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      Reply to the reviewers

      We thank the reviewer for their constructive comments and the fair and interesting discussion between reviewers.

      __Reviewer #1 __

      We are delighted to read that the reviewer finds the manuscript “very clear and of immediate impact […] and ready for publication” regarding this aspect. We have toned down the conclusion, proposing rather than concluding that “the incapacitation of Cmg2[KO] intestinal stem cells to function properly […] is due to their inability to transduce Wnt signals”.

      We have addressed the 3 points that were raised as well as the minor comments.

      Point #1

      The mouse mutant is just described as 'KO', referring to the previous work by the authors. The cited work simply states that this is a zygotic deletion of exon 3, which somehow leads to a decrease in protein abundance that is almost total in the lung but not so clear in the uterus. Exon 3 happens to be 72 bp long [https://www.ncbi.nlm.nih.gov/nuccore/NM_133738], so its deletion (assuming there are no cryptic splicing sites used) leads to an internal in-frame deletion of 24 amino acids. So, at best, this 'KO' is not a null, but a hypomorphic allele of context-dependent strength.

      Unfortunately, neither the previous work nor this paper (unless I have missed it!) contains information provided about the expression levels of Cmg2 in the intestine of KO mice - nor which cell types usually express it (see below). I think that using anti Cmg2 in WB and immunohistofluorescence of with ISC markers with intestine homogenate/sections of wild-type and mutant mice would be necessary to set the stage for the rest of the work.

      We now provide and explanation and characterization the Cmg2KO mice. Exon 3 indeed only encodes a short 24 amino acid sequence. This exon however encodes a ß-strand that is central to the vWA domain of CMG2, and therefore critical for the folding of this domain. As now shown in Fig. S1c, CMG2Dexon3 is produced in cells but cleared by the ER associated degradation pathway, therefore it is only detectable in cells treated with the proteasome inhibitor MG132, at a slightly lower molecular weight than the full-length protein. This is consistent, and was inspired by the fact that multiple Hyaline Fibromatosis missense mutations that map to the vWA domain lead to defective folding of CMG2, further illustrating that this domain is very vulnerable to modifications. In Fig. S1c, we moreover now show immunoprecipitation of Cmg2 from colonic tissue of wild-type (WT) and knockout (KO) mice, which confirm the absence of Cmg2 protein in Cmg2KO samples.

      Point #2

      Connected to the previous point, the expression pattern of Cmg2 in the intestine is not described. Maybe this is already established in the literature, but the authors do not refer to the data. This is important when considering that the previous work of the authors suggests that Cmg2 might contribute to Wnt signalling transduction through physical, cis interactions with the Wnt co-receptor LRP6. Therefore, one would expect that Cmg2 would be cell-autonomously required in the intestinal stem cells.

      The expression pattern of Cmg2 in the gut has not been characterized and is indeed essential to understanding its function. To address this gap, we now added a figure (Fig. 1) providing data from publicly available RNA-seq datasets and from our RNAscope experiments on Cmg2WT mice. Of note, we unfortunately have never managed to detect Cmg2 protein expression by immunohistochemistry of mouse tissue with any of the antibodies available, commercial or generated in the lab.

      In the RESULTS section we now mention:

      To investigate Cmg2 expression in the gut, we first analyzed publicly available spatial and scRNA-seq datasets to identify which cell types express Cmg2 across different gut regions. Spatial transcriptomic data from the mouse small intestine and colon revealed that Cmg2 is broadly expressed throughout the gut, including in the muscular, crypt, and epithelial layers (Fig. 1A–C). To validate these findings, we performed RNAscope in situ hybridization targeting Cmg2 in the duodenum and colon of wild-type mice. The expression pattern observed was consistent with the spatial transcriptomics data (Fig. 1D–E). We then analyzed scRNA-seq data from the same dataset to assess cell-type-specific expression in the mouse colon. Cmg2 was detected at varying levels across multiple cell types, including enterocytes and intestinal stem cells, as well as mesenchymal cells, notably fibroblasts.

      Of note for the reviewer, not mentioned in the manuscript, this wide-spread distribution of Cmg2 across the different cell types is not true for all organs. We have recently investigated the expression of Cmg2 in muscle and found that it is almost exclusively expressed in fibroblasts (so-called fibro-adipocyte progenitors) and very little in any other muscle cells, in particular fibers.

      Interestingly also, as now mentioned in the manuscript and shown in Fig. S1,the ANTXR1 protein, which is highly homologous to Cmg2 at the protein level and share its function of anthrax toxin receptor, displayed a much more restricted expression pattern, being confined primarily to fibroblasts and mural cells, and notably absent from epithelial cells. This differential expression highlights a potentially unique and epithelial-specific role for Cmg2 in maintaining intestinal homeostasis.

      Point #3

      The authors establish that the regenerating crypts of Cmg2[KO] mice are unable to transduce Wnt signalling, but it is not clear whether this situation is provoked by the DSS-induce injury or existed all along. Can Cmg2[KO] intestinal stem cells transduce Wnt signalling before the DSS challenge? If they were, it might suggest that the 'context-dependence' of the Cmg2 role in Wnt signalling is contextual not only because of the tissue, but because of the history of the tissue or its present structure. It would also suggest that Cmg2 mutant mice, unless reared in a germ-free facility for life, would eventually lose intestinal homeostasis, and maybe suggest the level of intervention/monitoring that HFS patients would require. It might also provide an explanation in case Cmg2 was not expressed in ISCs - if the state of the tissue was as important as the presence of the protein, then the effect on Wnt transduction could be indirect and therefore it might not be required cell-autonomously.

      We agree that understanding whether Cmg2KO intestinal stem cells are intrinsically unable to transduce Wnt signals, or whether this defect is contextually induced following injury (such as DSS treatment), is a critical point.

      As a first line of evidence, we show than under homeostatic condition, Wnt signaling appears largely intact in Cmg2KO crypts, with comparable levels of ß-catenin and expression levels of canonical Wnt target genes (e.g., Axin2, Lgr5) to those observed in WT animals (Figs. S1j-l and S3d-e). This indicates that Cmg2 is not essential for basal Wnt signaling under steady-state conditions.

      These findings thus support the idea that the requirement for Cmg2 in Wnt signal transduction is context-dependent—not only at the tissue level but also temporally, being specifically required during regenerative processes or in altered microenvironments such as during inflammation or epithelial damage. This context-dependence may reflect changes in the composition or accessibility of Wnt ligands, receptors, or matrix components during repair, where Cmg2 could play a scaffolding or stabilizing role.

      These aspects are now discussed in the text.

      I think points 1 and 2 are absolutely fundamental in a reverse genetics investigation. Point 3 would be nice to know but the outcome would not change the tenet of the paper. I believe that the work needed to deal these points can be performed on archival material. I do not think the mechanism proposed can be taken from 'plausible' to 'proven' without proposing substantial additional investigation, so I will not suggest any of it, as it could well be another paper.

      We have addressed points 1 and 2, and provided evidence and discussion for Point 3.

      __Minor points __

      1- Figure 1 legend says "In (c), results are mean {plus minus} SEM" - this seems applicable to (d) as (c) does not show error whiskers.

      We thank the reviewer for picking up this error. We modified : “In (c), results are median” and “In (d, f and g) Results are mean ± SEM.”

      2- Figure 1 legend says "(d) Body weight loss, (f) the aspect of the feces and presence of occult blood were monitored and used for the (e) DAI. Results are mean {plus minus} SEM. Each dot represents the mean of n = 12 mice per genotype". This part looks like has suffered some rearrangement of words. The first instance of (f) should be (e), I guess, and I am not sure what "(e) DAI" means. And for (e), "mean {plus minus} SEM" does not seem applicable. This needs some light revision.

      The legend was clarified as followed : “(d) __Body weight loss, and (e) aspect of the feces and presence of occult blood were monitored and used to evaluate Disease activity index in (f).__

      3 - Figure 1H legend does not say which statistical test was made in the survival experiment in (h) - presumably log-rank? A further comment on the survival statistics: euthanised animals should not be counted towards true mortality when that is what is recorded as an 'event'. They should be right-censored. However, in this case, reaching the euthanasia criterion is just as good an indicator of health as mortality itself. So, simply by changing the Y axis from 'survival' to 'event-free survival' (or something to that effect), where 'events' are either death or reaching the euthanasia criterion, leaves the analysis as it is, and authors do not need to clarify that figure 1H shows "apparent mortality", as it is straightforward "complication-free survival" (just not entirely orthogonal to weight loss).

      The Y axis was changed from 'survival' to “percentage of mice not reaching the euthanasia criterion”.

      4 - Some density measurements are made unnecessarily on arbitrary units (per field of view) - this should be simple to report in absolute measures (i.e. area of tissue screened or, better still, length of epithelium screened).

      Because the aera of tissue can vary significantly between damages, regenerating and undamaged tissue, we reported the length of epithelium screened as suggested : “per 800um tissue screened” in Fig S1c and Fig 2b.

      5 - Figure 2E should read "percent involvement"

      This has been corrected.

      6 - Figure 2J should read "lipocalin..."

      This has been corrected.

      7 - In section "CMG2 Is Dispensable for YAP/TAZ-Mediated Reprogramming to Fetal-Like Stem Cells", the authors write ""We measured the mRNA levels of two additional YAP target genes, Cyr61 and CTGF...". I presume the "additional" is because Ly6a is also a target of YAP/TAZ, but if the reader does not know, it is puzzling. I would suggest to make this link explicit.

      We added : “In addition to the fetal-like stem cell marker Ly6a, which is a YAP/TAZ target gene, we measured the mRNA levels of two others YAP target genes, Cyr61 and CTGF”

      8 - In Figures S2, 3 and S3, I think that the measures expressed as "% of homeostatic X in WT" really mean "% of average homeostatic X in WT". This should be made clear somewhere.

      We added: “Dotted line represents the average homeostatic levels of Cmg2 WT” in figure legends

      9 - In panel C, the nature of the data is not entirely clear. First, the corresponding part of the legend says "Representative images of n=4 mice per genotype" which I presume should refer to panel B. Then, the graph plots 4 data points, which suggests that they correspond to 4 mice - but how many fields of view? Also, the violin plot outline is not described - I presume it captures all the data points from the coarse-grained pixel analysis, but it should be clarified.

      It was modified as suggested : “(c) Results are presented as violin plot of the Ly6a mean intensity of all data points from the coarse-grain analysis. Each symbol represents the mean per mice of n=4 mice per condition. Results are mean ± SEM. Dotted line represents the average homeostatic levels of Cmg2WT. P values obtained by two-tailed unpaired t test.”

      10 - In Figure 3H and 3I, I would suggest to add the 7+3 timepoint where the data come from.

      We unfortunately do not understand the suggestion of the reviewer, given that these panels show the 7+3 time point.

      11 - In section "CMG2 Is Critical for Restoring the Lgr5+ Intestinal Stem Cell Pool", the authors say "...The mRNA levels of ... LRP6, β-catenin (Fig. S3a-b), and Wnt ligands (Wnt5a, 5b, and 2b) were comparable between the colons of Cmg2WT and Cmg2KO mice (Fig. S3c)..." without clarifying in which context - one needs to read the figure legend to realise this is "timepoint 7+3". I suggest to add "in the recovery phase" or "in regenerating colons" or something shorter, just to guide the reader.

      We added : “Initially, we quantified the expression of key molecular components involved in Wnt signaling in mice colon 3 days after DSS withdrawal using qPCR.”

      12 - Like with the previous point, it is not clear when the immunohistofluorescence of B-catenin is made - not even in the legend, as far as I could see. The only hint is that authors say "the nuclei of cells in the atrophic crypts of Cmg2KO..." with 'atrophic' probably indicating again the 7+3 timepoint.

      We have changed the text and now mention “Next, we analyzed β-catenin activation in the colon of Cmg2WT and Cmg2KO mice during the recovery phase.”

      13 - A typo in the discussion: tunning for tuning.

      This has been corrected.

      14 - In the discussion, the authors talk about the 'CMG2' protein (all caps - formatting convention for human proteins) but before they were referring to 'Cmg2' (formatting convention for mouse proteins). That is fine but some of the statements where "CMG2" is used clearly refer to observations made in the mouse.

      We have now used Cmg2, whenever referring to the mouse protein.

      15 - Typos in methods: "antigen retrieval by treating [with] Proteinase K"; "Image acquisition and analyze [analysis]"; "All details regarding code used for immunofluorescence analysis”.

      This has been corrected.

      __Reviewer #2 __

      We are very pleased to read that the reviewer found the study “overall well designed, meticulously carried out, and with clear and convincing results that are most reasonably and thoughtfully interpreted”.

      For this reader, one additional thought comes to mind. If I understand the field correctly it would be informative to know with greater confidence where - in what cell type, epithelial or mesenchymal - the CMG2-LRP6-WNT interaction occurs.

      This point was also raised by Reviewer I, and we have now added a new Figure 1, that describes Cmg2 expression in the gut, based both on from publicly available RNA-seq datasets and our RNAscope experiments on Cmg2WT mice. Of note, we unfortunately have never managed to detect Cmg2 protein expression by immunohistochemistry of mouse tissue with any of the antibodies available, commercial or generated in the lab.

      After injury the CMG2-KO mouse epithelium exhibits defective WNT signal transduction - as evidenced by failure of b-catenin to translocate into the nucleus. At first glance, this result is a disconnect with the paper by van Rijin that claims the defect in Hyaline Fibromatosis Syndrome cannot be due to loss of CMG2 expression/function in the barrier epithelial cell - a claim based on the mostly normal phenotypes of human CMG2 KO duodenal organoids. But the human organoids studied in the van Rijin paper, like all others, are established and cultured in very high WNT conditions, perhaps obscuring the lack of the CMG2-LRP6-WNT interaction. And in fact, the phenotypes of these human CMG2-KO duodenoids were not entirely normal - the CMG2-KO stem-like organoids (even when cultured in high WNT/R-spondin conditions) developed abnormal intercellular blisters consistent with a defect in epithelial structure/function - of unknown cause and not investigated.

      We thank the reviewer for raising this point and we fully agree. We now specify in the text that the human CMG2-KO duodenoids showed blisters, indeed consistent with a defect in epithelial structure/function, and that they were grown on high Wnt media which likely obscure the CMG2 requirement.

      I think it would be informative to prepare colon organoids (and duodenoids) from WT and CMG2-KO mice to quantify their WNT dependency during establishment and maintenance of the stem-like (and WNT-dependent) state. If CMG2 acts within the epithelial cell to affect WNT signaling (regardless of WNT source), organoids prepared from colons of CMG2-KO mice would require more WNT in culture media to establish and maintain the stem cell proliferative state - when compared to organoids prepared from WT mice. This can be quantified (and confirmed molecularly by transgene expression if successful). Enhanced dependency of high concentrations of exogenous WT would be evidence for a primary defect in WNT-(LRP2)-CMG2 signal transduction localized to the epithelial barrier cell - thus addressing the apparent discrepancy with the van Rijin paper - and for my part, advancing the field. And the discovery of a defect in the epithelium itself for WNT signal transduction would implicate a biologically most plausible mechanism for development of protein losing enteropathy.

      By no means do I consider these experiments to be required for publication (especially if considered to be incremental or already defined - WNT-CMG2 is not my field of research). This study already makes a meaningful contribution to the field as I state above. But in the absence of new experimentation, the issue should probably be discussed in greater depth.

      We are working out conditions to grow colon organoids that from WT and Cmg2 KO mice, indeed playing around with the concentrations of Wnt in the various media to identify those that would best mimic the regeneration conditions. This is indeed a study in itself. We have however included a discussion on this point in the manuscript as suggested.

      __Reviewer #3: __

      We thank the reviewer for her/his insightful comments.

      The premise is that the causative germline mutated gene, CMG2/ANTRX2, may have a functional role in colonic epithelium in addition to controlling the ECM composition. There is little background information but one study has shown no primary defect in epithelial organoids grown from patients with the syndrome. This leads the authors to wonder if non-homeostatic, conditions might reveal a function role for the gene in regeneration.

      Reviewer 2 commented on the fact that “human organoids studied in the van Rijin paper, like all others, are established and cultured in very high WNT conditions, perhaps obscuring the lack of the CMG2-LRP6-WNT interaction. And in fact, the phenotypes of these human CMG2-KO duodenoids were not entirely normal - the CMG2-KO stem-like organoids (even when cultured in high WNT/R-spondin conditions) developed abnormal intercellular blisters consistent with a defect in epithelial structure/function - of unknown cause and not investigated”.

      We have now added a discussion on this point in the manuscript.

      The authors' approach to test the hypothesis is to use a mouse germline knockout model and to induce colitis and regeneration by the established protocol of introducing dextran sodium sulfate (DSS) into the drinking water for five days. In brief there is no phenotype apparent in the untreated knockout (KO) but these animals show a more severe response to DSS that requires them to be killed by 10 days after the start of treatment. This effect following phenotypic characterisation of the colonic epithelium is interpreted as showing the CMG2 is a Wnt modifier required for the restoration of the intestinal stem cell population in the final stages of repair.

