, we have conducted additional experiments using progressively smaller subsets of the full training set (i.e., 25%, 50%, and 75%). When we used more than 50% of the training data, performance degrades gracefully rather than catastrophically with less data, which is promising for potential clinical scenarios where data collection may be limited. We add another figure (Fig. S4) to demonstrate this.

Edits:

Pages 15-16, Lines 427-452:

“There are several limitations in our study. The quality of the re-synthesized speech heavily relies on the performance of the generative model, indicating that future work should focus on refining and enhancing these models. Currently, our study utilized English speech sentences as input stimuli, and the performance of the system in other languages remains to be evaluated. Regarding signal modality and experimental methods, the clinical setting restricts us to collecting data during brief periods of awake neurosurgeries, which limits the amount of usable neural activity recordings. Overcoming this time constraint could facilitate the acquisition of larger datasets, thereby contributing to the re-synthesis of higher-quality natural speech. Furthermore, the inference speed of the current pipeline presents a challenge for real-time applications. On our hardware (a single NVIDIA GeForce RTX 3090 GPU), synthesizing speech from neural data takes approximately two to three times longer than the duration of the target speech segment itself. This latency is primarily attributed to the sequential processing in the autoregressive linguistic adaptor and the computationally intensive high-fidelity waveform generation in the vocoder (CosyVoice 2.0). While the current study focuses on offline reconstruction accuracy, achieving real-time or faster-than-real-time inference is a critical engineering goal for viable speech BCI prosthetics. Future work must therefore prioritize architectural optimizations, such as exploring non-autoregressive decoding strategies and more efficient neural vocoders, alongside potential hardware acceleration. Additionally, exploring non-invasive methods represents another frontier; with the accumulation of more data and the development of more powerful generative models, it may become feasible to achieve effective non-invasive neural decoding for speech resynthesis. Moreover, while our framework adopts specialized architectures (LSTM and Transformer) for distinct decoding tasks, an alternative approach is to employ a unified multimodal large language model (LLM) capable of joint acoustic-linguistic processing. Finally, the current framework requires training participant-specific adaptors, which limits its immediate applicability for new users. A critical next step is to develop methods that learn a shared, cross-participant neural feature encoder, for instance, by applying contrastive or selfsupervised learning techniques to larger aggregated ECoG datasets. Such an encoder could extract subject-invariant neural representations of speech, serving as a robust initialization before lightweight, personalized fine-tuning. This approach would dramatically reduce the amount of per-subject calibration data and time required, enhancing the practicality and scalability of the decoding framework for real-world BCI applications”

“In summary, our dual-path framework achieves high speech reconstruction quality by strategically integrating language models for lexical precision and voice cloning for vocal identity preservation, yielding a 37.4% improvement in MOS scores over conventional methods. This approach enables high-fidelity, sentence-level speech synthesis directly from cortical recordings while maintaining speaker-specific vocal characteristics. Despite current constraints in generative model dependency and intraoperative data collection, our work establishes a new foundation for neural decoding development. Future efforts should prioritize: (1) refining few-shot adaptation techniques, (2) developing non-invasive implementations, (3) expanding to dynamic dialogue contexts, and (4) cross-subject applications. The convergence of neurophysiological data with multimodal foundation models promises transformative advances, not only revolutionizing speech BCIs but potentially extending to cognitive prosthetics for memory augmentation and emotional communication. Ultimately, this paradigm will deepen our understanding of neural speech processing while creating clinically viable communication solutions for those with severe speech impairments”

Edits: 

add another section in Methods: Page 22, Line 681:

“Ablation study on training data volume”.

“To assess the impact of training data quantity on decoding performance, we conducted an additional ablation experiment. For each participant, we created subsets of the full training set corresponding to 25%, 50%, and 75% of the original data by random sampling while preserving the temporal continuity of speech segments. Personalized acoustic and linguistic adaptors were then independently trained from scratch on each subset, following the identical architecture and optimization procedures described above. All other components of the pipeline, including the frozen pre-trained generators (HiFi-GAN, Parler-TTS) and the CosyVoice 2.0 voice cloner, remained unchanged. Performance metrics (mel-spectrogram R², WER, PER) were evaluated on the same held-out test set for all data conditions. The results (Fig. S4) demonstrate that when more than 50% of the training data is utilized, performance degrades gracefully rather than catastrophically, which is a promising indicator for clinical applications with limited data collection time”.

(3) I appreciate that the author compared their model with the MLP, but more comparisons with previous models could be beneficial. Even simply summarizing some measures of earlier models, such as neural recording duration, WER, PER, etc., is ok.

Thank you for this suggestion. We agree that a broader comparison contextualizes our contribution. We also acknowledge that given the differences in tasks, signal modality, and amount of data, it’s hard to draw a direct comparison. The main goal of this table is to summarize major studies, their methods and results for reference. We have now added a new Supplementary Table that summarizes key metrics from several recent and relevant studies in neural speech decoding. The table includes:

- Neural modality (e.g., ECoG, sEEG, Utah array)

- Approximate amount of neural data used per subject for decoder training

- Primary task (perception vs. production)

-Decoding framework

-Reported Word Error Rate (WER) or similar intelligibility metrics (e.g., Character Error Rate)

-Reported acoustic fidelity metrics (if available, e.g., spectral correlation)

This table includes works such as Anumanchipalli et al., Nature 2019; Akbari et al., Sci Rep 2019; Willett et al., Nature 2023; and other contemporary studies. The table clearly shows that our dual-path framework achieves a highly competitive WER (~18.9%) using an exceptionally short neural recording duration (~20 minutes), highlighting its data efficiency. We will refer to this table in the revised manuscript.

Edits:

Page 14, Lines 374-376:

“Our framework establishes a framework for speech decoding by outperforming prior acousticonly or linguistic-only approaches (Table S3) through integrated pretraining-powered acoustic and linguistic decoding”

Minor:

(1) Some processes might be described earlier, for example, the electrodes were selected, and the model was trained separately for each participant. That information was only described in the Method section now.

Thank you for catching these. We have revised the manuscript accordingly.

Edits:

Page4, Lines 89-95:

“Our proposed framework for reconstructing speech from intracranial neural recordings is designed around two complementary decoding pathways: an acoustic pathway focused on preserving low-level spectral and prosodic detail, and a linguistic pathway focused on decoding high-level textual and semantic content. For every participant, our adaptor is independently trained, and we select speech-responsive electrodes (selection details are provided in the Methods section) to tailor the model to individual neural patterns. These two streams are ultimately fused to synthesize speech that is both natural-sounding and intelligible, capturing the full richness of spoken language. Fig. 1 provides a schematic overview of this dual-pathway architecture”

(2) Line 224-228 Figure 2 should be Figure 3

Thank you for catching these. We have revised the manuscript accordingly. The information about participant-specific training and electrode selection is now briefly mentioned in the "Results" overview (section: "The acoustic and linguistic performance..."), with details still in the Methods. The figure reference error has been corrected.

Edits:

Page7, Lines 224-228:

“However, exclusive reliance on acoustic reconstruction reveals fundamental limitations. Despite excellent spectral fidelity, the pathway produces critically impaired linguistic intelligibility. At the word level, intelligibility remains unacceptably low (WER = 74.6 ± 5.5%, Fig. 3D), while MOS and phoneme-level precision fares only marginally better (MOS = 2.878 ± 0.205, Fig. 3C; PER = 28.1 ± 2.2%, Fig. 3E)”.

(3) For Figure 3C, why does the MOS seem to be higher for baseline 3 than for ground truth? Is this significant?

This is a detailed observation. Baseline 3 achieves a mean opinion score of 4.822 ± 0.086 (Fig. 3C), significantly surpassing even the original human speech (4.234 ± 0.097, p = 6.674×10⁻33). We believe this trend arises because the TIMIT corpus, recorded decades ago, contains inherent acoustic noise and relatively lower fidelity compared to modern speech corpus. In contrast, the Parler-TTS model used in Baseline 3 is trained on massive, highquality, clean speech datasets. Therefore, it synthesizes speech that listeners may subjectively perceive as "cleaner" or more pleasant, even if it lacks the original speaker's voice. Crucially, as the reviewer implies, our final integrated output does not aim to maximize MOS at the cost of speaker identity; it successfully balances this subjective quality with high intelligibility and restored acoustic fidelity. We will add a brief note explaining this possible reason in the caption of Figure 3C.

Edits:

Page9, Lines 235-245:

“The linguistic pathway reconstructs high-intelligibility, higher-level linguistic information”

“The linguistic pathway, instantiated through a pre-trained TTS generator (Fig. 1B), excels in reconstructing abstract linguistic representations. This module operates at the phonological and lexical levels, converting discrete word tokens into continuous speech signals while preserving prosodic contours, syllable boundaries, and phonetic sequences. It achieves a mean opinion score of 4.822 ± 0.086 (Fig. 3C) - significantly surpassing even the original human speech (4.234 ± 0.097, p = 6.674×10⁻33) in that the TIMIT corpus, recorded decades ago, contains inherent acoustic noise and relatively lower fidelity compared to modern speech corpus.  Complementing this perceptual quality, objective intelligibility metrics confirm outstanding performance: WER reaches 17.7 ± 3.2%, with PER at 11.0 ± 2.3%”.

Reference

(1) Chen M X, Firat O, Bapna A, et al. The best of both worlds: Combining recent advances in neural machine translation[C]//Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long papers). 2018: 76-86

(2) P. Chen et al. Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain? 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024). 2225–2229 (2024).

(3) H. Akbari, B. Khalighinejad, J. L. Herrero, A. D. Mehta, N. Mesgarani, Towards reconstructing intelligible speech from the human auditory cortex. Scientific reports 9, 874 (2019).

(4) S. Komeiji et al., Transformer-Based Estimation of Spoken Sentences Using Electrocorticography. Int Conf Acoust Spee, 1311-1315 (2022).

(5) L. Bellier et al., Music can be reconstructed from human auditory cortex activity using nonlinear decoding models. Plos Biology 21,  (2023).

(6) F. R. Willett et al., A high-performance speech neuroprosthesis. Nature 620,  (2023).

(7) S. L. Metzger et al., A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620, 1037-1046 (2023).

(8) J. W. Li et al., Neural2speech: A Transfer Learning Framework for NeuralDriven Speech Reconstruction. Int Conf Acoust Spee, 2200-2204 (2024).

(9) X. P. Chen et al., A neural speech decoding framework leveraging deep learning and speech synthesis. Nat Mach Intell 6,  (2024).

(10) M. Wairagkar et al., An instantaneous voice-synthesis neuroprosthesis. Nature,  (2025).

Now we know its 100 percent confirmed that Omakakiins was the girl in the beginning

Nice interpretation I figured they would go deeper but they choose not to here

I find this whole section intriguing because to this day, the Chinese government operates like this, having officials ranks, each based on seniority.

PKM as tool for exploration and perhaps later reorientation, towards action / actionable things. Vgl [[Actionable sense als groep 20200801065550]] blog disc some of us had in 2003.

blogging as being comfortable with not knowing, holding questions. This comes close to writing as thinking itself.

refs [[Dave Snowden p]] aporia, disorientation as trigger to get a sense of what it is you don't know, through exploration. To get to the point where your knowledge need becomes actionable

Harold is working on a book on PKM. Seek and Share is working title.

Harold is nearing the end of his working life, what does it mean for his blogging? If it is a signboard more, or a professional writing outlet, then yes it may fall away. But if it is your primary space for expression, whatever the topic then the blog can morph with it, no?

signals the relative triviality of a lot of his writing in face of geopolitical upheaval. I feel him but also fully disagree, clearly signalling your humanity in the face of it all is key. vgl othering. life is 'small' by def. There is no 'need' to be part of the 'big' discussions for a blog to matter. Cat pictures ftw.

2026 decided to blog more again despite the aicrawlers. Can relate. I realised that my primary goal for blogging is distributed conversations as it was at the start, so whatever else happens is a 'don't care'.

[[Harold Jarche p]] describes how he came to blogging. He was in a place where there were no others to find. Blogging was finding the others. Early blogging scene was small and people helped eachother out. Did edutech first, then KM. Started in (early iirc) 2004.

smaller / trusted networks vs scale.

Reviewer #1 (Public review):

Summary:

Chen et al. engineered and characterized a suite of next-generation GECIs for the Drosophila NMJ that allow for the visualization of calcium dynamics within the presynaptic compartment, at presynaptic active zones, and in the postsynaptic compartment. These GECIs include ratiometric presynaptic Scar8m (targeted to synaptic vesicles), ratiometric active zone localized Bar8f (targeted to the scaffold molecule BRP), and postsynaptic SynapGCaMP8m. The authors demonstrate that these new indicators are a large improvement on the widely used GCaMP6 and GCaMP7 series GECIs, with increased speed and sensitivity. They show that presynaptic Scar8m accurately captures presynaptic calcium dynamics with superior sensitivity to the GCaMP6 and GCaMP7 series and with similar kinetics to chemical dyes. The active-zone targeted Bar8f sensor was assessed for the ability to detect release-site specific nanodomain changes, but the authors concluded that this sensor is still too slow to accurately do so. Lastly, the use of postsynaptic SynapGCaMP8m was shown to enable the detection of quantal events with similar resolution to electrophysiological recordings. Finally, the authors developed a Python-based analysis software, CaFire, that enables automated quantification of evoked and spontaneous calcium signals. These tools will greatly expand our ability to detect activity at individual synapses without the need for chemical dyes or electrophysiology.

Strengths:

In this study, the authors rigorously compare their newly engineered GECIs to those previously used at the Drosophila NMJ, highlighting improvements in localization, speed, and sensitivity. These comparisons appropriately substantiate the authors claim that their GECIs are superior to the ones currently in use.

The authors demonstrate the ability of Scar8m to capture subtle changes in presynaptic calcium resulting from differences between MN-Ib and MN-Is terminals and from the induction of presynaptic homeostatic potentiation (PHP), rivaling the sensitivity of chemical dyes.

The improved postsynaptic SynapGCaMP8m is shown to approach the resolution of electrophysiology in resolving quantal events.

The authors created a publicly available pipeline that streamlines and standardizes analysis of calcium imaging data.

Author response:

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

Reviewer #1

Chen et al. engineered and characterized a suite of next-generation GECIs for the Drosophila NMJ that allow for the visualization of calcium dynamics within the presynaptic compartment, at presynaptic active zones, and in the postsynaptic compartment. These GECIs include ratiometric presynaptic Scar8m (targeted to synaptic vesicles), ratiometric active zone localized Bar8f (targeted to the scaffold molecule BRP), and postsynaptic SynapGCaMP8m. The authors demonstrate that these new indicators are a large improvement on the widely used GCaMP6 and GCaMP7 series GECIs, with increased speed and sensitivity. They show that presynaptic Scar8m accurately captures presynaptic calcium dynamics with superior sensitivity to the GCaMP6 and GCaMP7 series and with similar kinetics to chemical dyes. The active-zone targeted Bar8f sensor was assessed for the ability to detect release-site-specific nanodomain changes, but the authors concluded that this sensor is still too slow to accurately do so. Lastly, the use of postsynaptic SynapGCaMP8m was shown to enable the detection of quantal events with similar resolution to electrophysiological recordings. Finally, the authors developed a Python-based analysis software, CaFire, that enables automated quantification of evoked and spontaneous calcium signals. These tools will greatly expand our ability to detect activity at individual synapses without the need for chemical dyes or electrophysiology.

We thank this Reviewer for the overall positive assessment of our manuscript and for the incisive comments.

(1) The role of Excel in the pipeline could be more clearly explained. Lines 182-187 could be better worded to indicate that CaFire provides analysis downstream of intensity detection in ImageJ. Moreover, the data type of the exported data, such as .csv or .xlsx, should be indicated instead of 'export to graphical program such as Microsoft Excel'.

We thank the Reviewer for these comments, many of which were shared by the other reviewers. In response, we have now 1) more clearly explained the role of Excel in the CaFire pipeline (lines 677-681), 2) revised the wording in lines 676-679 to indicate that CaFire provides analysis downsteam of intensity detection in ImageJ, and 3) Clarified the exported data type to Excel (lines 677-681). These efforts have improved the clarity and readability of the CaFire analysis pipeline.

(2) In Figure 2A, the 'Excel' step should either be deleted or included as 'data validation' as ImageJ exports don't require MS Excel or any specific software to be analysed. (Also, the graphic used to depict Excel software in Figure 2A is confusing.)

We thank the reviewer for this helpful suggestion. In the Fig. 2A, we have changed the Excel portion and clarified the processing steps in the revised methods. Specifically, we now indicate that ROIs are first selected in Fiji/ImageJ and analyzed to obtain time-series data containing both the time information and the corresponding imaging mean intensity values. These data are then exported to a spreadsheet file (e.g., Excel), which is used to organize the output before being imported into CaFire for subsequent analysis. These changes can be found in the Fig. 2A and methods (lines 676-681).

(3) Figure 2B should include the 'Partition Specification' window (as shown on the GitHub) as well as the threshold selection to give the readers a better understanding of how the tool works.

We absolutely agree with this comment, and have made the suggested changes to the Fig. 2B. In particular, we have replaced the software interface panels and now include windows illustrating the Load File, Peak Detection, and Partition functions. These updated screenshots provide a clearer view of how CaFire is used to load the data, detect events, and perform partition specification for subsequent analysis. We agree these changes will give the readers a better understanding of how the tool works, and we thank the reviewer for this comment.

(4) The presentation of data is well organized throughout the paper. However, in Figure 6C, it is unclear how the heatmaps represent the spatiotemporal fluorescence dynamics of each indicator. Does the signal correspond to a line drawn across the ROI shown in Figure 6B? If so, this should be indicated.

We apologize that the heatmaps were unclear in Fig panel 6C (Fig. 7C in the Current revision). Each heatmap is derived from a one-pixel-wide vertical line within a miniature-event ROI. These heatmaps correspond to the fluorescence change in the indicated SynapGCaMP variant of individual quantal events and their traces shown in Fig. 7C, with a representative image of the baseline and peak fluorescence shown in Fig. 7B. Specifically, we have added the following to the revised Fig. 7C legend:

The corresponding heatmaps below were generated from a single vertical line extracted from a representative miniature-event ROI, and visualize the spatiotemporal fluorescence dynamics (ΔF/F) along that line over time.

(5) In Figure 6D, the addition of non-matched electrophysiology recordings is confusing. Maybe add "at different time points" to the end of the 6D legend, or consider removing the electrophysiology trace from Figure 6D and referring the reader to the traces in Figure 7A for comparison (considering the same point is made more rigorously in Figure 7).

This is a good point, one shared with another reviewer. We apologize this was not clear, and have now revised this part of the figure to remove the electrophysiological traces in what is now Fig. 7 while keeping the paired ones still in what is now Fig. 8A as suggested by the reviewer. We agree this helps to clarify the quantal calcium transients.

(6) In GitHub, an example ImageJ Script for analyzing the images and creating the inputs for CaFire would be helpful to ensure formatting compatibility, especially given potential variability when exporting intensity information for two channels. In the Usage Guide, more information would be helpful, such as how to select ∆R/R, ideally with screenshots of the application being used to analyze example data for both single-channel and two-channel images.

We agree that additional details added to the GitHub would be helpful for users of CaFire. In response, we have now added the following improvements to the GitHub site: 

- ImageJ operation screenshots

Step-by-step illustrations of ROI drawing and Multi Measure extraction.

- Example Excel file with time and intensity values

Demonstrates the required data format for CaFire import, including proper headers.

- CaFire loading screenshots for single-channel and dual-channel imaging

Shows how to import GCaMP into Channel 1 and mScarlet into Channel 2.

- Peak Detection and Partition setting screenshots

Visual examples of automatic peak detection, manual correction, and trace partitioning.

- Instructions for ROI Extraction and CaFire Analysis

A written guide describing the full workflow from ROI selection to CaFire data export.

These changes have improved the usability and accessibility of CaFire, and we thank the reviewer for these points.

Reviewer #2

Calcium ions play a key role in synaptic transmission and plasticity. To improve calcium measurements at synaptic terminals, previous studies have targeted genetically encoded calcium indicators (GECIs) to pre- and postsynaptic locations. Here, Chen et al. improve these constructs by incorporating the latest GCaMP8 sensors and a stable red fluorescent protein to enable ratiometric measurements. In addition, they develop a new analysis platform, 'CaFire', to facilitate automated quantification. Using these tools, the authors demonstrate favorable properties of their sensors relative to earlier constructs. Impressively, by positioning postsynaptic GCaMP8m near glutamate receptors, they show that their sensors can report miniature synaptic events with speed and sensitivity approaching that of intracellular electrophysiological recordings. These new sensors and the analysis platform provide a valuable tool for resolving synaptic events using all-optical methods.

We thank the Reviewer for their overall positive evaluation and comments.

Major comments:

(1) While the authors rigorously compared the response amplitude, rise, and decay kinetics of several sensors, key parameters like brightness and photobleaching rates are not reported. I feel that including this information is important as synaptically tethered sensors, compared to freely diffusible cytosolic indicators, can be especially prone to photobleaching, particularly under the high-intensity illumination and high-magnification conditions required for synaptic imaging. Quantifying baseline brightness and photobleaching rates would add valuable information for researchers intending to adopt these tools, especially in the context of prolonged or high-speed imaging experiments.

This is a good point made by the reviewer, and one we agree will be useful for researchers to be aware. First, it is important to note that the photobleaching and brightness of the sensors will vary depending on the nature of the user’s imaging equipment, which can vary significantly between widefield microscopes (with various LED or halogen light sources for illumination), laser scanning systems (e.g., line scans with confocal systems), or area scanning systems using resonant scanners (as we use in our current study). Under the same imaging settings, GCaMP8f and 8m exhibit comparable baseline fluorescence, whereas GCaMP6f and 6s are noticeably dimmer; because our aim is to assess each reagent’s potential under optimal conditions, we routinely adjust excitation/camera parameters before acquisition to place baseline fluorescence in an appropriate dynamic range. As an important addition to this study, motivated by the reviewer’s comments above, we now directly compare neuronal cytosolic GCaMP8m expression with our Scar8m sensor, showing higher sensitivity with Scar8m (now shown in the new Fig. 3F-H).

Regarding photobleaching, GCaMP signals are generally stable, while mScarlet is more prone to bleaching: in presynaptic area scanned confocal recordings, the mScarlet channel drops by ~15% over 15 secs, whereas GCaMP6s/8f/8m show no obvious bleaching over the same window (lines 549-553). In contrast, presynaptic widefield imaging using an LED system (CCD), GCaMP8f shows ~8% loss over 15 secs (lines 610-611). Similarly, for postsynaptic SynapGCaMP6f/8f/8m, confocal resonant area scans show no obvious bleaching over 60 secs, while widefield shows ~2–5% bleaching over 60 secs (lines 634-638). Finally, in active-zone/BRP calcium imaging (confocal), mScarlet again bleaches by ~15% over 15 s, while GCaMP8f/8m show no obvious bleaching. The mScarlet-channel bleaching can be corrected in Huygens SVI (Bleaching correction or via the Deconvolution Wizard), whereas we avoid applying bleaching correction to the green GCaMP channel when no clear decay is present to prevent introducing artifacts. This information is now added to the methods (lines 548-553).

(2) In several places, the authors compare the performance of their sensors with synthetic calcium dyes, but these comparisons are based on literature values rather than on side-by-side measurements in the same preparation. Given differences in imaging conditions across studies (e.g., illumination, camera sensitivity, and noise), parameters like indicator brightness, SNR, and photobleaching are difficult to compare meaningfully. Additionally, the limited frame rate used in the present study may preclude accurate assessment of rise times relative to fast chemical dyes. These issues weaken the claim made in the abstract that "...a ratiometric presynaptic GCaMP8m sensor accurately captures .. Ca²⁺ changes with superior sensitivity and similar kinetics compared to chemical dyes." The authors should clearly acknowledge these limitations and soften their conclusions. A direct comparison in the same system, if feasible, would greatly strengthen the manuscript.

We absolutely agree with these points made the reviewer, and have made a concerted effort to address them through the following:

We have now directly compared presynaptic calcium responses on the same imaging system using the chemical dye Oregon Green Bapta-1 (OGB-1), one of the primary synthetic calcium indicators used in our field. These experiments reveal that Scar8f exhibits markedly faster kinetics and an improved signal-to-noise ratio compared to OGB-1, with higher peak fluorescence responses (Scar8f: 0.32, OGB-1: 0.23). The rise time constants of the two indicators are comparable (both ~3 msecs), whereas the decay of Scar8f is faster than that of OGB-1 (Scar8f: ~40, OGB-1: ~60), indicating more rapid signal recovery. These results now directly demonstrate the superiority of the new GCaMP8 sensors we have engineered over conventional synthetic dyes, and are now presented in the new Fig. 3A-E of the manuscript.

We agree with the reviewer that, in the original submission, the relatively slow resonant area scans (~115 fps) limited the temporal resolution of our rise time measurements. To address this, we have re-measured the rise time using higher frame-rate line scans (kHz). For Scar8f, the rise time constant was 6.736 msec at ~115 fps resonant area scanned, but shortened to 2.893 msec when imaged at ~303 fps, indicating that the original protocol underestimated the true kinetics. In addition, for Bar8m, area scans at ~118 fps yielded a rise time constant of 9.019 msec, whereas line scans at ~1085 fps reduced the rise time constant to 3.230 msec. These new measurements are now incorporated into the manuscript ( Figs. 3,4, and 6) to more accurately reflect the fast kinetics of these indicators.

(3) The authors state that their indicators can now achieve measurements previously attainable with chemical dyes and electrophysiology. I encourage the authors to also consider how their tools might enable new measurements beyond what these traditional techniques allow. For example, while electrophysiology can detect summed mEPSPs across synapses, imaging could go a step further by spatially resolving the synaptic origin of individual mEPSP events. One could, for instance, image MN-Ib and MN-Is simultaneously without silencing either input, and detect mEPSP events specific to each synapse. This would enable synapse-specific mapping of quantal events - something electrophysiology alone cannot provide. Demonstrating even a proof-of-principle along these lines could highlight the unique advantages of the new tools by showing that they not only match previous methods but also enable new types of measurements.

These are excellent points raised by the reviewer. In response, we have done the following: 

We have now included a supplemental video as “proof-of-principle” data showing simultaneous imaging of SynapGCaMP8m quantal events at both MN-Is and -Ib, demonstrating that synapse-specific spatial mapping of quantal events can be obtained with this tool (see new Supplemental Video 1). 

We have also included an additional discussion of the potential and limitations of these tools for new measurements beyond conventional approaches. This discussion is now presented in lines 419-421 in the manuscript.

(4) For ratiometric measurements, it is important to estimate and subtract background signals in each channel. Without this correction, the computed ratio may be skewed, as background adds an offset to both channels and can distort the ratio. However, it is not clear from the Methods section whether, or how, background fluorescence was measured and subtracted.