      The experiment and analysis seem reasonably well executed - although a few specific comments follow below. The narrative is simple and easy to understand. However, there are significant caveats that cast doubts on the interpretation made that loss of CMG2 impairs the transition of colonic epithelial cells from a fetal like state to adult ISCs.

      First there is only a single approach and single type of experiment performed. There is a lack of independent validation of the phenotype and how it is mediated.

      We do not fully understand what type of independent validation of the phenotype the reviewer would have liked to see. Is it the induction of intestinal damage using a stress other than DSS?

      The DSS dose in this kind of experiment is often determined empirically in individual units. Here the 3% used is within published range but at upper end. The control animals show a typical response with symptoms of colitis worsening for 2-3 days after the removal of DSS and then recovery commonly over another 5-7 days. Here the CMG2 KO mice fail to recover and are killed by 9 or 10 days. The authors attempt to exploit the time course by identifying normal initial (7days) and defective late (10days) repair phases in KO animals when compared to controls. It is from this comparison that conclusions are drawn. However, the alternative interpretation might be that the epithelium of KO animals is so badly damaged, and indeed non-existent (from viewing Fig2a), that it is incapable of mounting any other response other than death and that the profiling shown is of an epithelium in extremis. The repair capability and dynamics of the KO would have been better tested under more moderate DSS challenge, if this experiment had been regarded as a pilot rather than as definitive.

      The choice of 3% DSS was in fact based on a pilot experiment. As now shown in Fig. S4, we tested different concentrations and found that 3% DSS was the lowest concentration that reliably induced the full spectrum of colitis-associated symptoms, including significant body weight loss, diarrhea, rectal bleeding (summarized in the Disease Activity Index), as well as macroscopic signs such as colon shortening and spleen enlargement. Based on these criteria, we selected 3% DSS for the study described in the manuscript.

      In this model, WT mice showed a typical progression: body weight stabilized rapidly after DSS withdrawal, with resolution of diarrhea and rectal bleeding. Histological analysis at day 9 revealed signs of epithelial regeneration, including hypertrophic crypts and increased epithelial proliferation.

      In contrast, Cmg2KO mice failed to initiate this recovery phase. Clinical signs such as weight loss, diarrhea, and bleeding persisted after DSS withdrawal, ultimately necessitating euthanasia at day 9–10 due to humane endpoint criteria. Unfortunately, this prevented us from exploring later timepoints to determine whether regeneration was delayed or completely abrogated in the absence of Cmg2.

      Regarding the severity of epithelial damage, as raised by Reviewer 1, we now provide detailed histological scoring in the supplementary data. This analysis shows that the severity of inflammation and crypt damage was similar between WT and KO animals, as were inflammatory markers such as Lipocalin-2. The key difference lies in the extent of tissue involvement. While the lesions in WT mice were more localized, Cmg2KO mice displayed widespread and diffuse damage with no sign of regeneration as shown by the absence of hypertrophic crypts and a marked reduction in both epithelial coverage and proliferative cells. Importantly, at day 7, the percentage of epithelial and proliferating cells was comparable between genotypes, further supporting the idea that Cmg2KO mice failed to initiate this recovery phase and present a defective repair response.

      The animals used were young (8 weeks) and lacked any obvious defect in collagen deposition. Does this change with treatment? Even if not, is it possible that there is a defect in peristalsis or transit time of gut contents, resulting in longer dwell times and higher effective dose of DSS to the KO epithelium?

      Collagen deposition, particularly of collagen VI, is known to increase in response to intestinal injury and plays a critical role in promoting tissue repair following DSS-induced damage (Molon et al., PMID: 37272555). As suggested, we investigated whether Cmg2KO mice exhibit abnormal collagen VI accumulation following DSS treatment.

      Our results show that, consistent with published data, WT mice exhibit a marked increase in collagen VI expression during the acute phase of colitis, with levels returning toward baseline following DSS withdrawal. A similar expression pattern was observed in Cmg2KO mice, with no significant differences in Col6a1 mRNA levels between WT and KO animals throughout the entire time course of the experiment. This observation was further confirmed at the protein level by western blot and immunohistochemistry analyses, suggesting that the impaired regenerative capacity observed in Cmg2KO mice is independent of Collagen VI.

      Regarding the possibility of altered peristalsis or intestinal transit time contributing to increased DSS exposure in KO mice, this is indeed a possibility. Although we did not directly measure gut motility in this study, we did not observe any signs of intestinal obstruction or fecal retention in Cmg2KO mice. Indeed, during the experiment, animals were single caged for 30min in order to collect feces and no difference in the amount of feces collected was observed between WT and KO mice, arguing against a substantial difference in transit time (see figure below). The possible altered peristalsis and these observations are now mentioned in the discussion.

      Is CMG2 RNA and protein expressed in the colonic epithelium? It is not indicated or tested in the submitted manuscript. This reviewer struggled to find evidence, notably it did not seem to be referenced in the organoid paper they reference in introduction (ref 13).

      This very valid point was also raised by Reviewers 1 and 2. The expression pattern of Cmg2 in the gut has indeed not been characterized and is essential to understanding its function. To address this gap, we added a figure (Fig. 1) providing data from publicly available RNA-seq datasets and from our RNAscope experiments on Cmg2WT mice. Of note, we unfortunately have never managed to detect Cmg2 protein expression by immunohistochemistry of mouse tissue with any of the antibodies available, commercial or generated in the lab.

      __Specific comments: __

      Figure 3 c-e and associated text are confusing. In c the Y scale seems inappropriate to show percentages up to 15,000%.

      In this graph values are normalized to homeostatic level of WT mice which represent 100%

      In d and e the use of percentages may by correct. However, it is claimed in text that Cty61 and CTFG are upregulated in the KO. That is not what the plots appear to show as the compare to WT untreated cells, in which case the KO have not downregulated these genes in the way the controls have.

      As clarified in the text, under regenerative conditions, a transient activation of YAP signaling is crucial to induce a fetal-like reversion of intestinal stem cells. However, in a subsequent phase, the downregulation of YAP and the reactivation of Wnt signaling are necessary to complete intestinal regeneration. Several studies have highlighted a strong interplay between the Wnt and YAP pathways, suggesting that their coordinated regulation is essential for effective gut repair. Nevertheless, the precise mechanisms governing this interaction remain incompletely understood.

      In our model, this critical transition—YAP downregulation and Wnt reactivation—appears to be impaired. CMG2 may either hinder Wnt reactivation directly, or lead to sustained YAP signaling, which in turn suppresses activation of the Wnt pathway. Further studies, using in-vivo model and organoid models, will be necessary to understand the mechanistic role of Cmg2 in this regulatory process.

      A precision of the figure has been updated as followed: both of which were significantly upregulated in the injured colons of Cmg2KO mice compared to DSS-injured Cmg2WT mice

      __**Referees cross-commenting** __

      Rev2 Points 1 and 2 made by Referee 1 (and point 4 of Referee 3) appear most reasonable, and if not already done should be.

      We have indeed addressed these 2 points.

      I also noted the more severe morphology of DSS damaged epithelium shown in Fig 2a noted by Referee 3 - and this I agree is a confounding factor. […] For my part, the concern is understandable but likely not operating in a confounding way. And the evidence for the reprogramming of the damaged epithelium into "fetal-like stem cells" (the 1st step in restitution of lost stem cells) occurs in both WT and KO mice - and these data are strong. For this reader, the block convincingly shows up for KO mouse at the WNT dependent step

      The representative image has been updated, and a transverse section has been added to better illustrate that, although both epithelium and crypt structures can be present, the epithelial morphology differs significantly. Indeed, the regenerating epithelium of Cmg2WT mice displays a thick epithelial layer with well-polarized epithelial cells, whereas in cmg2KO mice, the epithelium appears atrophic, characterized by a thinner epithelial layer and elongated epithelial cells.

      __Rev 3 __

      This reviewer remains sceptical. I agree the authors performed the experiment well to confirm that DSS dosing was as equivalent as possible across the study. But DSS acts to induce colitis because it is concentrated in the colonic lumen as water is absorbed. Also ECM responses and remodelling are a central part of colitis models. And my concern is that the actual exposure in the KO group is influenced by transit of faeces/DSS is secondary to the known action of CMG2 on collagen deposition. The consequence of this being a protracted damage phase in which a restoration of adult stem cells would not be expected and leading to epithelial failure.

      However, we differ. I might propose that the authors are asked to investigate and confirm expression of CMG2 in the epithelium and to repeat the analysis of collagen levels they performed on untreated CMG2 KO mice on colons from CMG2 KO mice having received DSS to see if these differ from controls.

      This has now been done.

      __Rev 1 __

      Both reviewer #2 and reviewer #3 make relevant points, from the point of view of extracting as much biological knowledge as we can from the observations reported in the manuscript.

      Reviewer #2 suggestion to use Cmg2[KO] organoids to investigate the dependence of Wnt transduction on Cmg2 is the type of experiments I refrained to propose. However, I think the "skeleton" of the mechanism is there and is reasonably solid. Fleshing it out may well be another paper.

      I agree with Reviewer #3 objections to the timing and severity of the DSS damage. However, I am not sure how much they invalidate the main tenet of the paper:

      • DSS may affect Cmg2[KO] more severely, but the overall disease score is comparable during the DSS treatment. If this severity was enough to be the main driver of the phenotype, it should have left a mark in the Histological and Disease activity scores. In this regard, I think it would be helpful if the authors provided an expanded version of Figure 2A with examples of the different levels of "Crypt damage" scored, and the proportions for each. This could be in the supplementary material and would balance the impressions induced by a single image.

      As suggested, we included a detail of histological score including the crypt damage score in Supplementary Fig 3i showing no significant differences in crypt damage between Cmg2WT and Cmg2KO mice.

      • If DSS affected the recovery, this would also be compatible with having a more severe histological phenotype (which is not shown overall, just in Fig 2A) because one would also expect the tissue to attempt regeneration during the 7 days of DSS treatment.

      This is an interesting point, and we now allude to this aspect in the manuscript.

      • The only objection that I find difficult to argue is the effective duration of the treatment. If indeed peristalsis is affected, it may be that during the 'recovery' phase there is still DSS in the intestine. This could be perhaps verified using a DS detection assay (e.g. https://arxiv.org/pdf/1703.08663) on the intestinal contents or the faeces of the mice during the 3-day recovery period.

      We have attempted to obtain and purchase Heparin Red to perform this assay. Unfortunately, we have not obtained the reagent, which has never been delivered. We now also mention the following in the Discussion:

      One could envision that Cmg2KO mice have a defect in peristalsis resulting in longer dwell times and possibly higher effective dose of DSS to the KO epithelium. We however did not observe any signs of intestinal obstruction or fecal retention in Cmg2KO mice. Animals were single-caged for 30 min to collect feces. We did not observe any difference in amounts collected from WT and KO mice, arguing against a substantial difference in transit time of gut contents. Moreover, if DSS affected the recovery, one would have expected a more severe histological phenotype in the colon of Cmg2KO since the tissue likely already attempts regeneration during the 7 days of DSS treatment. But this was not the case. Therefore, while we cannot formally rule out the presence of residual DSS in Cmg2KO mice during the DSS withdrawal phase, there is currently no indication that this was the case.

      I think of what the aim of scholarly publication is, with this paper, and I find myself going back to a statement of the authors' discussion - that this work suggests that infants risking death may be offered (compassionate, I guess) IBD treatment. What does this hinge upon? I think, on the basic observation that diarrhoea (in the mouse model) is not intrinsic but caused by an inflammation-promoting insult. Is this substantiated? I think it is. Could we learn more biology from this disease model, about Wnt and about how ECM affects tissue regeneration? Certainly. Can this learning wait? I believe it can.

      We thank the reviewer for this statement.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript has a good rationale in trying to understand why infants with an inherited condition, Hyaline Fibromatosis Syndrome, that is primarily associated with turnover and deposition of extracellular collagen also develop severe diarrhoea that can contribute to their premature death. The premise is that the causative germline mutated gene, CMG2/ANTRX2, may have a functional role in colonic epithelium in addition to controlling the ECM composition. There is little background information but one study has shown no primary defect in epithelial organoids grown from patients with the syndrome. This leads the authors to wonder if non-homeostatic, conditions might reveal a function role for the gene in regeneration.

      The authors' approach to test the hypothesis is to use a mouse germline knockout model and to induce colitis and regeneration by the established protocol of introducing dextran sodium sulfate (DSS) into the drinking water for five days. In brief there is no phenotype apparent in the untreated knockout (KO) but these animals show a more severe response to DSS that requires them to be killed by 10 days after the start of treatment. This effect following phenotypic characterisation of the colonic epithelium is interpreted as showing the CMG2 is a Wnt modifier required for the restoration of the intestinal stem cell population in the final stages of repair.

      The experiment and analysis seem reasonably well executed - although a few specific comments follow below. The narrative is simple and easy to understand. However, there are significant caveats that cast doubts on the interpretation made that loss of CMG2 impairs the transition of colonic epithelial cells from a fetal like state to adult ISCs.

      Significance

      1. First there is only a single approach and single type of experiment performed. There is a lack of independent validation of the phenotype and how it is mediated.
      2. The DSS dose in this kind of experiment is often determined empirically in individual units. Here the 3% used is within published range but at upper end. The control animals show a typical response with symptoms of colitis worsening for 2-3 days after the removal of DSS and then recovery commonly over another 5-7 days.

      Here the CMG2 KO mice fail to recover and are killed by 9 or 10 days. The authors attempt to exploit the time course by identifying normal initial (7days) and defective late (10days) repair phases in KO animals when compared to controls. It is from this comparison that conclusions are drawn.

      However, the alternative interpretation might be that the epithelium of KO animals is so badly damaged, and indeed non-existent (from viewing Fig2a), that it is incapable of mounting any other response other than death and that the profiling shown is of an epithelium in extremis. The repair capability and dynamics of the KO would have been better tested under more moderate DSS challenge, if this experiment had been regarded as a pilot rather than as definitive. 3. The animals used were young (8 weeks) and lacked any obvious defect in collagen deposition. Does this change with treatment? Even if not, is it possible that there is a defect in peristalsis or transit time of gut contents, resulting in longer dwell times and higher effective dose of DSS to the KO epithelium? 4. Is CMG2 RNA and protein expressed in the colonic epithelium? It is not indicated or tested in the submitted manuscript. This reviewer struggled to find evidence, notably it did not seem to be referenced in the organoid paper they reference in introduction (ref 13).

      Specific comments:

      Figure 3 c-e and associated text are confusing. In c the Y scale seems inappropriate to show percentages up to 15,000%. In d and e the use of percentages may by correct. However, it is claimed in text that Cty61 and CTFG are upregulated in the KO. That is not what the plots appear to show as the compare to WT untreated cells, in which case the KO have not downregulated these genes in the way the controls have.

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      Referee #2

      Evidence, reproducibility and clarity

      The paper uses mice lacking Capillary Morphogenesis Gene 2 (CMG2- KO) mice to investigate the pathogenic mechanism underlying the protein losing enteropathy seen in children with severe Hyaline Fibromatosis Syndrome. Significance of the work is further enhanced as the intestinal phenotype induced by CMG2-KO provided a model system (with robust validated tools) for testing newly emerging (and paradigm shifting) ideas in mechanisms of tissue regeneration after injury - generalizable to tissue restitution beyond the intestine.

      The study shows that in the mouse colon CMG2 plays a critical role in recovery from mucosal/epithelial damage chemically induced by dextran-sulfate-sodium (DSS). Mice lacking CMG2 failed to recover from DSS colitis with no evidence for restitution of the DSS-damaged epithelium. WT mice recovered after DSS removal.

      The first step in restitution of epithelial damage in the intestine, when the epithelial stem-cell populations are depleted as in this model of DSS colitis, occurs by the transformation of surviving differentiating/differentiated epithelial cells back into a stem-cell-like (fetal-cell-like) state. This step in the process was found to occur normally in the CMG2 KO mouse. The block in restitution was located to the step where de-differentiated (fetal-cell-like) colonocytes are induced back into their WNT-dependent proliferative state - thus replenishing the normally proliferating stem (LGR5+) cells of the colonic crypt. The reason for this failure is explained by a defect in WNT signaling in the injured colons of CMG2 KO mice, as assessed by failure of -catenin translocation into the nucleus of barrier epithelial cells - a down-stream effect of WNT signaling and consistent with the dependence on CMG2 for WNT signaling in other experimental systems.

      The study is overall well designed, meticulously carried out, and with clear and convincing results that are most reasonably and thoughtfully interpreted. The paper makes a meaningful contribution to the field. It models an experiment of nature to test, delineate, and verify disease pathogenesis and a newly revised mechanism for mucosal tissue repair.

      For this reader, one additional thought comes to mind. If I understand the field correctly it would be informative to know with greater confidence where - in what cell type, epithelial or mesenchymal - the CMG2-LRP6-WNT interaction occurs.

      After injury the CMG2-KO mouse epithelium exhibits defective WNT signal transduction - as evidenced by failure of -catenin to translocate into the nucleus. At first glance, this result is a disconnect with the paper by van Rijin that claims the defect in Hyaline Fibromatosis Syndrome cannot be due to loss of CMG2 expression/function in the barrier epithelial cell - a claim based on the mostly normal phenotypes of human CMG2 KO duodenal organoids. But the human organoids studied in the van Rijin paper, like all others, are established and cultured in very high WNT conditions, perhaps obscuring the lack of the CMG2-LRP6-WNT interaction. And in fact, the phenotypes of these human CMG2-KO duodenoids were not entirely normal - the CMG2-KO stem-like organoids (even when cultured in high WNT/R-spondin conditions) developed abnormal intercellular blisters consistent with a defect in epithelial structure/function - of unknown cause and not investigated.

      I think it would be informative to prepare colon organoids (and duodenoids) from WT and CMG2-KO mice to quantify their WNT dependency during establishment and maintenance of the stem-like (and WNT-dependent) state. If CMG2 acts within the epithelial cell to affect WNT signaling (regardless of WNT source), organoids prepared from colons of CMG2-KO mice would require more WNT in culture media to establish and maintain the stem cell proliferative state - when compared to organoids prepared from WT mice. This can be quantified (and confirmed molecularly by transgene expression if successful). Enhanced dependency of high concentrations of exogenous WT would be evidence for a primary defect in WNT-(LRP2)-CMG2 signal transduction localized to the epithelial barrier cell - thus addressing the apparent discrepancy with the van Rijin paper - and for my part, advancing the field. And the discovery of a defect in the epithelium itself for WNT signal transduction would implicate a biologically most plausible mechanism for development of protein losing enteropathy.

      By no means do I consider these experiments to be required for publication (especially if considered to be incremental or already defined - WNT-CMG2 is not my field of research). This study already makes a meaningful contribution to the field as I state above.

      But in the absence of new experimentation, the issue should probably be discussed in greater depth.

      Significance

      The study is overall well designed, meticulously carried out, and with clear and convincing results that are most reasonably and thoughtfully interpreted. The paper makes a meaningful contribution to the field. It models an experiment of nature to test, delineate, and verify disease pathogenesis and a newly revised mechanism for mucosal tissue repair.

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      Referee #1

      Evidence, reproducibility and clarity

      In this work, Bracq and colleagues provide clear evidence that the persistent diarrhoea seen in a mouse model of Hyaline Fibromatosis Syndrome is related to the inability of their intestinal epithelium to properly regenerate. This is very clear and of immediate impact. This aspect of the paper, I think, is ready for publication, and would merit immediate dissemination on its own. It is great that the manuscript is in bioRxiv already.

      I am not so thoroughly convinced about the mechanism that the author propose to explain the incapacitation of Cmg2[KO] intestinal stem cells to function properly. The authors propose that it is due to their inability to transduce Wnt signals, and while this is plausible, I think there are few things that the paper should contain before this can be proposed firmly:

      Point #1

      The mouse mutant is just described as 'KO', referring to the previous work by the authors. The cited work simply states that this is a zygotic deletion of exon 3, which somehow leads to a decrease in protein abundance that is almost total in the lung but not so clear in the uterus. Exon 3 happens to be 72 bp long [https://www.ncbi.nlm.nih.gov/nuccore/NM_133738], so its deletion (assuming there are no cryptic splicing sites used) leads to an internal in-frame deletion of 24 amino acids. So, at best, this 'KO' is not a null, but a hypomorphic allele of context-dependent strength. Unfortunately, neither the previous work nor this paper (unless I have missed it!) contains information provided about the expression levels of Cmg2 in the intestine of KO mice - nor which cell types usually express it (see below). I think that using anti Cmg2 in WB and immunohistofluorescence of with ISC markers with intestine homogenate/sections of wild-type and mutant mice would be necessary to set the stage for the rest of the work.

      Point #2

      Connected to the previous point, the expression pattern of Cmg2 in the intestine is not described. Maybe this is already established in the literature, but the authors do not refer to the data. This is important when considering that the previous work of the authors suggests that Cmg2 might contribute to Wnt signalling transduction through physical, cis interactions with the Wnt co-receptor LRP6. Therefore, one would expect that Cmg2 would be cell-autonomously required in the intestinal stem cells.

      Point #3

      The authors establish that the regenerating crypts of Cmg2[KO] mice are unable to transduce Wnt signalling, but it is not clear whether this situation is provoked by the DSS-induce injury or existed all along. Can Cmg2[KO] intestinal stem cells transduce Wnt signalling before the DSS challenge? If they were, it might suggest that the 'context-dependence' of the Cmg2 role in Wnt signalling is contextual not only because of the tissue, but because of the history of the tissue or its present structure. It would also suggest that Cmg2 mutant mice, unless reared in a germ-free facility for life, would eventually lose intestinal homeostasis, and maybe suggest the level of intervention/monitoring that HFS patients would require. It might also provide an explanation in case Cmg2 was not expressed in ISCs - if the state of the tissue was as important as the presence of the protein, then the effect on Wnt transduction could be indirect and therefore it might not be required cell-autonomously.

      I think points 1 and 2 are absolutely fundamental in a reverse genetics investigation. Point 3 would be nice to know but the outcome would not change the tenet of the paper. I believe that the work needed to deal these points can be performed on archival material. I do not think the mechanism proposed can be taken from 'plausible' to 'proven' without proposing substantial additional investigation, so I will not suggest any of it, as it could well be another paper.

      A few minor points picked along the way:

      1. Figure 1 legend says "In (c), results are mean {plus minus} SEM" - this seems applicable to (d) as (c) does not show error whiskers.
      2. Figure 1 legend says "(d) Body weight loss, (f) the aspect of the feces and presence of occult blood were monitored and used for the (e) DAI. Results are mean {plus minus} SEM. Each dot represents the mean of n = 12 mice per genotype". This part looks like has suffered some rearrangement of words. The first instance of (f) should be (e), I guess, and I am not sure what "(e) DAI" means. And for (e), "mean {plus minus} SEM" does not seem applicable. This needs some light revision.
      3. Figure 1H legend does not say which statistical test was made in the survival experiment in (h) - presumably log-rank? A further comment on the survival statistics: euthanised animals should not be counted towards true mortality when that is what is recorded as an 'event'. They should be right-censored. However, in this case, reaching the euthanasia criterion is just as good an indicator of health as mortality itself. So, simply by changing the Y axis from 'survival' to 'event-free survival' (or something to that effect), where 'events' are either death or reaching the euthanasia criterion, leaves the analysis as it is, and authors do not need to clarify that figure 1H shows "apparent mortality", as it is straightforward "complication-free survival" (just not entirely orthogonal to weight loss).
      4. Some density measurements are made unnecessarily on arbitrary units (per field of view) - this should be simple to report in absolute measures (i.e. area of tissue screened or, better still, length of epithelium screened).
      5. Figure 2E should read "percent involvement"
      6. Figure 2J should read "lipocalin..."
      7. In section "CMG2 Is Dispensable for YAP/TAZ-Mediated Reprogramming to Fetal-Like Stem Cells", the authors write ""We measured the mRNA levels of two additional YAP target genes, Cyr61 and CTGF...". I presume the "additional" is because Ly6a is also a target of YAP/TAZ, but if the reader does not know, it is puzzling. I would suggest to make this link explicit.
      8. In Figures S2, 3 and S3, I think that the measures expressed as "% of homeostatic X in WT" really mean "% of average homeostatic X in WT". This should be made clear somewhere.
      9. In panel C, the nature of the data is not entirely clear. First, the corresponding part of the legend says "Representative images of n=4 mice per genotype" which I presume should refer to panel B. Then, the graph plots 4 data points, which suggests that they correspond to 4 mice - but how many fields of view? Also, the violin plot outline is not described - I presume it captures all the data points from the coarse-grained pixel analysis, but it should be clarified.
      10. In Figure 3H and 3I, I would suggest to add the 7+3 timepoint where the data come from.
      11. In section "CMG2 Is Critical for Restoring the Lgr5+ Intestinal Stem Cell Pool", the authors say "...The mRNA levels of ... LRP6, β-catenin (Fig. S3a-b), and Wnt ligands (Wnt5a, 5b, and 2b) were comparable between the colons of Cmg2WT and Cmg2KO mice (Fig. S3c)..." without clarifying in which context - one needs to read the figure legend to realise this is "timepoint 7+3". I suggest to add "in the recovery phase" or "in regenerating colons" or something shorter, just to guide the reader.
      12. Like with the previous point, it is not clear when the immunohistofluorescence of B-catenin is made - not even in the legend, as far as I could see. The only hint is that authors say "the nuclei of cells in the atrophic crypts of Cmg2KO..." with 'atrophic' probably indicating again the 7+3 timepoint.
      13. A typo in the discussion: tunning for tuning.
      14. In the discussion, the authors talk about the 'CMG2' protein (all caps - formatting convention for human proteins) but before they were referring to 'Cmg2' (formatting convention for mouse proteins). That is fine but some of the statements where "CMG2" is used clearly refer to observations made in the mouse.
      15. Typos in methods: "antigen retrieval by treating [with] Proteinase K"; "Image acquisition and analyze [analysis]"; "All details regarding code[s] used for immunofluorescence analysis"

      Referees cross-commenting

      *this session contains comments from ALL the reviewers"

      Rev2

      Points 1 and 2 made by Referee 1 (and point 4 of Referee 3) appear most reasonable, and if not already done should be.

      I also noted the more severe morphology of DSS damaged epithelium shown in Fig 2a noted by Referee 3 - and this I agree is a confounding factor. But overall, multiple lines of evidence were assembled to show that the KO mice and WT mice suffered DSS-induced colitis with equal severity - and with closely equal severity of damage to the intestinal epithelium (though the image in Fig 2a is disturbing). For my part, the concern is understandable but likely not operating in a confounding way. And the evidence for the reprogramming of the damaged epithelium into "fetal-like stem cells" (the 1st step in restitution of lost stem cells) occurs in both WT and KO mice - and these data are strong. For this reader, the block convincingly shows up for KO mouse at the WNT dependent step

      Rev 3 This reviewer remains sceptical. I agree the authors performed the experiment well to confirm that DSS dosing was as equivalent as possible across the study. But DSS acts to induce colitis because it is concentrated in the colonic lumen as water is absorbed. Also ECM responses and remodelling are a central part of colitis models. And my concern is that the actual exposure in the KO group is influenced by transit of faeces/DSS is secondary to the known action of CMG2 on collagen deposition. The consequence of this being a protracted damage phase in which a restoration of adult stem cells would not be expected and leading to epithelial failure.

      However, we differ. I might propose that the authors are asked to investigate and confirm expression of CMG2 in the epithelium and to repeat the analysis of collagen levels they performed on untreated CMG2 KO mice on colons from CMG2 KO mice having received DSS to see if these differ from controls.

      Rev 1 Both reviewer #2 and reviewer #3 make relevant points, from the point of view of extracting as much biological knowledge as we can from the observations reported in the manuscript.

      Reviewer #2 suggestion to use Cmg2[KO] organoids to investigate the dependence of Wnt transduction on Cmg2 is the type of experiments I refrained to propose. However, I think the "skeleton" of the mechanism is there and is reasonably solid. Fleshing it out may well be another paper.

      I agree with Reviewer #3 objections to the timing and severity of the DSS damage. However, I am not sure how much they invalidate the main tenet of the paper:

      • DSS may affect Cmg2[KO] more severely, but the overall disease score is comparable during the DSS treatment. If this severity was enough to be the main driver of the phenotype, it should have left a mark in the Histological and Disease activity scores. In this regard, I think it would be helpful if the authors provided an expanded version of Figure 2A with examples of the different levels of "Crypt damage" scored, and the proportions for each. This could be in the supplementary material and would balance the impressions induced by a single image.

      • If DSS affected the recovery, this would also be compatible with having a more severe histological phenotype (which is not shown overall, just in Fig 2A) because one would also expect the tissue to attempt regeneration during the 7 days of DSS treatment.

      • The only objection that I find difficult to argue is the effective duration of the treatment. If indeed peristalsis is affected, it may be that during the 'recovery' phase there is still DSS in the intestine. This could be perhaps verified using a DS detection assay (e.g. https://arxiv.org/pdf/1703.08663) on the intestinal contents or the faeces of the mice during the 3-day recovery period.

      I think of what the aim of scholarly publication is, with this paper, and I find myself going back to a statement of the authors' discussion - that this work suggests that infants risking death may be offered (compassionate, I guess) IBD treatment. What does this hinge upon? I think, on the basic observation that diarrhoea (in the mouse model) is not intrinsic but caused by an inflammation-promoting insult. Is this substantiated? I think it is. Could we learn more biology from this disease model, about Wnt and about how ECM affects tissue regeneration? Certainly. Can this learning wait? I believe it can.

      Significance

      In this work, Bracq and colleagues provide clear evidence that the persistent diarrhoea seen in a mouse model of Hyaline Fibromatosis Syndrome is related to the inability of their intestinal epithelium to properly regenerate. This is very clear and of immediate impact. For instance, the authors themselves point at the possibility of applying treatments for Inflammatory Bowel Disease to HFS patients. While what happens in a mouse model is not necessarily the same as in human patients, the fact that persistent diarrhoea is a life-threatening symptom in HFS make this proposal, at least in compassionate use of the therapies and until its efficacy is disproven, very plausible. This is a clear gap of knowledge that addresses an unmet medical need.

      I find that the work shows clearly that HFS mouse model subjects have normal intestinal function until challenged with a standard chemically-induced colitis. Then, the histological and health deterioration of the HFS mouse model is clear in comparison with normal mice, which can regenerate appropriately. This is shown with a multiplicity of orthogonal techniques spanning molecular, histological and organismal, which are standard and very well reported in the paper.

      The authors propose a specific cellular and molecular mechanism to explain the incapacity of the intestinal epithelium in the mouse model of HFS to regenerate. According to this mechanism, the protein Cmg2, whose mutation causes HFS in humans, would be necessary for intestinal stem cells to transduce the signal of Wnt ligands and therefore support their behaviour as regenerative cells. This mechanism is plausible, but more basic and advanced work would be needed to take it as proven.

      This work would be of interest to both the clinical, biomedical, and basic research communities interested in rare diseases, the gastrointestinal system, collagen and extracellular matrix, and Wnt signalling.

      My general expertise is in developmental and stem cell biology using reverse genetics, transgenesis and immunohistological and molecular methods of data production, and lineage tracing, digital imaging and bioinformatic analytical methods; I work with Drosophila melanogaster and its adult gastrointestinal system.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time

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      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript entitled " The hepatitis E virus capsid protein ORF2 counteracts cell-intrinsic antiviral responses to enable persistence in hepatocytes ", Ann-Kathrin Mehnert interrogated that HEV pORF2 can inhibit host antiviral response. They found interaction of HEV ORF2 and TBK1. The finding is interesting and echoed with some previous studies that ORF2 can inhibit innate immunity.

      The study could benefit from the consideration of some major and specific points, as indicated below:

      Major issues:

      1. The researchers used p6, a cell-adapted clone, which was isolated form a chronic HEV patient. As previous studies suggested, p6 may behave differently than wild-type strains. Did the authors tried other HEV strains, as they used ips-induced model that was reported supportive to wild-type HEV?
      2. Figure 1F, ORF2 can interact with TBK1 as showed. But the prediction from Alphafold is weak. Also, could the author more evidence than the co-IP?
      3. Figure 2C and 2D, at 5 dpi, one can observed a stronger antiviral response, but at 7 dpi, no obvious difference was observed. Could the authors comment on this? 4.Figure 2H and 2I, detailed description of how the authors measured the positive cells should be provided. Did the authors selected whole plate of cells for counting? As showed in Figure 2H, the signals of IF were stronger at 5 and 7 dpi when compared at 3 dpi, but why the proportion of positive cells was reduced in Figure 2I?
      4. The study emphasized the function of ORF2 on HEV "persistence". However, this cannot be fully supported by cell models. In future, study on chronic HEV infection animal models may be conducted.
      5. The authors study ORF2 in whole. It will be of benefit to the readers that the authors could specified the function of secreted ORF2 and ORF2 capsid in the current study.

      Minor issues:

      1. Figure 3A, this is an elegant design. More data may provide for the validation of the formation of the virions.
      2. Figure 1, data should be provided for the successful expression of HEV-1 or HEV-3 ORF2, and ORF3.
      3. line 219, the current evidence that supported this statement is weak, especially for ORF2.
      4. Suppl Figure 3F-3H, statistical analysis is needed
      5. Suppl Figure 3F-3H, it seems that when no treatment was admistrated, the level of ISG15 in ΔORF2 group was higher than those of the WT and ΔORF3 group. Could the authors comment?
      6. Figure 3D and 3E, the starting time of the detection is not aligned.
      7. Figure 3F, scale bar is missing.
      8. In M&M, statistical method should be provided with more details and cover all the experiments used.