This is a good point, and we agree more clarification about how ratiometric measurements were made is needed. In response, we have now added the following to the Methods section (lines 548-568):

Time-lapse videos were stabilized and bleach-corrected prior to analysis, which visibly reduced frame-toframe motion and intensity drift. In the presynaptic and active-zone mScarlet channel, a bleaching factor of ~1.15 was observed during the 15 sec recording. This bleaching can be corrected using the “Bleaching correction” tool in Huygens SVI. For presynaptic and active-zone GCaMP signals, there was minimal bleaching over these short imaging periods. Therefore, the bleaching correction step for GCaMP was skipped. Both GCaMP and mScarlet channels were processed using the default settings in the Huygens SVI “Deconvolution Wizard” (with the exception of the bleaching correction option). Deconvolution was performed using the CMLE algorithm with the Huygens default stopping criterion and a maximum of 30 iterations, such that the algorithm either converged earlier or, if convergence was not reached, was terminated at this 30iteration limit; no other iteration settings were used across the GCaMP series. ROIs were drawn on the processed images using Fiji ImageJ software, and mean fluorescence time courses were extracted for the GCaMP and mScarlet channels, yielding F_GCaMP(t) and F_mScarlet(t). F(t)s were imported into CaFire with GCaMP assigned to Channel #1 (signal; required) and mScarlet to Channel #2 (baseline/reference; optional). If desired, the mScarlet signal could be smoothed in CaFire using a user-specified moving-average window to reduce high-frequency noise. In CaFire’s ΔR/R mode, the per-frame ratio was computed as R(t)=F_GCaMP(t) and F_mScarlet(t); a baseline ratio R0 was estimated from the pre-stimulus period, and the final response was reported as ΔR/R(t)=[R(t)−R0]/R0, which normalizes GCaMP signals to the co-expressed mScarlet reference and thereby reduces variability arising from differences in sensor expression level or illumination across AZs.

(5) At line 212, the authors claim "... GCaMP8m showing 345.7% higher SNR over GCaMP6s....(Fig. 3D and E) ", yet the cited figure panels do not present any SNR quantification. Figures 3D and E only show response amplitudes and kinetics, which are distinct from SNR. The methods section also does not describe details for how SNR was defined or computed.

This is another good point. We define SNR operationally as the fractional fluorescence change (ΔF/F). Traces were processed with CaFire, which estimates a per-frame baseline F₀(t) with a user-configurable sliding window and percentile. In the Load File panel, users can specify both the length of the moving baseline window and the desired percentile; the default settings are a 50-point window and the 30th percentile, representing a 101-point window centered on each time point (previous 50 to next 50 samples) and took the lower 30% of values within that window to estimate F₀(t). The signal was then computed as ΔF/F=[F(t)−F0(t)]/F0(t). This ΔF/F value is what we report as SNR throughout the manuscript and is now discussed explicitly in the revised methods (lines 686-693).

(6) Lines 285-287 "As expected, summed ΔF values scaled strongly and positively with AZ size (Fig. 5F), reflecting a greater number of Cav2 channels at larger AZs". I am not sure about this conclusion. A positive correlation between summed ΔF values and AZ size could simply reflect more GCaMP molecules in larger AZs, which would give rise to larger total fluorescence change even at a given level of calcium increase.

The reviewer makes a good point, one that we agree should be clarified. The reviewer is indeed correct that larger active zones should have more abundant BRP protein, which in turn will lead to a higher abundance of the Bar8f sensor, which should lead to a higher GCaMP response simply by having more of this sensor. However, the inclusion of the ratiometric mScarlet protein should normalize the response accurately, correcting for this confound, in which the higher abundance of GCaMP should be offset (normalized) by the equally (stoichiometric) higher abundance of mScarlet. Therefore, when the ∆R/R is calculated, the differences in GCaMP abundance at each AZ should be corrected for the ratiometric analysis. We now use an improved BRP::mScarlet3::GCaMP8m (Bar8m) and compute ΔR/R with R(t)=F_GCaMP8m/F_mScarlet3. ROIs were drawn over individual AZs (Fig. 6B). CaFire estimated R0 with a sliding 101-point window using the lowest 10% of values, and responses were reported as ΔR/R=[R−R0]/R0. Area-scan examples (118 fps) show robust ΔR/R transients (peaks ≈1.90 and 3.28; tau rise ≈9.0–9.3 ms; Fig. 6C, middle).

We have now made these points more clearly in the manuscript (lines 700-704) and moved the Bar8f intensity vs active zone size data to Table S1. Together, these revisions improve the indicator-abundance confound (via mScarlet normalization). 

(6) Lines 313-314: "SynapGCaMP quantal signals appeared to qualitatively reflect the same events measured with electrophysiological recordings (Fig. 6D)." This statement is quite confusing. In Figure 6D, the corresponding calcium and ephys traces look completely different and appear to reflect distinct sets of events. It was only after reading Figure 7 that I realized the traces shown in Figure 6D might not have been recorded simultaneously. The authors should clarify this point.

Yes, we absolutely agree with this point, one shared by Reviewer 1. In response, we have removed the electrophysiological traces in Fig. 6 to clarify that just the calcium responses are shown, and save the direct comparison for the Fig. 7 data (now revised Fig. 8).

(8) Lines 310-313: "SynapGCaMP8m .... striking an optimal balance between speed and sensitivity", and Lines 314-316: "We conclude that SynapGCaMP8m is an optimal indicator to measure quantal transmission events at the synapse." Statements like these are subjective. In the authors' own comparison, GCaMP8m is significantly slower than GCaMP8f (at least in terms of decay time), despite having a moderately higher response amplitude. It is therefore unclear why GCaMP8m is considered 'optimal'. The authors should clarify this point or explain their rationale for prioritizing response amplitude over speed in the context of their application.

This is another good point that we agree with, as the “optimal” sensor will of course depend on the user’s objectives. Hence, we used the term “an optimal sensor” to indicate it is what we believed to be the best one for our own uses. However, this point should be clarified and better discussed. In response, we have revised the relevant sections of the manuscript to better define why we chose the 8m sensors to strike an optimal balance of speed and sensitivity for our uses, and go on to discuss situations in which other sensor variants might be better suited. These are now presented in lines 223-236 in the revised manuscript, and we thank the reviewer for making these comments, which have improved our study.

Minor comments

(1)  Please include the following information in the Methods section:

(a) For Figures 3 and 4, specify how action potentials were evoked. What type of electrodes were used, where were they placed, and what amount of current or voltage was applied?

We apologize for neglecting to include this information in the original submission. We have now added this information to the revised Methods section (lines 537-543).

(b) For imaging experiments, provide information on the filter sets used for each imaging channel, and describe how acquisition was alternated or synchronized between the green and red channels in ratiometric measurements. Additionally, please report the typical illumination intensity (in mW/mm²) for each experimental condition.

We thank the reviewer for this helpful comment. We have now added detailed information about the imaging configuration to the Methods (lines 512-528) with the following:

Ca2+ imaging was conducted using a Nikon A1R resonant scanning confocal microscope equipped with a 60x/1.0 NA water-immersion objective (refractive index 1.33). GCaMP signals were acquired using the FITC/GFP channel (488-nm laser excitation; emission collected with a 525/50-nm band-pass filter), and mScarlet/mCherry signals were acquired using the TRITC/mCherry channel (561-nm laser excitation; emission collected with a 595/50-nm band-pass filter). ROIs focused on terminal boutons of MN-Ib or -Is motor neurons. For both channels, the confocal pinhole was set to a fixed diameter of 117.5 µm (approximately three Airy units under these conditions), which increases signal collection while maintaining adequate optical sectioning. Images were acquired as 256 × 64 pixel frames (two 12-bit channels) using bidirectional resonant scanning at a frame rate of ~118 frames/s; the scan zoom in NIS-Elements was adjusted so that this field of view encompassed the entire neuromuscular junction and was kept constant across experiments. In ratiometric recordings, the 488-nm (GCaMP) and 561-nm (mScarlet) channels were acquired in a sequential dual-channel mode using the same bidirectional resonant scan settings: for each time point, a frame was first collected in the green channel and then immediately in the red channel, introducing a small, fixed frame-to-frame temporal offset while preserving matched spatial sampling of the two channels.

Directly measuring the absolute laser power at the specimen plane (and thus reporting illumination intensity in mW/mm²) is technically challenging on this resonant-scanning system, because it would require inserting a power sensor into the beam path and perturbing the optical alignment; consequently, we are unable to provide reliable absolute mW/mm² values. Instead, we now report all relevant acquisition parameters (objective, numerical aperture, refractive index, pinhole size, scan format, frame rate, and fixed laser/detector settings) and note that laser powers were kept constant within each experimental series and chosen to minimize bleaching and phototoxicity while maintaining an adequate signal-to-noise ratio. We have now added the details requested in the revised Methods section (lines 512-535), including information about the filter sets, acquisition settings, and typical illumination intensity.

(2) Please clarify what the thin versus thick traces represent in Figures 3D, 3F, 4C, and 4E. Are the thin traces individual trials from the same experiment, or from different experiments/animals? Does the thick trace represent the mean/median across those trials, a fitted curve, or a representative example?

We apologize this was not more clear in the original submission. Thin traces are individual stimulus-evoked trials (“sweeps”) acquired sequentially from the same muscle/NMJ in a single preparation; the panel is shown as a representative example of recordings collected across animals. The thick colored trace is the trialaveraged waveform (arithmetic mean) of those thin traces after alignment to stimulus onset and baseline subtraction (no additional smoothing beyond what is stated in Methods). The thick black curve over the decay phase is a single-exponential fit used to estimate τ. Specifically, we fit the decay segment by linear regression on the natural-log–transformed baseline-subtracted signal, which is equivalent to fitting y = y_peak·e^−t/τdecay over the decay window (revised Fig.4D and Fig.5C legends).

(3) Please clarify what the reported sample size (n) represents. Does it indicate the number of experimental repeats, the number of boutons or PSDs, or the number of animals?

Again, we apologize this was not clear. (n) refers to the number of animals (biological replicates), which is reported in Supplementary Table 1. All imaging was performed at muscle 6, abdominal segment A3. Per preparation, we imaged 1-2 NMJs in total, with each imaging targeting 2–3 terminal boutons at the target NMJ and acquired 2–3 imaging stacks choosing different terminal boutons per NMJ. For the standard stimulation protocol, we delivered 1 Hz stimulation for 1ms and captured 14 stimuli in a 15s time series imaging (lines 730-736).

Reviewer #3

Genetically encoded calcium indicators (GECIs) are essential tools in neurobiology and physiology. Technological constraints in targeting and kinetics of previous versions of GECIs have limited their application at the subcellular level. Chen et al. present a set of novel tools that overcome many of these limitations. Through systematic testing in the Drosophila NMJ, they demonstrate improved targeting of GCaMP variants to synaptic compartments and report enhanced brightness and temporal fidelity using members of the GCaMP8 series. These advancements are likely to facilitate more precise investigation of synaptic physiology.

This is a comprehensive and detailed manuscript that introduces and validates new GECI tools optimized for the study of neurotransmission and neuronal excitability. These tools are likely to be highly impactful across neuroscience subfields. The authors are commended for publicly sharing their imaging software.

This manuscript could be improved by further testing the GECIs across physiologically relevant ranges of activity, including at high frequency and over long imaging sessions. The authors provide a custom software package (CaFire) for Ca2+ imaging analysis; however, to improve clarity and utility for future users, we recommend providing references to existing Ca2+ imaging tools for context and elaborating on some conceptual and methodological aspects, with more guidance for broader usability. These enhancements would strengthen this already strong manuscript.

We thank the Reviewer for their overall positive evaluation and comments. 

Major comments:

(1) Evaluation of the performance of new GECI variants using physiologically relevant stimuli and frequency. The authors took initial steps towards this goal, but it would be helpful to determine the performance of the different GECIs at higher electrical stimulation frequencies (at least as high as 20 Hz) and for longer (10 seconds) (Newman et al, 2017). This will help scientists choose the right GECI for studies testing the reliability of synaptic transmission, which generally requires prolonged highfrequency stimulation.

We appreciate this point by the reviewer and agree it would be of interest to evaluate sensor performance with higher frequency stimulation and for a longer duration. In response, we performed a variety of stimulation protocols at high intensities and times, but found the data to be difficult to separate individual responses given the decay kinetics of all calcium sensors. Hence, we elected not to include these in the revised manuscript. However, we have now included an evaluation of the sensors with 20 Hz electrical stimulation for ~1 sec using a direct comparison of Scar8f with OGB-1. These data are now presented in a new Fig. 3D,E and discussed in the manuscript (lines 396-403).

(2) CaFire.

The authors mention, in line 182: 'Current approaches to analyze synaptic Ca2+ imaging data either repurpose software designed to analyze electrophysiological data or use custom software developed by groups for their own specific needs.' References should be provided. CaImAn comes to mind (Giovannucci et al., 2019, eLife), but we think there are other software programs aimed at analyzing Ca2+ imaging data that would permit such analysis.

Thank you for the thoughtful question. At this stage, we’re unable to provide a direct comparison with existing analysis workflows. In surveying prior studies that analyze Drosophila NMJ Ca²⁺ imaging traces, we found that most groups preprocess images in Fiji/ImageJ and then rely on their own custom-made MATLAB or Python scripts for downstream analysis (see Blum et al. 2021; Xing and Wu 2018). Because these pipelines vary widely across labs, a standardized head-to-head evaluation isn’t currently feasible. With CaFire, our goal is to offer a simple, accessible tool that does not require coding experience and minimizes variability introduced by custom scripts. We designed CaFire to lower the barrier to entry, promote reproducibility, and make quantal event analysis more consistent across users. We have added references to the sentence mentioned above.

Regarding existing software that the reviewer mentioned – CaImAn (Giovannucci et al. 2019): We evaluated CaImAn, which is a powerful framework designed for large-scale, multicellular calcium imaging (e.g., motion correction, denoising, and automated cell/ROI extraction). However, it is not optimized for the per-event kinetics central to our project - such as extracting rise and decay times for individual quantal events at single synapses. Achieving this level of granularity would typically require additional custom Python scripting and parameter tuning within CaImAn’s code-centric interface. This runs counter to CaFire’s design goals of a nocode, task-focused workflow that enables users to analyze miniature events quickly and consistently without specialized programming expertise.

Regarding Igor Pro (WaveMetrics), (Müller et al. 2012): Igor Pro is another platform that can be used to analyze calcium imaging signals. However, it is commercial (paid) software and generally requires substantial custom scripting to fit the specific analyses we need. In practice, it does not offer a simple, open-source, point-and-click path to per-event kinetic quantification, which is what CaFire is designed to provide.

The authors should be commended for making their software publicly available, but there are some questions:

How does CaFire compare to existing tools?

As mentioned above, we have not been able to adapt the custom scripts used by various labs for our purposes, including software developed in MatLab (Blum et al. 2021), Python (Xing and Wu 2018), and Igor (Müller et al. 2012). Some in the field do use semi-publically available software, including Nikon Elements (Chen and Huang 2017) and CaImAn (Giovannucci et al. 2019). However, these platforms are not optimized for the per-event kinetics central to our project - such as extracting rise and decay times for individual quantal events at single synapses. We have added more details about CaFire, mainly focusing on the workflow and measurements, highlighting the superiority of CaFire, showing that CaFire provides a no-code, standardized pipeline with automated miniature-event detection and per-event metrics (e.g., amplitude, rise time τ, decay time τ), optional ΔR/R support, and auto-partition feature. Collectively, these features make CaFire simpler to operate without programming expertise, more transparent and reproducible across users, and better aligned with the event-level kinetics required for this project.

Very few details about the Huygens deconvolution algorithms and input settings were provided in the methods or text (outside of MLE algorithm used in STED images, which was not Ca2+ imaging). Was it blind deconvolution? Did the team distill the point-spread function for the fluorophores? Were both channels processed for ratiometric imaging? Were the same settings used for each channel? Importantly, please include SVI Huygens in the 'Software and Algorithms' Section of the methods.

We thank the reviewer for raising this important point. We have now expanded the Methods to describe our use of Huygens in more detail and have added SVI Huygens Professional (Scientific Volume Imaging, Hilversum, The Netherlands) to the “Software and Algorithms” section. For Ca²⁺ imaging data, time-lapse stacks were processed in the Huygens Deconvolution Wizard using the standard estimation algorithm (CMLE). This is not a blind deconvolution procedure. Instead, Huygens computes a theoretical point-spread function (PSF) from the full acquisition metadata (objective NA, refractive index, voxel size/sampling, pinhole, excitation/emission wavelengths, etc.); if refractive index values are provided and there is a mismatch, the PSF is adjusted to account for spherical aberration. We did not experimentally distill PSFs from bead measurements, as Huygens’ theoretical PSFs are sufficient for our data.

Both green (GCaMP) and red (mScarlet) channels were processed for ratiometric imaging using the same workflow (stabilization, optional bleaching correction, and deconvolution within Huygens). For each channel, the PSF, background, and SNR were estimated automatically by the same built-in algorithms, so the underlying procedures were identical even though the numerical values differ between channels because of their distinct wavelengths and noise characteristics. Importantly, Huygens normalizes each PSF to unit total intensity, such that the deconvolution itself does not add or remove signal and therefore preserves intensity ratios between channels; only background subtraction and bleaching correction can change absolute fluorescence values. For the mScarlet channel, where we observed modest bleaching (~1.10 over 15 sec), we applied Huygens’ bleaching correction and visually verified that similar structures maintained comparable intensities after correction. For presynaptic GCaMP signals, bleaching over these short recordings was negligible, so we omitted the bleaching-correction step to avoid introducing multiplicative artifacts. This workflow ensures that ratiometric ΔR/R measurements are based on consistently processed, intensity-conserving deconvolved images in both channels.

The number of deconvolution iterations could have had an effect when comparing GCAMP series; please provide an average number of iterations used for at least one experiment. For example, Figure 3, Syt::GCAMP6s, Scar8f & Scar8m, and, if applicable, the maximum number of permissible iterations.

We thank the reviewer for this comment. For all Ca²⁺ imaging datasets, deconvolution in Huygens was performed using the recommended default settings of the CMLE algorithm with a maximum of 30 iterations. The stopping criterion was left at the Huygens default, so the algorithm either converged earlier or, if convergence was not reached, terminated at this 30-iteration limit. No other iteration settings were used across the GCaMP series (lines 555-559).

Please clarify if the 'Express' settings in Huygens changed algorithms or shifted input parameters.

We appreciate the reviewer’s question regarding the Huygens “Express” settings. For clarity, we note that all Ca²⁺ imaging data reported in this manuscript were deconvolved using the “Deconvolution Wizard”, not the “Deconvolution Express” mode. In the Wizard, we explicitly selected the CMLE algorithm (or GMLE in a few STED-related cases as recommended by SVI), using the recommended maximum of 30 iterations, and other recommended settings while allowing Huygens to auto-estimate background and SNR for each channel.Bleaching correction was toggled manually per channel (applied to mScarlet when bleaching was evident, omitted for GCaMP when bleaching was negligible), as described in the revised Methods (lines 553-559).

By contrast, the Deconvolution Express tool in Huygens is a fully automated front-end that can internally adjust both the choice of deconvolution algorithm (e.g., CMLE vs. GMLE/QMLE) and key input parameters such as SNR, number of iterations, and quality threshold based on the selected “smart profile” and the image metadata. In preliminary tests on our datasets, Express sometimes produced results that were either overly smoothed or showed subtle artifacts, so we did not use it for any data included in this study. Instead, we relied exclusively on the Wizard with explicitly controlled settings to ensure consistency and transparency across all GCaMP series and ratiometric analyses.

We suggest including a sample data set, perhaps in Excel, so that future users can beta test on and organize their data in a similar fashion.

We agree that this would be useful, a point shared by R1 above. In response, we have added a sample data set to the GitHub site and included sample ImageJ data along with screenshots to explain the analysis in more detail. These improvements are discussed in the manuscript (lines 705-708).

(3) While the challenges of AZ imaging are mentioned, it is not discussed how the authors tackled each one. What is defined as an active zone? Active zones are usually identified under electron microscopy. Arguably, the limitation of GCaMP-based sensors targeted to individual AZs, being unable to resolve local Ca2+ changes at individual boutons reliably, might be incorrect. This could be a limitation of the optical setup being used here. Please discuss further. What sensor performance do we need to achieve this performance level, and/or what optical setup would we need to resolve such signals?

We appreciate the reviewer’s thoughtful comments and agree that the technical challenges of active zone (AZ) Ca²⁺ imaging merit further clarification. We defined AZs, as is the convention in our field, as individual BRP puncta at NMJs. These BRP puncta co-colocalize with individual puncta of other AZ components, including CAC, RBP, Unc13, etc. ROIs were drawn tightly over individual BRP puncta and only clearly separable spots were included.

To tackle the specific obstacles of AZ imaging (small signal volume, high AZ density, and limited photon budget at high frame rates), we implemented both improved sensors and optimized analysis (Fig. 6). First, we introduced a ratiometric AZ-targeted indicator, BRP::mScarlet3::GCaMP8m (Bar8m), and computed ΔR/R with ΔR/R with R(t)=F_GCaMP8m/F_mScarlet3. ROIs were drawn over individual AZs (Fig. 6B). Under our standard resonant area-scan conditions (~118 fps), Bar8m produces robust ΔR/R transients at individual AZs (example peaks ≈ 3.28; τ_rise≈9.0 ms; Fig. 6C, middle), indicating that single-AZ signals can be detected reproducibly when AZs are optically resolvable.

Second, we increased temporal resolution using high-speed Galvano line-scan imaging (~1058 fps), which markedly sharpened the apparent kinetics (τ_rise≈3.23 ms) and revealed greater between-AZ variability (Fig. 6C, right; 6D–E). Population analyses show that line scans yield much faster rise times than area scans (Fig. 6D) and a dramatically higher fraction of significantly different AZ pairs (8.28% and 4.14% in 8f and 8m areascan vs 78.62% in 8m line-scan, lines 721-725), uncovering pronounced AZ-to-AZ heterogeneity in Ca²⁺ signals. Together, these revisions demonstrate that under our current confocal configuration, AZ-targeted GCaMP8m can indeed resolve local Ca²⁺ changes at individual, optically isolated boutons.

We have revised the Discussion to clarify that our original statement about the limitations of AZ-targeted GCaMPs refers specifically to this combination of sensor and optical setup, rather than an absolute limitation of AZ-level Ca²⁺ imaging. In our view, further improvements in baseline brightness and dynamic range (ΔF/F or ΔR/R per action potential), combined with sub-millisecond kinetics and minimal buffering, together with optical configurations that provide smaller effective PSFs and higher photon collection (e.g., higher-NA objectives, optimized 2-photon or fast line-scan modalities, and potentially super-resolution approaches applied to AZ-localized indicators), are likely to be required to achieve routine, high-fidelity Ca²⁺ measurements at every individual AZ within a neuromuscular junction.

(4) In Figure 5: Only GCAMP8f (Bar8f fusion protein) is tested here. Consider including testing with GCAMP8m. This is particularly relevant given that GCAMP8m was a more successful GECI for subcellular post-synaptic imaging in Figure 6.

We appreciate this point and request by Reviewer 3. The main limitation for detecting local calcium changes at AZs is the speed of the calcium sensor, and hence we used the fastest available (GCaMP8f) to test the Bar8f sensor. While replacing GCaMP8f with GCaMP8m would indeed be predicted to enhance sensitivity (SNR), since GCaMP8m does not have faster kinetics relative to GCaMP8f, it is unlikely to be a more successful GECI for visualizing local calcium differences at AZs. 

That being said, we agree that the Bar8m tool, including the improved mScarlet3 indicator, would likely be of interest and use to the field. Fortunately, we had engineered the Bar8m sensor while this manuscript was in review, and just recently received transgenic flies. We have evaluated this sensor, as requested by the reviewer, and included our findings in Fig. 1 and 6. In short, while the sensitivity is indeed enhanced in Bar8m compared to Bar8f, the kinetics remain insufficient to capture local AZ signals. These findings are discussed in the revised manuscript (lines 424-442, 719-730), and we appreciate the reviewer for raising these important points.

In earlier experiments, Bar8f yielded relatively weak fluorescence, so we traded frame rate for image quality during resonant area scans (~60 fps). After switching to Bar8m, the signal was bright enough to restore our standard 118 fps area-scan setting. Nevertheless, even with dual-channel resonant area scans and ratiometric (GCaMP/mScarlet) analysis, AZ-to-AZ heterogeneity remained difficult to resolve. Because Ca²⁺ influx at individual active zones evolves on sub-millisecond timescales, we adopted a high-speed singlechannel Galvano line-scan (~1 kHz) to capture these rapid transients. We first acquired a brief area image to localize AZ puncta, then positioned the line-scan ROI through the center of the selected AZ. This configuration provided the temporal resolution needed to uncover heterogeneity that was under-sampled in area-scan data. Consistent with this, Bar8m line-scan data showed markedly higher AZ heterogeneity (significant AZ-pair rate ~79%, vs. ~8% for Bar8f area scans and ~4% for Bar8m area scans), highlighting Bar8m’s suitability for quantifying AZ diversity. We have updated the text, Methods, and figure legend accordingly (tell reviewer where to find everything).

(5) Figure 5D and associated datasets: Why was Interquartile Range (IQR) testing used instead of ZScoring? Generally, IQR is used when the data is heavily skewed or is not normally distributed. Normality was tested using the D'Agostino & Pearson omnibus normality test and found that normality was not violated. Please explain your reasoning for the approach in statistical testing. Correlation coefficients in Figures 5 E & F should also be reported on the graph, not just the table. In Supplementary Table 1. The sub-table between 4D-F and 5E-F, which describes the IQR, should be labeled as such and contain identifiers in the rows describing which quartile is described. The table description should be below. We would recommend a brief table description for each sub-table.

Thank you for this helpful suggestion. We have updated the analysis in two complementary ways. First, we now perform paired two-tailed t-tests between every two AZs within the same preparation (pairwise AZ–AZ comparisons of peak responses). At α<0.05, the fraction of significant AZ pairs is ~79% for Bar8m line-scan data versus ~8% for Bar8f area-scan data, indicating markedly greater AZ-to-AZ diversity when measured at high temporal resolution. Second, for visually marking the outlying AZs, we re-computed the IQR (Q1–Q3) based on the individual values collected from each AZs(15 data points per AZ, 30 AZs for each genotype), and marked AZs whose mean response falls above Q3 or below Q1; IQR is used here solely as a robust dispersion reference rather than for hypothesis testing. Both analyses support the same observation: Bar8m line-scan data reveal substantially higher AZ heterogeneity than Bar8f and Bar8m area-scan data. We have revised the Methods, figure panels, and legends accordingly (t-test details; explicit “IQR (Q1–Q3)” labeling; significant AZ-pair rates reported on the plots) (lines 719-730).

(6) Figure 6 and associated data. The authors mention: ' SynapGCaMP quantal signals appeared to qualitatively reflect the same events measured with electrophysiological recordings (Fig. 6D).' If that was the case, shouldn't the ephys and optical signal show some sort of correlation? The data presented in Figure 6D show no such correlation. Where do these signals come from? It is important to show the ROIs on a reference image.

We apologize this was not clear, as similar points were raised by R1 and R2. We were just showing separate (uncorrelated) sample traces of electrophysiological and calcium imaging data. Given how confusing this presentation turned out to be, and the fact that we show the correlated ephys and calcium imaging events in Fig. 7, we have elected to remove the uncorrelated electrophysiological events in Fig. 6 to just focus on the calcium imaging events (now Figures 7 and 8).

Figure 7B: Were Ca2+ transients not associated with mEPSPs ever detected? What is the rate of such events?

This is an astute question. Yes indeed, during simultaneous calcium imaging and current clamp electrophysiology recordings, we occasionally observed GCaMP transients without a detectable mEPSP in the electrophysiological trace. This may reflect the detection limit of electrophysiology for very small minis; with our noise level and the technical limitation of the recording rig, events < ~0.2 mV cannot be reliably detected, whereas the optical signal from the same quantal event might still be detected. The fraction of calcium-only events was ~1–10% of all optical miniature events, depending on genotype (higher in lines with smaller average minis). These calcium-only detections were low-amplitude and clustered near the optical threshold (lines 361-365).

Minor comments

(1) It should be mentioned in the text or figure legend whether images in Figure 1 were deconvolved, particularly since image pre-processing is only discussed in Figure 2 and after.

We thank the reviewer for pointing this out. Yes, the confocal images shown in Figure 1 were also deconvolved in Huygens using the CMLE-based workflow described in the revised Methods. We applied deconvolution to improve contrast, reduce out-of-focus blur, and better resolve the morphology of presynaptic boutons, active zones, and postsynaptic structures, so that the localization of each sensor is more clearly visualized. We have now explicitly stated in the Fig. 1 legend and Methods (lines 575-577) that these images were deconvolved prior to display. 

(2) The abbreviation, SNR, signal-to-noise ratio, is not defined in the text.

We have corrected this error and thank the reviewer for pointing this out.

(3) Please comment on the availability of fly stocks and molecular constructs.

We have clarified that all fly stocks and molecular constructs will be shared upon request (lines 747-750). We are also in the process of depositing the new Scar8f/m, Bar8f/m, and SynapGCaMP sensors to the Bloomington Drosophila Stock Center for public dissemination.

(4) Please add detection wavelengths and filter cube information for live imaging experiments for both confocal and widefield.

We thank the reviewer for this helpful suggestion. We have now added the detection wavelengths and filter cube configurations for both confocal and widefield live imaging to the Methods.

For confocal imaging, GCaMP signals were acquired on a Nikon A1R system using the FITC/GFP channel (488-nm laser excitation; emission collected with a 525/50-nm band-pass filter), and mScarlet signals were acquired using the TRITC/mCherry channel (561-nm laser excitation; emission collected with a 595/50-nm band-pass filter). Both channels were detected with GaAsP detectors under the same pinhole and scan settings described above (lines 512-517).

For widefield imaging, GCaMP was recorded using a GFP filter cube (LED excitation ~470/40 nm; emission ~525/50 nm), which is now explicitly described in the revised Methods section (lines 632-633).

(5) Please include a mini frequency analysis in Supplemental Figure S1.

We apologize for not including this information in the original submission. This is now included in the Supplemental Figure S1.

(6) In Figure S1B, consider flipping the order of EPSP (currently middle) and mEPSP (currently left), to easily guide the reader through the quantification of Figure S1A (EPSPs, top traces & mEPSPs, bottom traces).

We agree these modifications would improve readability and clarity. We have now re-ordered the electrophysiological quantifications in Fig. S1B as requested by the reviewer.

(7) Figure 6C: Consider labeling with sensor name instead of GFP.

We agree here as well, and have removed “GFP” and instead added the GCaMP variant to the heatmap in Fig. 7C.

(8) Figure 6E, 7B, 7E: Main statistical differences highlighting sensor performance should be represented on the figures for clarity.

We did not show these differences in the original submission in an effort to keep the figures “clean” and for clarity, putting the detailed statistical significance in Table S1. However, we agree with the reviewer that it would be easier to see these in the Fig. 6E and 7B,E graphs. This information has now been added the Figs. 7 and 8.

(9) Please report if the significance tested between the ephys mini (WT vs IIB-/-, WT vs IIA-/-, IIB-/- vs IIA-/-) is the same as for Ca2+ mini (WT vs IIB-/-, WT vs IIA-/-, IIB-/- vs IIA-/-). These should also exhibit a very high correlation (mEPSP (mV) vs Ca2+ mini deltaF/F). These tests would significantly strengthen the final statement of "SynapGCaMP8m can capture physiologically relevant differences in quantal events with similar sensitivity as electrophysiology."

We agree that adding the more detailed statistical analysis requested by the reviewer would strengthen the evidence for the resolution of quantal calcium imaging using SynapGCaMP8m. We have included the statistical significance between the ephys and calcium minis in Fig. 8 and included the following in the revised methods (lines 358-361), the Fig. 8 legend and Table S1:

Using two-sample Kolmogorov–Smirnov (K–S) tests, we found that SynapGCaMP8m Ca²⁺ minis (ΔF/F, Fig. 8E) differ significantly across all genotype pairs (WT vs IIB^-/-, WT vs IIA^-/-, IIB^-/- vs IIA^-/-; all p < 0.0001). The genotype rank order of the group means (±SEM) is IIB^-/- > WT > IIA^-/- (0.967 ± 0.036; 0.713 ± 0.021; 0.427 ± 0.017; n=69, 65, 59). For electrophysiological minis (mEPSP amplitude, Fig. 8F), K–S tests likewise show significant differences for the same comparisons (all p < 0.0001) with D statistics of 0.1854, 0.3647, and 0.4043 (WT vs IIB^-/-, WT vs IIA^-/-, IIB^-/- vs IIA^-/-, respectively). Group means (±SEM) again follow IIB^-/- > WT > IIA^-/- (0.824 ± 0.017 mV; 0.636 ± 0.015 mV; 0.383 ± 0.007 mV; n=41 each). These K–S results demonstrate identical significance and rank order across modalities, supporting our conclusion that SynapGCaMP8m resolves physiologically relevant quantal differences with sensitivity comparable to electrophysiology.

References

Blum, Ian D., Mehmet F. Keleş, El-Sayed Baz, Emily Han, Kristen Park, Skylar Luu, Habon Issa, Matt Brown, Margaret C. W. Ho, Masashi Tabuchi, Sha Liu, and Mark N. Wu. 2021. 'Astroglial Calcium Signaling Encodes Sleep Need in Drosophila', Current Biology, 31: 150-62.e7.

Chen, Y., and L. M. Huang. 2017. 'A simple and fast method to image calcium activity of neurons from intact dorsal root ganglia using fluorescent chemical Ca(2+) indicators', Mol Pain, 13: 1744806917748051.

Giovannucci, Andrea, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L. Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L. Gauthier, Pengcheng Zhou, Baljit S. Khakh, David W. Tank, Dmitri B. Chklovskii, and Eftychios A. Pnevmatikakis. 2019. 'CaImAn an open source tool for scalable calcium imaging data analysis', eLife, 8: e38173.

Müller, M., K. S. Liu, S. J. Sigrist, and G. W. Davis. 2012. 'RIM controls homeostatic plasticity through modulation of the readily-releasable vesicle pool', J Neurosci, 32: 16574-85.

Wu, Yifan, Keimpe Wierda, Katlijn Vints, Yu-Chun Huang, Valerie Uytterhoeven, Sahil Loomba, Fran Laenen, Marieke Hoekstra, Miranda C. Dyson, Sheng Huang, Chengji Piao, Jiawen Chen, Sambashiva Banala, Chien-Chun Chen, El-Sayed Baz, Luke Lavis, Dion Dickman, Natalia V. Gounko, Stephan Sigrist, Patrik Verstreken, and Sha Liu. 2025. 'Presynaptic Release Probability Determines the Need for Sleep', bioRxiv: 2025.10.16.682770.

Xing, Xiaomin, and Chun-Fang Wu. 2018. 'Unraveling Synaptic GCaMP Signals: Differential Excitability and Clearance Mechanisms Underlying Distinct Ca²⁺ Dynamics in Tonic and Phasic Excitatory, and Aminergic Modulatory Motor Terminals in Drosophila', eneuro, 5: ENEURO.0362-17.2018.

Nicely acid

Some nicely acid comments.

Alice identifies herself as a little girl which seems weird to her as maybe much is expected of her?

Alice's identity comes into question again. Could be a reference to her being a child growing up in a victorian society.

But it is true that faith is scarce today, even in the realm of the proletarian, of YOUNG hearts

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study presents a system for delivering precisely controlled cutaneous stimuli to freely moving mice by coupling markerless real-time tracking to transdermal optogenetic stimulation, using the tracking signal to direct a laser via galvanometer mirrors. The principal claims are that the system achieves sub-mm targeting accuracy with a latency of <100 ms. The nature of mouse gait enables accurate targeting of forepaws even when mice are moving.

Strengths:

The study is of high quality and the evidence for the claims is convincing. There is increasing focus in neurobiology in studying neural function in freely moving animals, engaged in natural behaviour. However, a substantial challenge is how to deliver controlled stimuli to sense organs under such conditions. The system presented here constitutes notable progress towards such experiments in the somatosensory system and is, in my view, a highly significant development that will be of interest to a broad readership.

Weaknesses:

(1) "laser spot size was set to 2.00 } 0.08 mm2 diameter (coefficient of variation = 3.85)" is unclear. Is the 0.08 SD or SEM? (not stated). Also, is this systematic variation across the arena (or something else)? Readers will want to know how much the spot size varies across the arena - ie SD. CV=4 implies that SD~7 mm. ie non-trivial variation in spot size, implying substantial differences in power delivery (and hence stimulus intensity) when the mouse is in different locations. If I misunderstood, perhaps this helps the authors to clarify. Similarly, it would be informative to have mean & SD (or mean & CV) for power and power density. In future refinements of the system, would it be possible/useful to vary laser power according to arena location?

We thank the reviewer for their comments and for identifying areas needing more clarity. The previous version was ambiguous: 0.08 refers to the standard deviation (SD). We have removed the ambiguity by stating mean ± SD and reporting a unitless coefficient of variation (CV).

The revised text reads “laser spot size was set to 2.00 ± 0.08 mm² (mean ± SD; coefficient of variation = 0.039).” This makes clear that the variability in spot size is minimal: it is 0.08 mm² SD (≈0.03 mm SD in diameter). This should help clarify that spot size variability across the arena is minute and unlikely to contribute meaningfully to differences in stimulus intensity across locations. The power was modulated depending on the experiment, so we provide the unitless CV here in “The absolute optical power and power density were uniform across the glass platform (coefficient of variation 0.035 and 0.029, respectively; Figure 2—figure supplement)”. We are grateful to the reviewer for spotting these omissions.

The reviewer also asks whether, in the future, it is “possible/useful to vary laser power according to arena location”. This is already possible in our system for infrared cutaneous stimulation using analog modulation (Figure 4). We have added the following sentence to make this clearer: “Laser power could be modulated using the analog control.”

(2) "The video resolution (1920 x 1200) required a processing time higher than the frame interval (33.33 ms), resulting in real-time pose estimation on a sub-sample of all frames recorded". Given this, how was it possible to achieve 84 ms latency? An important issue for closed-loop research will relate to such delays. Therefore please explain in more depth and (in Discussion) comment on how the latency of the current system might be improved/generalised. For example, although the current system works well for paws it would seem to be less suited to body parts such as the snout that do not naturally have a stationary period during the gait cycle.

We captured and stored video with a frame-to-frame interval of 33.33 ms (30 fps). DeepLabCut-live! was run in a latency-optimization mode, meaning that new frames are not processed while the network is busy - only the most recent frame is processed when free. The processing latency is measured per processed frame, and intermediate frames are thus skipped while the network is busy. Although a wide field of view and high resolution is required to capture the large environment, increasing the per-frame compute time, the processing latency remained small enough to track and stimulate moving mice. This processing latency of 84 ± 12 ms (mean ± SD) was calculated using the timestamps stored in the output files from DeepLabCut-live!: subtracting the frame acquisition timestamp from the frame processing timestamp across 16,000 processed frames recorded across four mice (4,000 each). In addition, there is a small delay to move the galvanometers and trigger the laser, calculated as 3.3 ± 0.5 ms (mean ± SD; 245 trials). This is described in the manuscript, but can be combined with the processing latency to indicate a total closed-loop delay of ≈87 ms so we have expanded on the ‘Optical system characterization’ subsection in the Methods, adding “We estimated a processing latency of 84 ± 12 ms (mean ± SD) by subtracting…” and that “In the current configuration the end-to-end closed-loop delay is ≈87 ms from the combination of the processing latency and other delays”. To the Discussion, we now comment on how this latency can be reduced and how this can allow for generalization to more rapidly moving body parts.

Reviewer #2 (Public review):

Parkes et al. combined real-time keypoint tracking with transdermal activation of sensory neurons to examine the effects of recruitment of sensory neurons in freely moving mice. This builds on the authors' previous investigations involving transdermal stimulation of sensory neurons in stationary mice. They illustrate multiple scenarios in which their engineering improvements enable more sophisticated behavioral assessments, including (1) stimulation of animals in multiple states in large arenas, (2) multi-animal nociceptive behavior screening through thermal and optogenetic activation, and (3) stimulation of animals running through maze corridors. Overall, the experiments and the methodology, in particular, are written clearly. However, there are multiple concerns and opportunities to fully describe their newfound capabilities that, if addressed, would make it more likely for the community to adopt this methodology:

The characterization of laser spot size and power density is reported as a coefficient of variation, in which a value of ~3 is interpreted as uniform. My interpretation would differ - data spread so that the standard deviation is three times larger than the mean indicates there is substantial variability in the data. The 2D polynomial fit is shown in Figure 2 - Figure Supplement 1A and, if the fit is good, this does support the uniformity claim (range of spot size is 1.97 to 2.08 mm2 and range of power densities is 66.60 to 73.80 mW). The inclusion of the raw data for these measurements and an estimate of the goodness of fit to the polynomials would better help the reader evaluate whether these parameters are uniform across space and how stable the power density is across repeated stimulations of the same location. Even more helpful would be an estimate of whether the variation in the power density is expected to meaningfully affect the responses of ChR2-expressing sensory neurons.

We thank the reviewer for their comments. As also noted in response to Reviewer 1, the coefficient of variation (CV) is now reported in unitless form (rather than a percentage) to ensure clarity. For avoidance of doubt, the CV is 0.039 (3.9%), so the variation in laser spot size is minimal – there is negligible spot size variability across the system. The ranges are indeed consistent with uniformity. We have included the goodness-of-fit estimates in the appropriate figure legend “fit with a two-dimensional polynomial (area R² = 0.91; power R² = 0.75)”. This indicates that the polynomials fit well overall.

The system already allows for control of spot size. To examine whether the variation in the power density affects the responses of ChR2-expressing sensory neurons, we examined this in our previous work that focused more on input-output relationships, demonstrating a steep relationship between spot size (range of 0.02 mm² to 2.30 mm²) and the probability of paw response, demonstrating a meaningful change in response probability (Schorscher-Petcu et al. eLife, 2021). In future studies, we aim to use this approach to “titrate” cutaneous inputs as mice move through their environments.

While the error between the keypoint and laser spot error was reported as ~0.7 to 0.8 mm MAE in Figure 2L, in the methods, the authors report that there is an additional error between predicted keypoints and ground-truth labeling of 1.36 mm MAE during real-time tracking. This suggests that the overall error is not submillimeter, as claimed by the authors, but rather on the order of 1.5 - 2.5 mm, which is considerable given the width of a hind paw is ~5-6 mm and fore paws are even smaller. In my opinion, the claim for submillimeter precision should be softened and the authors should consider that the area of the paw stimulated may differ from trial to trial if, for example, the error is substantial enough that the spot overlaps with the edge of the paw.

We thank the reviewer for identifying a discrepancy in these reported errors. We clarify this below and in the manuscript

The real-time tracking error is the mean absolute Euclidean distance (MAE) between ground truth and DLC on the left hind paw where likelihood was relatively high. More specifically, ground truth was obtained by manual annotation of the left hind paw center. The corresponding DLC keypoint was evaluated in frames with likelihood >0.8 (the stimulation threshold). Across 1,281 frames from five videos of freely exploring mice (30 fps), the MAE was 1.36 mm.

The targeting error is the MAE between ground truth and the laser spot location, so should reflect the real-time tracking error plus errors from targeting the laser. More specifically, this metric was determined by comparing the manually determined ground truth keypoint of the left hind paw and the actual center of the laser spot. Importantly, this metric was calculated using four five-minute high-speed videos recorded at 270 fps of mice freely exploring the open arena (463 frames) and frames were selected with a likelihood threshold >0.8. This allowed us to resolve the brief laser pulses but inadvertently introduced a difference in spatial scaling. After rescaling, the values give a targeting error MAE now in line with the real-time tracking error  (see corrected Figure 2L). This is approximately 1.3 mm across all locomotion speeds categories. These errors are small and are limited by the spatial resolution of the cameras. We thank the reviewer for noting this discrepancy and prompting us to get to its root cause.

We have amended the subtitle on Figure 2L as “Ground truth keypoint to laser spot error” and have avoided the use of submillimeter throughout. We have added the following sentence to clarify this point: “As laser targeting relies on real-time tracking to direct the laser to the specified body part, this metric includes any errors introduced by tracking and targeting”.

As the major advance of this paper is the ability to stimulate animals during ongoing movement, it seems that the Figure 3 experiment misses an opportunity to evaluate state-dependent whole-body reactions to nociceptor activation. How does the behavioral response relate to the animal's activity just prior to stimulation?

The reviewers suggest analysis of state-dependent responses. In the Figure 3 experiment, mice were stimulated up to five times when stationary. Analysis of whole body reactions in stationary mice has been described in (Schorscher-Petcu et al. eLife, 2021) and doing this here would be redundant, so instead we now analyse the responses of moving mice in Figure 5. This new analysis shows robust state-dependent responses during movement as suggested by the reviewer. We find two behavioral clusters: one that is for faster, direct (coherent) movement and the other that is for slower assessment (incoherent) movement. Stimulation during the former results in robust and consistent slowing and shift towards assessment, whereas stimulation during the former results in a reduction in assessment. We describe and interpret these new data in the Results and Discussion sections and add information in the Methods and Figure legend, as given below. We believe that demonstrating movement statedependence is a valuable addition to the paper and thank the reviewer for suggesting this.

Given the characterization of full-body responses to activation of TrpV1 sensory neurons in Figure 4 and in the authors' previous work, stimulation of TrpV1 sensory neurons has surprisingly subtle effects as the mice run through the alternating T maze. The authors indicate that the mice are moving quickly and thus that precise targeting is required, but no evidence is shared about the precision of targeting in this context beyond images of four trials. From the characterization in Figure 2, at max speed (reported at 241 +/- 53 mm/s, which is faster than the high speeds in Figure 2), successful targeting occurs less than 50% of the time. Is the initial characterization consistent with the accuracy in this context? To what extent does inaccuracy in targeting contribute to the subtlety of affecting trajectory coherence and speed? Is there a relationship between animal speed and disruption of the trajectory?

We thank the reviewer for pointing out the discrepancy in the reported maximum speed. We have corrected the error in the main text: the average maximum speed is 142 ± 26 mm/s (four mice).

The self-paced T-maze alternation task in Figure 5 demonstrates that mice running in a maze can be stimulated using this method. We did not optimize the particular experimental design to assess the hit accuracy, as this was determined in Figure 2. Instead, we optimized for the pulse frequencies, meaning the galvanometers tracked with processed frames but the laser was triggered whether or not the paw was actually targeted. However, even in this case with the system pulsing in the free-run mode, the laser hit rate was 54 ± 6% (mean ± sem, n = 7 mice). We have weakened references to submillimeter as it was only inferred from other experiments and was not directly measured here. We find in this experiment that stimulation in freely moving mice can cause them to briefly halt and evaluate. In the future, we will use experimental designs to more optimally examine learning.

The reviewer also asks if there is a relationship between speed and disruption of the trajectory. We find that this is the case as described above with our additional analysis.

Reviewer #3 (Public review):

Summary:

To explore the diverse nature of somatosensation, Parkes et al. established and characterized a system for precise cutaneous stimulation of mice as they walk and run in naturalistic settings. This paper provides a framework for real-time body part tracking and targeted optical stimuli with high precision, ensuring reliable and consistent cutaneous stimulation. It can be adapted in somatosensation labs as a general technique to explore somatosensory stimulation and its impact on behavior, enabling rigorous investigation of behaviors that were previously difficult or impossible to study.

Strengths:

The authors characterized the closed-loop system to ensure that it is optically precise and can precisely target moving mice. The integration of accurate and consistent optogenetic stimulation of the cutaneous afferents allows systematic investigation of somatosensory subtypes during a variety of naturalistic behaviors. Although this study focused on nociceptors innervating the skin (Trpv1::ChR2 animals), this setup can be extended to other cutaneous sensory neuron subtypes, such as low-threshold mechanoreceptors and pruriceptors. This system can also be adapted for studying more complex behaviors, such as the maze assay and goal-directed movements.

Weaknesses:

Although the paper has strengths, its weakness is that some behavioral outputs could be analyzed in more detail to reveal different types of responses to painful cutaneous stimuli. For example, paw withdrawals were detected after optogenetically stimulating the paw (Figures 3E and 3F). Animals exhibit different types of responses to painful stimuli on the hind paw in standard pain assays, such as paw lifting, biting, and flicking, each indicating a different level of pain. Improving the behavioral readouts from body part tracking would greatly strengthen this system by providing deeper insights into the role of somatosensation in naturalistic behaviors. Additionally, if the laser spot size could be reduced to a diameter of 2 mm², it would allow the activation of a smaller number of cutaneous afferents, or even a single one, across different skin types in the paw, such as glabrous or hairy skin.

We thank the reviewer for highlighting how our system can be combined with improved readouts of coping behavior to provide deeper insights. Optogenetic and infrared cutaneous stimulation are well established generators of coping behaviors (lifting, flicking, licking, biting, guarding). Detection of these behaviors is an active and evolving field with progress being made regularly (e.g. Jones et al., eLife 2020 [PAWS];  Wotton et al., Mol Pain 2020; Zhang et al., Pain 2022; Oswell et al., bioRxiv 2024 [LUPE]; Barkai et al., Cell Reports Methods 2025 [BAREfoot], along with more general tools like Hsu et al., Nature Communications 2021 [B-SOiD]; Luxem et al., Communications Biology 2022 [VAME]; Weinreb et al,. Nature Methods 2024 [Keypoints-MoSeq]). One output of our system is bodypart keypoints, which are the typical input to many of these tools. We will leave the readers and users of the system to decide which tools are appropriate for their experimental designs - the focus of this current manuscript is describing the novel stimulation approach in moving animals.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) It is hard to see how the rig is arranged from the render of Figure 2AB due to the components being black on black. A particularly useful part of Fig2AB is the aerial view in panel B that shows the light paths. I suggest adding the labelling of Figure 2A also to that. The side/rear views could perhaps be deleted, allowing the aerial view to be larger.

We appreciate this suggestion and have revised Figure 2B to improve the visibility of the optomechanical components. We have enlarged the side and aerial views, removed the rear view, and added further labels to the aerial view.

(2) MAE - to interpret the 0.54 result, it would be useful to state the arena size in this paragraph.

Thank you. We have added the arena size in this paragraph and also added scales in the relevant figure (Figure 2).

(3) "pairwise correlations of R = 0.999 along both x- and y-axes". Is this correlation between hindpaw keypoint and galvo coordinates?

Yes, we have added the following to clarify: “...between galvanometer coordinates and hind paw keypoints”

(4) Latency was 84 ms. Is this mainly/entirely the delay between DLC receiving the camera image and outputting key point coordinates?

Yes, we hope that the additional detail in the Methods and Discussion described above will now clarify the current closed-loop latencies.

(5) "Mice move at variable speeds": in this sentence, spell out when "speed" refers to mouse and when it refers to hindpaw. Similarly, Fig 2i. The sentence is potentially confusing to general readers (paws stationary although the mouse is moving). Presumably, it's due to gait. I suggest explaining this here.

The speed values that relate to the mouse body and paws are now clearer in the main text and in the legend for Figure 2I.

(6) Figure 2k and associated main text. It is not clear what "success/hit rate" means here.

We have added the following sentence in the main text: “Hit accuracy refers to the percentage of trials in which the laser successfully targeted (‘hit’) the intended hind paw.” and use hit accuracy throughout instead of success rate.

(7) Figure 2L. All these points are greater than the "average" 0.54 reported in the text. How is this possible?

The MAE of 0.54 mm refers to the “predicted and actual laser spot locations” (that is, the difference between where the calibration map should place the laser spot and where it actually fell), while Figure 2L MAE values refers to the error between the ground truth keypoint to laser spot (that is, the error between the human-observed paw target and where the laser spot fell). The latter error will include the former error so is expected to be larger. We have clarified this point throughout the text, for example, stating “As laser targeting relies on real-time tracking to direct the laser to the specified body part, this metric inherently accounts for any errors introduced by the tracking and targeting.”. This is also discussed above in response to Reviewer 2.

(8) "large circular arena". State the size here

We have added this to the Figure 2 legend.

(9) Figure 3c-left. Can the contrast between the mouse and floor be increased here?

We have improved the contrast in this image.

(10) Figure 5c. It is unclear what C1, C2, etc refers to. Mice?

Yes, these refer to mice. We have removed reference to these now as they are not needed.

(11) Discussion. A comment. There is scope for elaborating on the potential for new research by combining it with new methods for measurements of neural activity in freely moving animals in the somatosensory system.

Thank you. We agree and have added more detail on this in the discussion stating “The system may be combined with existing tools to record neural activity in freely-moving mice, such as fiber photometry, miniscopes, or large-scale electrophysiology, and manipulations of this neural activity, such as optogenetics and chemogenetics. This can allow mechanistic dissection of cell and circuit biology in the context of naturalistic behaviors.”

Reviewer #3 (Recommendations for the authors):

(1) Include the number of animals for behavior assays for the panels (e.g., Figures 4G).

Where missing, we now state the number of animals in panels.

(2) If representative responses are shown, such as in Figures 3E and 4F, include the average response with standard deviation so readers can appreciate the variation in the responses.

We appreciate the suggestion to show variability in the responses. We have made several changes to Figures 3 and 4. Specifically, to illustrate the variability across multiple trials more clearly, Figure 3E now shows representative keypoint traces for each body part from two mice during their 5 trials. For Figure 4, we have re-analyzed the thermal stimulation trials and shown a raster plot of keypoint-based local motion energy (Figure 4E) sorted by response latency for hundreds of trials. Figure 4G now presents the cumulative distribution for all trials and animals for thermal (18 wild-type mice, 315 trials) and optogenetic stimulation trials (9 Trpv1::ChR2 mice, 181 trials). We also now provide means ± SD for the key metrics for optogenetic and thermal stimulation trials in Figure 4 in the Results section. This keeps the manuscript focused on the methodological advances while showing the trial variability.

(3) "optical targeting of freely-moving mice in a large environments" should be "optical targeting of freely-moving mice in a large environment".