      Significance

      In the manuscript entitled " The hepatitis E virus capsid protein ORF2 counteracts cell-intrinsic antiviral responses to enable persistence in hepatocytes ", Ann-Kathrin Mehnert interrogated that HEV pORF2 can inhibit host antiviral response. They found interaction of HEV ORF2 and TBK1. The finding is interesting and echoed with some previous studies that ORF2 can inhibit innate immunity.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: Authors described the protective mechanism mediated by ORF2 that protects viral replication from the antiviral responses. They have utilized the advanced single-cell RNA sequencing to decipher the dampened antiviral responses in the presence of ORF2 HEV. I believe the study is important for the HEV literature and believe that the manuscript can be considered for publication after authors (1) rewrites the results and discussions separately until the journal wants it to be together. (2) answer the below questions.

      Minor comments:

      Line 69, 71 - I have never seen in any paper including reference in this way!

      Line 72 and 73 - missing reference!

      Line 92, 93 - missing reference!

      Line 95 to 99 - missing references!

      Major comments:

      I would like the authors to answer few questions: 1. Did the authors study only the P6 HEV genome? Have they done anything comparative with the other strain to understand if the proposed mechanism is not the strain specific? 2. Can the authors explain why we do not see any band in the Fig. 1F B-actin?

      Significance

      The paper uses advanced technique as single cell RNA seq to understand the mechanism of ORF2 assisting in the HEV replication.

      The study is well designed.

      This study will add up to understand some of the persistence infection seen in solid organ transplant patients. This study gives a mechanistic overview of HEV avoidance of antiviral response.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, the authors investigated how HEV ORF2 interferes with host antiviral responses to sustain viral infection. They employed several models, including pattern recognition receptors (PRRs) KO cell lines, immunodeficient cells and stem cell-derived models, to prove that: 1) ORF2 is essential for viral replication and 2) ORF2 dampens the interferon and inflammatory signaling pathways. They confirmed the interaction between ORF2 and TBK1, a central mediator of innate immune responses and identified residues in ORF2 that affect its interaction with TBK1. Finally, through single cell RNAseq, they demonstrated that ORF2 is a viral antagonist that inhibits host ISG expression in both infected and bystander cells. Interestingly, the sets of genes that are upregulated in WT vs ORF2-deficient virus infected cells are not entirely identical, suggesting that ORF2 may also modulate host gene expression in addition to suppressing the immune response. This research confers new immune antagonism mechanisms mediated by HEV capsid for sustainable HEV replication in host cells and provides potential therapeutic targets for HEV treatments.

      Major comments:

      1. The authors conclude from Figure 1 that the HEV ORF2 protein antagonizes both antiviral and inflammatory signaling pathways. The authors comprehensively investigated PRRs-mediated activation of type I interferon by viruses or poly(I:C) through overexpression of MDA5, RIG-I and TLR3. However, they only investigated the impact of ORF2 on host inflammatory response through evaluating the levels of TNFAIP3 RNA in the presence of MDA5 overexpression. It would be informative if the authors also check for NFkB activation/phosphorylation and expression of classical pro-inflammatory cytokines such as IL-1b and IL6. Interestingly, changes in IFNB secretion after ORF2 overexpression appear more dramatic compared to changes in IFNB1 RNA levels (compare Figure 1A-C with Supplementary Figure 1A and C). Are the IFN-beta protein expression changes statistically significant in Supplementary Figure 1?
      2. Changes in the IFN response do not always translate into changes in the viral RNA levels. In Figure 2B-D, the authors attributed the higher induction of IFNL1 and ISG15 on day 5 to the absence of ORF2 inhibition. However, the expression of these two genes drops to the same levels as the ones in WT viral RNA-electroporated cells on day 7, which is strange as ORF2-deficient viral RNA levels continue to be inhibited on day 7. This is different from the stem cell derived hepatocytes infected with the trans-complementation viruses in Figure 3G-H where there are significant differences in ISG15 levels between WT and ORF2-deficient virus infected cells on both days 5 and 7. To support their hypothesis, the authors need to further confirm the sudden upregulated antiviral activity on day 5 in electroporated HepG2/C3A cells by testing JAK/STAT phosphorylation and type I interferon secretion.
      3. The authors used different hepatocyte systems coupled with viral RNA electroporation or trans-complementation virus infection to investigate ORF2-mediated interference of the IFN pathway, which is highly complementary. However, while the electroporation of viral RNA into HepG2/C3A (Figure 2B-D) and infection of stem cell-derived hepatocytes with trans-complementation viruses (Figure 3F-H) result in similar upregulation of ISG expression on day 5, that wasn't observed in HepG2/C3A cells infected with trans-complementation viruses (Figure 3C-E) on day 5. The authors need to discuss the discrepancy among these different systems. Since the ORF2-deficient trans-complementation virus still brings in ORF2 proteins from the producer cells but cannot generate new ORF2 proteins, do ORF2 proteins from these two different sources have different functions in different hepatocyte systems? In addition, other than the data points that are shown to be not significantly different in Figure 3D-E, are any of the other data points significantly different?
      4. The single cell RNAseq data are very informative and revealed two interesting groups of genes. First, the ISGs that are further induced in the cells infected with ORF2-deficient HEV compared to cells infected with WT HEV (Figure 4N) are likely suppressed by ORF2. Second, the ISGs that are uniquely induced in the absence of ORF2 are different from the genes that are uniquely induced by WT HEV (Supplementary Table 2), suggesting that ORF2 may also modulate host gene expression. The authors can further characterize these two groups of ISGs by performing gene knockdown or knockout and investigating whether ORF2 directly interacts with these ISG products to determine the functional consequences of their upregulation. Related to that, are there other gene expression changes beyond ISG signatures which would suggest that ORF2 can regulate host gene expression? Figure 4A-C only shows comparisons for WT or ORF2-deficient vs. uninfected cells. The authors can perform GO and KEGG analyses to see if certain biological processes/pathways are enriched among the WT vs ORF2-deficient HEV induced genes. Further characterization of these genes (ISGs or not) would shed light on the novel roles of ORF2 in both immune antagonism and gene regulation and greatly increase the significance of the study.
      5. In Supplementary Figure 3F-H, the authors used BX795 to inhibit TBK1 (a target of ORF2) and found decreases in IFNL1 and ISG15 expression whether cells are electroporated with WT, ORF2-deficient, or ORF3-deficient viral RNA. However, this does not correlate with the data in Figure 2E-G where TBK1 inhibition results in significant differences in viral RNA levels only in the absence of ORF2 or ORF3. These results would suggest that the effects of TBK1 inhibition on viral RNA levels is independent of changes in the IFN/ISG expression levels.

      Significance

      The study addresses a long-standing question in the field about the immune antagonism activities of HEV ORF2 and ORF3 which previous studies have conflicting results on. The strength of this study is the use of complementary approaches such as ORF2 trans complementation system and single cell sequencing, and more relevant models such as stem cell derived hepatocytes to rigorously dissect the role of newly synthesized ORF2 protein in immunocompetent cell context. The manuscript is well written and would appeal to researchers in the HEV and innate immunity fields. However, the significance of the study is dampened by changes in the IFN response not always correlate with the inconsistency of ORF2-mediated inhibitory effects in different models and the still poorly defined mechanism of ORF2 suppression of the IFN pathway. The study would make conceptual advance if the authors can address the discrepancies in their findings and perform additional characterization to determine the functional consequences of ORF2-mediated immune suppression and gene regulation.

      My expertise is in innate immunity and host-virus interactions.

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      Reply to the reviewers

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)): __ Summary In this work, the authors present a careful study of the lattice of the indirect flight muscle (IFM) in Drosophila using data from a morphometric analysis. To this end, an automated tool is developed for precise, high-throughput measurements of sarcomere length and myofibril width, and various microscopy techniques are used to assess sub-sarcomeric structures. These methods are applied to analyze sarcomere structure at multiple stages in the process of myofibrillogenesis. In addition, the authors present various factors and experimental methods that may affect the accurate measurement of IFM structures. Although the comprehensive structural study is appreciated, there are major issues with the presentation/scope of the work that need to be addressed: Major Comments 1. The main weakness of the paper is in its claim of presenting a model of the sarcomere. Indeed, the paper reports a structural study that is drawn onto a 3D schematic. There is no myofibrillogenesis model that would provide insights into mechanisms. Therefore, the use of the word model is grossly overstated.

      In biology, the term “model” is used in various contexts, but it generally refers to a simplified representation of a biological system, a structure or a process. Accordingly, we consider “model” the most fitting phrase for what we present in Figure 4 (Figure 7 in the revised manuscript). These are not arbitrary 3D schematics; they are scaled representations in which the length, the number and the relative three-dimensional arrangement of thin and thick filaments are based on measurements. These measurements are primarily based on our own data (presented in the main text and provided in the supplementary materials), as published data were either lacking or inconsistent. Moreover, we would like to highlight that we do not claim to present a conceptual or mechanistic model of myofibrillogenesis, but we do present structural reconstructions or models for four developmental time points. Therefore, we disagree with the remark that “the use of the word model is grossly overstated”, as our wording fully corresponds to the common sense.

      In general, the major focus and contribution of the work is unclear. How does the comprehensive nature of the measurements contribute to existing literature?

      We significantly revised the text to highlight the main points more firmly, and added an additional section to help non-specialist readers to better understand our aims and findings.

      Figure labels are often rather confusing - for example it is unclear why there is a B, B', B' etc instead of B,C,D, etc.

      The figure labels have been revised in accordance with the reviewer’s recommendation.

      Some comments in the text are not clearly tied to the figures. For example, in lines 108-109, are the authors referring to the shadow along the edges of the myofibril when saying they are not clearly defined (Figure 1C)?

      The lines refer to the fact that identifying the boundary of an “object” in a fluorescence microscopy image is inherently challenging - even under ideal conditions where the object’s image is not affected by nearby signals or background noise. To improve clarity, we revised this section and now it reads: The other key parameter - myofibril diameter - is typically measured using phalloidin staining. However, accurately delineating their boundaries in micrographs is difficult - even under optimal conditions (high signal‑to‑noise ratio, no overlapping fibers, etc.; Fig. 1C). This limitation arises from the fundamental nature of light microscopy as the image produced is a blurred version of the actual structure, due to convolution with the microscope’s point spread function.

      In line 116, it is unclear what "surrounding structures" the authors are referring to if the myofibrils are isolated.

      We revised the text for clarity. It now states: Once isolated, myofibrils lie flat on the coverslip, aligning with the focal plane of the objective lens. This orientation allows for high-resolution, undistorted imaging and accurate two-dimensional measurements, free from interference by neighboring biological structures (e.g.: other myofibrils).

      In lines 141-142, there is no reference of data to back up the claim of validation.

      We addressed this mistake by including a reference to Fig. S1E (Fig. S1D in the revised manuscript).

      In line 170, the authors mention the mef2-Gal4/+ strain as a Gal4 driver line but do not clearly state how this strain is different from the wildtypes or how this impacts their results.

      Mef2-Gal4 is a muscle-specific Gal4 driver, often used in Drosophila muscle studies. It is a convention between Drosophila geneticists that presence of a transgene (i.e. Mef2-Gal4) changes the genetic background, and although it does not necessariliy cause any phenotypic effect, it is clearly distinguished from the wild type situation, and whenever relevant, Mef2-Gal4/+ is the preferred choice (if not the correct choice) as a control instead of wild type. As clear from our data, presence of the Mef2-Gal4 driver line does not affect the length or width of IFM sarcomeres as compared to wild type.

      In lines 182-185, the authors discuss the effects of tissue embedding on morphometrics. Were factors such as animal sex, age, fiber type, etc. conserved in these experiments? If not, any differences in results may be confounding.

      We fully agree with the reviewer that when testing the effect of a single variable, all other variables should remain constant. This is actually one of the main points emphasized in the results section. Additionally, this information is already provided in the Source Data files for each panel.

      In lines 199-201, the authors discuss results of myofibril diameter using different preparation methods, yet no data is cited to support the claims. In line 220, the phrase "6 independent experiments" is unclear. Is each independent experiment performed using a different animal? Furthermore, are 6 experiments performed for each time point?

      We substantially revised the relevant paragraphs and ensured that the corresponding data (Figure 2A in the revised manuscript) is cited each time when it is discussed. We conducted six independent experiments at each time point. This is consistently indicated in the figures and can be verified in the SourceData files (specifically, Fig3SourceData in this case). To clarify what we mean by "independent experiments," we added the following sentence to the Methods section: Experiments were considered independent when specimens came from different parental crosses, and each experiment included approximately six animals to capture individual variability.

      In line 254, the authors refer to "number of sarcomeres". It must be clearly stated if this refers to sarcomeres per myofibril, image area, etc.

      It is now clearly stated as: "number of sarcomeres per myofibril".

      In line 274, the authors refer to "myofilament number". It must be clearly stated if this refers to myofilaments per myofibril, image area, etc.

      We counted the number of myofilaments in developing myofibrils, and this is now clearly stated in the text and in the legend of Figure 3 (Figure 4 in the revised manuscript).

      In line 299, the authors mention that thin filaments measured less than 560 nm in length, yet no data is cited to support this.

      The previously missing reference to Figure 4 (Figure 7 in the revised manuscript) has now been added in addition to the revised Supplementary Figure 5.

      In the "Quantifying sarcomere growth dynamics" section of the summary (starting from line 402) the authors introduce data that would be more naturally placed in the results and discussion section.

      As suggested by the reviewer, we incorporated the key aspects of sarcomere growth dynamics into the Results and Discussion section.

      In lines 422-423, it is not mentioned what the controls are for.

      This was already explained in the main text between lines 167 and 173.

      In the caption of Figure 1C, it is not mentioned what the red dashed lines in the microscope images represent.

      The caption has been updated to include the following clarification: The red dashed lines border the ROI used for generating the intensity profiles.

      In the caption of Figure 1D, the difference between the lighter and darker grey points is not mentioned.

      This was already explained in each relevant figure legend. In this specific case, it is stated between lines 850 and 852: “Light gray dots represent individual measurements of sarcomere length and myofibril diameter, while the larger dots indicate the mean values from independent experiments.”

      In line 849, the stated p-value (0.003) does not match that mentioned in the figure (0.0003).

      We thank the reviewer for noticing this small mistake; correction was made to display the accurate p-value of 0.0003 at both places.

      In line 874, it is not clear what an "independent experiment" refers to (different animal, etc.?).

      We refer the reviewer to point 9, where this question has already been addressed.

      Figure 2A is hard to read. Using different colored dots for different time points might help.

      As suggested by the reviewer, we generated a plot with the individual points color-coded by time.

      The significant figures presented in Figure 4 give a completely inaccurate representation of the variability of the measurements achieved with these techniques.

      Certainly, each measured parameter exhibits inherent biological and technical variability. We have made all the raw data available to the reader through the SourceData files, and this variability is also evident in Figures 1, 2, 3, Supplementary Figure 1, 3, and 5 (Figure 1, 2, 3, 4, 6, and Supplementary Figure 1 in the revised manuscript). Also we have included an additional plot (Supplementary Figure 5 in the revised manuscript) that presents the calculated thin and thick filament lengths and their uncertainty. However, in Figure 4 (Figure 7 in the revised manuscript), our goal was to present an easily understandable visual representation of the sarcomeric structures for each time point, based on the averages of the relevant measurements.

      In line 877, it should be mentioned that the number of filaments is counted per myofibril. The y-axes in the figure should also be adjusted to clarify this.

      As suggested by the reviewer, both the figure legend and the plot have been updated to clearly indicate that the filament count refers to the number per myofibril.

      In line 883, it is not clear what an "independent experiment" refers to (different animal, etc.?).

      We refer the reviewer to point 9, where this question has already been addressed.

      The statement of sample sizes in all figures is a little confusing.

      Following general guidelines, we used SuperPlots to effectively present the data, as nicely demonstrated in the JCB viewpoint article by Lord et al., 2020 (PMID: 32346721). Individual measurements are shown as pooled data points, allowing readers to appreciate the spread, distribution and number of measurements. Overlaid on these pooled dot plots are the mean values from each independent experiment, with error bars representing variability between independent experiments. Sample sizes are provided for both individual measurements and independent experiments. This is now clearly explained in the Materials and Methods section, and we corrected the legends to improve clarity (“n” indicates the number of independent experiments/individual measurements).

      In lines 1007-1008, the authors imply that the lattice model is needed for calculation of myofilament length. However, from the equations and previous data, it seems that this can be estimated using the confocal and dSTORM images.

      As the reviewer correctly noted, myofilament length can be estimated using measurements from confocal and dSTORM images, following the equations provided. However, constructing even a simplified model requires multiple constraints to be defined and applied in a specific order. In practice, one must first determine the number and arrangement of myofilaments in a cross-sectional view of an “average sarcomere” before attempting to build a longitudinal model, where length calculations become relevant. This is now clarified in the text.

      A more specific discussion of future directions is needed to put this paper in context. For example: Can anything from the overall process be used to better understand sarcomere dynamics in larger animals/humans? Can this be applied to disease modelling?

      To address these questions, we have added a section titled STUDY LIMITATIONS, which states: “Our study is focused on describing the growth of IFM sarcomeres during myofibrillogenesis at the level of individual myofilaments. Additionally, we developed a user-friendly software tool for precise sarcomere size measurements and demonstrate that these measurements are sensitive to varying conditions. Whereas, this tool can be used successfully on whole muscle fiber preparations as well, our pipeline was intentionally optimized for individual IFM myofibrils ensuring higher measurement precision in our hands than other type of preparations. Thus, we predict that future work will be required to extend it to sarcomeres from other muscle tissues or species. Nevertheless, our study exemplifies a workflow how to measure sarcomere dimensions precisely. With some variations, it should be possible to adopt it for other muscles, including vertebrate and human striated muscles. To facilitate this and to enhance the accessibility and usability of this dataset, we welcome any feedback and suggestions from researchers in the field.”

      One of the major claims of the paper is that there is a measurable variability with sex and other parameters. However, this data is never clearly summarized, presented (except for supplement), or discussed for its implications.

      We followed the suggestion of the reviewer, and we moved this supplementary data into a main figure, and thoroughly revised the corresponding paragraphs to present and discuss the findings more clearly.

      Minor Comments: 1. Lines 60-65 seem to break the flow of the introduction. As the authors discuss existing methods in literature for IFM analysis in the previous couple sentences, the following sentences should clearly state the limitations of existing methods/current gap in literature and a general idea of what the current work is contributing.

      We agree with this remark, and we substantially revised the Introduction to clearly define the existing gap in the literature and to articulate how our work addresses this gap.

      In line 104, the acronym for ZASPs is not spelled out.

      The acronym has now been spelled out for clarity.

      **Referee Cross-commenting**

      I agree as well.

      Reviewer #1 (Significance (Required)):

      In summary, this paper provides a multi-scale characterization of Drosophila flight muscle sarcomere structure under a variety of conditions, which is potentially a significant contribution for the field. However, the paper scope is overstated in that it does not provide an actual sarcomere model. Further, there are multiple issues with data presentation that impact the readability of the manuscript.

      Although it is somewhat unclear what would be “an actual sarcomere model” for the reviewer, but we cannot accept that we made on overstatement by using the word “model”, because one of the main outcomes of our work are indeed the myofilament level sarcomere models depicted in Figure 4 (Figure 7 in the revised manuscript). As said above, we do not claim that these would be molecular models, or mechanistic models or developmental models, but it makes absolutely nonsense (even in common terms!) that our scaled graphical representations (based on a wealth of measurements) should not be or cannot be called models.

      As to the comment with data presentation, we thank the reviewer for the numerous suggestions, and we substantially revised the manuscript to increase clarity and overall readability.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ Summary: In this manuscript titled "A myofilament lattice model of Drosophila flight muscle sarcomeres based on multiscale morphometric analysis during development," Görög et al. perform a detailed analysis of morphological parameters of the indirect flight muscle (IFM) of D. melanogaster. The authors start by illustrating the range of measurements reported in the literature for mature IFM sarcomere length and width, showing a need to revisit and determine a standardized measurement. They develop a new Python-based tool, IMA, to analyze sarcomere lengths from confocal micrographs of isolated myofibrils stained with phalloidin and a z-disc marker. Using this tool, they demonstrate that sample preparation (especially mounting medium), as well as fiber type, sex, and age influence sarcomere measurements. Combining IMA, TEM, and STORM data, they measure sarcomere parameters across development, providing a comprehensive and up-to-date set of "standardized" sarcomere measurements. Using these data, they generate a model integrating all of the parameters to model sarcomeres at four discrete timepoints of development, recapitulating key phases of sarcomere formation and growth.

      Major comments: Line 200 & 901 - Figure S1B - The authors make a strong statement about the use of liquid versus hardening media, and it is clear from the image provided in Figure S1 that there is a difference in the apparent sarcomere width. The identity of the "liquid media" versus the "hardening media" should be clearly identified in the Results, in addition to the legend for Figure S1. The authors show that "glycerol-based solutions" increase sarcomere width, but the Materials only list 90% glycerol and PBS. However, a frequently used liquid mounting media is Vectashield. Based on the literature, measurements in liquid Vectashield show diameters significantly less than 2.2 microns observed here with presumably 90% glycerol or PBS. Can the authors qualify this statement, or provide data that all forms of liquid mounting media cause this effect? Does this also apply to hemi-thorax and sectioned preparations, or just isolated myofibrils?

      We used a PBS-based solution containing 90% glycerol as our liquid medium, as now stated in the main text. In response to the reviewer’s suggestion, we also tested a non-hardening version of Vectashield (H-1000). Myofibrils in Vectashield were significantly thicker than those in ProLong Gold but still thinner than those in the 90% glycerol–PBS solution, shown in Figure 2B. The mechanisms that could potentially explain these observations have been described in several studies (Miller et al., 2008; Tanner et al., 2011, 2012). Briefly, IFM is a densely packed macromolecular assembly. Upon removal of the cell membrane, myofibrillar proteins attract water, leading to overhydration of the myofilament lattice. This increases the spacing between filaments, resulting in an expansion of overall myofibril diameter. The extent of hydration depends on the osmolarity of the surrounding medium, as the system eventually reaches osmotic equilibrium. While both liquid media induced significant swelling, the observed differences likely reflect variations in their osmotic properties. In contrast, dehydration - an essential step in electron microscopy sample preparation - reduces the spacing between filaments, making myofibrils appear thinner. This explains why EM micrographs consistently show significantly smaller myofibril diameters (Chakravorty et al., 2017).

              Hardening media such as ProLong Gold introduce additional artifacts: during polymerization, these media shrink, exerting compressive forces on the tissue (Jonkman et al., 2020). We therefore propose that isolated myofibrils first expand due to overhydration in the dissection solution, and are then compressed back toward their *in vivo* dimensions during incubation in ProLong Gold. The average *in vivo* diameter of IFM myofibrils can be estimated without direct measurements, as it is determined by two key factors: (i) the number of myofilaments, which has been quantified in EM cross-sections in several studies (Fernandes & Schöck, 2014; Shwartz et al., 2016; Chakravorty et al., 2017) including our own, and (ii) the spacing between filaments, which can be measured by X-ray diffraction even in live *Drosophila* or under various experimental conditions (Irving & Maughan, 2000; Miller et al., 2008; Tanner et al., 2011, 2012). Our findings suggest that the effects of lattice overhydration and media-induced shrinkage are most pronounced in isolated myofibrils. In larger tissue preparations, the inter-myofibrillar space likely acts as a mechanical and osmotic buffer, reducing the extent of such distortions
      

      Can the authors comment on whether the length of fixation or fixation buffer solution, in addition to the mounting medium, make a difference on sarcomere length and diameter measurements? This is another source of variation in published protocols.

      The effect of fixation time on sarcomere morphometrics in whole-mount IFM preparations has been previously demonstrated by DeAguero et al. (2019), as briefly noted in our manuscript. To extend these findings, we performed a comparison using isolated myofibrils, assessing morphometric parameters after fixation for 10, 20 (standard) and 60 minutes. We found no difference between the 10- and 20-minute fixation conditions; however, fixation for 60 minutes resulted in significantly increased myofibril diameter (and these data are now shown in Supplementary Figure 1C). A comparable increase in thickness was also observed when using a glutaraldehyde-based fixative. These results suggest that more extensively fixed myofibrils may better resist the compressive forces exerted by hardening media.

      Line 237-238. The authors conclude that premyofibrils are much thinner than previously measured. The use of Airyscan to more accurately measure myofibril width at this timepoint is a good contribution, as indeed diffraction and light scatter likely contribute to increased width measured in light microscopy images. I also wonder, though, how well the IMP software performs in measuring width at 36h APF, given how irregular the isolated myofibrils at this stage look (wide z-lines but thinner and weaker H and I bands as shown in Fig. 2B)?

      The reviewer is correct that measurements during the early stages of myofibrillogenesis require additional effort. However, in addition to its automatic mode, IMA can also operate in semi-automatic or manual modes, ensuring complete control over the measurements. Myofibril width is determined from the phalloidin channel at the Z-line (as described in the software’s User Guide and Supplementary Figure 2), where it is at its thickest.

      Also, how much of the difference in sarcomere width arises due to effects of "stripping" components off of the sarcomere at the earliest timepoint (for example alpha-actinin or Zasp proteins)?

      A comparison between isolated myofibrils and those from microdissected muscles (Supplementary Figure 3B, Figure 3C in the revised manuscript) shows that the isolation process does not alter the morphometric measurements of sarcomeres. Moreover, the measured myofibril width aligns well with what we expect based on the number of myofilaments observed in TEM cross-sections of myofibrils at 36 hours APF (Figure 3A, now Figure 4A in the revised manuscript), supporting the consistency of our model.

      Myofibrils at early timepoints do contain more than 4-12 sarcomeres in a line (they extend the full length of the myofiber), so it is possible they are breaking due to the detergent and mechanical disruption induced by the isolation method.

      The reviewer is correct - myofibrils likely span the full length of the myofiber from the onset of myofibrillogenesis. However, during the isolation of individual myofibrils, they often break, and even mature myofibrils typically fragment into pieces of about 300 µm in length (illustrated in Figure 1E, now Figure 2A in the revised manuscript). Importantly, our measurements show that this fragmentation does not affect the assessed sarcomere length or width (as shown in Supplementary Figure 3B, now Figure 3C in the revised manuscript).

      Line 312 - What does "stable association" mean in this context? The authors mention early timepoints lack stable association of alpha-Actinin or Zasp52, and they reference Fig. S4C, but this figure only shows 72h and 24 AE, not 36h and 48 h APF. Previous reports have seen localization of both alpha-Actinin and Zasp52, so presumably the detergent or mechanical isolation is stripping these components off of the isolated myofibrils up until 72h.

      In agreement with previous reports, we also detected both α-Actinin (as shown in former Supplementary Figure 3B, now Figure 3C) and Zasp52 in microdissected IFM starting from 36 hours APF. However, these markers were largely absent from the isolated myofibrils of young pupae (36 to 60 hours APF). By 60 hours APF, strong α-Actinin and Zasp52 staining became evident in isolated myofibrils, whereas dTitin epitopes were clearly detectable from the earliest time point examined. This indicates that some proteins, such as α-Actinin and Zasp52, can be lost during the isolation process, whereas others like dTitin are retained and this differential sensitivity appears to depend on developmental stage. A likely explanation is that α-Actinin and Zasp52 are recruited early to Z-bodies but are only fully incorporated as more mature Z-disks form between 48 and 60 hours APF. This incomplete incorporation at the earlier stages could account for their loss during the isolation process. This interpretation is supported by our morphological analysis of the Z-discs, as shown in the dSTORM dataset (former Figure 3B, B’’, now Figure 4C, E) and in longitudinal TEM sections (former Supplementary Figure 5B, now in Figure 6B). Because α-Actinin and Zasp52 are not detected in isolated myofibrils at 36 and 48 hours APF, they are not included in Figure S4C (Figure 5C in the revised manuscript). This is explained in the updated figure legend.

      This same type of issue comes up again in Lines 325-334, where the authors talk about 3E8 and MAC147. They state that 3E8 signal significantly declines in later stages and that MAC147 is not suitable to label myofibrils in young pupae, but they only show data from 72 APF and 24 AE (which looks to have decent staining for both 3E8 and MAC147). A clearer explanation here would be helpful.

      To put it simply: we used one myosin antibody to label the A-band in the IFM of 36h APF and 48h APF animals, and a different antibody for the 72h APF and 24h AE stages. In more detail: Myosin 3E8 is a monoclonal antibody targeting the myosin heavy chain and labels the entire length of mature thick filaments except for the bare zone (former Supplementary Figure 4D, now in Figure 5D), suggesting its epitope is near the head domain. As a result, we expect a uniform A-band staining - excluding the bare zone - which is exactly what we observe in the IFM of young pupae (36h APF and 48h APF; formerly Figure 3B, now Figure 4C in the revised manuscript). However, at 72h APF and 24h AE, Myosin 3E8 produces a different staining pattern: two narrow stripes flanking the bare zone and two broader, more diffuse stripes near the A/I band junction (former Supplementary Figure 4D, now Figure 5D). This change is likely due to restricted antigen accessibility at these later developmental stages - a common issue in the densely packed IFM - making this antibody unsuitable for reliably measuring thick filament length in these stages.

      MAC147 is another monoclonal antibody against Mhc that recognizes an epitope near the head domain. However, it only works reliably in more mature myofibrils (72h APF and 24h AE; formerly Figure 3B, now Figure 4C in the revised manuscript), likely due to its specificity for a particular Mhc isoform. This is why we do not include images from earlier developmental stages using this antibody. We added a revised, concise explanation in the main text for general readers, and provided a more detailed description for specialist readers in the legend of Supplementary Figure 4D (updated as Figure 5D in the revised manuscript).

      Figure 3B. The authors show the H, Z, and I lengths in B', B', and B' and discuss these lengths in the text (lines 305-320). It would also be nice to actually have the plots showing the measured/calculated lengths for thin and thick filaments. These are mentioned in the results, but I cannot find the plots in the figures and there is no panel reference.

      A summary table of the measured and calculated parameters is provided in Fig4SourceData (Fig7Source Data in the revised manuscript). However, following the reviewer’s suggestion, we also generated an additional plot (Supplementary Figure 5 in the revised manuscript) that displays the calculated thin and thick filament lengths.

      Line 400. Does the model in Figure 4 actually have molecular resolution as the authors claim? From these views, thick and thin filaments appear to be represented by cylindrical objects. Localization of specific molecules would require further modeling with individual proteins. Or do the authors mean localization from STORM imaging relative to the ends of the thick and/or thin filaments? The model itself is a useful contribution, but based on Figure 4, resolution of individual molecules is not evident.

      The reviewer is correct; and we fully agree that we do not present a molecular model of sarcomeres in this study - nor do we claim to. Instead we present a myofilament level model. Nevertheless, the scaled myofilament lattice model we introduce could serve as a geometric constraint when constructing supramolecular models of sarcomeres. As the reviewer rightly notes, implementing such an approach would require additional effort.

      The main Results section of the text is condensed into 4 figures. However, I found myself flipping back and forth between the main figures and the supplement continuously, especially parts of Supplemental Figures 1, 3, 4, and 5. With such large amounts of detail in the Results relying on the supplement, it may be worth considering reorganizing the main and supplemental figures, and having 7 main figures, to include important panels that are currently in the supplement (esp. Fig S1B, S1C, S1D, S3B, S4, S5).

      We found it a very useful suggestion, and we substantially reorganized the figures in the revised manuscript according to the recommendations of the reviewer.

      Minor comments: On the plots in Fig. S1B, D, and F, it is hard to see the color of the dots because the red error bars are on top of them. Can the other distribution dots be tinted the correct color or the x-axis labels be added, so it is clear which dataset is which?

      We significantly enlarged the dots to enhance visual clarity.

      Line 142 needs a reference to Figure S1, Panel E, which shows the accuracy and precision measurements.

      The requested panel reference has now been included in the revised manuscript.

      Lines 198 - is this range from the above publications? Needs to be clearly cited.

      The range has indeed been estimated using measurements from the aforementioned publications, and this point is now further clarified in the revised text.

      Figure S3B is confusing - why do the blow-ups overlap both the top (presumably microdissected) and the bottom (presumably isolated) images? The identity of microdissected images should be labeled, as they are hard to see underneath of the blown-up images and the identity of individual image planes wasn't immediately obvious.

      We refined the panel structure of Figure S3B (Figure 3C in the revised manuscript) to enhance clarity as the reviewer suggested.

      Line 298. By "misaligned," do the authors mean the pointed ends are not uniformly anchored in the z-disc, leading to the wide z-disc measurements? At this early stage, I'm not sure "misaligned" is the right word - perhaps "were not yet aligned in register at the z-disc" or something similar.

      We revised the text for clarity. It now reads: At 36 hours APF, thin filaments had not yet aligned in perfect register at the Z-disc, with most measuring less than 560 nm in length - and exhibiting considerable variability.

      Figure S6 - spelling mistake in label of panel A, "sarcomer" should be "sarcomere"

      The typo is corrected.

      Line 487. Spelling "Zaps52" should be "Zasp52"

      The typo is corrected.

      Line 887. Spelling "Myofilement" should be "Myofilament"

      The typo is corrected.