Corrected

(4) Define fps when you first mention this in the manuscript.

Added

(5) Data needs to be shown for the claim "Mice concurrently turned their heads toward the stimulus location while repositioning their bodies away from it".

We state this observation to qualify that the stimulation of stationary mice resulted in behavioral responses “consistent with previous studies”. It would be redundant to repeat our full analysis and might distract from the novelty of the current manuscript. We have restricted this sentence to make it clearer: “Consistent with previous studies, we observed the whole-body behaviors like head orienting concurrent with local withdrawal (Browne et al., Cell Reports 2017; Blivis et al., eLife, 2017.)”

"…on GitHub" (as with all things from people who speak myopically about "open source" but run all their projects on GitHub). They're talking about the GitHub userbase.

Reviewer #1 (Public review):

Summary:

The study by Druker et al. shows that siRNA depletion of PHD1, but not PHD2, increases H3T3 phosphorylation in cells arrested in prometaphase. Additionally, the expression of wild-type RepoMan, but not the RepoMan P604A mutant, restored normal H3T3 phosphorylation localization in cells arrested in prometaphase. Furthermore, the study demonstrates that expression of the RepoMan P604A mutant leads to defects in chromosome alignment and segregation, resulting in increased cell death. These data support a role for PHD1-mediated prolyl hydroxylation in controlling progression through mitosis. This occurs, at least in part, by hydroxylating RepoMan at P604, which regulates its interaction with PP2A during chromosome alignment.

Strengths:

The data support most of the conclusions made.

Comments on revisions:

Actually, I am still concerned that PHD1 binds to RepoMan endogenously and directly. Furthermore, the authors have not yet provided genetic evidence demonstrating that PHD1 controls progression through mitosis by catalyzing the hydroxylation of RepoMan.

Reviewer #3 (Public review):

Summary:

The manuscript is a comprehensive molecular and cell biological characterisation of the effects of P604 hydroxylation by PHD1 on RepoMan, a regulatory subunit of the PPIgamma complex. Conclusions are generally supported by results. Overall, a timely study that demonstrates the interplay between hydroxylase signalling and the cell cycle. The study extends the scope of prolyl hydroxylase signalling beyond canonical hypoxia pathways, providing a concrete example of hydroxylation regulating PP1 holoenzyme composition and function during mitosis.

The work would benefit from additional biochemical validation of direct targeting to characterise the specificity and mode of recognition, but this is beyond the scope of the study.

Strengths:

Compelling data, characterisation of how P604 hydroxylation induces the interaction between RepoMan and a phosphatase complex, resulting in loading of RepoMan on Chromatin. Knockdown of PHD1 mimics the disruption of the complex and loss of the regulation of the hydroxylation site by PHD1, resulting in mitotic defects.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Druker et al. shows that siRNA depletion of PHD1, but not PHD2, increases H3T3 phosphorylation in cells arrested in prometaphase. Additionally, the expression of wild-type RepoMan, but not the RepoMan P604A mutant, restored normal H3T3 phosphorylation localization in cells arrested in prometaphase. Furthermore, the study demonstrates that expression of the RepoMan P604A mutant leads to defects in chromosome alignment and segregation, resulting in increased cell death. These data support a role for PHD1-mediated prolyl hydroxylation in controlling progression through mitosis. This occurs, at least in part, by hydroxylating RepoMan at P604, which regulates its interaction with PP2A during chromosome alignment.

Strengths:

The data support most of the conclusions made. However, some issues need to be addressed.

Weaknesses:

(1) Although ectopically expressed PHD1 interacts with ectopically expressed RepoMan, there is no evidence that endogenous PHD1 binds to endogenous RepoMan or that PHD1 directly binds to RepoMan.

We do not fully agree that this comment is accurate - the implication is that we only show interaction between two exogenously expressed proteins, i.e. both exogenous PHD1 and RepoMan, when in fact we show that tagged PHD1 interacts with endogenous RepoMan. The major technical challenge here is the well-known difficulty of detecting endogenous PHD1 in such cell lines. We agree that co-IP studies do not prove that this interaction is direct and never claim to have shown this, though we do feel that a direct interaction is most likely, albeit not proven.

(2) There is no genetic evidence indicating that PHD1 controls progression through mitosis by catalyzing the hydroxylation of RepoMan.

We agree that our current study is primarily a biochemical and cell biological study, rather than a genetic study. Nonetheless, similar biochemical and cellular approaches have been widely used and validated in previous studies in mechanisms regulating cell cycle progression and we are confident in the conclusions drawn based on the data obtained so far.

(3) Data demonstrating the correlation between dynamic changes in RepoMan hydroxylation and H3T3 phosphorylation throughout the cell cycle are needed.

We agree that it will be very interesting to analyse in more detail the cell cycle dynamics of RepoMan hydroxylation and H3T3 phosphorylation - along with other cell cycle parameters. We view this as outside the scope of our present study and are actively engaged in raising the additional funding needed to pursue such future experiments.

(4) The authors should provide biochemical evidence of the difference in binding ability between RepoMan WT/PP2A and RepoMan P604A/PP2A.

Here again we agree that it will be very interesting to analyse in future the detailed binding interactions between wt and mutant RepoMan and other interacting proteins, including PP2A. We show reduced interaction in cells by PLA (Figure 5A) and in biochemical analysis (Figure 5C). More in vitro analysis is, in our view, outside the scope of our present study and we are actively engaged in raising the additional funding needed to pursue such future experiments.

(5) PHD2 is the primary proline hydroxylase in cells. Why does PHD1, but not PHD2, affect RepoMan hydroxylation and subsequent control of mitotic progression? The authors should discuss this issue further.

We agree with the main point underpinning this comment, i.e., that there are still many things to be learned concerning the specific roles and mechanisms of the different PHD enzymes in vivo. We address this in the Discussion section and look forward to addressing these questions experimentally in future studies.

Reviewer #2 (Public review):

Summary:

This is a concise and interesting article on the role of PHD1-mediated proline hydroxylation of proline residue 604 on RepoMan and its impact on RepoMan-PP1 interactions with phosphatase PP2A-B56 complex leading to dephosphorylation of H3T3 on chromosomes during mitosis. Through biochemical and imaging tools, the authors delineate a key mechanism in the regulation of the progression of the cell cycle. The experiments performed are conclusive with well-designed controls.

Strengths:

The authors have utilized cutting-edge imaging and colocalization detection technologies to infer the conclusions in the manuscript.

Weaknesses:

Lack of in vitro reconstitution and binding data.

We agree that it will be very interesting to pursue in vitro reconstitution studies and detailed binding data. We view this as outside the scope of our present study and are actively engaged in raising the additional funding needed to pursue such future experiments. We do provide in vitro hydroxylation data in our accompanying manuscript by Jiang et al, 2025 Elife.

Reviewer #3 (Public review):

Summary:

The manuscript is a comprehensive molecular and cell biological characterisation of the effects of P604 hydroxylation by PHD1 on RepoMan, a regulatory subunit of the PPIgamma complex. The identification and molecular characterisation of the hydroxylation site have been written up and deposited in BioRxiv in a separate manuscript. I reviewed the data and came to the conclusion that the hydroxylation site has been identified and characterised to a very high standard by LC-MS, in cells and in vitro reactions. I conclude that we should have no question about the validity of the PHD1-mediated hydroxylation. 

In the context of the presented manuscript, the authors postulate that hydroxylation on P604 by PHD1 leads to the inactivation of the complex, resulting in the retention of pThr3 in H3. 

Strengths:

Compelling data, characterisation of how P604 hydroxylation is likely to induce the interaction between RepoMan and a phosphatase complex, resulting in loading of RepoMan on Chromatin. Loss of the regulation of the hydroxylation site by PHD1 results in mitotic defects.

Weaknesses:

Reliance on a Proline-Alanine mutation in RepoMan to mimic an unhydroxylatable protein. The mutation will introduce structural alterations, and inhibition or knockdown of PHD1 would be necessary to strengthen the data on how hydroxylates regulate chromatin loading and interactions with B56/PP2A.

We do not agree that we rely solely on analysis of the single site pro-ala mutant in RepoMan for our conclusions, since we also present a raft of additional experimental evidence, including knock-down data and experiments using both fumarate and FG. We would also reference the data we present on RepoMan in the parallel study by Jiang et al, which has also published in eLife(https://doi.org/10.7554/eLife.108128.1)). Of course, we agree with the reviewer that even although the mutant RepoMan features only a single amino acid change, this could still result in undetermined structural effects on the RepoMan protein that could conceivably contribute, at least in part, to some of the phenotypic effects observed. We now provide evidence in the current revision (new Figure 5D) that reduced interaction between RepoMan and B56gamma/PP2A is also evident when PHD1 is depleted from cells.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) The manuscript can benefit from improved quality of writing and avoidance of grammatical errors.

We have checked through the manuscript again and corrected any mistakes we have encountered in the Current revision.

(2) Although the data in the manuscript is compelling, it is difficult to rule out indirect effects in the interactions. Hence, in vitro binding assays with purified proteins are important to validate the findings, along with in vitro reconstitution of phosphatase activity.

It is possible that cofactors and / or additional PTMs are required to promote these interactions in vivo. We have provided in vitro hydroxylation analysis and the additional experiments suggested will be the subject of follow-on future studies.

(3) Proline to alanine is a drastic mutation in the amino acid backbone. The authors could purify PHD1 and reconstitute P604 hydroxylation to show if it performs as expected.

This is likely to be a challenging experiment technically, given that RepoMan is a component of multiple distinct complexes, some of which are dynamic. We did not feel able to address this within the scope of the current study.

(4) The confocal images showing the overlap of two fluorescent signals need to show some sort of quantification and statistics to prove that the overlap is significant.

We now provide Pearson correlation measurements for Figure 2A in new Figure 2B in the Current revision.

(5) Kindly provide a clearer panel for the Western blot of H3T3ph in Figure 3c.

We have now included a new panel for this Figure in the Current revision.

(6) Kindly also include the figures for validation of siRNAs used in the study

We have added this throughout in supplementary figures.

Reviewer #3 (Recommendations for the authors):

(1) The authors have shown that PHD1 and RepoMan interact; can the interaction be "trapped" by the addition of DMOG? Generally, hydroxylase substrates can be trapped, which would add an additional layer of confidence that PHD1 and RepoMan form an enzyme-substrate complex. 

This is something we are planning to do for follow-up studies using the established methods from the von Kriesgheim laboratory.

(2) How does P604A mutation affect the interaction with PHD1? One would expect a reduction in interaction. 

Another interesting point we are planning to investigate in the future.

(3) The effects of expression of the wt and P604A mutant repoman are well-characterised. Could the authors check the effects of overexpressing PHD1 and deadPHD1, inhibition on the mitosis/H3 phosphorylation? My concerns are that a P-A mutation will disrupt the secondary structure, and although it is a good tool, data should be backed up by increasing/decreasing the hydroxylation of RepoMan over the mutation. Repeat some of the most salient experiments where the P604A mutation has been used and modulate the hydP604 by modulating PHD1 activity/expression (such as Chromatin interaction, PLA assay, B56gamma interaction, H3 phosphorylation localisation, Monastrol release, etc.)

We agree, the PA mutant can potentially affect the protein structure. In our manuscript we have provided pH3 analysis for PHD inhibition using siRNA, FG4592 and Fumarate. In the Current revision ee also data showing that depletion of PHD1 results in a reduction in interaction between RepoMan and B56gamma/PP2A. This is now presented in new figure 5D.

(4) I also have a general question, as a point of interest, as the interaction between PHD1 and RepoMan appears to be cell cycle dependent, is it possible that the hydroxylation status cycles as well? Could this explain how some sub-stochiometric hydroxylation events observed may be masked by assessing unsynchronised cells in bulk?

Indeed, a very good question. We believe this is an interesting question for follow up studies. Given our previous publication showing phosphorylation of PHD1 by CDKs alters substrate binding (Ortmann et al, 2016 JCS), this is our current hypothesis.

eLife Assessment

IL21R, being a key cytokine receptor for shaping the T follicular helper and B cell functions, utilizes two STAT family members, STAT1 and STAT3. The authors utilize the IL21R ENU-induced mutant, together with relevant in vitro and in vivo experiments, to dissect the function of STAT1 and STAT3. The approach by itself sounds reasonable, but the main conclusions are incompletely supported by the data presented in this manuscript.

Cultural capital therefore stems from monetary and time capital. You need both, and then you are allowed new forms of communication, new forms of convincing others, of sharing a framework, an ideology, of not just performing but coming learned. This is a current pervasive ideal: The fact that different motivations and experiential situations are to be homogenised, and that when you come to an educational activity, you must do so with specific requirements and mindset.

This is one of the most notable wings of meritocratic thought and efficiency, when in actuality, lack of retraining often makes senior workers stagnate, and replace about collective innovation for top-down imposition, whereas newer entrants are judged harshly and demobilised, sterilised, as if they were playing pretend with toys and not engaging with the real material.

a package to install nextcloud quickly on a new server w back end tools. Debian 13 required.

Aimed at small groups / companies / associations / schools. Would work on a Hetzner cloud server. But works for larger set-ups too. By people in Schleswig-Holstein (where the public admin has switched to Nextcloud)

Reviewer #1 (Public review):

Summary:

In this article by Xiao et al., the authors aimed to identify the precise targets by which magnesium isoglycyrrhizinate (MgIG) functions to improve liver injury in response to ethanol treatment. The authors found through a series of in vivo and molecular approaches that MgIG treatment attenuates alcohol-induced liver injury through a potential SREBP2-IdI1 axis. This manuscript adds to a previous set of literature showing MgIG improves liver function across a variety of etiologies, and also provides mechanistic insight into its mechanism of action.

Strengths:

(1) The authors use a combination of approaches from both in-vivo mouse models to in-vitro approaches with AML12 hepatocytes to support the notion that MgIG does improve liver function in response to ethanol treatment.

(2) The authors use both knockdown and overexpression approaches, in vivo and in vitro, to support most of the claims provided.

(3) Identification of HSD11B1 as the protein target of MgIG, as well as confirmation of direct protein-protein interactions between HSD11B1/SREBP2/IDI1, is novel.

Weaknesses:

Major weaknesses can be classified into 3 groups:

(1) The results do not support some claims made.

(2) Qualitative analyses of some of the lipid measures, as opposed to more quantitative analyses.

(3) There are no appropriate readouts of Srebp2 translocation and/or activity.

More specific comments:

(1) A few of the claims made are not supported by the references provided. For instance, line 76 states MgIG has hepatoprotective properties and improved liver function, but the reference provided is in the context of myocardial fibrosis.

(2) MgIG is clinically used for the treatment of liver inflammatory disease in China and Japan. In the first line of the abstract, the authors noted that MgIG is clinically approved for ALD. In which countries is MgIG approved for clinical utility in this space?

(3) Serum TGs are not an indicator of liver function. Alterations in serum TGs can occur despite changes in liver function.

(4) There are discrepancies in the results section and the figure legends. For example, line 302 states Idil is upregulated in alcohol fed mice relative to the control group. The figure legend states that the comparison for Figure 2A is that of ALD+MgIG and ALD only.

(5) Oil Red O staining provided does not appear to be consistent with the quantification in Figure 1D. ORO is nonspecific and can be highly subjective. The representative image in Figure 1C appears to have a much greater than 30% ORO (+) area.

(6) The connection between Idil expression in response to EtOH/PA treatment in AML12 cells with viability and apoptosis isn't entirely clear. MgIG treatment completely reduces Idi1 expression in response to EtOH/PA, but only moderate changes, at best, are observed in viability and apoptosis. This suggests the primary mechanism related to MgIG treatment may not be via Idi1.

(7) The nile red stained images also do not appear representative with its quantification. Several claims about more or less lipid accumulation across these studies are not supported by clear differences in nile red.

(8) The authors make a comment that Hsd11b1 expression is quite low in AML12 cells. So why did the authors choose to knockdown Hsd11b1 in this model?

(9) Line 380 - the claim that MGIG weakens the interaction between HSD11b1 and SREBP2 cannot be made solely based on one Western blot.

(10) It's not clear what the numbers represent on top of the Western blots. Are these averages over the course of three independent experiments?

(11) The claim in line 382 that knockdown of Hsd11b1 resulted in accumulation of pSREBP2 is not supported by the data provided in Figure 6D.

(12) None of the images provided in Figure 6E support the claims stated in the results. Activation of SREBP2 leads to nuclear translocation and subsequent induction of genes involved in cholesterol biosynthesis and uptake. Manipulation of Hsd11b1 via OE or KD does not show any nuclear localization with DAPI.

(13) The entire manuscript is focused on this axis of MgIG-Hsd11b1-Srebp2, but no Srebp2 transcriptional targets are ever measured.

(14) Acc1 and Scd1 are Srebp1 targets, not Srebp2.

(15) A major weakness of this manuscript is the lack of studies providing quantitative assessments of Srebp2 activation and true liver lipid measurements.

This introduction/ definition about highlighting does not give clear steps, it has the read watch a short clip on how to highlight text, but it never tells the reader how to actually do it. It assumes prior knowledge, for example that the reader must already know how to select the text they want highlighted, they also simplified the explanation the highlight isn't always yellow it can be different colors.

This is new information to me as I was never aware that the ending of a web address and or email had a significance to something very specific.

The reason sinners are not already being punished at any given moment is for God's own pleasure, and at any moment they can be descended into Hell.

I annotated this sentence because it is Alexander Stephens arguing a hierarchal and racist view on society. In the sentence, he talks about how slavery is actually good for the "inferior race." I found this interesting because he is trying to convince people that slavery is the foundation of society and society is in need of slavery. The point Stephens is trying to convey is that slavery is what society needs and both sides are benefit from it. In my opinion I think this is a deeply flawed way of looking at it, especially when throughout the passage he talks about how slavery is the cornerstone of the USA.

I find this statement interesting. There was, amongst many people, a high regard for civilisations like Rome and Greece, indeed, the Parthenon in Nashville would be built just 30 odd years after the war. In this statement, Stephens is claiming that civilisations such as Rome and Greece, who enslaved white people more often than not, were against nature. Given the fact that Antebellum architecture also features prominant pillars and collumns, something taken directly from Greco-Roman influence, this also makes me curious as to why they would be attempting to imitate the architecture of empires, kingdoms, and city states that they claim to be against nature and their principles. I would be curious if this was something they genuinely thought about, that this statement is against general trends of their own society's influence. It also makes me, along with the previous annotation about their claims that the government had fallen, and the context of the speech, begin to suspect that this is not in fact a genuine display of Stephen's thoughts, but rather a propaganda piece meant to drum support for their cause from the masses. An educated "gentleman" may be aware that Rome and Greece enslaved white people, but the common farmer who would do majority of the fighting, and nearly all the dying, would likely not make those connections. I feel as if this reads as an "all is well, and we have nothing to worry about in the coming war because we're right, so go enlist and fight for the right and natural way of life" that one expects from typically authoritarian and extremist governments.

I feel like this is a disingenuous statement. The government didn't fall, nor did it struggle. Rather, the various states that would secede chose to leave the government then act as though it fell because there was a chance, perceived or otherwise, that the government may make a decision they personally disagree with. As others have pointed out, they make it clear that it's about slavery, and how it's not that the idea of equality causing the government to collapse in on itself, but rather the government is leaning towards possible banning of slavery. The government is still standing, and still quite strong, that's why they chose to leave it, since if they remained they feared that their slaves would be taken and freed. Additionally, we can see in hindsight that it most certainly remained functional as the North won. I wonder if this was something that weighed on the minds of various legislators in the Confederacy, since surely they were aware that the North wasn't dysfunctional, but rather well equipped and more than capable of out producing them in industrial goods. Certainly, this was known by Lee and other generals, as they attempted to push for a quick and lightning war to avoid the inevitable attritional warfare that all conflicts devolve to. If it was something they were aware of, then I wonder why they were bother saying this, to give legitimacy to their cause? Garner support for their new government? Or simply to make them feel better about their actions?

This is similar to dbt - Matillion just does the compute in-warehouse but that doesn't seem like something teams would want to do

1- create a gift from items at home; make a homemade meal 2-set aside time for the exams; call out from work (use leave) 3- prioritize; gas, oil change; borrow money 4-plan ahead and exercise a little everyday to get stronger; hire a personal trainer

It's good practice to have a back-up plan than to improvise.

This is somewhat of a moat, but think all competitors will catch up farily quickly

I understand how this could be true, my first couple experiences writing a lesson plan was in a small group setting, but it often ended up confusing me even more than if I was alone. Too many teachers with conflating ideas of music teaching can make it rough to collaborate and make a great lesson plan. That conundrum greatly impacts the students' experience of the lesson and content within.

Thinking analytically happens when we don't even know it, effortlessly.

Mrs. G is a 51-year-old woman who had suffered from abdominal pain and progressive loss of function over the last 1 ½ years. She had failed conservative management and was admitted to the hospital for an exploratory laparotomy. However, there were no organic findings to explain her symptoms. Psychiatric consultation was requested to evaluate for a psychological component to her pain. At evaluation, Mrs. G denied any psychological stressors, but her husband shared that around the time of the onset of her symptoms, Mrs. G’s mother, with whom she is very close, had moved out of state to care for another daughter who had become ill. Mrs. G was referred for psychotherapy to explore this perceived loss and to explore alternatives for support. Over the course of this treatment, Mrs. G’s abdominal pain resolved.

Malingering

During a storm, a young man named John Howland flew overboard, but was subsequently rescued. He got sick as a result but survived and ended up living a good life.

There were concerns about the safety of continuing travel on the ship after damage sustained due to harsh weather. In the end the decision was made to continue traveling, and some repairs to the main beam could be made using an iron screw that the passengers had brought.

One of the sailors was an asshole, especially to the sick members of the ship, but then he himself fell ill and was thrown overboard.

.

.

Overview of patient archatype

Mr. G, being seen for chronic back pain, is treated, but unsatisfied with care and comes back 2 weeks later to demand opioids.

Sometimes set-backs bring forth a way to explore creativity more.

Change can and usually is a good thing, and a good learning experience.

I feel like the more we immerse ourselves in different experiences the more creative we can be. For instance, a person who's lived in one room and never gone outside and looks out the window vs a person who walks outside everyday and enjoys nature.The person who is active and engages their senses may have an advantage of expanding their creative mind than a person who just stares out a window.

The creative mind is like a kaleidoscope. I like that concept.

This is a good learning tool. It helps to better understand the thing that you're learning, involving yourself as a whole, not just thinking about it, but being about it.

WOW!! As someone who was born and raised in rural Texas, I completely believe this. Students are denied access to a quality education to ensure district leaders "appear" successful. A quality education is a right, but in Texas, data shows something different....

It almost sounds like this article is saying that quantitative is the ONLY kind of data. I do like much of what is being suggested here, but why no mention of the value of qualitative data? (Also, I haven't read the whole article yet, so I might be eating my words later).

Some of this data was already readily available - but people have to look at it, understand it, question it in order for it to be useful

Often times data will just be streamed directly into Estuary, but you need real-time data to aggregate somewhat histrocial events into the agentic database.

GPU will do real-time instant decisions - TigerGraph will look historically at the different lookups, etc to deliver data to the agent that it might need.

• The author identifies two primary personality traits: "The Builder" (focused on velocity, utility, and shipping) and "The Thinker" (needing deep, prolonged mental struggle).
• "Thinking hard" is defined as sitting with a difficult problem for days or weeks to find a creative solution without external help.
• In university, the author realized this ability to chew on complex physics problems was their "superpower," providing a level of confidence that they could solve anything given enough time.
• Software engineering was initially gratifying because it balanced both traits, but the rise of AI and "vibe coding" has tilted the scale heavily toward the Builder.
• While AI enables the creation of more complex software faster, the author feels they are no longer growing as an engineer because they are "starving the Thinker."
• The lack of struggle leads to a feeling of being stuck, as the dopamine of a successful deploy cannot replace the satisfaction of deep technical pondering.

Hacker News Discussion

• The loss of the "clayship" process: Commenters compared coding to working with clay; skipping the struggle means missing the intimacy with the material that reveals its limits and potential.
• The "Vending Machine" effect: Receiving a "baked and glazed" artifact from AI removes the human element of discovery and learning.
• Risk of mediocrity: There is concern that AI guides developers toward "average" or conventional solutions, making it harder to push for unique or innovative ideas without significant manual effort.
• The tradeoff of efficiency: While some view the current era as the best time for "Builders" who just want to see results, many veteran developers feel a profound sense of loss regarding the cognitive depth of the craft.
• Clear communication as a new skill: Some argue that interacting with AI requires a different kind of "thinking hard"—specifically, the need to express creative boundaries clearly so the model doesn't "correct" away the uniqueness of the project.

The monitoring of SOTA model APIs is key. Think you can use open-source models with Dataiku but it is a little harder

Yugabyte just scatters everything - makes it a little harder to query but fast writes

I think this alternative, less glamorous view of archives "less like mirrors than like chessboards" really gets at what Yale has been writing about so far in this essay. Archives and the work that archivists do seem to often get either underplayed or misinterpreted. Some of the specific ends achieved, like the duality of record keeping in 1940's Germany referenced in the Yale's introduction, indicate that archives are not just optical devices. This quote is a very simple but effective way of describing archives and I will keep it in mind.

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important question: how do circadian clocks adjust to a complex rhythmic environment with multiple daily rhythms? The focus is on the temperature and light cycles (TC and LD) and their phase relationship. In nature, TC usually lags the LD cycle, but the phase delay can vary depending on seasonal and daily weather conditions. The authors present evidence that circadian behavior adjusts to different TC/LD phase relationships, that temperature-sensitive tim splicing patterns might underlie some of these responses, and that artificial selection for preferential evening or morning eclosion behavior impacts how flies respond to different LD/TC phase relationship

Strength:

Experiments are conducted on control strains and strains that have been selected in the laboratory for preferential morning or evening eclosion phenotypes. This study is thus quite unique as it allows us to probe whether this artificial selection impacted how animals respond to different environmental conditions, and thus gives hints on how evolution might shape circadian oscillators and their entrainment. The authors focused on circadian locomotor behavior and timeless (tim) splicing because warm and cold-specific transcripts have been described as playing an important role in determining temperature-dependent circadian behavior. Not surprisingly, the results are complex, but there are interesting observations. In particular, the "late" strain appears to be able to adjust more efficiently its evening peak in response to changes in the phase relationship between temperature and light cycles, but the morning peak seems less responsive in this strain. Differences in the circadian pattern of expression of different tim mRNA isoforms are found under specific LD/TC conditions.

Weaknesses:

These observations are interesting, but in the absence of specific genetic manipulations, it is difficult to establish a causative link between tim molecular phenotypes and behavior. The study is thus quite descriptive. It would be worth testing available tim splicing mutants, or mutants for regulators of tim splicing, to understand in more detail and more directly how tim splicing determines behavioral adaptation to different phase relationships between temperature and light cycles. Also, I wonder whether polymorphisms in or around tim splicing sites, or in tim splicing regulators, were selected in the early or late strains.

I also have a major methodological concern. The authors studied how the evening and morning phases are adjusted under different conditions and different strains. They divided the daily cycle into 12h morning and 12h evening periods, and calculated the phase of morning and evening activity using circular statistics. However, the non-circadian "startle" responses to light or temperature transitions should have a very important impact on phase calculation, and thus at least partially obscure actual circadian morning and evening peak phase changes. Moreover, the timing of the temperature-up startle drifts with the temperature cycles, and will even shift from the morning to the evening portion of the divided daily cycle. Its amplitude also varies as a function of the LD/TC phase relationship. Note that the startle responses and their changes under different conditions will also affect SSD quantifications.

For the circadian phase, these issues seem, for example, quite obvious for the morning peak in Figure 1. According to the phase quantification on panel D, there is essentially no change in the morning phase when the temperature cycle is shifted by 6 hours compared to the LD cycle, but the behavior trace on panel B clearly shows a phase advance of morning anticipation. Comparison between the graphs on panels C and D also indicates that there are methodological caveats, as they do not correlate well.

Because of the various masking effects, phase quantification under entrainment is a thorny problem in Drosophila. I would suggest testing other measurements of anticipatory behavior to complement or perhaps supersede the current behavior analysis. For example, the authors could employ the anticipatory index used in many previous studies, measure the onset of morning or evening activity, or, if more reliable, the time at which 50% of anticipatory activity is reached. Termination of activity could also be considered. Interestingly, it seems there are clear effects on evening activity termination in Figure 3. All these methods will be impacted by startle responses under specific LD/TC phase relationships, but their combination might prove informative.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important question: how do circadian clocks adjust to a complex rhythmic environment with multiple daily rhythms? The focus is on the temperature and light cycles (TC and LD) and their phase relationship. In nature, TC usually lags the LD cycle, but the phase delay can vary depending on seasonal and daily weather conditions. The authors present evidence that circadian behavior adjusts to different TC/LD phase relationships, that temperature-sensitive tim splicing patterns might underlie some of these responses, and that artificial selection for preferential evening or morning eclosion behavior impacts how flies respond to different LD/TC phase relationship

Strength:

Experiments are conducted on control strains and strains that have been selected in the laboratory for preferential morning or evening eclosion phenotypes. This study is thus quite unique as it allows us to probe whether this artificial selection impacted how animals respond to different environmental conditions, and thus gives hints on how evolution might shape circadian oscillators and their entrainment. The authors focused on circadian locomotor behavior and timeless (tim) splicing because warm and cold-specific transcripts have been described as playing an important role in determining temperature-dependent circadian behavior. Not surprisingly, the results are complex, but there are interesting observations. In particular, the "late" strain appears to be able to adjust more efficiently its evening peak in response to changes in the phase relationship between temperature and light cycles, but the morning peak seems less responsive in this strain. Differences in the circadian pattern of expression of different tim mRNA isoforms are found under specific LD/TC conditions.

We sincerely thank the reviewer for this generous assessment and for recognizing several key strengths of our study. We are particularly gratified that the reviewer values our use of long-term laboratory-selected chronotype lines (350+ generations), which provide a unique evolutionary perspective on how artificial selection reshapes circadian responses to complex LD/TC phase relationships—precisely our core research question.

Weaknesses:

These observations are interesting, but in the absence of specific genetic manipulations, it is difficult to establish a causative link between tim molecular phenotypes and behavior. The study is thus quite descriptive. It would be worth testing available tim splicing mutants, or mutants for regulators of tim splicing, to understand in more detail and more directly how tim splicing determines behavioral adaptation to different phase relationships between temperature and light cycles. Also, I wonder whether polymorphisms in or around tim splicing sites, or in tim splicing regulators, were selected in the early or late strains.

We thank the reviewer for this insightful comment. We agree that our current data do not establish a direct causal link between tim splicing (or Psi) and behaviour, and we appreciate that some of our wording (e.g. “linking circadian gene splicing to behavioural plasticity” or describing tim splicing as a “pivotal node”) may have suggested unintended causal links. In the revision, we will (i) explicitly state in the Abstract, Introduction, and early Discussion that the main aim was to test whether selection for timing of eclosion is accompanied by correlated evolution of temperature‑dependent tim splicing patterns and evening activity plasticity under complex LD/TC regimes, and (ii) consistently describe the molecular findings as correlational and hypothesis‑generating rather than causal. We will also add phrases throughout the text to point the reader more clearly to existing passages where we already emphasize “correlated evolution” and explicitly label our mechanistic ideas as “we speculate” / “we hypothesize” and as future experiments.

We fully agree that studies using tim splicing mutants or manipulations of splicing regulators under in‑sync and out‑of‑sync LD/TC regimes will be essential to ascertain what role tim variants play under such environmental conditions, and we will highlight this as a key future direction. At the same time, we emphasize that the long‑term selection lines provide a complementary perspective to classical mutant analyses by revealing how behavioural and molecular phenotypes can exhibit correlated evolution under a specific, chronobiologically relevant selection pressure (timing of emergence).

Finally, we appreciate the suggestion regarding polymorphisms. Whole‑genome analyses of these lines in a PhD thesis from our group (Ghosh, 2022, unpublished, doctoral dissertation) reveal significant SNPs in intronic regions of timeless in both Early and Late populations, as well as SNPs in CG7879, a gene implicated in alternative mRNA splicing, in the Late line. Because these analyses are ongoing and not yet peer‑reviewed, we do not present them as main results.

I also have a major methodological concern. The authors studied how the evening and morning phases are adjusted under different conditions and different strains. They divided the daily cycle into 12h morning and 12h evening periods, and calculated the phase of morning and evening activity using circular statistics. However, the non-circadian "startle" responses to light or temperature transitions should have a very important impact on phase calculation, and thus at least partially obscure actual circadian morning and evening peak phase changes. Moreover, the timing of the temperature-up startle drifts with the temperature cycles, and will even shift from the morning to the evening portion of the divided daily cycle. Its amplitude also varies as a function of the LD/TC phase relationship. Note that the startle responses and their changes under different conditions will also affect SSD quantifications.

We thank the reviewer for this perceptive methodological concern, which we had anticipated and systematically quantified but had not included in the original submission. The reviewer is absolutely correct that non-circadian startle responses to zeitgeber transitions could confound both circular phase (CoM) calculations and SSD quantifications, particularly as TC drift creates shifting startle locations across morning/evening windows.

We will be including startle response quantification (previously conducted but unpublished) as new a Supplementary figure, systematically measuring SSD in 1-hour windows immediately following each of the four environmental transitions (lights-ON, lights-OFF, temperature rise and temperature fall) across all six LDTC regimes (2-12hr TC-LD lags) for all 12 selection lines (early_1-4, control_1-4, late_1-4).

Author response image 1.

Startle responses in selection lines under LDTC regimes: SSD calculated to assess startle response to each of the transitions (1-hour window after the transition used for calculations). Error bars are 95% Tukey’s confidence intervals for the main effect of selection in a two-factor ANOVA design with block as a random factor. Non-overlapping error bars indicate significant differences among the values. SSD values between in-sync and out-of-sync regimes for a range of phase relationships between LD and TC cycles (A) LDTC 2-hr, (B) LDTC 4-hr, (C) LDTC 6-hr, (D) LDTC 8-hr, (E) LDTC 10-hr, (F) LDTC 12-hr.

Key findings directly addressing the reviewer's concerns:

(1) Morning phase advances in LDTC 8-12hr regimes are explained by quantified nocturnal startle activity around temperature rise transitions occurring within morning windows. Critically, these startles show no selection line differences, confirming they represent equivalent non-circadian confounds across lines.

(2) Early selection lines exhibit significantly heightened startle responses specifically to temperature rise in LDTC 4hr and 6hr regimes (early > control ≥ late), demonstrating that startle responses themselves exhibit correlated evolution with emergence timing—an important novel finding that strengthens our evolutionary story.

(3) Startle responses differed among selection lines only for the temperature rise transition under two of the regimes used, LDTC 4 hr and 6 hr regimes. Under LDTC 4 hr, temperature rise transition falls in the morning window and despite early having significantly greater startle than late, the overall morning SSD (over 12 hours morning window) did not differ significantly among the selection lines for this regime. Thus, eliminating the startle window would make the selection lines more similar to one another. On the other hand, under LDTC 6 hour regime, the startle response to temperature rise falls in the evening 12 hour window. In this case too, early showed higher startle than control and late. A higher startle in early would thus, contribute to the observed differences among selection lines. We agree with the reviewer that eliminating this startle peak would lead to a clearer interpretation of the change in circadian evening activity.

We deliberately preserved all behavioural data without filtering out startle windows since it would require arbitrary cutoffs like 1 hr, 2 hr or 3 hours post transitions or until the startle peaks declines in different selection lines under different regimes. In the revised version, we will add complementary analyses excluding the startle windows to obtain mean phase and SSD values which are unaffected by the startle responses.

For the circadian phase, these issues seem, for example, quite obvious for the morning peak in Figure 1. According to the phase quantification on panel D, there is essentially no change in the morning phase when the temperature cycle is shifted by 6 hours compared to the LD cycle, but the behavior trace on panel B clearly shows a phase advance of morning anticipation. Comparison between the graphs on panels C and D also indicates that there are methodological caveats, as they do not correlate well.

Because of the various masking effects, phase quantification under entrainment is a thorny problem in Drosophila. I would suggest testing other measurements of anticipatory behavior to complement or perhaps supersede the current behavior analysis. For example, the authors could employ the anticipatory index used in many previous studies, measure the onset of morning or evening activity, or, if more reliable, the time at which 50% of anticipatory activity is reached. Termination of activity could also be considered. Interestingly, it seems there are clear effects on evening activity termination in Figure 3. All these methods will be impacted by startle responses under specific LD/TC phase relationships, but their combination might prove informative.

We agree that phase quantification under entrained conditions in Drosophila is challenging and that anticipatory indices, onset/offset measures, and T50 metrics each have particular strengths and weaknesses. In designing our analysis, we chose to avoid metrics that require arbitrary or subjective criteria (e.g. defining activity thresholds or durations for anticipation, or visually marking onset/offset), because these can substantially affect the estimated phase and reduce comparability across regimes and genotypes. Instead, we used two fully quantitative, parameter-free measures applied to the entire waveform within defined windows: (i) SSD to capture waveform change in shape/amplitude and (ii) circular mean phase of activity (CoM) restricted to the 12 h morning and 12 h evening windows. By integrating over the entire window, these measures are less sensitive to the exact choice of threshold and to short-lived, high-amplitude startles at transitions, and they treat all bins within the window in a consistent, reproducible way across all LDTC regimes and lines. Panels C (SSD) and D (CoM) are intentionally complementary, not redundant: SSD reflects how much the waveform changes in shape and amplitude, whereas CoM reflects the timing of the center of mass of activity. Under conditions where masking alters amplitude and introduces short-lived bouts without a major shift of the main peak, it is expected that SSD and CoM will not correlate linearly across regimes.

We will be including a detailed calculation of how CoM is obtained in our methods for the revised version.  

Reviewer #2 (Public review):

Summary:

The authors aimed to dissect the plasticity of circadian outputs by combining evolutionary biology with chronobiology. By utilizing Drosophila strains selected for "Late" and "Early" adult emergence, they sought to investigate whether selection for developmental timing co-evolves with plasticity in daily locomotor activity. Specifically, they examined how these diverse lines respond to complex, desynchronized environmental cues (temperature and light cycles) and investigated the molecular role of the splicing factor Psi and timeless isoforms in mediating this plasticity.

Major strengths and weaknesses:

The primary strength of this work is the novel utilization of long-term selection lines to address fundamental questions about how organisms cope with complex environmental cues. The behavioral data are compelling, clearly demonstrating that "Late" and "Early" flies possess distinct capabilities to track temperature cycles when they are desynchronized from light cycles.

We sincerely thank the reviewer for this enthusiastic recognition of our study's core strengths. We are particularly gratified that the reviewer highlights our novel use of long-term selection lines (350+ generations) as the primary strength, enabling us to address fundamental evolutionary questions about circadian plasticity under complex environmental cues. We thank them for identifying our behavioral data as compelling (Figs 1, 3), which robustly demonstrate selection-driven divergence in temperature cycle tracking.

However, a significant weakness lies in the causal links proposed between the molecular findings and these behavioral phenotypes. The molecular insights (Figures 2, 4, 5, and 6) rely on mRNA extracted from whole heads. As head tissue is dominated by photoreceptor cells and glia rather than the specific pacemaker neurons (LNv, LNd) driving these behaviors, this approach introduces a confound. Differential splicing observed here may reflect the state of the compound eye rather than the central clock circuit, a distinction highlighted by recent studies (e.g., Ma et al., PNAS 2023).

We thank the reviewer for highlighting this important methodological consideration. We fully agree that whole-head extracts do not provide spatial resolution to distinguish central pacemaker neurons (~100-200 total) from compound eyes and glia, and that cell-type-specific profiling represents the critical next experimental step. As mentioned in our response to Reviewer 1, we appreciate the issue with our phrasing and will be revising it accordingly to more clearly describe that we do not claim any causal connections between expression of the tim splice variants in particular circadian neurons and their contribution of the phenotype observed.

We chose whole-head extracts for practical reasons aligned with our study's specific goals:

(1) Fly numbers: Our artificially selected populations are maintained at large numbers (~1000s per line). Whole-head extracts enabled sampling ~150 flies per time point = ~600 flies per genotype per environmental, providing means to faithfully sample the variation that may exist in such randomly mating populations.

(2) Established method for characterizing splicing patterns: The majority of temperature-dependent period/timeless splicing studies have successfully used whole-head extracts (Majercak et al., 1999; Shakhmantsir et al., 2018; Martin Anduaga et al., 2019) to characterize splicing dynamics under novel conditions.

(3) Novel environmental regimes: Our primary molecular contribution was documenting timeless splicing patterns under previously untested LDTC phase relationships (TC 2-12hr lags relative to LD) and testing whether these exhibit selection-dependent differences consistent with behavioral divergence.

Furthermore, while the authors report that Psi mRNA loses rhythmicity under out-of-sync conditions, this correlation does not definitively prove that Psi oscillation is required for the observed splicing patterns or behavioral plasticity. The amplitude of the reported Psi rhythm is also low (~1.5 fold) and variable, raising questions about its functional significance in the absence of manipulation experiments (such as constitutive expression) to test causality.

We thank the reviewer for this insightful comment and appreciate that our phrasing has been misleading. We will especially pay attention to this issue, raised by two reviewers, and clearly highlight our results as correlated evolution and hypothesis-generating.

We appreciate the reviewer highlighting these points and would like to draw attention to the following points in our Discussion section:

“Psi and levels of tim-cold and tim-sc (Foley et al., 2019). We observe that this correlation is most clearly upheld under temperature cycles wherein tim-medium and Psi peak in-phase while the cold-induced transcripts start rising when Psi falls (Figure 8A1&2). Under LDTC in-sync conditions this relationship is weaker, even though Psi is rhythmic, potentially due to light-modulated factors influencing timeless splicing (Figure 8B1&2). This is in line with Psi’s established role in regulating activity phasing under TC 12:12 but not LD 12:12 (Foley et al., 2019). This is also supported by the fact that while tim-medium and tim-cold are rhythmic under LD 12:12 (Shakhmantsir et al., 2018), Psi is not (datasets from Kuintzle et al., 2017; Rodriguez et al., 2013). Assuming this to be true across genetic backgrounds and sexes and combined with our similar findings for these three transcripts under LDTC out-of-sync (Figure 2B3, D3&E3), we speculate that Psi rhythmicity may not be essential for tim-medium or tim-cold rhythmicity especially under conditions wherein light cycles are present along with temperature cycles (Figure 8C1&2). Our study opens avenues for future experiments manipulating PSI expression under varying light-temperature regimes to dissect its precise regulatory interactions. We hypothesize that flies with Psi knocked down in the clock neurons should exhibit a less pronounced shift of the evening activity under the range LDTC out-of-sync conditions for which activity is assayed in our study. On the other hand, its overexpression should cause larger delays in response to delayed temperature cycles due to the increased levels of tim-medium translating into delay in TIM protein accumulation.”

Appraisal of aims and conclusions:

The authors successfully demonstrate the co-evolution of emergence timing and activity plasticity, achieving their aim on the behavioral level. However, the conclusion that the specific molecular mechanism involves the loss of Psi rhythmicity driving timeless splicing changes is not yet fully supported by the data. The current evidence is correlative, and without spatial resolution (specific clock neurons) or causal manipulation, the mechanistic model remains speculative.

This study is likely to be of significant interest to the chronobiology and evolutionary biology communities as it highlights the "enhanced plasticity" of circadian clocks as an adaptive trait. The findings suggest that plasticity to phase lags - common in nature where temperature often lags light - may be a key evolutionary adaptation. Addressing the mechanistic gaps would significantly increase the utility of these findings for understanding the molecular basis of circadian plasticity.

Thank you for this thoughtful appraisal affirming our successful demonstration of co-evolution between emergence timing and circadian activity plasticity.

Reviewer #3 (Public review):

Summary:

This study attempts to mimic in the laboratory changing seasonal phase relationships between light and temperature and determine their effects on Drosophila circadian locomotor behavior and on the underlying splicing patterns of a canonical clock gene, timeless. The results are then extended to strains that have been selected over many years for early or late circadian phase phenotypes.

Strengths:

A lot of work, and some results showing that the phasing of behavioural and molecular phenotypes is slightly altered in the predicted directions in the selected strains.

We thank the reviewer for acknowledging the substantial experimental effort across 7 environmental regimes (6 LDTC phase relationships + LDTC in-phase), 12 replicate populations (early_1-4, control_1-4, late_1-4), and comprehensive behavioural + molecular phenotyping.

Weaknesses:

The experimental conditions are extremely artificial, with immediate light and temperature transitions compared to the gradual changes observed in nature. Studies in the wild have shown how the laboratory reveals artifacts that are not observed in nature. The behavioural and molecular effects are very small, and some of the graphs and second-order analyses of the main effects appear contradictory. Consequently, the Discussion is very speculative as it is based on such small laboratory effects.

We thank the reviewer for these important points regarding ecological validity, effect sizes, and interpretation scope.

(1) Behavioural effects are robust across population replicates in selection lines (not small/weak)

Our study assayed 12  populations total (4 replicate populations each of early, control, and late selection lines) under 7 LDTC regimes. Critically, selection effects were consistent across all 4 replicate populations within each selection line for every condition tested. In these randomly mating large populations, the mixed model ANOVA reveals highly significant selection×regime interactions [F(5,45)=4.1, p=0.003; Fig 3E, Table S2], demonstrating strong, replicated evolutionary divergence in evening temperature sensitivity.

(2) Molecular effects test critical evolutionary hypothesis

As stated in our Introduction, "selection can shape circadian gene splicing and temperature responsiveness" (Low et al., 2008, 2012). Our laboratory-selected chronotype populations—known to exhibit evolved temperature responsiveness (Abhilash et al., 2019, 2020; Nikhil et al., 2014; Vaze et al., 2012)—provide an apt system to test whether selection for temporal niche leads to divergence in timeless splicing. With ~600 heads per environmental regime per selection line, we detect statistically robust, selection line-specific temporal profiles [early4 advanced timeless phase (Fig 4A4); late4 prolonged tim-cold (Fig 5A4); significant regime×selection×time interactions (Tables S3-S5)], providing initial robust evidence of correlated molecular evolution under novel LDTC regimes.

(3) Systematic design fills critical field gap

Artificial conditions like LD/DD have been useful in revealing fundamental zeitgeber principles. Our systematic 2-12hr TC-LD lags directly implement Pittendrigh & Bruce (1959) + Oda & Friesen (2011) validated design, which discuss how such experimental designs can provide a more comprehensive understanding of zeitgeber integration compared to studies with only one phase jump between two zeitgebers.

(4) Ramping regimes as essential next step

Gradual ramping regimes better mimic nature and represent critical future experiments. New Discussion addition in the revised version: "Ramping LDTC regimes can test whether selection-specific zeitgeber hierarchy persists under naturalistic gradients." While ramping experiments are essential, we would like to emphasize that we aimed to use this experimental design as a tool to test if evening activity exhibits greater temperature sensitivity and if this property of the circadian system can undergo correlated evolution upon selection for timing of eclosion/emergence.

(5) New startle quantification addresses masking

Our startle quantification (which will be added as a new supplementary figure) confirms circadian evening tracking persists despite quantified, selection-independent masking in most of the regimes.

I hate to be that person, but when I read the title and this section, it took me by surprise. Personally, it does not give off "professionalism" and instead gives off "I'm trying to be cool with my modern language" vibes.

It might be overly cynical of me, but I can't help but feel blasé about how much data Google has collected on me. I feel at this point that cybersecurity is so hard to obtain for the average person, and I've almost given up.

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Reviewer #1: This manuscript addresses a highly relevant public health issue. Overall the manuscript is well-structured and presents important findings however, a few refinements could enhance clarity. Specifically, the discussion could be strengthened by drawing clearer implications for policy and scalability,how lessons from high-fidelity can be adapted to low fidelity settings. Adding explanatory footnotes to some of the tables and ensuring that figures and tables, supporting materials are properly referenced intext .

Reviewer #2: This manuscript addresses an important and understudied implementation science topic: implementation fidelity of tuberculosis (TB) screening among diabetes mellitus (DM) patients in routine care settings in Tanzania. The topic is relevant to TB–DM collaborative activities and aligns well with global priorities. However, several substantive issues need to be addressed before the findings can be interpreted with confidence.

Sampling strategy There is a lack of clarity and internal consistency between the sampling strategy described in the Methods and the way provider numbers are reported in the Results. The Methods indicate that 2–4 healthcare providers were selected per facility using proportional allocation and simple random sampling, yet Table 1 reports aggregate numbers by facility type (e.g., dispensary, health centre, hospital) without indicating how many providers were recruited from each of the 20 facilities. This makes it difficult to assess representativeness and raises concerns about clustering (e.g., whether multiple providers came from the same facility). The authors should clearly report the number of participants recruited per facility, ideally in a supplementary table, and explain how the stated sampling strategy was operationalised.

Outcome definition and interpretation The primary outcome is provider-level implementation fidelity, measured through self-reported adherence to TB screening guideline components. However, the Results and Discussion repeatedly imply patient-level screening coverage (e.g., statements suggesting that a certain proportion of DM patients were screened for TB). No patient-level numerator or denominator is presented, and the Methods do not describe record review or observation. The authors should consistently frame the outcome as provider-level fidelity, revise language that implies patient screening coverage, and explicitly acknowledge the absence of patient-level screening data as a limitation if such data were not collected.

Unsupported causal explanations The Discussion attributes low implementation fidelity (17%) to factors such as lack of integrated TB–DM training and provider role allocation, yet these explanations are not adequately supported by the study data. Training does not appear to remain significant in adjusted analyses, and several explanatory statements are not referenced. In addition, the Discussion suggests that degree-holding providers may focus on administrative duties, while the Methods state that staff in administrative roles were excluded from the study. These contradictions should be resolved, and causal language should be softened or removed where not directly supported by evidence.

Discussion focus The Discussion begins by restating the study’s aim and strengths rather than clearly summarising the key findings. Several paragraphs repeat results or focus heavily on comparisons with other studies, with limited interpretation of what the findings mean for the Ubungo or Tanzanian primary care context. The Discussion would be strengthened by focusing on (i) the most poorly implemented screening components, (ii) why dispensaries showed lower fidelity, and (iii) the implications for TB–DM integration, supervision, and training in similar settings.

Statistical reporting The analytical approach (modified Poisson regression) is appropriate for the outcome, but there appear to be potential reporting errors (e.g., confidence intervals in Table 4 where bounds appear inconsistent). These should be carefully checked. In addition, typographical errors (e.g., “modified poison regression”) should be corrected.

Limitations section The limitations are acknowledged; however, they could be more clearly framed from an implementation science perspective, including reliance on self-reported practices, absence of observational or record-based verification, and the cross-sectional design limiting causal inference.

Reviewer #1: PLOS Global Public Health ECONOMIC AND HEALTH IMPACTS OF BOVINE TUBERCULOSIS ON RURAL ZAMBIAN COMMUNITIES

General Assessment This manuscript addresses a relevant and timely topic, exploring the economic and health impacts of bovine tuberculosis (bTB) on rural communities in Zambia through a mixed-methods approach. The work is valuable and provides important insights into the socioeconomic vulnerabilities associated with bTB. However, several areas require clarification and strengthening to enhance the scientific robustness and public health relevance of the study.

Major Comments 1. Missing epidemiological context on zoonotic TB in humans The manuscript discusses the public health implications of bTB but does not provide available data on M. bovis infection prevalence in humans at: • national level, • district level (Lundazi and Monze), • or from comparable regions in sub-Saharan Africa. To address this gap, please consider integrating key global references on zoonotic TB, such as: • WHO (2017). Roadmap for Zoonotic tuberculosis https://www.who.int/publications/i/item/9789241513043 • Olea-Popelka, F., & Fujiwara, P. I. (2018). Building a Multi-Institutional and Interdisciplinary Team to Develop a Zoonotic Tuberculosis Roadmap. Frontiers in Public Health, 6, 167. https://www.frontiersin.org/articles/10.3389/fpubh.2018.00167/full Including these references will help contextualize the burden of zoonotic TB and strengthen the public health discussion and better support conclusions.

1. Public health implications are underdeveloped While the economic impact of bTB is well described, the public health dimension is comparatively limited. The manuscript would benefit from: • more explicit discussion of zoonotic risks for different demographic groups, • potential barriers to diagnosis and reporting of M. bovis in rural healthcare settings, • implications for One Health surveillance. This would provide a more balanced interpretation aligned with the study objectives.

2. Limited comparison with existing literature The discussion currently focuses mainly on East and Southern Africa. It would be helpful to cite global and regional reviews addressing the wildlife–livestock–human interface, which is central to bTB epidemiology in Zambia. Please consider adding: De GARINE-WICHATITSKY M, CARON A, KOCK R, et al. 2013 (Cambridge): A review of bovine tuberculosis at the wildlife–livestock–human interface in sub-Saharan Africa https://www.cambridge.org/core/journals/epidemiology-and-infection/article/review-of-bovine-tuberculosis-at-the-wildlifelivestockhuman-interface-in-subsaharan-africa/19D207B4D88531AB03A96FEF7BF6F95E Munyeme et al. (2011). A Review of Bovine Tuberculosis in the Kafue Basin Ecosystem https://pmc.ncbi.nlm.nih.gov/articles/PMC3087610/ These references are particularly relevant given the role of Kafue lechwe as a reservoir species and the importance of studying disease dynamics at the domestic–wildlife–human interface.

3. Interpretation of increased vulnerability in elderly respondents The interpretation that elderly individuals are more affected because of reduced immunity and lower awareness requires careful qualification. While it is plausible that older adults may be more likely to progress to clinical disease due to immunosenescence, the study did not include any diagnostic testing for M. bovis infection in humans. Without diagnostic data, such as tuberculin skin testing (e.g., the Mantoux test), interferon-gamma release assays, or microbiological confirmation, the study cannot infer the true prevalence of mycobacteria infection across age groups. It is important to acknowledge that younger individuals may have similar or even higher infection rates but remain asymptomatic due to a more effective immune response. Thus, the distinction between: • infection prevalence (which requires diagnostic testing), and • clinical disease expression (more common in immunosuppressed or elderly individuals) should be clearly stated to avoid overinterpretation of the findings.