      Line 946-947. In the legend for Supp. Fig. 3., the authors should specify which published datasets on sarcomere length are shown in the figure by including the references in the legend. Presumably the "isolated individual myofibrils" are the blue "this study" lines, leaving the "microdissected muscles" as the magenta "previous reports" on the figure. Without the reference, it is not clear if these are microdissected, isolated myofibrils, hemi-thorax sections, cryosections, or another preparation method for the "previous reports" data.

      The references have now been added to both the figure and its legend.

      **Referee Cross-commenting**

      I agree with the comments from the other reviewers. Many of the major themes are consistent across the reviews, including regarding the model, preparation methods, and the software tool.

      Reviewer #2 (Significance (Required)):

      Strengths: This manuscript is an important contribution to the field of sarcomere development. The authors use modern technologies to revisit variation in morphometric measurements in the literature, and they identify parameters that influence this variation. Notably, sex-specific differences, DLM versus DVM measurements, and mouting media are potential contributors to the variability. Combining TEM and STORM with a confocal timecourse of isolated myofibrils, they refine previously published values of sarcomere length and width, and add more comprehensive data for filament length, number and spacing. This highly accurate timecourse demonstrates continual growth of sarcomeres after 48 h APF, and correct some inconsistencies from previous large-scale timecourse datasets. These data are very valuable to the field, especially Drosophila muscle biologists, and will serve as a comparative resource for future studies. Weaknesses: At early timepoints, loss of sarcomere components through mechanical or detergent-mediated artifacts may influence the authors' measurements. In addition, isolating myofibrils is not always the most ideal approach, as it loses information on myofiber structure as well as organization and structure of the myofibrils in vivo.

      We believe that the control experiments we presented here adequately demonstrate that sarcomere measurements are not affected by the myofibril isolation process at early timepoints (Figure 3C). Nevertheless, we certainly agree with the reviewer that isolated myofibrils alone cannot capture the entire complexity of muscle tissues, and additional approaches should also be applied in complex projects. Yet, we are confident that our approach offers the most reliable and efficient method for precise morphometric analysis of the sarcomeres, and although alone it is very unlikely to be sufficient to address all questions of a muscle development project, it can still be applied as a very useful and robust tool.

      The point regarding liquid versus hardening mounting media is valuable, but remains to be tested and validated with the diverse liquid and hardening media used by other labs.

      Whereas it would not be feasible for us to test all possible liquid and hardening media used by others in all possible conditions, we tested the effect of Vectashield (the most commonly used liquid media) according to the suggestion of the reviewer, and the results are now included in the manuscript. We think that this is a valuable extension of the list of the materials and conditions we tested, although we need to point out that our primary goal was not necessarily to test as many conditions as possible (because the number of those conditions is virtually endless), rather to raise awareness among colleagues that these variables can significantly impact the data obtained and affect their comparability.

      The IMA software seems to be designed specifically for analysis of isolated myofibrils, and it is unclear if it would work for other types of IFM preparations.

      As stated in the manuscript, IMA is a specialized tool designed for the analysis of individual myofibrils. While it can also process other types of IFM preparations in semi-automatic or manual modes, we believe these approaches compromise both efficiency and accuracy. This is further clarified in the revised manuscript.

      A last point is that TEM and STORM may not be available on a regular basis to many labs, hindering wide implementation of the approach used in this manuscript to generate very accurate and detailed measurements of sarcomere morphometrics.

      Regarding the availability of TEM and STORM, we acknowledge that these techniques are not universally accessible. However, that is exactly one major value of our work that our open-source software tool now allows researchers to generate valuable data using only a confocal microscope in combination with our published datasets.

      Audience: Scientists who study sarcomerogenesis or Drosophila muscle biology.

      My expertise: I study muscle development in the Drosophila model.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ Summary: This manuscripts presents a computational tool to quantify sarcomere length and myofibril width of the Drosophila indirect flight muscles, including developmental samples. This tool was applied to confocal and STORM super-resolution images of isolated myofibrils from adult and developing flight muscles. Thick filament numbers per myofibril were counted during development of flight muscles. A myofilament model of developing flight muscle myofibrils is presented that remains speculative for the early developmental stages.

      Major comments: 1. The title of the manuscript appears unclear. What is a lattice model? Lattice is an ordered array. The filament array parameters for mature flight muscles was aready measured. It appears that the authors speculate how this order might be generated during sarcomere assembly, which is not studied in this manuscript as it is limited to periodic arrays after 36h APF.

      As the reviewer correctly points out, a lattice refers to an ordered array - in the case of IFM sarcomeres, this includes both thin and thick filaments. Therefore, the phrase "myofilament lattice model of Drosophila flight muscle sarcomeres" specifically describes a model representing the spatial organization of these filament arrays within the sarcomere. To provide additional clarity for readers, we have revised the title to include more context. It now reads: Developmental Remodeling of Drosophila Flight Muscle Sarcomeres: A Scaled Myofilament Lattice Model Based on Multiscale Morphometrics

      To create a model of these arrays, three essential pieces of information are required:

      1) The length of the filaments,

      2) The number of filaments, and

      3) The relative position of the filaments.

      While some direct measurements are available in the literature, and others can be used to calculate the necessary values, available data is often contradictory or simply different from each other (as described in our ms) making them unsuitable for constructing scaled models of the myofilament arrays. In contrast to that, here we present a comprehensive and consistent set of measurements that enabled us to build models not only of mature sarcomeres but also of sarcomeres at three other significant developmental time points.

      Regarding the mention of "sarcomere assembly" in line 37, we intended it to refer to the growth of the sarcomeres, not their initial formation. We do not speculate about sarcomere assembly anywhere in the text. In fact, we have clearly stated multiple times that our focus is on the growth of the IFM myofilament array during myofibrillogenesis. Nevertheless, to avoid confusion, we revised the phrase in line 37 to "sarcomere growth".

      The authors review the flight muscle sarcomere length literature and conclude it is variable because of imprecise measurements. Likely this is partially true, however, more importantly is that the sarcomere length and width changes during isolation methods of the myofibrils, as well as by various embedding methods, as the authors show here as well in Figure 1B-E.

      We dedicated two sections of the Results - “An automated method to accurately measure sarcomeric parameters” and “IFM sarcomere morphometrics are affected by sex, age, fiber type, and sample preparation” - to exploring potential sources of variability in published IFM sarcomere measurements. Based on these analyses, we conclude that such variability stems from both measurement imprecision and biological or technical factors, including sex, age, fiber type and, of foremost, sample preparation. Because it is difficult to quantify the relative impact of each variable across published studies, we have refrained from speculations about the relative contribution of the different factors in the revised manuscript.

      Hence, I find the strongly claims the authors make here surprising, while they are isolating the myofibrils. Hence, these myofibrils are ruptured at the ends, relaxed or contracted, depending on buffer choice and passive tension is released. On page 8, the authors correctly state that the embedding medium causes shrinkage of the myofibrils. While isolation is state of the art for electron microscopy techniques, other methods including sectioning or even whole mount preparation have been developed for high resolution microscopy of IFMs that avoid these artifacts. Unfortunately, this manuscript only uses isolated myofibrils that were fixed and then mechanically dissociated by pipetting. This method likely induces variations as seen by the large spread of sarcomere length reported in Figure 1C (2.8-3.9µm?) and even bigger spreads for myofibril widths. Are these also seen in tissue without dissections? Unfortunately, no comparision to intact flight muscles are reported with the here presented quantification tool. The sarcomere length spread in the developmental samples is even larger.

      The major issue raised in this paragraph is the use of isolated myofibril versus intact flight muscle preparations. The reviewer claims that the latter might be superior because the isolated myofibrils are ruptured at their ends. Clearly, the intact IFMs cannot be imaged in vivo by light microscopy because the adult fly cuticle is opaque. To visualize these muscles, one must open the thorax, but neither microdissection nor sectioning preserves them perfectly, even the cleanest longitudinal cuts sever some myofibrils, and dissection itself can damage the tissue. Although published images often show only the most pristine regions, the practice of selective cropping cannot be taken as a scientific argument. Here, by comparing sarcomere lengths measured in isolated myofibrils with those from whole-mount longitudinal DLM sections and microdissected IFM myofibers, we demonstrate that isolation does not alter sarcomere length (Figure 1E, now Figure 2A in the revised manuscript). As to myofibril width, it is determined by two parameters: the number of myofilaments and the spacing between them. In vivo filament spacing has been measured directly, and filament counts can be obtained from EM cross-sections of DLM fibers. Combining these values gives an expected in vivo myofibril diameter. While isolated myofibrils measure thinner than those in whole-mount or microdissected samples (Figure 1E, now Figure 2A in the revised manuscript), their diameter closely matches this in vivo estimate (see manuscript, lines 187–198). Therefore, we conclude that isolated myofibrils (even if it seems counterintuitive for this reviewer) are superior for sarcomere measurements than whole-mount preparations - and that is why we primarily rely on them here.

      Despite that, we certainly recognize that isolated myofibrils cannot recapitulate every aspect of an IFM fiber, and the need for whole-mount preparations during our IFM studies is not questioned by us.

              In addition to this general answer to the issues raised in the above paragraph of the reviewer, we would like to specifically reflect for some of the remarks:
      

      „Unfortunately, this manuscript only uses isolated myofibrils that were fixed and then mechanically dissociated by pipetting.”

      This is a false statement that “this manuscript only uses isolated myofibrils” as we used different preparation methods for initial comparisons (see Figure 1E, now Figure 2A in the revised manuscript). Additionally, unlike the reviewer assumed, the myofibrils were first dissociated and then fixed, and not vice versa (as described in the Materials and Methods section).

      „This method likely induces variations as seen by the large spread of sarcomere length reported in Figure 1C (2.8-3.9µm?) and even bigger spreads for myofibril widths. Are these also seen in tissue without dissections?”

      This remark makes absolutely no sense, as we do not report sarcomere length values in Figure 1C at all. By assuming that the reviewer meant to refer to Figure 1B, it still remains a misunderstanding or a false statement, because that panel refers to the variations found in published data (not in our current data), and this is clearly explained both in the figure legend and the main text. Regardless of that, the stated spread does not appear unusual. In the article by Spletter et al. (2018), the authors report a similar spread (2.576–3.542 µm) for sarcomere length in mature IFM using whole-mount DLM cross-sections. As to the second question here, we do observe a comparable spread in other preparations as well (see Figure 1E, now Figure 2A in the revised manuscript), which is again the opposite conclusion as compared to the (clearly false) assumption of the reviewer.

      „Unfortunately, no comparision to intact flight muscles are reported with the here presented quantification tool. „

      This is also a false statement; as we do report comparison to whole mount cross sections which we belive the reviewer considers „intact” in Figure 1E (Figure 2A in the revised manuscript).

      „The sarcomere length spread in the developmental samples is even larger.”

      The spread is not larger at all than in previous reports, as clearly shown in Supplementary Figure 3A.

      The authors suggest that there are sex differences in sarcomere length and pupal development duration. This is potentially interesting, unfortunately they then use mixed sex samples to analyse sarcomeres during flight muscle development.

      In the revised manuscript, we now provide a more detailed description of a subtle post-eclosion difference in IFM sarcomere metrics between male and female Drosophila. We attribute this variation to the well-established observation that female pupae develop slightly faster than males, a property that may last till shortly after eclosion. Confirming this experimentally would require considerable effort with limited scientific benefit. Nonetheless, the subtle nature of this sex-linked variation reinforced our decision to include IFM sarcomeres from both male and female flies in our comprehensive developmental analysis.

      The IMA software tool lacks critical assessment of its performance compared to other tools and the validation presented is too limited. IMA seems to generate systematic errors, based on Fig S1E, as it does not report the ground truth. These have to be discussed and compared to available tools. The principles of fitting used in IMA seem well adapted to IFM myofibrils in low noise conditions, but may not be usable in other situations. This should be assessed and discussed.

      IMA is a specialized software tool developed to address a specific need, notably, to accurately and efficiently measure sarcomere length and myofibril diameter in individual IFM myofibril images labeled with both phalloidin and Z-disc markers. For our purposes, it remains the most suitable and reliable option, and we are confident that IMA outperforms all other available tools. To demonstrate this, we have included a table comparing the few alternatives (MyofibrilJ, SarcGraph, and sarcApp) capable of both measurements, which further supports our conclusion. Given IMA's focused application, extensive validation under artificially low signal-to-noise conditions is unnecessary. While IMA may introduce minor systematic errors (~0.01 µm for sarcomere length and ~0.03 µm for myofibril diameter), these are negligible errors relative to the limitations of the simulated ground truth data used for benchmarking. This point is now addressed in the manuscript.

      It is claimed that validation was achieved on simulated IFM images: do the authors rather mean simulated isolated IFM myofibril images? This is not quite the same in terms of algorithm complexity and this should be corrected if this is the case.

      Indeed, we used simulated individual IFM myofibril images, where both phalloidin labeling and Z-disc labeling are present. This is clearly shown in Supplementary Figure 1A, and stated in the text when first introduced: „we generated artificial images of IFM myofibrils with known dimensions, simulating the image formation process”

      The authors need to revise their comparison to other tools. It is incomplete and seemingly incorrect. It should be clearly stated that IMA is limited to isolated myofibrils, which is a far easier segmentation task than what other tools can do, such as sarcApp (Neininger-Castro et al. 2023, PMID: 37921850). Defining the acronym would be valuable in that sense. The claim line 129-130 "none can adequately measure myofibril diameter from regular side view images" is unclear. What do the authors refer to as "side view images"? Sarc-Graph from Zhao et al 2021, PMID: 34613960, and sarcApp from Neininger-Castro et al. 2023 provide sarcomere width, in conditions that are very similar to what IMA does, e.g. on xy images based on the documentation provided on github. A performance comparison with these tools would be valuable. Does installation and use of IMA require computational skills?

      Motivated by the reviewer’s comments, we revised the section introducing IMA. However, we chose not to include an extensive comparison with other software tools, as this would divert the manuscript’s focus without impacting the main conclusions. Instead, we added a summary table highlighting the key requirements for analyzing IFM sarcomere morphometrics from Z-stacks of phalloidin- and Z-line-labeled individual myofibrils and compared the available tools accordingly. In our experience, most software tools are developed to address very specific problems, even those marketed as general-purpose solutions. Consequently, applying them beyond their intended scope often results in reduced efficiency and suboptimal performance. Although sarcApp was initially available as a free tool, one of its dependencies (PySimpleGUI 5) has since adopted a commercial license model. Using a trial version of PySimpleGUI 5, we evaluated sarcApp on our dataset. The software is limited to single-plane image input, hence raw image stacks must be preprocessed into a suitable format, which is a time consuming step. Furthermore, implementation requires basic programming proficiency, as parameter adjustments must be performed directly within the source code to accommodate dataset-specific configurations. Once appropriately configured, sarcApp reliably quantifies both sarcomere length and myofibril width with accuracy comparable to that of IMA. However, it lacks built-in diagnostic feedback or visualization tools to facilitate measurement verification or troubleshooting during batch processing. SarcGraph also supports only single-plane image inputs and requires prior image preprocessing. Additionally, images must be loaded manually one by one, which further reduces processing efficiency. Parameter optimization relies on direct code modification through a trial-and-error process, demanding a certain level of programming proficiency. Even with these adjustments, the software frequently introduces artifacts - such as Z-line splitting - when applied to our dataset. Even when segmentation is successful, sarcomere length is often overestimated, whereas myofibril diameter is consistently underestimated. As compared to these issues, IMA was designed for ease of use and does not require any programming experience to install or operate. It can automatically handle raw microscopic image formats without the need for preprocessing. Segmentation is fully automated, with no requirement for parameter tuning. The tool provides visual feedback during both the segmentation and fitting steps, allowing users to confidently assess and validate the results. IMA produces accurate and precise measurements of sarcomere length and diameter. Batch processing is enabled by default, significantly improving efficiency when analyzing multiple images. Finally, unlike the reviewer stated, IMA is not limited to isolated myofibrils. It is optimized for isolated myofibrils (i.e. full performance is achieved on these samples), but it can also work on whole-mount preparations in semi-automatic and manual mode, which still allow precise measurements (with some reduction in processing efficiency).

      As to the minor comments, the acronym IMA was already defined in lines 541 and 917–918 of the original submission, as well as on the software’s GitHub page. Additionally, we replaced the phrase "side view images" with "longitudinal myofibril projections" to improve clarity.

      How do the authors know that the bright phallodin signal visible that the Z-disc at 36h and 48h APF is due to actin filament overlap, as suggested? An alternative solution are more short actin filaments at the early Z-discs.

      It is widely accepted that the bright phalloidin signal at the Z-line in mature sarcomeres reflects actin filament overlap (e.g., Littlefield and Fowler, 2002; PMID: 11964243). Accordingly, in slightly stretched myofibrils, this bright signal diminishes, and in more significantly stretched myofibrils, a small gap appears (e.g., Kulke et al., 2001; PMID: 11535621). The width of this bright phalloidin signal corresponds to the electron-dense band seen in longitudinal EM sections (Figure 3B and Supplementary Figure 5B, now Figure 4B and Figure 6B in the revised manuscript) and matches the actin filament overlap observed in Z-disc cryo-EM reconstructions from other species (Yeganeh et al., 2023; Rusu et al., 2017), where individual thin filaments can be resolved. By extension, we interpret the bright phalloidin signals at the Z-discs observed at 36 h and 48 h APF as arising from similar actin filament overlaps, given their comparable width to the electron-dense Z-bodies described both in our study (Supplemantary Figure 5B, now Figure 6B in the revised manuscript) and by Reedy and Beall (1993). While we cannot fully rule out the reviewer’s alternative interpretation, for the time being it remains a bold speculation without supporting evidence, and therefore we prefer to stay with the conventional view.

      The authors seem to doubt their own interpretation that actin filaments shrink when reading line 304 and following. This is obviously critical for the "model" presented.

      Unlike the reviewer implies, we certainly do not doubt our own interpretation, but to avoid confusion we revised the corresponding paragraph in the manuscript and provided more details on our explanation, and we also provide a brief overview of it here. Between 36 h and 48 h APF we observe a pronounced structural transition in the IFM sarcomeres. In EM cross-sections, the previously irregular myofilament lattice becomes organized into a regular hexagonal pattern (Figure 3A, now Figure 4A in the revised manuscript) with filament spacing typical of mature myofibrils (Supplementary Figure 5A, now Figure 6A in the revised manuscript). In longitudinal EM sections, the elongated, amorphous Z-bodies condense along the myofibril axis to form well-defined, adult-like Z-discs (Supplementary Figure 5B, now Figure 6B in the revised manuscript). Similarly, dSTORM imaging shows that the Z-disc associated D-Titin epitopes become more compact and organized during this period (Supplementary Figure 4E, now Figure 5E in the revised manuscript). The edges of the thick filament arrays also become more sharply defined, and the appearance of a distinct bare zone indicates the establishment of a regular register (Figure 3B, now Figure 4B in the revised manuscript). By assuming that a similar reorganization occurs within the thin filament array, the apparent length of the thin filament array would decrease—not due to shortening of individual filaments, rather due to improved alignment. Although we cannot directly resolve single thin filaments, this reorganization offers the most plausible explanation for the observed change.

      Minor comments: 1. Figure S1B is not called out in the text.

      The reviewer might have missed this, but in fact, it is explicitly called out in line 181.

      Fig. 1: Please state whenever images are simulations?

      We appreciate the reviewer’s observation that the simulated IFM myofibril images are indistinguishable from the real ones, as this confirms the adequacy of these images for testing our software tool. However, this is already clearly indicated: Figure 1B features simulated images, as noted in the figure legend (line 824), and Supplementary Figure 1A similarly shows simulated images, as stated both in the legend (line 886) and in the figure.

      Fig. 2: Length-width correlation - please provide individual points color-coded by time point?

      As suggested by the reviewer, we generated a plot with the individual points color-coded by time.

      "newly eclosed males and females, we observed that males have slightly shorter sarcomeres and narrower myofibrils". Please provide a statistical test supporting the difference.

      In the revised manuscript, we compared sarcomere length and myofibril width between males and females from 0 to 96 hours AE using a two-way ANOVA with Sidak’s multiple comparisons test. We expanded our description of these observations in the main text, and details of the statistical analysis are now included in the revised figure legend (Figure 1E). Briefly, newly eclosed males showed slightly shorter sarcomeres than females - a consistent but non-significant trend (p = 0.9846) - which resolved by 12 h AE, with sarcomere lengths remaining similar thereafter (p = 0.1533; Figure 1E). In contrast, myofibril width was significantly narrower in the newly eclosed males (p = 0.0374), but this difference disappeared between 24 and 48 h AE as myofibrils expanded in diameter during post-eclosion development (p

      Were statistical tests performed using animals as sample numbers? Please clarify in the images what are animal and what are sarcomere numbers.

      Following standard guidelines, statistical tests were performed using the means of independent experiments, as noted in the figure legends. For each experiment, we used approximately 6 animals, and this information is now included in the Materials and Methods section.

      mef2-Gal4 should be spelled Mef2-GAL4 according to Flybase.

      This has been corrected in the revised text and figures.

      Are the images shown in Figure 2B representative? 96h AE appears thicker than 24h AE but the graph reports no difference.

      We aimed to show representative images, however, in the case of 96h APF we may have selected a wrong example. We now changed the image for a more appropriate one.

      The authors only found Zasp52 and alpha-Actinin at the Z-discs from 72h APF onwards, which is different to what others have reported.

      Similarly to former reports, we detected both α-Actinin (see Supplementary Figure 3B, now Figure 3C in the revised manuscript) and Zasp52 in microdissected IFMs as early as 36 hours APF. However, these markers were largely absent in isolated myofibrils from the early pupal stages (36–60 hours APF). By 60 hours APF, strong α-Actinin and Zasp52 signals were clearly visible in isolated myofibrils (the closest timepoint captured by dSTORM is 72h APF). As discussed in the manuscript, a likely explanation is that α-Actinin and Zasp52 are recruited to developing Z-bodies early on but are only fully incorporated into mature Z-discs between 48 and 60 hours APF. Their incomplete integration at earlier stages may lead to their loss during the isolation procedure.

      Thick filament length during development has also been estimated by Orfanos and Sparrow, which should be cited (PMID: 23178940)

      Contrary to the reviewer’s claim, the article 'Myosin isoform switching during assembly of the Drosophila flight muscle thick filament lattice' does not provide any measurements or estimates of thick filament length; it only includes a schematic illustration where the length of the thick filaments is not based on empirical data.

      **Referee Cross-commenting**

      I also agree with my colleagues comments, which are largely consistent.

      Reviewer #3 (Significance (Required)):

      This paper introduces a tool to measure sarcomere length. Easy to use tools that do this as well already exist. The tool can also measure sarcomere width, which it claims as unique point, which is not the case, see above comment.

      We are aware that other tools exist to measure sarcomere parameters (and we did not claim the opposite in our ms), nevertheless, we need to emphasize that based on our comparisons, IMA is superior to all three alternatives. Three software tools could, in principle, be used to measure both sarcomere length and myofibril diameter: MyofibrilJ, SarcGraph, and sarcApp. However, two of them - MyofibrilJ and SarcGraph - consistently under- or overestimate these values. The only tool capable of performing these measurements reliably, sarcApp, is no longer freely available, it requires programming expertise, and it does not support raw image file formats, making it difficult to use in practice (see above comments for more details). In contrast, IMA is user-friendly and does not require any programming expertise to install or operate. It can automatically process raw microscopic image formats without the need for preprocessing. Segmentation is fully automated, and no parameter tuning is necessary. The tool offers visual feedback on both the segmentation and fitting processes, enabling users to validate results with confidence. IMA delivers accurate and precise measurements of sarcomere length and diameter. Additionally, batch processing is enabled by default, significantly enhancing workflow efficiency.

      This manuscript shows that depending on the isolation and embedding media sarcomere and myofibrils width changes and hence artifacts can be introduced. While this is not suprising, it has not been well controlled in a number of previous publications.

      Furthermore, this paper measures sarcomere length and width during flight muscle development and consolidates what was already known from previous publications. Sarcomeres are added until 48 h APF, then they grow in diameter. Despite strong claims in the text, I do not see any significant novel findings how sarcomeres grow in length or width or any significant deviations from what has been published before. This is even documented in the supplementary graphs by comparing to published data. It is close to identical.

      The overall process has been quantitatively described in four previous studies (Reedy and Beall, 1993, Orfanos et al., 2015, Spletter et al., 2018, Nikonova et al., 2024). While there is general agreement on the pattern of sarcomere development, significant discrepancies exist among these datasets; differences that become particularly problematic when attempting to build structural models. More specifically: Reedy and Beall (1993) report substantially shorter sarcomeres compared to all other datasets, including ours. This discrepancy likely stems from two factors: (i) their use of longitudinal EM sections, where sample preparation is known to cause considerable tissue shrinkage; and (ii) the maintenance of their flies at 23 °C, a temperature that clearly delays development relative to the more commonly used 25 °C. Interestingly, Spletter et al. (2018) and Nikonova et al. (2024) conducted their experiments at 27 °C, which also deviates from standard conditions and may complicate comparisons. Orfanos et al. (2015) suggested that mature sarcomere length is reached by approximately 88 hours after puparium formation (APF). In contrast, our measurements show that sarcomeres continue to elongate beyond this point, reaching mature length between 12 and 24 hours post-eclosion. All four earlier studies report a mature sarcomere length around 3.2-3.3 µm, only slightly longer than the ~3.2 µm length of thick filaments (Katzemich et al., 2012; Gasek et al., 2016). This would imply an I-band length below ~100 nm, which is an implausibly short distance. In contrast, our data, along with several recent studies (González-Morales et al., 2019; Deng et al., 2021; Dhanyasi et al., 2020; DeAguero et al., 2019), support a mature sarcomere length of approximately 3.45 µm, placing the length of the I-band at around 250 nm. This estimate is more consistent with high-resolution structural observations from longitudinal EM sections and fluorescent nanoscopy (Szikora et al., 2020; Schueder et al., 2023). Although Reedy and Beall (1993) provide limited data on myofibril diameter during myofibrillogenesis, a more detailed quantitative analysis is presented by Spletter et al. (2018) and by Nikonova et al. (2024). Interestingly, Spletter et al. report two separate datasets - one based on longitudinal sections and another on cross-sections of DLM fibers. While the measurements are consistent during early pupal stages, they diverge significantly in mature IFMs (1.116 ± 0.1025 µm vs. 1.428 ± 0.0995 µm), a discrepancy that is not addressed in their publication. Nikonova et al. (2024) report even narrower myofibril widths (0.9887 ± 0.1273 µm). Moreover, the reported diameters of early myofibrils in all three datasets are nearly twice as large as those reported by Reedy and Beall (1993) and in our own measurements, directly contradicting the reviewer's claim that the values are “close to identical.” Finally, our data clearly demonstrate that both the length and diameter of IFM sarcomeres reach a plateau in young adults, which is a key developmental feature not examined in previous studies.

      In summary, we did not and we do not intend to claim that our conclusions are novel as to the general mechanisms of myofibril and sarcomere growth. Rather, our contribution lies in providing a high-precision, robust analysis of the growth process using a state-of-the-art toolkit, resulting in a comprehensive description that aligns with structural data obtained from TEM and dSTORM. We therefore believe that expert readers will recognize numerous valuable aspects of our approaches that will advance research in the field.

      Counting the total number of thick filaments during myofibril development is nice, however, this also has been done (REEDY, M. C. & BEALL, C. 1993, PMID: 8253277). In this old study, the authors reported the amount of filament across one myofibril. How does this compare to the new data here counting all filaments? Unfortunatley, this is not discussed.

      Indeed, the study by Reedy and Beall (1993) was primarily based on longitudinal DLM sections, which were used to estimate myofibril width and count the number of thick filaments on this lateral view images (e.g., ~15 thick filaments wide at 75 hours APF), but total thick filament numbers were not provided. While such data could theoretically be used to estimate the number of myofilaments per myofibril, these estimations would depend on the unverified assumption that the section includes the full width of the myofibril. Additionally, the study did not provide standard deviations or the number of measurements, limiting the interpretability and reproducibility of their findings. These points highlight the need for a more rigorous and quantitative approach. For these reasons, we chose to quantify myofilament number using cross-sections, providing more accurate and reliable assessments.

      Besides the difference between the lateral versus cross sections, a direct comparison of our studies is further complicated by differences in the developmental time points and experimental conditions used. Reedy and Beall (1993) reports data from pupae aged 42, 60, 75 and 100 hours, as well as from adults, whereas we present data from 36, 48, and 72 hours APF, and from 24 hours after eclosion, which corresponds to approximately 124 hours APF. Moreover, their experiments were carried out at 23 °C, a temperature that somewhat slows down pupal development and results in adult eclosion at around 112 hours APF, as stated in their study. In contrast, our experiments were carried out at the more commonly used 25 °C, where adults typically emerge around 100 hours APF.

      Collectively, these differences prevented meaningful comparisons between the two datasets, and therefore we preferred to avoid lengthy discussions on this issue.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This manuscripts presents a computational tool to quantify sarcomere length and myofibril width of the Drosophila indirect flight muscles, including developmental samples. This tool was applied to confocal and STORM super-resolution images of isolated myofibrils from adult and developing flight muscles. Thick filament numbers per myofibril were counted during development of flight muscles. A myofilament model of developing flight muscle myofibrils is presented that remains speculative for the early developmental stages.

      Major comments:

      1. The title of the manuscript appears unclear. What is a lattice model? Lattice is an ordered array. The filament array parameters for mature flight muscles was aready measured. It appears that the authors speculate how this order might be generated during sarcomere assembly, which is not studied in this manuscript as it is limited to periodic arrays after 36h APF.

      2. The authors review the flight muscle sarcomere length literature and conclude it is variable because of imprecise measurements. Likely this is partially true, however, more importantly is that the sarcomere length and width changes during isolation methods of the myofibrils, as well as by various embedding methods, as the authors show here as well in Figure 1B-E.

      Hence, I find the strongly claims the authors make here surprising, while they are isolating the myofibrils. Hence, these myofibrils are ruptured at the ends, relaxed or contracted, depending on buffer choice and passive tension is released. On page 8, the authors correctly state that the embedding medium causes shrinkage of the myofibrils. While isolation is state of the art for electron microscopy techniques, other methods including sectioning or even whole mount preparation have been developed for high resolution microscopy of IFMs that avoid these artifacts. Unfortunately, this manuscript only uses isolated myofibrils that were fixed and then mechanically dissociated by pipetting. This method likely induces variations as seen by the large spread of sarcomere length reported in Figure 1C (2.8-3.9µm?) and even bigger spreads for myofibril widths. Are these also seen in tissue without dissections? Unfortunately, no comparision to intact flight muscles are reported with the here presented quantification tool. The sarcomere length spread in the developmental samples is even larger.

      1. The authors suggest that there are sex differences in sarcomere length and pupal development duration. This is potentially interesting, unfortunately they then use mixed sex samples to analyse sarcomeres during flight muscle development.

      2. The IMA software tool lacks critical assessment of its performance compared to other tools and the validation presented is too limited. IMA seems to generate systematic errors, based on Fig S1E, as it does not report the ground truth. These have to be discussed and compared to available tools. The principles of fitting used in IMA seem well adapted to IFM myofibrils in low noise conditions, but may not be usable in other situations. This should be assessed and discussed.

      It is claimed that validation was achieved on simulated IFM images: do the authors rather mean simulated isolated IFM myofibril images? This is not quite the same in terms of algorithm complexity and this should be corrected if this is the case.

      1. The authors need to revise their comparison to other tools. It is incomplete and seemingly incorrect. It should be clearly stated that IMA is limited to isolated myofibrils, which is a far easier segmentation task than what other tools can do, such as sarcApp (Neininger-Castro et al. 2023, PMID: 37921850). Defining the acronym would be valuable in that sense. The claim line 129-130 "none can adequately measure myofibril diameter from regular side view images" is unclear. What do the authors refer to as "side view images"? Sarc-Graph from Zhao et al 2021, PMID: 34613960, and sarcApp from Neininger-Castro et al. 2023 provide sarcomere width, in conditions that are very similar to what IMA does, e.g. on xy images based on the documentation provided on github. A performance comparison with these tools would be valuable. Does installation and use of IMA require computational skills?

      2. How do the authors know that the bright phallodin signal visible that the Z-disc at 36h and 48h APF is due to actin filament overlap, as suggested? An alternative solution are more short actin filaments at the early Z-discs. The authors seem to doubt their own interpretation that actin filaments shrink when reading line 304 and following. This is obviously critical for the "model" presented.

      Minor comments:

      1. Figure S1B is not called out in the text.

      2. Fig. 1: Please state whenever images are simulations?

      3. Fig. 2: Length-width correlation - please provide individual points color-coded by time point?

      4. "newly eclosed males and females, we observed that males have slightly shorter sarcomeres and narrower myofibrils". Please provide a statistical test supporting the difference.

      5. Were statistical tests performed using animals as sample numbers? Please clarify in the images what are animal and what are sarcomere numbers.

      6. mef2-Gal4 should be spelled Mef2-GAL4 according to Flybase.

      7. Are the images shown in Figure 2B representative? 96h AE appears thicker than 24h AE but the graph reports no difference.

      8. The authors only found Zasp52 and alpha-Actinin at the Z-discs from 72h APF onwards, which is different to what others have reported.

      9. Thick filament length during development has also been estimated by Orfanos and Sparrow, which should be cited (PMID: 23178940)

      Referee Cross-commenting

      I also agree with my colleagues comments, which are largely consistent.

      Significance

      This paper introduces a tool to measure sarcomere length. Easy to use tools that do this as well already exist. The tool can also measure sarcomere width, which it claims as unique point, which is not the case, see above comment.