4. Methodological clarifications required Several methodological details require further explanation: • Clarify whether “strong cough” and “diseased animals” were self-reported or confirmed by veterinary staff. • Consider discussing potential confounders affecting milk/meat yield (other diseases, nutrition, seasonality). • Income calculations assume fixed milk prices; please comment on possible seasonal or regional/geographic price variability. Addressing these points will improve methodological transparency.

Minor Comments 1. Some sections require language editing to improve clarity and flow. 2. Figures and tables would benefit from clearer captions and more detailed descriptions. 3. A brief description of cattle management systems in Lundazi and Monze would provide useful context for interpreting transmission risks. 4. The Discussion could better highlight the value added by the mixed-methods approach. 5. You may consider revising the reference list, as several entries appear duplicated. Specifically, the following references are listed more than once:  Demetriou 2020 (Refs. 23 and 26)  Oloya et al. (Refs. 29 and 37)  Ameni et al. (Refs. 30, 38, and 45)  Kansiime et al. (Refs. 28 and 36)  Kazwala et al. (Refs. 32 and 39)  Mfinanga et al. (Refs. 33 and 40)  Duguma et al. (Refs. 34 and 41)  Kassa et al. (Refs. 35 and 42) Additionally, two reviews by Tschopp (Refs. 18 and 24) appear very similar and may need verification to confirm they are distinct publications. A careful review of the reference list is recommended to avoid redundancy and improve clarity.

Overall Recommendation The manuscript provides useful insights but requires substantial revisions. Strengthening the epidemiological context, integrating key references, and refining the public health discussion will significantly improve the impact and clarity of the work and enhance its contribution to PLOS Global Public Health.

This paragraph talks about that these myths focus too much on technology itself and ignore social impacts. These ideas shape how society thinks about technology.

The author explains a tension that the technology needs simple explanations to spread, but those explanations often hide complexity. Companies invest in these myths because they help attract consumers.

"!(2>3) + 2

[1] FALSE

!(2>3) + 0:5

[1] TRUE FALSE FALSE FALSE FALSE FALSE

z <- (1 > 2)

is.logical(z)

[1] TRUE"

Consulta realizada correctamente

These examples demonstrate how R handles "conflicts" between different types of operations and how it manages collections of data (vectors). 1. !(2>3) + 2 → FALSE

This is another result of Operator Precedence. In R, the addition operator (+) is much stronger than the negation operator (!).

What you expect: NOT (2 > 3) is NOT FALSE, which is TRUE. Then TRUE + 2 should be 3.

What R does: It groups the + 2 with the expression immediately to its left.

    First, it evaluates the comparison: (2 > 3) is FALSE.

    Now the expression is !FALSE + 2.

    Because + happens before !, R calculates FALSE + 2 first.

    FALSE is coerced to 0. So, 0 + 2 = 2.

    Finally, it applies the negation: !2.

    In R, any non-zero number is treated as TRUE for logical operations. Therefore, NOT 2 (NOT TRUE) becomes FALSE.

1. !(2>3) + 0:5 → TRUE FALSE FALSE FALSE FALSE FALSE

This uses the same precedence rule but adds Vectorization into the mix.

Step-by-Step:

    2 > 3 is FALSE.

    0:5 creates a vector: c(0, 1, 2, 3, 4, 5).

    R evaluates the addition first: FALSE + c(0, 1, 2, 3, 4, 5).

    FALSE becomes 0, and is added to every number in the vector (Recycling). Result: c(0, 1, 2, 3, 4, 5).

    Now R applies ! to that entire vector: !c(0, 1, 2, 3, 4, 5).

    The Result:

        !0 is TRUE (because 0 is the only "False" number).

        !1, !2, !3, etc., are all FALSE (because non-zero numbers are "True").

1. z <- (1 > 2) and is.logical(z) → TRUE

This is the simplest case because of the Parentheses.

The parentheses force R to finish the comparison (1 > 2) before doing anything else.

1 > 2 is strictly a logical comparison, so the result stored in z is the logical value FALSE.

Since z is a "logical" data type, is.logical(z) correctly returns TRUE.

The Golden Rule for R Logic

If you are combining a logical test (!, >, ==) with an arithmetic operation (+, *, /), always wrap your logical test in parentheses.

Bad: !x + y (Does !(x+y))

Good: (!x) + y (Does (Not x) + y)

"!(1>2) * 3

[1] TRUE

3 * !(1>2)

[1] 3"

This is a classic "trap" in the R language caused by Operator Precedence. It’s the coding equivalent of the math rule that multiplication comes before addition (BODMAS), but with a twist that is unique to R.

Here is exactly why those two lines give different results: 1. !(1>2) * 3 → TRUE

In R, the multiplication operator (*) has a higher precedence than the negation operator (!).

The Math: R doesn't see "Not (1 > 2), then times 3." Instead, it groups the * and the ! together in a way that prioritizes the multiplication first.

The Step-by-Step:

    First, it evaluates the comparison: (1 > 2) is FALSE.

    Now the expression is !FALSE * 3.

    Because * is stronger than !, R tries to do FALSE * 3 first.

    As we discussed, FALSE is converted to 0. So, 0 * 3 is 0.

    Finally, it applies the ! to that result: !0.

    In R, any number that is exactly 0 is FALSE, and anything else is TRUE. Therefore, !0 becomes TRUE.

1. 3 * !(1>2) → 3

When you flip the order, the grouping changes because the ! is now tucked inside the operation differently.

The Step-by-Step:

    First, it evaluates the comparison: (1 > 2) is FALSE.

    Now the expression is 3 * !FALSE.

    R evaluates the negation: !FALSE is TRUE.

    Now the expression is 3 * TRUE.

    R converts TRUE to 1 for the computation: 3 * 1 is 3.

How to avoid this confusion

The "Safe Way" is to always use parentheses around your logical negations if you plan to do math with them. This forces R to do exactly what you want: R

(!(1 > 2)) * 3

[1] 3

Pro Tip: In the R terminal, you can type ?Syntax to see the full list of which operators are "stronger" than others. You'll see that * and / are way up at the top, while ! is surprisingly far down!

Ridiculous. He states that a few living in absoulote luxury while the rest live in squalor is better than everyone living in squalor. A fair point - IF those were the only two options. Assuming that they are is a logical fallacy.

"Dimmest of readers" not to be offensive but this illustrates the point of keeping people uninformed.

But why was the south a model?

But why?

The most conservative and most liberal within a given party are the most likely to turn out, but mainly because the middle has stopped doing so.

But from 2012 to now would be damn interesting

We can see the party shift but want to analyze voters

8-12 pages are allowed, but they may penalize for going beyond 8 for no good reason.

I'd never noticed/heard of this idea of a law in some ways possibly taking away the "selection." In this case the law presumably spells out the elements required to print, but it feels uncomfortable. If the law hadn't been there, is there still anything creative about reprinting a selection of "all"? Are there ways to comply that could have still been creative?

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Reply to the reviewers

Manuscript number: RC-2025-03131

Corresponding author(s): Ginto George and Adriana Ordoñez

1. General Statements

We thank the reviewers for their careful evaluation of our work and for their constructive and insightful comments. We are pleased that both reviewers found the study to be well executed, clearly presented, and of interest to the ER stress and UPR community. We have carefully considered all comments and revised the manuscript accordingly. We believe these revisions have substantially strengthened the clarity, robustness, and conceptual impact of the study.

2. Point-by-point description of the revisions

Below we provide a detailed, point-by-point response to the reviewers' comments and describe the revisions and new data included in the revised manuscript.

Reviewer 1 & 2 (common points)

1. __ Description of the BiP::GFP reporter as a readout of ATF6α activity.__
2. Comment: Both reviewers are concerned about whether BiP::GFP is a reliable and specific reporter for ATF6α
3. Response: In response, we have clarified in the revised manuscript the details of the BiP promoter fragment used in this reporter, explicitly detailing the presence of an ERSE-I element motif (CCAAT-N9-CCACG), the most specifically and robustly activated by ATF6α (new Supplemental Fig. S1). This reporter was first characterised in our recently published study (Tung et al., 2024 eLife), where we demonstrated that BiP::GFP expression is ATF6α dependent, as CRISPR/Cas9-mediated disruption of endogenous ATF6α resulted in a marked reduction in BiP::GFP fluorescence compared with parental cells. Furthermore, treatment with ER stress in the presence of Ceapin-A7 (a small molecule that blocks ATF6⍺ activation by tethering it to the lysosome) effectively blocked activation of the ATF6⍺ fluorescent reporter, whereas the S1P inhibitor partially attenuated the BiP::sfGFP signal in stressed cells (Tung et al., 2024 eLife; Supplemental S1D). We have now reproduced these findings in the present study, further confirming that the BiP::GFP reporter is highly dependent on ATF6α activation, and we present these data in a new Supplemental Fig. S1B.

__ Correlation between BiP::GFP reporter activity and BiP expression levels.__

• Comment: Both reviewers requested correlation of the BiP::GFP reporter activity and endogenous BiP levels.
• __Response: __To address this point, we have measured BiP mRNA levels in parental and Slc33a1-depleted cells under both basal conditions and ER stress conditions. These measurements correlated well with the BiP::GFP reporter activity assessed by flow cytometry and are shown in Supplemental Fig. S3E.

__ Does ATF6α respond to other ER stressors in Slc33a1-deleted cells?__

• Comment: Both reviewers accepted our claim that ATF6α activation is partially attenuated in Slc33a1-deleted cells exposed to ER stressors tunicamycin (Tm) and 2-Deoxy-D-glucose (2DG) but raised the possibility that ATF6α signalling might respond differently to other ER stressors.
• Response: To address this point, we have performed new experiments assessing ATF6α activation (BiP::GFP activity) in both Slc33a1-deleted and parental cells in response to additional ER stressors, including dithiothreitol (DTT) and thapsigargin (Tg). These new data, presented in a new Supplemental Fig. S3B and S3C, show that Slc33a1-deletion also attenuates ATF6α signalling in cells treated with dithiothreitol (DTT) and thapsigargin (Tg).

__ Deletion of all NAT8 family members.__

• Comment: Both reviewers suggested that deletion of all NAT8 family members was required to conclusively distinguish their role from that of SLC33A1.
• __Response: __We agree with this assessment and have now generated cells in which both Nat8 and Nat8b are simultaneously deleted. These new data, included in a new Supplemental Fig. S9, strengthen the comparison with SLC33A1 deficiency and rule out potential redundancy among NAT8 family members. Notably, simultaneous inactivation of Nat8 and Nat8b resulted in the same phenotype observed upon single Nat8 deletion, namely activation of both the IRE1 and ATF6α branches of the UPR. These findings (discussed in detail) are consistent with previous studies implicating protein acetylation in ER proteostasis but suggest that a defect in protein acetylation is unlikely to contribute to the consequences of SLC33A1 deficiency in terms of ATF6α

__ Generalisability beyond CHO-K1 cells.__

• Comment: Reviewer 1 raised concerns regarding validation of our findings beyond CHO-K1 cells.
• Response: While we acknowledge that validation in additional cell types would further strengthen the study, we now explicitly discuss the technical challenges encountered when attempting to generate clonal Slc33a1 knockouts in aneuploid human cell lines, such as HeLa. This limitation is now clearly acknowledged in the revised version, and our conclusions are framed accordingly.

__ Relationship between basal ATF6 and IRE1 signalling.__

• Comment: Both reviewers argued that BiP::GFP does not appear to be active under basal conditions in parental cells, and therefore a failure to activate ATF6 would not be expected to affect the conditions of the cells basally. Thereby questioning how attenuated basal ATF6 activity in the SLC33a1 deleted cells could account for the derepression observed in the IRE1 pathway.

• Response: The logic of the reviewer's critique is impeccable, and we thank them for the opportunity to clarify this important issue. Whilst the basal fluorescent signal arising from BiP::GFP (the ATF6α reporter) is indeed weak, it is not null. This is evident by comparing the BiP::GFP signal in wildtype and ATF6α -deleted cells (new Supplemental Fig. S1B) These experiments revealed a significant reduction in basal BiP::GFP fluorescence in ATF6αΔ cells compared with parental dual-reporter cells, indicating that the BiP::GFP reporter has basal activity that is dependent on ATF6α. These new data are consistent with previous published observations demonstrated that treatment with Ceapin, an ATF6α-specific inhibitor, lowered BiP::GFP fluorescence in tunicamycin-treated cells to levels below those observed in untreated controls (Tung et al., eLife 2024). Together these observations indicate that ATF6α is active basally in CHO-K1 cells. Given the established cross-pathway repression of IRE1 by ATF6α signalling, it renders plausible our suggestion that the basal activation of the XBP1::mCherry (IRE1-reporter) observed basally in the SLC33a1 deleted cells arises from the partial interruption of ATF6α Reviewer 1 (additional points)

• __ Effect of deleting sialic acid-modifying acetyltransferases.__

• Comment: Reviewer 1 suggested that comparing the consequences of deleting SLC33a1 and the sialic acid- modifying acetyltransferases that operate downstream of the putative acetyl-CoA transporter could be informative.
• Response: In response to this valuable suggestion, we have now examined the impact of deleting Casd1, the gene encoding the Golgi acetyltransferase responsible for modifying sialic acids on ATF6α activity, comparing the consequences to Slc33a1. New Supplemental Fig. S8 reveals partial phenotypic overlap between the two deletions, suggesting that the loss of SLC33A1 exerts some of its effects on CHO cells by compromising sialic acid modification.

__ Potential effects on ATF6-like proteins (SREBP1/2, CREB3L).__

• Comment: Reviewer 1 suggested that we evaluate the effect of SLC33A1 loss on other ATF6-like transcription factors.

• Response: We took this advice to heart, but our attempts to compare SREBP2 processing in wildtype and SLC33A1 knockout cells were frustrated by the low quality of the antibodies available to us. Reviewer 2 (additional points)

• __ Physiological state and clonal adaptation of Slc33a1-deleted cells.__

• __Comment: __Reviewer 2 raised concerns regarding the physiological state of the Slc33a1-deleted cells and the potential impact of clonal adaptation or selection pressure on the consequences of genetic manipulation.
• Response: This is a valid concern. Deconvoluting direct from indirect effects are a challenge in any genetics-based experiment. To try and address this point, we compared the proliferation capacity of three pairs of parental CHO-K1 clones with their derivative Slc33a1-deletion variants using the IncuCyte assay. As shown in new Supplemental Fig. S2D, the Slc33a1 deletion variants had no consistent fitness disadvantage revealed by this assay. Whilst cell mass accretion is only one measure of comparability between cell lines, we deem these observations to indicate that a comparison between SLC33A1 wildtype and mutant CHO-K1 cells is unlikely to be compromised by gross underlying differences in cell fitness.

__ Responsiveness of PERK signalling to ER stress.__

• Comment: Reviewer 2 asked whether PERK signalling, which appears basally activated due to higher basal IRE1 signalling in the Slc33a1-deleted cells, remains responsive to ER stress.
• Response: To address this point, we treated cells with ER stressors and assessed PERK pathway activation. As shown in new Supplemental Fig. S4C, PERK signalling remains functional and responsive to ER stress in Slc33a1-depleted cells.

In addition to the points above, we have addressed several presentation and clarity issues raised by the reviewers, including figure labelling, image presentation, and schematic models. The Discussion has also been revised to more explicitly acknowledge the current limitations of the study while emphasising its central conceptual advance: namely, that loss of SLC33A1 results in a discordant UPR state in which IRE1 and PERK are activated, whereas ATF6α trafficking and transcriptional output are selectively compromised.

The following table summarises the major changes made to the figures in the revised manuscript to facilitate tracking the modifications introduced

Figure

Figure Panels

Amendment (if any)

Fig 4

4B (modified)

Scale bar added.

Fig 5

5B (modified)

Labelling correction according to the reviewer.

Fig S1 (new)

S1A-S1B

New data detailing the BiP promoter fragment and the reliability of the BiP::GFP reporter as a readout for ATF6α activity in cells.

Fig S2 (modified)

S2D (new)

New IncuCyte data added.

Fig S3 (modified)

S3B, S3C and S3E (new)

Panels B and C: New data from DTT and thapsigargin treatments, respectively. __Panel E: __New data from BiP mRNA levels under 2DG treatment in parental and Slc33a1-deleted cells.

Fig S4 (new)

S4C (new)

__Panels A and B: __Previously shown as panels in Fig. S2C and S2D.

__Panel C: __New data on the PERK response to ER stress in Slc33a1-deleted cells.

Fig S7 (new)

S7A-S7C (new)

New sanger sequencing chromatograms displaying the targeted exonic regions of the Casd1, Nat8 and Nat8b. * *

Fig S8 (new)

S8A-S8B (new)

Casd1-deleted data added.

Fig S9 (new)

Unique panel

New data comparing Nat8/Nat8b-deleted cells with single Nat8-deleted cells.

We thank the reviewers again for their insightful comments, which have significantly strengthened the manuscript. We believe the revised study clarifies key mechanistic points and provides a stronger conceptual advance regarding the role of SLC33A1 in UPR regulation.

Sincerely,

Adriana Ordóñez

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors employed a genome-wide CRISPR-Cas9 screen to search for the genes selectively involved in the activation of ER stress sensor ATF6. Deletion of Slc33a1, which encodes a transporter of acetyl-CoA into the ER lumen, compromised the ATF6 pathway (as assessed by BiP::GFP reporter), while IRE1 and PERK were activated in basal conditions, in the absence of ER stress (as assessed by XBP1s::mCherry reporter and endogenous XBP1s and CHOP::GFP reporter). Moreover, IRE1, but not ATF6, replied to ER stress. Consistently, in Slc33a1Δ cells upon ER stress the levels of the processed N-ATF6α were significantly lowered compared to the parental cells, and microscopy study showed that in Slc33a1-deficient cells ATF6 is translocated to Golgi even in the absence of ER stress, but fails to reach the nucleus even after ER stress is imposed. Golgi-type sugar modification of ATF6α is decreased in Slc33a1Δ cells. These data show the importance of SLC33A1 for ATF6 processing and functioning through the mechanism which remains to be revealed.

Major comments.

Taken together, the reported data do support the conclusion about the role of SLC33A1 functioning in post-ER maturation of ATF6. Data and methods are presented in a reproducible way. Still, there are several issues worth attention.

1. While BiP::GFP reporter is very useful, it would be more convincing to show the level of BiP in Slc33a1Δ cells by WB.
2. Another concern is the state of Slc33a1Δ cells. While adaptation is a general problem of clonal cells, the cells used in this study (with XBP1 highly spliced, CHOP upregulated, and ATF6 pro-survival pathway inhibited) are probably very sick, and the selection pressure/adaptation is very strong in this cell line. I would suggest the authors to clarify this issue.
3. Authors showed that, based on CHOP::GFP reporter data, PERK was activated in the absence of ER stress and the activation was due to IRE1 signalling. But did PERK reply to the ER stress?
4. An important question is a subcellular location of SLC33A1. Huppke et al. (cited in the manuscript) showed that FLAG- and GFP-tagged SLC33A1 was colocalized with Golgi markers. While that may be due to overexpression of the protein, it deserves consideration, given that ATF6 is stuck in Golgi upon depletion of SLC33A1.
5. OPTIONAL. Regarding the role of acetylation in compromising ATF6 function: since both SLC33A1 deficiency and depletion of Nat8 have broad effects, glycosylation of ATF6 upon depletion of Nat8 should be assessed (similarly to Fig 5), to demonstrate the difference in glycosylation pattern upon the absence of SLC33A1 and Nat8 and strengthen the conclusions.

Minor comments.

1. Apart from the table of the cell lines, it would be useful to add to the supplementary a simple-minded scheme of the reporters used in this study (BiP::GFP, CHOP::GFP, XBP1s::mCherry) specifying the mechanism of the readout and the harbored protein and other important details (e.g., whether mRNA of XBP1s::mCherry reporter could be processed by IRE1).
2. Fig 2B and Fig 3A - the percentage of spliced XBP1 in parental cells is about 30% according to the graphs, but it looks more like 5%.
3. Fig 3B - It would probably be better to demonstrate the processing of endogenous ATF6. It could help to avoid the problems with alternative translation (even though anti-ATF6 antibodies are known to be tricky).
4. In Fig 4B - could be better to show Golgi marker separately. In Fig 4B and E the bars are missing (and cells in Fig 4B look bigger than in Fig 4E). Magnification of the insets should be further increased.
5. As the authors mention, 2-deoxy-D-glucose (2DG) is known to be the ER stress inducer, acting via prevention of N-glycosylation of proteins. Also, N-glycosylation state of ATF6 has been suggested to influence its trafficking. Thus, even if the control cells were treated in the same way, 2DG may not be the best ER-stress inducer to study ATF6 trafficking. Indeed, altered sugar modification of ATF6α in Slc33a1Δ cells (Fig 5) was tracked using Thapsigargin.
6. Minor comment on Fig 7 - recent data (Belyy et al., 2022) suggest IRE1 is a dimer even in the absence of ER stress.

Referee cross-commenting

I agree with Reviewer 1 that the authors need to clarify that authors need to clarify better how exactly BiP::GFP reporter works and whether it reflects ATF6 activation (rev 1 pointed to unclear responsiveness of the reporter to ATF6 and I asked to show the level of BiP by WB and the scheme of the mechanisms of readouts of the reporters)

I also agree with the comment on 2-DG which for some experiments may not be the best choice to activate UPR (or as Reviewer 1 pointed out shouldn't be the only one used to induce UPR). I still think that there's no contradiction in partial cleavage of ATF6 and suppression of BiP::GFP in Slc33a1Δ cells if then (as authors show) it doesn't reach nucleus.

Significance

General assessment. The article shows the necessity of SLC33A1, a transporter of acetyl-CoA in ER lumen, for ATF6 processing and functioning. It is well-written. However, the molecular mechanism which underlies the link is yet to be discovered (and this is clearly mentioned by the authors).

The study is of interest for the basic research and of potential interest for clinical research.

My main field of expertise is UPR. While I have broad knowledge and interest in protein science in general, my experience with protein glycosylation is rather limited.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors follow up on the results from a previous CRISPR screen in CHO-K1 cells demonstrating that knockout of the ER acetyl-CoA transporter Slc33a1 suppresses ATF6 activation. The authors show in these cells that, in response to 2-DG, the Slc33a1 deletion results in constitutive activation of the UPR except for the ATF6 pathway, which appears to traffic constitutively to the Golgi but to not be cleaved there. They show using an uncleavable ATF6 that loss of Slc33a1 delays formation of an O-glycosylated form of at least this version of the protein, and they also find that single deletion of the ER acetyltransferases NAT8 and NAT8B also constitutively activates the UPR, but that activation in this case includes activation of ATF6. The mechanism by which Acetyl-CoA might impact ATF6 activation is not elucidated.

Major Comments:

The following conclusions are well-supported:

• That loss of Slc33a1 results in IRE1 and PERK activation but not ATF6 activation
• That ATF6 traffics at least to some degree constitutively to the Golgi when Slc33a1 is deleted, which is a counterintuitive finding given the apparent lack of ATF6 activation
• That loss of Slc33a1 can alter the level O-glycosylation and the preponderance of sialylated N-glycans on at least ATF6
• Generally speaking, I find the wording to be careful and precise

The following claims are less convincing:

• That loss of Slc33a1 results in universal suppression of ATF6 activation. The effect in response to 2-DG is unquestionably strong at least at the level of Bip-GFP reporter (although it's not clear from this paper nor the previous one from this group how much of the Bip promoter this reporter encodes-which is important because only a minimal Bip promoter is exclusively responsive to ATF6). However, the impairment of ATF6 activation in response to tunicamycin (Fig. 1C) is very modest, and no other stressors were tested (DTT and TG were used for other purposes, not to test ATF6 activation). One might actually expect this pathway, if it affects glycosylation pathways, to be particularly sensitive to a stressor like 2-DG that would have knock-on effects on glycosylation. Admittedly, it does seem to be true in the basal condition (i.e., absent an exogenous ER stress) that IRE1 and PERK are activated where ATF6 is not. At some level, it's hard to reconcile the almost complete suppression of Bip-GFP induction in Slc33a1 cells in response to 2DG with the fact that in Fig. 3, cleavage clearly seems to be occurring, albeit to a lesser extent
• That regulation of ATF6 is a broadly applicable consequence of Slc33a1 action. Unless I've missed it, all experiments are performed in CHO-K1 cells, so how broadly applicable this pathway is not clear.
• That loss of Slc33a1 "deregulated activation of the IRE1 branch of the UPR." It is clear that IRE1 is activated when Slc33a1 is deleted (that the authors show this repeatedly in different parental cell lines provides a high degree of rigor). However, at least through the CHOP-GFP reporter, PERK is activated as well. Although 4u8C suppresses this activation, the suppression is not complete, there are no orthogonal ways of showing this (e.g., loss of KD of IRE1), and the converse experiment (examining IRE1 activation when PERK is lost or inhibited) was not done. Thus, while I agree that the data shown are consistent with PERK activation being downstream of IRE1, they are not definitive enough to, in my opinion, rule out the more parsimonious explanation for their own data and what is already published in the field that loss of Slc33a1 causes ER stress (thus in principle activating all 3 pathways of the UPR-including ATF6 transit to the Golgi) but that it also, separately, inhibits activation of ATF6 (and possibly other things? See below)-a possibility acknowledged towards the end of the Discussion.
• That "Nat8 and Slc33a1 influence ER homeostasis and ATF6 signaling through distinct mechanisms". This conclusion would require simultaneous deletion of both Nat8 and NAT8B because of possible redundancy/compensatory effects.
• If I'm understanding the authors' argument correctly, they seem to be invoking that the ATF6 activation defect underlies/is upstream of the activation of IRE1 in Slc33a1 KO cells. But if that understanding is correct, it seems fairly unlikely, as the authors' data show no evidence that ATF6 is activated in parental cells under basal conditions (Fig. 3B) and thus no reason to expect that failure to activate ATF6 by itself would result in appreciable phenotype in cells-an idea also consistent with the general lack of phenotype in ATF6-null MEF and other cells.

Minor Comments:

• The alteration in O-glycosylation levels of ATF6 is interesting, but it might or might not be relevant to ATF6 activation, and if it isn't, then the paper provides no mechanism for why loss of Slc33a1 has the effects on ATF6 that it does. What about other similar molecules, like ATF6B (surprising that this was not examined), SREBP1/2, a non-glycoyslatable ATF6, and/or one of the other CREB3L proteins?
• Does Slc33a1 deletion cause other ER resident proteins to constitutively mislocalize to the Golgi?
• As mentioned above, does loss/knockdown of Slc33a1 activate IRE1 and PERK but not ATF6 in other cell types?
• Also as mentioned above, how do the UPR (all 3 branches) in cells lacking Slc33a1 respond to TG or DTT? This and the preceding comments are important toward making the claim that Slc33a1 is actually a regulator of ATF6. The time required to do these experiments will depend on whether creation of more stable lines is required, and whether they are worth doing depends on how broad the authors wish the scope of the paper to be.
• It's surprising that the authors didn't do comparable experiments to what is shown in Fig. 6 but deleting the acetyltransferases that modify sialic acids, which I believe are known.
• The authors mis-describe the data from Fig. 5B. EndoH and PNGaseF should collapse ATF6 to a 0N form, not a 1N form (what is labeled as 2N should be 1N, and it looks like the true 2N band is partially obscured by the strong 3N band.

Referee cross-commenting

While reviewer #2 and I have somewhat different opinions on the strength of the evidence, we seem fairly well-aligned on the overall significance of the work.

Significance

The conceptual advance in this paper is that, while loss of Slc33a1 seems widely disruptive to ER function-an idea that has been advanced in the literature before-it seems to have unique and discordant effects on ATF6 relative to the other UPR pathways. The paper does not offer a conclusive mechanism by which these effects are realized, and the sole focus on ATF6 makes it difficult to fully contextualize the findings, but the data are of high quality and, while the scope is somewhat narrow, the phenotype is likely to be of interest to those concerned with ER stress and UPR signaling, which also describes my own expertise.

LinkedIn API november 2025 version, mentioned as element in complying with DMA portability requirements. This is meant for companies not members then? But it says members. And it was offered to me when trying to download my personal account.

It seems LinkedIn has put this in place of a regular download, actually making it harder to get at your data.

These are all positive solutions, but in our world and communities today, do you think we see more microresistence strategies, or negative consequences?

i’m surprised that microinterventions has that definition, but i’m glad that this is listed as a solution. I feel like while it’s crucial to address the ones creating tension, the initially more efficient solution is to invest in microinterventions. It's so important to open up those bridges and build one, and it might be easier before trying to revive another's burned bridge.

Zivver is originally Dutch, but got bought by the US-ian Kiteworks https://www.zivver.com/nl/blog/persbericht-zivver-sluit-zich-aan-bij-kiteworks in #2025/06 Kiteworks is run by ex-intelligence cadre of the IDF. Zivver specialises in secure comms for various gov entities and hospitals etc.

Reviewer #3 (Public review):

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides requires further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses. Experiments must show reproducibility and contain appropriate controls wherever necessary.

Comments on revisions:

I thank the authors for their clarifications and opinions on my questions and suggestions. Based on the response, the following points are important while considering the significance of this manuscript:

- The manuscript provides a novel method to detect and identify proline hydroxylation residues in the proteome. While this provides several advances over previous methods, the probability of false positives, loss of true positives and incomplete removal of the interference of methionine oxidation in this strategy need to be addressed clearly in the discussion section of the manuscript, so that the strengths and limitations of this method are made aware to the reader.

- Going by the standards of publication in eLife, reproducibility is very important in the experiments done. Hence, I strongly recommend that the authors perform the experiments in triplicate with error bars to confirm reproducibility. Graphs with single data points do not convey that, and this is very important for eLife.

- As for Figure 9C, the authors have rejected the request for a control lane in the figure. It may sound trivial to the authors, but for completeness of the experiment, all applicable controls must be performed and shown alongside the main data. It is essential to show the PHD1 only control to rule out the possibility of the contribution of any non-specific signal in the dot blot by PHD1.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript by Hao Jiang et al described a systematic approach to identify proline hydroxylation proteins. The authors implemented a proteomic strategy with HILIC-chromatographic separation and reported an identification of 4993 sites from HEK293 cells (4 replicates) and 3247 sites from RCC4 sites (3 replicates) with 1412 sites overlapping between the two cell lines. From the analysis, the authors identified 225 sites and 184 sites respectively from 293 and RCC4 cells with HyPro diagnostic ion. The identifications were validated by analyzing a few synthetic peptides, with a specific focus on Repo-man (CDCA2) through comparing MS/MS spectra, retention time, and diagnostic ions. With SILAC analysis and recombinant enzyme assay, the study showed that Repo-man HyPro604 is a target of the PHD1 enzyme.

Strengths:

The study involved extensive LC-MS analysis and was carefully implemented. The identification of over 4000 confident proline hydroxylation sites would be a valuable resource for the community. The characterization of Repo-man proline hydroxylation is a novel finding.

Weaknesses:

However, as a study mainly focused on methodology, the findings from the experimental data did not convincingly demonstrate the sensitivity and specificity of the workflow for site-specific identification of proline hydroxylation in global studies.

Proline hydroxylation is an enzymatic post translational protein modification, catalysed by prolyl Hydroxylases (PHDs), which can have profound biological significance, e.g. altering protein half-life and/or the stability of protein-protein interactions. Furthermore, there has been controversy in the field as to the true number of protein targets for PHDs in cells. Thus, there is a clear need for methods to enable the robust identification of genuine PHD targets and to reliably map sites of PHD-catalysed proline hydroxylation in proteins. We believe, therefore, that our methodology, as reported here in Jiang et al., is an important contribution towards this goal. We note that our methodology has already been used successfully by others

(https://doi.org/10.1016/j.mcpro.2025.100969). While further improvements in this methodology may of course be developed in future, we are not currently aware of any superior methods that have been reported previously in the literature. The criticism made by the reviewer notably does not include reference to any such alternative published methodology that interested researchers can use which would offer superior results to the approach we document in this study.

Major concerns:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations. 

The data we present in this study demonstrate clearly that peptides with hydroxylated prolines are enriched in specific HILIC fractions (F10-F18), in comparison with total unfractionated peptides derived from cell extracts. We also refer the reviewer to our previously published study by Bensaddek et al (International Journal of Mass Spectrometry: doi:10.1016/j.ijms.2015.07.029), which was reference 41 in this study, in which we compared directly the performance of both HILIC and strong anionic exchange chromatography, (hSAX). This showed that HILIC provided superior enrichment to hSAX for enrichment of peptides containing hydroxylated proline residues. To clarify this point for readers, we have now included a specific reference to our previous study at the start of the Results section in our current revision. Currently, we use HILIC to provide a degree of enrichment for proline hydroxylated peptides because we are not aware of alternative chromatographic methods that in our hands provide better results.

We have included descriptions of the information shown in the heatmaps in the associated figure legends and captions.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyl-lysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

The reviewer appears to have misunderstood the point we make with regard to the identification of the immonium ion and its use as a diagnostic marker for proline hydroxylation in MS analyses. We do not claim that this immonium ion is an essential diagnostic marker for proline hydroxylation. As the reviewer notes, with respect to the acetyl-lysine modification, the corresponding immonium ion is often used in MS studies as a diagnostic for identification of specific post translational modifications. Previous studies have reported that the immonium ion for hydroxylated proline is detected when the transcription factor HIF is analysed, but is often absent with other putative PHD targets, which has been used as an argument that these targets are not genuine proline hydroxylation sites. We are not, therefore, introducing the idea in this study that the hydroxy-proline immonium ion is a required diagnostic marker for proline hydroxylation, but instead demonstrating that detection of this ion, at least in some peptide sequences, may require the use of higher MS collision energies than are typically required for routine peptide identification. We believe that this is an interesting observation that can help to clear up discussions in the literature regarding the true prevalence of PHD-catalysed proline hydroxylation in different target proteins. Our data suggest that, in future MS studies analysing suspected PHD target proteins, two different collision energy might need to be used, i.e., normal collision energy for the routine identification of a peptide, combined with use of a higher collision energy if the hydroxy-proline immonium ion was not already detected.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

We refer the reviewer to the data we present in this study using SILAC analysis, combined with our MS workflow. to achieve a more accurate quantitative picture of proline hydroxylation levels. While we agree that the point the reviewer makes is valid, regarding our data dependent LC-MS/MS analysis potentially not being comprehensive, this means, however, that we are potentially underestimating the true prevalence of proline hydroxylated peptides, not overestimating the level of these modified peptides. We also refer the reviewer to the accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1)  in which we present a detailed follow-on study demonstrating the functional significance of the novel proline hydroxylation site we detected in the protein RepoMan (CDCA2). Therefore, even if we have not achieved a fully comprehensive analysis of all proline hydroxylated peptides catalysed by PHD enzymes, we believe that we have advanced the field by documenting a workflow that is able to identify and validate novel PHD targets.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

The analysis shown in Figure 9 was not intended to quantify the efficiency of in vitro hydroxylation of RepoMan by PHD1, but rather to answer the question, ‘Can recombinant PHD1 alone hydroxylate P604 on RepoMan in vitro, yes or no?’. The data show that the answer here is ‘yes’. Clearly, the HIF peptide is a more efficient substrate in vitro for recombinant PHD1 than the RepoMan peptide and we have now included a statement in the Discussion that addresses the significance of this observation more directly.

Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present.

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Weaknesses:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

This is a good suggestion and we have now included a figure (Figure 10) with a summary of our workflow in the current revision.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

While we had mentioned the Cockman et al., paper in the Introduction, we had not focussed on this somewhat controversial issue. However, in response to the Reviewer’s request, we have now added a comment in the Discussion section in the current revision of how our new data address the proposal discussed previously by Cockman et al. In brief, we believe that our findings are not consistent with a model in which PHDs have no protein targets other than HIFs.

Reviewer #3 (Public review): 

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses.

We of course agree that false positives may arise, as is true for essentially all PTM studies. There are two issues here; first, are identified sites technically correct? (i.e. not misidentifications from the MS data) and second, are the identified modifications of biological significance? We have addressed this using the popular MaxQuant software suite to evaluate the modifications identified and to control the false discovery rate (FDR) at both the precursor and protein level, as described in the manuscript. We are aware that false positives could arise from confusing oxidation of methionine with hydroxylation of proline. Therefore, to address the issue as to whether we could identify bona fide PHD protein targets outside of the HIF family, we adopted a conservative approach by simply filtering out peptides where there was a methionine residue within three amino acids of the predicted proline hydroxylation site. This was a pragmatic decision made to reduce the likelihood of false positives in our dataset and we recognise that this likely results in us overlooking some genuine proline hydroxylation sites that occur nearby methionine residues. To address the potential biological relevance of the proline hydroxylation sites identified, we analysed extracts from cells treated with FG inhibitors. Of course a detailed understanding of biological significance relies upon follow-on experimental analyses for each site, which we have performed for P604 on RepoMan in accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The finding that the immonium ion intensities of L/I did not increase with increasing collision energy was surprising. Was this specific to this synthetic peptide?

We agree this is an interesting and unexpected finding. We have no reason to believe that it is specific to synthetic peptides per se, but rather think this reflects an effect of amino acid composition in the peptides analysed. It will be interesting to explore this phenomenon in more detail in future.

(2) The sequence logos in Figure 4 seemed to lack any amino acid enrichment in most positions except for collagen peptides. Have these findings been tested with statistical analysis?

The results we show for sequence logo analysis were generated using WebLogo (10.1101/gr.849004) and correspond to an analysis of all proline hydroxylated peptides we detected across all cell lines and replicates analysed. The fact that collagens are highly abundant proteins with very high levels of proline hydroxylation likely explains why collagen peptides dominated the outcome of the sequence logo analysis. There is clearly scope for more detailed follow up analysis in future of the sequence specificity of proline hydroxylation sites in no- collagen proteins that are validated PHD targets.

(3) Overall figure quality was not ideal. The resolution and font sizes of figures should be carefully evaluated and adjusted. The figure legend should contain a title for the figure. Annotations of the figures were somewhat confusing. 

We agree with the criticism of the figure resolution in the review copies - the lower resolution appears to have been generated after we had uploaded higher resolution original images. We are providing again higher resolution versions of all figures for the current revision.

Reviewer #3 (Recommendations for the authors):

Certain concerns regarding portions of the manuscript that need addressing:

(1) " These data show that two different cell lines show unique profiles of proteins with hydroxylated peptides." - It is difficult to conclusively say this statement after profiling the prolyl hydroxy proteome from just two cell lines, especially since the amino acids with the highest frequency in the most enriched peptides are similar in both cell lines.

We agree with this point and have changed the current revision to state instead, “This shows that each of the two cell lines analysed have distinct profiles.”

(2) "We noted that there was a high frequency of a methionine residues appearing either at the first, second, or even third positions after the HyPro site.." - according to the authors, claim, the advantage of their method was that they were able to overcome the limitation of older methods that couldn't separate methionine oxidation from proline hydroxylation. However, in this statement, they say that the high frequency of methionine residues may be because of the similar mass shift. These statements are contradictory. The authors should either tone down the claim or prove that those are indeed hydroxyproline sites. Is it possible that in the filtering step of excluding these high-frequency of methionine - containing peptides, we are losing potential positive hits for hydroxy-proline sites? What is the authors' take on this?

We respectfully do not agree that our, “statements are contradictory”, with respect to the potential confusion between identification of methionine oxidation and proline hydroxylation, but acknowledge that we have not explained this issue clearly enough. It is a fact that the similar mass shift resulting from proline hydroxylation and methionine oxidation is a technical challenge that can potentially lead to misidentifications in MS studies and that is what we state clearly in the manuscript. We have addressed this issue head on experimentally in this study and show using synthetic peptides how detailed analysis of specific proline hydroxylation sites in target proteins can be distinguished from methionine oxidation, based upon differential chromatographic behaviour of peptides with either hydroxylated proline or oxidised methionine, as well as by detailed analysis of fragmentation spectra. However, in the case of our global analysis, as we were not able to perform synthetic peptide comparisons for every putative site identified, we took the pragmatic approach of filtering out examples of peptides where a methionine residue was present within three residues of a potential proline hydroxylation site. This was done simply to reduce the possibility of misidentification in the set of novel proline hydroxylated peptides identified and we accept that as a consequence we are likely filtering out peptides that include bona fide proline hydroxylation sites. We have clarified this point in the current revision and hope to be able to address this issue more comprehensively in future studies.

(3) "Accordingly, a score cut-off of 40 for hydroxylated peptides and a localisation probability cut-off of more than 0.5 for hydroxylated peptides was performed." Could the authors shed more light and clarify what was the basis for this value of cut-off to be used in this filtering step? Is this sample dependent? What should be the criteria to determine this value?

We used MaxQuant software (10.1016/j.cell.2006.09.026), for PTM analysis, in which a localization probability score of 0.75 and score cut-off of 40 is a commonly used threshold to define high confidence. The reason that we used 0.5 at the first step was to investigate how likely it might be that the misassignment of delta m/z +16 Da (oxidation) on Methionine would affect the identification of hydroxylation on Proline. However, we note that in the final results set used for analysis, all putative proline hydroxylated peptides that had a Methionine residue near to the hydroxylated proline were disregarded as a pragmatic step to reduce the probability of false identifications.

(4) The authors are requested to kindly make the HPLC and MS traces more legible and use highresolution images, with clearly labeled values on the peaks. Kindly extract coordinates from the underlying data files to plot the curves if needed to make it clearer.

We have reviewed the clarity of all images and figures in the current revision.

(5) There seems to be no error bars in Figure 3, Figure 7E, and panels of Figure 8 with bar graphs. Are those single replicate data?

These specific figures are from single replicate data.

(6) For Figure 9C, the control with only PHD1 (no peptide) is missing. 

The ‘no peptide control’ was not included in the figure because it is simply a blank lane and there is nothing to see.

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Reply to the reviewers

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

Summary:

Damaris et al. perform what is effectively an eQTL analysis on microbial pangenomes of E. coli and P. aeruginosa. Specifically, they leverage a large dataset of paired DNA/RNA-seq information for hundreds of strains of these microbes to establish correlations between genetic variants and changes in gene expression. Ultimately, their claim is that this approach identifies non-coding variants that affect expression of genes in a predictable manner and explain differences in phenotypes. They attempt to reinforce these claims through use of a widely regarded promoter calculator to quantify promoter effects, as well as some validation studies in living cells. Lastly, they show that these non-coding variations can explain some cases of antibiotic resistance in these microbes.

Major comments

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The authors convincingly demonstrate that they can identify non-coding variation in pangenomes of bacteria and associate these with phenotypes of interest. What is unclear is the extent by which they account for covariation of genetic variation? Are the SNPs they implicate truly responsible for the changes in expression they observe? Or are they merely genetically linked to the true causal variants. This has been solved by other GWAS studies but isn't discussed as far as I can tell here.

We thank the reviewer for their effective summary of our study. Regarding our ability to identify variants that are causal for gene expression changes versus those that only “tag” the causal ones, here we have to again offer our apologies for not spelling out the limitation of GWAS approaches, namely the difficulty in separating associated with causal variants. This inherent difficulty is the main reason why we added the in-silico and in-vitro validation experiments; while they each have their own limitations, we argue that they all point towards providing a causal link between some of our associations and measured gene expression changes. We have amended the discussion (e.g. at L548) section to spell our intention out better and provide better context for readers that are not familiar with the pitfalls of (bacterial) GWAS.

They need to justify why they consider the 30bp downstream of the start codon as non-coding. While this region certainly has regulatory impact, it is also definitely coding. To what extent could this confound results and how many significant associations to expression are in this region vs upstream?

We agree with the reviewer that defining this region as “non-coding” is formally not correct, as it includes the first 10 codons of the focal gene. We have amended the text to change the definition to “cis regulatory region” and avoided using the term “non-coding” throughout the manuscript. Regarding the relevance of this including the early coding region, we have looked at the distribution of associated hits in the cis regulatory regions we have defined; the results are shown in Supplementary Figure 3.

We quantified the distribution of cis associated variants and compared them to a 2,000 permutations restricted to the -200bp and +30bp window in both E. coli * (panel A) and P. aeruginosa* (panel B). As it can be seen, the associated variants that we have identified are mostly present in the 200bp region and the +30bp region shows a mild depletion relative to the random expectation, which we derived through a variant position shuffling approach (2,000 replicates). Therefore, we believe that the inclusion of the early coding region results in an appreciable number of associations, and in our opinion justify its inclusion as a putative “cis regulatory region”.

The claim that promoter variation correlates with changes in measured gene expression is not convincingly demonstrated (although, yes, very intuitive). Figure 3 is a convoluted way of demonstrating that predicted transcription rates correlate with measured gene expression. For each variant, can you do the basic analysis of just comparing differences in promoter calculator predictions and actual gene expression? I.e. correlation between (promoter activity variant X)-(promoter activity variant Y) vs (measured gene expression variant X)-(measured gene expression variant Y). You'll probably have to

We realize that we may not have failed to properly explain how we carried out this analysis, which we did exactly in the way the reviewer suggests here. We had in fact provided four example scatterplots of the kind the reviewer was requesting as part of Figure 4. We have added a mention of their presence in the caption of Figure 3.

Figure 7 it is unclear what this experiment was. How were they tested? Did you generate the data themselves? Did you do RNA-seq (which is what is described in the methods) or just test and compare known genomic data?

We apologize for the lack of clarity here; we have amended the figure’s caption and the corresponding section of the results (i.e. L411 and L418) to better highlight how the underlying drug susceptibility data and genomes came from previously published studies.

Are the data and the methods presented in such a way that they can be reproduced?

No, this is the biggest flaw of the work. The RNA-Seq experiment to start this project is not described at all as well as other key experiments. Descriptions of methods in the text are far too vague to understand the approach or rationale at many points in the text. The scripts are available on github but there is no description of what they correspond to outside of the file names and none of the data files are found to replicate the plots.

We have taken this critique to heart, and have given more details about the experimental setup for the generation of the RNA-seq data in the methods as well as the results sections. We have also thoroughly reviewed any description of the methods we have employed to make sure they are more clearly presented to the readers. We have also updated our code repository in order to provide more information about the meaning of each script provided, although we would like to point out that we have not made the code to be general purpose, but rather as an open documentation on how the data was analyzed.

Figure 8B is intended to show that the WaaQ operon is connected to known Abx resistance genes but uses the STRING method. This requires a list of genes but how did they build this list? Why look at these known ABx genes in particular? STRING does not really show evidence, these need to be substantiated or at least need to justify why this analysis was performed.

We have amended the Methods section (“Gene interaction analysis”, L799) to better clarify how the network shown in this panel was obtained. In short, we have filtered the STRING database to identify genes connected to members of the waa operon with an interaction score of at least 0.4 (“moderate confidence”), excluding the “text mining” field. Antimicrobial resistance genes were identified according to the CARD database. We believe these changes will help the readers to better understand how we derived this interaction.

Are the experiments adequately replicated and statistical analysis adequate?

An important claim on MIC of variants for supplementary table 8 has no raw data and no clear replicates available. Only figure 6, the in vitro testing of variant expression, mentions any replicates.

We have expanded the relevant section in the Methods (“Antibiotic exposure and RNA extraction”, L778) to provide more information on the way these assays were carried out. In short, we carried out three biological replicates, the average MIC of two replicates in closest agreement was the representative MIC for the strain. We believe that we have followed standard practice in the field of microbiology, but we agree that more details were needed to be provided in order for readers to appreciate this.

Minor comments

Specific experimental issues that are easily addressable..

Are prior studies referenced appropriately?

There should be a discussion of eQTLs in this. Although these have mostly been in eukaryotes a. https://doi.org/10.1038/s41588-024-01769-9 ; https://doi.org/10.1038/nrg3891.

We have added these two references, which provide a broader context to our study and methodology, in the introduction.

Line 67. Missing important citation for Ireland et al. 2020 https://doi.org/10.7554/eLife.55308

Line 69. Should mention Johns et al. 2018 (https://doi.org/10.1038/nmeth.4633) where they study promoter sequences outside of E. coli

Line 90 - replace 'hypothesis-free' with unbiased

We have implemented these changes.

Line 102 - state % of DEGs relative to the entire pan-genome

Given that the study is focused on identifying variants that were associated with changes in expression for reference genes (i.e. those present in the reference genome), we think that providing this percentage would give the false impression that our analysis include accessory genes that are not encoded by the reference isolate, which is not what we have done.

Figure 1A is not discussed in the text

We have added an explicit mention of the panels in the relevant section of the results.

Line 111: it is unclear what enrichment was being compared between, FIgures 1C/D have 'Gene counts' but is of the total DEGs? How is the p-value derived? Comparing and what statistical test was performed? Comparing DEG enrichment vs the pangenome? K12 genome?

We have amended the results and methods section, as well as Figure 1’s caption to provide more details on how this analysis was carried out.

Line 122-123: State what letters correspond to these COG categories here

We have implemented the clarifications and edits suggested above

Line 155: Need to clarify how you use k-mers in this and how they are different than SNPs. are you looking at k-mer content of these regions? K-mers up to hexamers or what? How are these compared. You can't just say we used k-mers.

We have amended that line in the results section to more explicitly refer to the actual encoding of the k-mer variants, which were presence/absence patterns for k-mers extracted from each target gene’s promoter region separately, using our own developed method, called panfeed. We note that more details were already given in the methods section, but we do recognize that it’s better to clarify things in the results section, so that more distracted readers get the proper information about this class of genetic variants.

Line 172: It would be VERY helpful to have a supplementary figure describing these types of variants, perhaps a multiple-sequence alignment containing each example

We thank the reviewer for this suggestion. We have now added Supplementary Figure 3, which shows the sequence alignments of the cis-regulatory regions underlying each class of the genetic marker for both E. coli and P. aeruginosa.

Figure 4: THis figure is too small. Why are WaaQ and UlaE being used as examples here when you are supposed to be explicitly showing variants with strong positive correlations?

We rearranged the figure’s layout to improve its readability. We agree that the correlation for waaQ and ulaE is weaker than for yfgJ and kgtP, but our intention was to not simply cherry-pick strong examples, but also those for which the link between predicted promoter strength and recorded gene expression was less obvious.

Figure 4: Why is there variation between variants present and variant absent? Is this due to other changes in the variant? Should mention this in the text somewhere

Variability in the predicted transcription rate for isolates encoding for the same variant is due to the presence of other (different) variants in the region surrounding the target variant. PromoterCalculator uses nucleotide regions of variable length (78 to 83bp) to make its predictions, while the variants we are focusing on are typically shorter (as shown in Figure 4). This results in other variants being included in the calculation and therefore slightly different predicted transcription rates for each strain. We have amended the caption of Figure 4 to provide a succinct explanation of these differences.

Line 359: Need to talk about each supplementary figure 4 to 9 and how they demonstrate your point.

We have expanded this section to more explicitly mention the contents of these supplementary figures and why they are relevant for the findings of this section (L425).

Are the text and figures clear and accurate?

Figure 4 too small

We have fixed the figure, as described above

Acronyms are defined multiple times in the manuscript, sometimes not the first time they are used (e.g. SNP, InDel)

Figure 8A - Remove red box, increase label size

Figure 8B - Low resolution, grey text is unreadable and should be darker and higher resolution

Line 35 - be more specific about types of carbon metabolism and catabolite repression

Line 67 - include citation for ireland et al. 2020 https://doi.org/10.7554/eLife.55308

Line 74 - You talk about looking in cis but don't specify how mar away cis is

Line 75 - we encoded genetic variants..... It is unclear what you mean here

Line 104 - 'were apart of operons' should clarify you mean polycistronic or multi-gene operons. Single genes may be considered operonic units as well.

We have addressed all the issues indicated above.

Figure 2: THere is no axis for the percents and the percents don't make sense relative to the bars they represent??

We realize that this visualization might not have been the most clear for readers, and have made the following improvement: we have added the number of genes with at least one association before the percentage. We note that the x-axis is in log scale, which may make it seem like the light-colored bars are off. With the addition of the actual number of associated genes we think that this confusion has been removed.

Figure 2: Figure 2B legend should clarify that these are individual examples of Differential expression between variants

Line 198-199: This sentence doesn't make sense, 'encoded using kmers' is not descriptive enough

Line 205: Should be upfront about that you're using the Promoter Calculator that models biophysical properties of promoter sequences to predict activity.

Line 251: 'Scanned the non-coding sequences of the DEGs'. This is far too vague of a description of an approach. Need to clarify how you did this and I didn't see in the method. Is this an HMM? Perfect sequence match to consensus sequence? Some type of alignment?

Line 257-259: This sentence lacks clarity

We have implemented all the suggested changes and clarified the points that the reviewer has highlighted above.

Line346: How were the E. coli isolates tested? Was this an experiment you did? This is a massive undertaking (1600 isolates * 12 conditions) if so so should be clearly defined

While we have indicated in the previous paragraph that the genomes and antimicrobial susceptibility data were obtained from previously published studies, we have now modified this paragraph (e.g. L411 and L418) slightly to make this point even clearer.

Figure 6A: The tile plot on the right side is not clearly labeled and it is unclear what it is showing and how that relates to the bar plots.

In the revised figure, we have clarified the labeling of the heatmap to now read “Log2(Fold Change) (measured expression)” to indicate that it represents each gene’s fold changes obtained from our initial transcriptomic analysis. We have also included this information in the caption of the figure, making the relationship between the measured gene expression (heatmap) and the reporter assay data (bar plots) clear to the reader.

FIgure 6B: typo in legend 'Downreglation'

We thank the review for pointing this out. The typo has been corrected to “Down regulation” in the revised figure.

Line 398: Need to state rationale for why Waaq operon is being investigated here. WHy did you look into individual example?

We thank the reviewer for asking for a clarification here. Our decision to investigate the waaQ gene was one of both biological relevance and empirical evidence. In our analysis associating non-coding variants with antimicrobial resistance using the Moradigaravand et al. dataset, we identified a T>C variant at position 3808241 that was associated with resistance to Tobramycin. We also observed this variant in our strain collection, where it was associated with expression changes of the gene, suggesting a possible functional impact. The waa operon is involved in LPS synthesis, a central determinant of the bacteria’s outer membrane integrity and a well established virulence factor. This provided a plausible biological mechanism through which variation could influence antimicrobial susceptibility. As its role in resistance has not been extensively characterized, this represents a good candidate for our experimental validation. We have now included this rationale in our revised manuscript (i.e. L476).

Figure 8: Can get rid of red box

We have now removed the red box from Figure 8 in the revised version.

Line 463 - 'account for all kinds' is too informal

Mix of font styles throughout document

We have implemented all the suggestions and formatting changes indicated above.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In their manuscript "Cis non-coding genetic variation drives gene expression changes in the E. coli and P. aeruginosa pangenomes", Damaris and co-authors present an extensive meta-analysis, plus some useful follow up experiments, attempting to apply GWAS principles to identify the extent to which differences in gene expression between different strains within a given species can be directly assigned to cis-regulatory mutations. The overall principle, and the question raised by the study, is one of substantial interest, and the manuscript here represents a careful and fascinating effort at unravelling these important questions. I want to preface my review below (which may otherwise sound more harsh than I intend) with the acknowledgment that this is an EXTREMELY difficult and challenging problem that the authors are approaching, and they have clearly put in a substantial amount of high quality work in their efforts to address it. I applaud the work done here, I think it presents some very interesting findings, and I acknowledge fully that there is no one perfect approach to addressing these challenges, and while I will object to some of the decisions made by the authors below, I readily admit that others might challenge my own suggestions and approaches here. With that said, however, there is one fundamental decision that the authors made which I simply cannot agree with, and which in my view undermines much of the analysis and utility of the study: that decision is to treat both gene expression and the identification of cis-regulatory regions at the level of individual genes, rather than transcriptional units. Below I will expand on why I find this problematic, how it might be addressed, and what other areas for improvement I see in the manuscript:

We thank the reviewer for their praise of our work. A careful set of replies to the major and minor critiques are reported below each point.

In the entire discussion from lines roughly 100-130, the authors frequently dissect out apparently differentially expressed genes from non differentially expressed genes within the same operons... I honestly wonder whether this is a useful distinction. I understand that by the criteria set forth by the authors it is technically correct, and yet, I wonder if this is more due to thresholding artifacts (i.e., some genes passing the authors' reasonable-yet-arbitrary thresholds whereas others in the same operon do not), and in the process causing a distraction from an operon that is in fact largely moving in the same direction. The authors might wish to either aggregate data in some way across known transcriptional units for the purposes of their analysis, and/or consider a more lenient 'rescue' set of significance thresholds for genes that are in the same operons as differentially expressed genes. I would favor the former approach, performing virtually all of their analysis at the level of transcriptional units rather than individual genes, as much of their analysis in any case relies upon proper assignment of genes to promoters, and this way they could focus on the most important signals rather than get lots sometimes in the weeds of looking at every single gene when really what they seem to be looking at in this paper is a property OF THE PROMOTERS, not the genes. (of course there are phenomena, such as rho dependent termination specifically titrating expression of late genes in operons, but I think on the balance the operon-level analysis might provide more insights and a cleaner analysis and discussion).

We agree with the reviewer that the peculiar nature of transcription in bacteria has to be taken into account in order to properly quantify the influence of cis variants in gene expression changes. We therefore added the exact analysis the reviewer suggested; that is, we ran associations between the variants in cis to the first gene of each operon and a phenotype that considered the fold-change of all genes in the operon, via a weighted average (see Methods for more details). As reported in the results section (L223), we found a similar trend as with the original analysis: we found the highest proportion of associations when encoding cis variants using k-mers (42% for E. coli and 45% for P. aeruginosa). More importantly, we found a high degree of overlap between this new “operon-level” association analysis and the original one (only including the first gene in each operon). We found a range of 90%-94% of associations overlapping for E. coli and between 75% and 91% for P. aeruginosa, depending on the variant type. We note that operon definitions are less precise for P. aeruginosa, which might explain the higher variability in the level of overlap. We have added the results of this analysis in the results section.

This also leads to a more general point, however, which I think is potentially more deeply problematic. At the end of the day, all of the analysis being done here centers on the cis regulatory logic upstream of each individual open reading frame, even though in many cases (i.e., genes after the first one in multi-gene operons), this is not where the relevant promoter is. This problem, in turn, raises potentially misattributions of causality running in both directions, where the causal impact on a bona fide promoter mutation on many genes in an operon may only be associated with the first gene, or on the other side, where a mutation that co-occurs with, but is causally independent from, an actual promoter mutation may be flagged as the one driving an expression change. This becomes an especially serious issue in cases like ulaE, for genes that are not the first gene in an operon (at least according to standard annotations, the UlaE transcript should be part of a polycistronic mRNA beginning from the ulaA promoter, and the role played by cis-regulatory logic immediately upstream of ulaE is uncertain and certainly merits deeper consideration. I suspect that many other similar cases likewise lurk in the dataset used here (perhaps even moreso for the Pseudomonas data, where the operon definitions are likely less robust). Of course there are many possible explanations, such as a separate ulaE promoter only in some strains, but this should perhaps be carefully stated and explored, and seems likely to be the exception rather than the rule.

While we again agree with the reviewer that some of our associations might not result in a direct causal link because the focal variant may not belong to an actual promoter element, we also want to point out how the ability to identify the composition of transcriptional units in bacteria is far from a solved problem (see references at the bottom of this comment, two in general terms, and one characterizing a specific example), even for a well-studied species such as E. coli. Therefore, even if carrying out associations at the operon level (e.g. by focusing exclusively on variants in cis for the first gene in the operon) might be theoretically correct, a number of the associations we find further down the putative operons might be the result of a true biological signal.

1. Conway, T., Creecy, J. P., Maddox, S. M., Grissom, J. E., Conkle, T. L., Shadid, T. M., Teramoto, J., San Miguel, P., Shimada, T., Ishihama, A., Mori, H., & Wanner, B. L. (2014). Unprecedented High-Resolution View of Bacterial Operon Architecture Revealed by RNA Sequencing. mBio, 5(4), 10.1128/mbio.01442-14. https://doi.org/10.1128/mbio.01442-14

2. Sáenz-Lahoya, S., Bitarte, N., García, B., Burgui, S., Vergara-Irigaray, M., Valle, J., Solano, C., Toledo-Arana, A., & Lasa, I. (2019). Noncontiguous operon is a genetic organization for coordinating bacterial gene expression. Proceedings of the National Academy of Sciences, 116(5), 1733–1738. https://doi.org/10.1073/pnas.1812746116

3. Zehentner, B., Scherer, S., & Neuhaus, K. (2023). Non-canonical transcriptional start sites in E. coli O157:H7 EDL933 are regulated and appear in surprisingly high numbers. BMC Microbiology, 23(1), 243. https://doi.org/10.1186/s12866-023-02988-6

Another issue with the current definition of regulatory regions, which should perhaps also be accounted for, is that it is likely that for many operons, the 'regulatory regions' of one gene might overlap the ORF of the previous gene, and in some cases actual coding mutations in an upstream gene may contaminate the set of potential regulatory mutations identified in this dataset.

We agree that defining regulatory regions might be challenging, and that those regions might overlap with coding regions, either for the focal gene or the one immediately upstream. For these reasons we have defined a wide region to identify putative regulatory variants (-200 to +30 bp around the start codon of the focal gene). We believe this relatively wide region allows us to capture the most cis genetic variation.

Taken together, I feel that all of the above concerns need to be addressed in some way. At the absolute barest minimum, the authors need to acknowledge the weaknesses that I have pointed out in the definition of cis-regulatory logic at a gene level. I think it would be far BETTER if they performed a re-analysis at the level of transcriptional units, which I think might substantially strengthen the work as a whole, but I recognize that this would also constitute a substantial amount of additional effort.

As indicated above, we have added a section in the results section to report on the analysis carried out at the level of operons as individual units, with more details provided in the methods section. We believe these results, which largely overlap with the original analysis, are a good way to recognize the limitation of our approach and to acknowledge the importance of gaining a better knowledge on the number and composition of transcriptional units in bacteria, for which, as the reference above indicates, we still have an incomplete understanding.

Having reached the end of the paper, and considering the evidence and arguments of the authors in their totality, I find myself wondering how much local x background interactions - that is, the effects of cis regulatory mutations (like those being considered here, with or without the modified definitions that I proposed above) IN THE CONTEXT OF A PARTICULAR STRAIN BACKGROUND, might matter more than the effects of the cis regulatory mutations per se. This is a particularly tricky problem to address because it would require a moderate number of targeted experiments with a moderate number of promoters in a moderate number of strains (which of course makes it maximally annoying since one can't simply scale up hugely on either axis individually and really expect to tease things out). I think that trying to address this question experimentally is FAR beyond the scope of the current paper, but I think perhaps the authors could at least begin to address it by acknowledging it as a challenge in their discussion section, and possibly even identify candidate promoters that might show the largest divergence of activities across strains when there IS no detectable cis regulatory mutation (which might be indicative of local x background interactions), or those with the largest divergences of effect for a given mutation across strains. A differential expression model incorporating shrinkage is essential in such analysis to avoid putting too much weight on low expression genes with a lot of Poisson noise.

We again thank the reviewer for their thoughtful comments on the limitations of correlative studies in general, and microbial GWAS in particular. In regards to microbial GWAS we feel we may have failed to properly explain how the implementation we have used allows to, at least partially, correct for population structure effects. That is, the linear mixed model we have used relies on population structure to remove the part of the association signal that is due to the genetic background and thus focus the analysis on the specific loci. Obviously examples in which strong epistatic interactions are present would not be accounted for, but those would be extremely challenging to measure or predict at scale, as the reviewer rightfully suggests. We have added a brief recap of the ability of microbial GWAS to account for population structure in the results section (“A large fraction of gene expression changes can be attributed to genetic variations in cis regulatory regions”, e.g. L195).

I also have some more minor concerns and suggestions, which I outline below:

It seems that the differential expression analysis treats the lab reference strains as the 'centerpoint' against which everything else is compared, and yet I wonder if this is the best approach... it might be interesting to see how the results differ if the authors instead take a more 'average' strain (either chosen based on genetics or transcriptomics) as a reference and compared everything else to that.

While we don’t necessarily disagree with the reviewer that a “wild” strain would be better to compare against, we think that our choice to go for the reference isolates is still justified on two grounds. First, while it is true that comparing against a reference introduces biases in the analysis, this concern would not be removed had we chosen another strain as reference; which strain would then be best as a reference to compare against? We think that the second point provides an answer to this question; the “traditional” reference isolates have a rich ecosystem of annotations, experimental data, and computational predictions. These can in turn be used for validation and hypothesis generation, which we have done extensively in the manuscript. Had we chosen a different reference isolate we would have had to still map associations to the traditional reference, resulting in a probable reduction in precision. An example that will likely resonate with this reviewer is that we have used experimentally-validated and high quality computational operon predictions to look into likely associations between cis-variants and “operon DEGs”. This analysis would have likely been of worse quality had we used another strain as reference, for which operon definitions would have had to come from lower-quality predictions or be “lifted” from the traditional reference.

Line 104 - the statement about the differentially expressed genes being "part of operons with diverse biological functions" seems unclear - it is not apparent whether the authors are referring to diversity of function within each operon, or between the different operons, and in any case one should consider whether the observation reflects any useful information or is just an apparently random collection of operons.

We agree that this formulation could create confusion and we have elected to remove the expression “with diverse biological functions”, given that we discuss those functions immediately after that sentence.

Line 292 - I find the argument here somewhat unconvincing, for two reasons. First, the fact that only half of the observed changes went in the same direction as the GWAS results would indicate, which is trivially a result that would be expected by random chance, does not lend much confidence to the overall premise of the study that there are meaningful cis regulatory changes being detected (in fact, it seems to argue that the background in which a variant occurs may matter a great deal, at least as much as the cis regulatory logic itself). Second, in order to even assess whether the GWAS is useful to "find the genetic determinants of gene expression changes" as the authors indicate, it would be necessary to compare to a reasonable, non-straw-man, null approach simply identifying common sequence variants that are predicted to cause major changes in sigma 70 binding at known promoters; such a test would be especially important given the lack of directional accuracy observed here. Along these same lines, it is perhaps worth noting, in the discussion beginning on line 329, that the comparison is perhaps biased in favor of the GWAS study, since the validation targets here were prioritized based on (presumably strong) GWAS data.

We thank the reviewer for prompting us into reasoning about the results of the in-vitro validation experiments. We agree that the agreement between the measured gene expression changes agree only partly with those measured with the reporter system, and that this discrepancy could likely be attributed to regulatory elements that are not in cis, and thus that were not present in the in-vitro reporter system. We have noted this possibility in the discussion. Additionally, we have amended the results section to note that even though the prediction in the direction of gene expression change was not as accurate as it could be expected, the prediction of whether a change would be present (thus ignoring directionality) was much higher.

I don't find the Venn diagrams in Fig 7C-D useful or clear given the large number of zero-overlap regions, and would strongly advocate that the authors find another way to show these data.

While we are aware that alternative ways to show overlap between sets, such as upset plots, we don’t actually find them that much easier to parse. We actually think that the simple and direct Venn diagrams we have drawn convey the clear message that overlaps only exist between certain drug classes in E. coli, and virtually none for P. aeruginosa. We have added a comment on the lack of overlap between all drug classes and the differences between the two species in the results section (i.e. L436 and L465).

In the analysis of waa operon gene expression beginning on line 400, it is perhaps important to note that most of the waa operon doesn't do anything in laboratory K12 strains due to the lack of complete O-antigen... the same is not true, however, for many wild/clinical isolates. It would be interesting to see how those results compare, and also how the absolute TPMs (rather than just LFCs) of genes in this operon vary across the strains being investigated during TOB treatment.

We thank the reviewer for this helpful suggestion. We examined the absolute expression (TPMs) of waa operon genes under the baseline (A) and following exposure to Tobramycin (B). The representative TPMs per strain were obtained by averaging across biological replicates. We observed a constitutive expression of the genes in the reference strain (MG1655) and the other isolates containing the variant of interest (MC4100, BW25113). In contrast, strains lacking the variants of interest (IAI76 and IAI78), showed lower expression of these operon genes under both conditions. Strain IAI77, on the other hand, displayed increased expression of a subset of waa genes post Tobramycin exposure, indicating strain-specific variation in transcriptional response. While the reference isolate might not have the O-antigen, it certainly expresses the waa operon, both constitutively and under TOB exposure.

I don't think that the second conclusion on lines 479-480 is fully justified by the data, given both the disparity in available annotation information between the two species, AND the fact that only two species were considered.

While we feel that the “Discussion” section of a research paper allows for speculative statements, we have to concede that we have perhaps overreached here. We have amended this sentence to be more cautious and not mislead readers.

Line 118: "Double of DEGs"

Line 288 - presumably these are LOG fold changes

Fig 6b - legend contains typos

Line 661 - please report the read count (more relevant for RNA-seq analysis) rather than Gb

We thank the reviewer for pointing out the need to make these edits. We have implemented them all.

Source code - I appreciate that the authors provide their source code on github, but it is very poorly documented - both a license and some top-level documentation about which code goes with each major operation/conclusion/figure should be provided. Also, ipython notebooks are in general a poor way in my view to distribute code, due to their encouragement of nonlinear development practices; while they are fine for software development, actual complete python programs along with accompanying source data would be preferrable.

We agree with the reviewer that a software license and some documentation about what each notebook is about is warranted, and we have added them both. While we agree that for “consumer-grade” software jupyter notebooks are not the most ergonomic format, we believe that as a documentation of how one-time analyses were carried out they are actually one of the best formats we could think of. They in fact allow for code and outputs to be presented alongside each other, which greatly helped us to iterate on our research and to ensure that what was presented in the manuscript matched the analyses we reported in the code. This is of course up for debate and ultimately specific to someone’s taste, and so we will keep the reviewer’s critique in mind for our next manuscript. And, if we ever decide to package the analyses presented in the manuscript as a “consumer-grade” application for others to use, we would follow higher standards of documentation and design.

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

In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance.

A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

We thank the reviewer for highlighting the strengths of our study. We hope that our replies to their comments and the other two reviewers will address some of the limitations.

Major comments:

1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

In order to better guide readers through the different kinds of genetic variants we considered, we have added a brief explanation about what “promoter switches” are in the introduction (“meaning that the entire promoter region may differ between isolates due to recombination events”, L56). We believe this clarifies how they are very different in character from a large deletion. We have kept the reference to the original study (10.1073/pnas.1413272111) describing how widespread these switches are in E. coli as a way for readers to discover more about them.

• I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

We indeed agree with the reviewer in thinking that representing genetic variants as k-mers would encompass short variants (SNP/InDels) as well as larger variants and promoters presence/absence patterns. We believe that this assumption is validated by the fact that we identify the highest proportion of DEGs with a significant association when using this representation of variants (Figure 2A, 39% for both species). We have added a reference to a recent review on the advantages of k-mer methods for population genetics (10.1093/molbev/msaf047) in the introduction. Regarding the issue of multiple testing correction, we have employed a commonly recognized approach that, unlike a crude Bonferroni correction using the number of tested variants, allows for a realistic correction of association p-values. We used the number of unique presence/absence patterns, which can be shared between multiple genetic variants, and applied a Bonferroni correction using this number rather than the number of variants tested. We have expanded the corresponding section in the methods (e.g. L697) to better explain this point for readers not familiar with this approach.

1. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?

We appreciate the suggestion made by the reviewer to look into the distribution of effect sizes divided by variant type. We have now evaluated the distribution of the effect sizes and allele frequencies for the genetic markers (SNPs/InDels, IGRs, and k-mers) for both species (Supplementary Figure 2). In E. coli, IGR variants showed somewhat larger median effect sizes (|β| = 4.5) than SNPs (|β| = 3.8), whereas k-mers displayed the widest distribution (median |β| = 5.2). In P. aeruginosa, the trend differed with IGRs exhibiting smaller effects (median |β| = 3.2), compared to SNPs/InDels (median |β| =5.1) and k-mers (median |β| = 6.2). With respect to allele frequencies, SNPs/InDels generally occured at lower frequencies (median AF = 0.34 for E.coli, median AF = 0.33 for P. aeruginosa), whereas IGRs (median AF = 0.65 for E. coli and 0.75 for P. aeruginosa) and k-mers (median AF = 0.71 for E. coli and 0.65 for P. aeruginosa) were more often at the intermediate to higher frequencies respectively. We have added a visualization for the distribution of effect sizes (Supplementary Figure 2).

1. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.

We thanks the reviewer for their comment, which is similar in nature to that raised by reviewer #2 above. As noted in our reply above we have amended the results and discussion to indicate that although the direction of gene expression change was not highly accurate, focusing on the magnitude (or rather whether there would be a change in gene expression, regardless of the direction), resulted in a higher accuracy. We postulate that the cases in which the direction of the change was not correctly identified could be due to the influence of other genetic elements in trans with the gene of interest.

1. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?

We thank the reviewer for this comment. We agree that estimating the null expectation for the functional promoter would require testing promoter alleles with sequence variation that are not associated in the GWAS. Such experiments, which would directly address if the observed effects in our study exceeds background, would have required us to prepare multiple constructs, which was unfortunately not possible for us due to staff constraints. We therefore elected to clarify the scope of our GFP reporter assays instead. These experiments were designed as a paired comparison of the wild-type and the GWAS-associated variant alleles of the same promoter in an identical reporter background, with the aim of testing allele-specific functional effects for GWAS hits (Supplementary Figure 6). We also included a comparison in GFP fluorescence between the promoterless vector (pOT2) and promoter-containing constructs; we observed higher GFP signals in all but four (yfgJ, fimI, agaI, and yfdQ) variant-containing promoter constructs, which indicates that for most of the construct we cloned active promoter elements. We have revised the manuscript text accordingly to reflect this clarification and included the control in the supplementary information as Supplementary Figure 6.

1. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.

We thank the reviewer for pointing this out. We have reviewed Figure 6 to indicate significant differences between the test and control reporter constructs. We used the paired student’s t-test to match the matched plate/time point measurements. We also corrected for multiple testing using the Benhamini-Hochberg correction. As seen in the updated Figure 6A, 16 out of the 18 reporter constructs displayed significant differences (adjusted p-value

1. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?

We thank the reviewer for this helpful suggestion, which allows us to more closely look into the results of our in-vitro validation. We performed a direct comparison of RNAseq fold changes from the GWAS (x-axis) with the GFP reporter measurements (y-axis) as depicted in the figure above. The overall correlation between the two was weak (Pearson r = 0.17), reflecting the lack of thorough agreement between the associations and the reporter construct. We however note that the two metrics are not directly comparable in our opinion, since on the x-axis we are measuring changes in gene expression and on the y-axis changes in fluorescence expression, which is downstream from it. As mentioned above and in reply to a comment from reviewer 2, the agreement between measured gene expression and all other in-silico and in-vitro techniques increases when ignoring the direction of the change. Overall, we believe that these results partly validate our associations and predictions, while indicating that other factors in trans with the regulatory region contribute to changes in gene expression, which is to be expected. The scatter plot has been included as a new supplementary figure (Supplementary Figure 7).

1. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?

The T>C SNP upstream of waaQ did not show significant association with gene expression in our cis GWAS analysis. Instead, this variant was associated with resistance to tobramycin when referencing data from Danesh et al, and we observed the variant in our strain collection. We subsequently investigated whether this variant also influenced expression of the waa operon under sub-inhibitory tobramycin exposure. The differential expression results shown in the final section therefore represent a functional follow-up experiment, and not a direct replication of the GWAS presented in the first part of the manuscript.

1. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?

The awkwardness of that sentence is perhaps the consequence of our assumption that readers would be aware of the differences in population genetics differences between the two species. We however have realized that not much literature is available (if at all!) about these differences, which we have observed during the course of this and other studies we have carried out. As a result, we agree that we cannot assume that the reader is similarly familiar with these differences, and have changed that sentence (i.e. L548) to more directly address the differences between the two species, which will presumably result in a diverse population structure. We thank the reviewer for letting us be aware of a gap in the literature concerning the comparison of pangenome structures across relevant species.

1. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.

We have amended this sentence to be more adherent to what we can conclude from our analyses.

1. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.

We thank the reviewer for highlighting the need for clearer methodological detail. We have expanded this section (i.e. L608) to fully describe the generation and quality control of the E. coli RNA-seq data including RNA extraction and sequencing platform.

1. How were the DEG p-values adjusted for multiple testing?

As indicated in the methods section (“Differential gene expression and functional enrichment analysis”), we have used DEseq2 for E. coli, and LPEseq for P. aeruginosa. Both methods use the statistical framework of the False Discovery Rate (FDR) to compute an adjusted p-value for each gene. We have added a brief mention of us following the standard practice indicated by both software packages in the methods.

1. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.

In the context of providing more information about the transcriptomics experiments for E. coli, we have also more clearly indicated that we have two biological replicates for the E. coli dataset.

1. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?

In line with an earlier comment from this reviewer, we have expanded the section in the Methods (e.g. L689) that explains how this correction worked to include as many details as possible, in order to provide the readers with the full context under which our analyses were carried out.

1. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.

We thank both reviewer #1 and #3 for pointing out this lack of references to the extensive literature on the subject. We have added a number of references about the applications of eQTL studies, and specifically its application in microbial pangenomes, which we believe is more relevant to our study, in the introduction.

Minor comments:

1. I wasn't able to understand the top panels in Figure 4. For ulaE, most strains have the solid colors, and the corresponding bottom panel shows mostly red points. But for waaQ, most strains have solid color in the top panel, but only a few strains in the bottom panel are red. So solid color in the top does not indicate a variant allele? And why are there so many solid alleles; are these all indels? Even if so, for kgtP, the same colors (i.e., nucleotides) seem to seamlessly continue into the bottom, pale part of the top panel. How are these strains different genotypically? Are these blocks of solid color counted as one indel or several SNPs, or somehow as k-mer differences? As the authors can see, these figures are really hard to understand and should be reworked. The same comment applies to Figure 5, where it seems that all (!) strains have the "variant"?

We thank the reviewer for pointing out some limitations with our visualizations, most importantly with the way we explained how to read those two figures. We have amended the captions to more explicitly explain what is shown. The solid colors in the “sequence pseudo-alignment” panels indicate the focal cis variant, which is indicated in red in the corresponding “predicted transcription rate” panels below. In the case of Figure 5, the solid color indicates instead the position of the TFBS in the reference.

1. Figure 1A & B: It would be helpful to add the total number of analyzed genes somewhere so that the numbers denoted in the colored outer rings can be interpreted in comparison to the total.

We have added the total number of genes being considered for either species in the legend.

1. Figure 1C & D: It would be better to spell out the COG names in the figure, as it is cumbersome for the reader to have to look up what the letters stand for in a supplementary table in a separate file.

While we do not disagree with the awkwardness of having to move to a supplementary table to identify the full name of a COG category, we also would like to point out that the very long names of each category would clutter the figure to a degree that would make it difficult to read. We had indeed attempted something similar to what the reviewer suggests in early drafts of this manuscript, leading to small and hard to read labels. We have therefore left the full names of each COG category in Supplementary Table 3.

1. Line 107: "Similarly," does not fit here as the following example (with one differentially expressed gene in an operon) is conceptually different from the one before, where all genes in the operon were differentially expressed.

We agree and have amended the sentence accordingly.

1. Figure 5 bottom panel: it is odd that on the left the swarm plots (i.e., the dots) are on the inside of the boxplots while on the right they are on the outside.

We have fixed the position of the dots so that they are centered with respect to the underlying boxplots.

1. It is not clear to me how only one or a few genes in an operon can show differential mRNA abundance. Aren't all genes in an operon encoded by the same mRNA? If so, shouldn't this mRNA be up- or downregulated in the same manner for all genes it encodes? As I am not closely familiar with bacterial systems, it is well possible that I am missing some critical fact about bacterial gene expression here. If this is not an analysis artifact, the authors could briefly explain how this observation is possible.

We thanks the reviewer for their comment, which again echoes one of the main concerns from reviewer #2. As noted in our reply above, it has been established in multiple studies (see the three we have indicated above in our reply to reviewer #2) how bacteria encode for multiple “non-canonical” transcriptional units (i.e. operons), due to the presence of accessory terminators and promoters. This, together with other biological effects such as the presence of mRNA molecules of different lengths due to active transcription and degradation and technical noise induced by RNA isolation and sequencing can result in variability in the estimation of abundance for each gene.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance. A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

Major comments:

1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

3. I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

4. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?
5. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.
6. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?
7. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.
8. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?
9. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?
10. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?
11. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.
12. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.
13. How were the DEG p-values adjusted for multiple testing?
14. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.
15. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?
16. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.