      This manuscript shows that depending on the isolation and embedding media sarcomere and myofibrils width changes and hence artifacts can be introduced. While this is not suprising, it has not been well controlled in a number of previous publications.

      Furthermore, this paper measures sarcomere length and width during flight muscle development and consolidates what was already known from previous publications. Sarcomeres are added until 48 h APF, then they grow in diameter. Despite strong claims in the text, I do not see any significant novel findings how sarcomeres grow in length or width or any significant deviations from what has been published before. This is even documented in the supplementary graphs by comparing to published data. It is close to identical.

      Counting the total number of thick filaments during myofibril development is nice, however, this also has been done (REEDY, M. C. & BEALL, C. 1993, PMID: 8253277). In this old study, the authors reported the amount of filament across one myofibril. How does this compare to the new data here counting all filaments? Unfortunatley, this is not discussed.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript titled "A myofilament lattice model of Drosophila flight muscle sarcomeres based on multiscale morphometric analysis during development," Görög et al. perform a detailed analysis of morphological parameters of the indirect flight muscle (IFM) of D. melanogaster. The authors start by illustrating the range of measurements reported in the literature for mature IFM sarcomere length and width, showing a need to revisit and determine a standardized measurement. They develop a new Python-based tool, IMA, to analyze sarcomere lengths from confocal micrographs of isolated myofibrils stained with phalloidin and a z-disc marker. Using this tool, they demonstrate that sample preparation (especially mounting medium), as well as fiber type, sex, and age influence sarcomere measurements. Combining IMA, TEM, and STORM data, they measure sarcomere parameters across development, providing a comprehensive and up-to-date set of "standardized" sarcomere measurements. Using these data, they generate a model integrating all of the parameters to model sarcomeres at four discrete timepoints of development, recapitulating key phases of sarcomere formation and growth.

      Major comments:

      • Line 200 & 901 - Figure S1B - The authors make a strong statement about the use of liquid versus hardening media, and it is clear from the image provided in Figure S1 that there is a difference in the apparent sarcomere width. The identity of the "liquid media" versus the "hardening media" should be clearly identified in the Results, in addition to the legend for Figure S1. The authors show that "glycerol-based solutions" increase sarcomere width, but the Materials only list 90% glycerol and PBS. However, a frequently used liquid mounting media is Vectashield. Based on the literature, measurements in liquid Vectashield show diameters significantly less than 2.2 microns observed here with presumably 90% glycerol or PBS. Can the authors qualify this statement, or provide data that all forms of liquid mounting media cause this effect? Does this also apply to hemi-thorax and sectioned preparations, or just isolated myofibrils?

      • Can the authors comment on whether the length of fixation or fixation buffer solution, in addition to the mounting medium, make a difference on sarcomere length and diameter measurements? This is another source of variation in published protocols.

      • Line 237-238. The authors conclude that premyofibrils are much thinner than previously measured. The use of Airyscan to more accurately measure myofibril width at this timepoint is a good contribution, as indeed diffraction and light scatter likely contribute to increased width measured in light microscopy images. I also wonder, though, how well the IMP software performs in measuring width at 36h APF, given how irregular the isolated myofibrils at this stage look (wide z-lines but thinner and weaker H and I bands as shown in Fig. 2B)? Also, how much of the difference in sarcomere width arises due to effects of "stripping" components off of the sarcomere at the earliest timepoint (for example alpha-actinin or Zasp proteins)? Myofibrils at early timepoints do contain more than 4-12 sarcomeres in a line (they extend the full length of the myofiber), so it is possible they are breaking due to the detergent and mechanical disruption induced by the isolation method.

      • Line 312 - What does "stable association" mean in this context? The authors mention early timepoints lack stable association of alpha-Actinin or Zasp52, and they reference Fig. S4C, but this figure only shows 72h and 24 AE, not 36h and 48 h APF. Previous reports have seen localization of both alpha-Actinin and Zasp52, so presumably the detergent or mechanical isolation is stripping these components off of the isolated myofibrils up until 72h. This same type of issue comes up again in

      • Lines 325-334, where the authors talk about 3E8 and MAC147. They state that 3E8 signal significantly declines in later stages and that MAC147 is not suitable to label myofibrils in young pupae, but they only show data from 72 APF and 24 AE (which looks to have decent staining for both 3E8 and MAC147). A clearer explanation here would be helpful. Figure 3B. The authors show the H, Z, and I lengths in B', B', and B' and discuss these lengths in the text (lines 305-320). It would also be nice to actually have the plots showing the measured/calculated lengths for thin and thick filaments. These are mentioned in the results, but I cannot find the plots in the figures and there is no panel reference.

      • Line 400. Does the model in Figure 4 actually have molecular resolution as the authors claim? From these views, thick and thin filaments appear to be represented by cylindrical objects. Localization of specific molecules would require further modeling with individual proteins. Or do the authors mean localization from STORM imaging relative to the ends of the thick and/or thin filaments? The model itself is a useful contribution, but based on Figure 4, resolution of individual molecules is not evident.

      • The main Results section of the text is condensed into 4 figures. However, I found myself flipping back and forth between the main figures and the supplement continuously, especially parts of Supplemental Figures 1, 3, 4, and 5. With such large amounts of detail in the Results relying on the supplement, it may be worth considering reorganizing the main and supplemental figures, and having 7 main figures, to include important panels that are currently in the supplement (esp. Fig S1B, S1C, S1D, S3B, S4, S5).

      Minor comments:

      • On the plots in Fig. S1B, D, and F, it is hard to see the color of the dots because the red error bars are on top of them. Can the other distribution dots be tinted the correct color or the x-axis labels be added, so it is clear which dataset is which?

      • Line 142 needs a reference to Figure S1, Panel E, which shows the accuracy and precision measurements.

      • Lines 198 - is this range from the above publications? Needs to be clearly cited.

      • Figure S3B is confusing - why do the blow-ups overlap both the top (presumably microdissected) and the bottom (presumably isolated) images? The identity of microdissected images should be labeled, as they are hard to see underneath of the blown-up images and the identity of individual image planes wasn't immediately obvious.

      • Line 298. By "misaligned," do the authors mean the pointed ends are not uniformly anchored in the z-disc, leading to the wide z-disc measurements? At this early stage, I'm not sure "misaligned" is the right word - perhaps "were not yet aligned in register at the z-disc" or something similar.

      • Figure S6 - spelling mistake in label of panel A, "sarcomer" should be "sarcomere"

      • Line 487. Spelling "Zaps52" should be "Zasp52"

      • Line 887. Spelling "Myofilement" should be "Myofilament"

      • Line 946-947. In the legend for Supp. Fig. 3., the authors should specify which published datasets on sarcomere length are shown in the figure by including the references in the legend. Presumably the "isolated individual myofibrils" are the blue "this study" lines, leaving the "microdissected muscles" as the magenta "previous reports" on the figure. Without the reference, it is not clear if these are microdissected, isolated myofibrils, hemi-thorax sections, cryosections, or another preparation method for the "previous reports" data.

      Referee Cross-commenting

      I agree with the comments from the other reviewers. Many of the major themes are consistent across the reviews, including regarding the model, preparation methods, and the software tool.

      Significance

      Strengths: This manuscript is an important contribution to the field of sarcomere development. The authors use modern technologies to revisit variation in morphometric measurements in the literature, and they identify parameters that influence this variation. Notably, sex-specific differences, DLM versus DVM measurements, and mouting media are potential contributors to the variability. Combining TEM and STORM with a confocal timecourse of isolated myofibrils, they refine previously published values of sarcomere length and width, and add more comprehensive data for filament length, number and spacing. This highly accurate timecourse demonstrates continual growth of sarcomeres after 48 h APF, and correct some inconsistencies from previous large-scale timecourse datasets. These data are very valuable to the field, especially Drosophila muscle biologists, and will serve as a comparative resource for future studies.

      Weaknesses: At early timepoints, loss of sarcomere components through mechanical or detergent-mediated artifacts may influence the authors' measurements. In addition, isolating myofibrils is not always the most ideal approach, as it loses information on myofiber structure as well as organization and structure of the myofibrils in vivo. The point regarding liquid versus hardening mounting media is valuable, but remains to be tested and validated with the diverse liquid and hardening media used by other labs. The IMA software seems to be designed specifically for analysis of isolated myofibrils, and it is unclear if it would work for other types of IFM preparations. A last point is that TEM and STORM may not be available on a regular basis to many labs, hindering wide implementation of the approach used in this manuscript to generate very accurate and detailed measurements of sarcomere morphometrics.

      Audience: Scientists who study sarcomerogenesis or Drosophila muscle biology.

      My expertise: I study muscle development in the Drosophila model.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      In this work, the authors present a careful study of the lattice of the indirect flight muscle (IFM) in Drosophila using data from a morphometric analysis. To this end, an automated tool is developed for precise, high-throughput measurements of sarcomere length and myofibril width, and various microscopy techniques are used to assess sub-sarcomeric structures. These methods are applied to analyze sarcomere structure at multiple stages in the process of myofibrillogenesis. In addition, the authors present various factors and experimental methods that may affect the accurate measurement of IFM structures. Although the comprehensive structural study is appreciated, there are major issues with the presentation/scope of the work that need to be addressed:

      Major Comments

      1. The main weakness of the paper is in its claim of presenting a model of the sarcomere. Indeed, the paper reports a structural study that is drawn onto a 3D schematic. There is no myofibrillogenesis model that would provide insights into mechanisms. Therefore, the use of the word model is grossly overstated.

      2. In general, the major focus and contribution of the work is unclear. How does the comprehensive nature of the measurements contribute to existing literature?

      3. Figure labels are often rather confusing - for example it is unclear why there is a B, B', B' etc instead of B,C,D, etc.

      4. Some comments in the text are not clearly tied to the figures. For example, in lines 108-109, are the authors referring to the shadow along the edges of the myofibril when saying they are not clearly defined (Figure 1C)?

      5. In line 116, it is unclear what "surrounding structures" the authors are referring to if the myofibrils are isolated.

      6. In lines 141-142, there is no reference of data to back up the claim of validation.

      7. In line 170, the authors mention the mef2-Gal4/+ strain as a Gal4 driver line but do not clearly state how this strain is different from the wildtypes or how this impacts their results.

      8. In lines 182-185, the authors discuss the effects of tissue embedding on morphometrics. Were factors such as animal sex, age, fiber type, etc. conserved in these experiments? If not, any differences in results may be confounding.

      9. In lines 199-201, the authors discuss results of myofibril diameter using different preparation methods, yet no data is cited to support the claims. In line 220, the phrase "6 independent experiments" is unclear. Is each independent experiment performed using a different animal? Furthermore, are 6 experiments performed for each time point?

      10. In line 254, the authors refer to "number of sarcomeres". It must be clearly stated if this refers to sarcomeres per myofibril, image area, etc.

      11. In line 274, the authors refer to "myofilament number". It must be clearly stated if this refers to myofilaments per myofibril, image area, etc.

      12. In line 299, the authors mention that thin filaments measured less than 560 nm in length, yet no data is cited to support this.

      13. In the "Quantifying sarcomere growth dynamics" section of the summary (starting from line 402) the authors introduce data that would be more naturally placed in the results and discussion section.

      14. In lines 422-423, it is not mentioned what the controls are for.

      15. In the caption of Figure 1C, it is not mentioned what the red dashed lines in the microscope images represent.

      16. In the caption of Figure 1D, the difference between the lighter and darker grey points is not mentioned.

      17. In line 849, the stated p-value (0.003) does not match that mentioned in the figure (0.0003).

      18. In line 874, it is not clear what an "independent experiment" refers to (different animal, etc.?).

      19. Figure 2A is hard to read. Using different colored dots for different time points might help.

      20. The significant figures presented in Figure 4 give a completely inaccurate representation of the variability of the measurements achieved with these techniques.

      21. In line 877, it should be mentioned that the number of filaments is counted per myofibril. The y-axes in the figure should also be adjusted to clarify this.

      22. In line 883, it is not clear what an "independent experiment" refers to (different animal, etc.?).

      23. The statement of sample sizes in all figures is a little confusing.

      24. In lines 1007-1008, the authors imply that the lattice model is needed for calculation of myofilament length. However, from the equations and previous data, it seems that this can be estimated using the confocal and dSTORM images.

      25. A more specific discussion of future directions is needed to put this paper in context. For example: Can anything from the overall process be used to better understand sarcomere dynamics in larger animals/humans? Can this be applied to disease modelling?

      26. One of the major claims of the paper is that there is a measurable variability with sex and other parameters. However, this data is never clearly summarized, presented (except for supplement), or discussed for its implications.

      Minor Comments

      1. Lines 60-65 seem to break the flow of the introduction. As the authors discuss existing methods in literature for IFM analysis in the previous couple sentences, the following sentences should clearly state the limitations of existing methods/current gap in literature and a general idea of what the current work is contributing.

      2. In line 104, the acronym for ZASPs is not spelled out.

      Referee Cross-commenting

      I agree as well.

      Significance

      In summary, this paper provides a multi-scale characterization of Drosophila flight muscle sarcomere structure under a variety of conditions, which is potentially a significant contribution for the field. However, the paper scope is overstated in that it does not provide an actual sarcomere model. Further, there are multiple issues with data presentation that impact the readability of the manuscript.

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      Reply to the reviewers

      Manuscript number: RC-2025-02953

      Corresponding author(s): Andreas, Villunger

      1. General Statements [optional]

      *We would like to thank the reviewers for their constructive input and overall support. We appreciate to provide a provisional revision plan, as outlined here, and are happy to engage in additional communication with journal editors via video call, in case further clarifications are needed. *

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer #1

      __Evidence, reproducibility and clarity __

      Summary: This manuscript by Leone et al describes the role of the PIDDosome in cardiomyocytes. Using a series of whole body and cardiomyocyte specific knockouts, the authors show that the PIDDosome maintains correct ploidy in these cells. It achieves this through inducing cell cycle arrest in cardiomyocytes in a p53 dependent manner. Despite this effect on ploidy, PIDDosome-deficient hearts show no structural or functional defects. Statistics and rigor appear to be adequate.

      We thank this referee for taking the time to evaluate our work and their valuable comments. We assume that this reviewer by mistake indicates that the phenomenon we describe, depends on p53. As outlined in the abstract and throughout the manuscript, the effect is independent of p53, but may additionally still involve p21, acting along or parallel to the PIDDosome.

      Major comments: 1. Figure 1 uses fluorescent intensity of a nuclear stain to determine ploidy per nucleus and they further separate the results into mononucleated, binucleated or multinucleated cells. It is hard to know how to interpret these results without further information or controls. Is there a good positive control that can be used to help to show whether this assay is quantitative? The differences are larger with the Raidd and caspase-2 knockouts than with the Pidd knockouts but this is not addressed.

      *We appreciate this concern. Regarding a "good positive control" we can say that we follow state-of the art in the cardiomyocyte field and studies by the Evans (PMID: 36622904), Kuhn (PMID: 32109383), Bergmann (PMID: 26544945) and Patterson labs (PMID: 28783163, 36912240) all use the identical approach to discriminate 2n from 4n nuclei in microscopy images at the cellular level. The fact that the majority of rodent CM nuclei is indeed diploid (PMID: 31175264, 31585517 and 32078450) and a large number of nuclei has been evaluated to assess their mean fluorescence intensity (MFI) reduces the risk of a systematic bias in our analysis. Moreover, we have used an orthogonal approach that is indeed quantitative to define DNA content, i.e,. flow-cytometry based evaluation of DNA content in isolated CM nuclei (Fig. 1C). We hence are confident our assays are quantitative. *

      Regarding the fact that loss of Pidd1 causes a more saddle phenotype, we can offer to discuss this in light of the fact that Pidd1 has additional functions, outside the PIDDosome (PMID: 35343572), and that we made similar observations when analyzing ploidy in hepatocytes (PMID: *31983631). Given the fact that all components of the PIDDosome show a similar phenotype, and that this phenotype is mimicked by loss of the protein that connects PIDD1 and centrosomes, ANKRD26 (Fig. 4a), we are confident that this biological variation in our analysis is not affecting our conclusions. *

      On line 459 the authors state that the increase in polyploidy in PIDDosome knockouts occurs in adult hood but this is not directly tested. In fact, in the next section the polyploidy is assessed in early postnatal development. This statement should be explained or removed.

      We see that we have made an unclear statement here. In fact, we first noted increases in ploidy in adult heart and then define the time window in development when this happens. This sentence will be rephrased.

      In Figure 4. The authors obtained RNAseq data for P1, P7 and P14 but only show the differences with and without caspase-2 at P7. Given that the differences in ploidy are more significant at P14 (Fig 3D), all the comparisons should be shown along with analysis of whether the same genes/gene families are altered in the absence of caspase-2.

      The reason why we focus on postnatal day 7 (P7) is that data from Alkass et al (PMID: 26544945) and other labs (PMID: 31175264 ) document that on this day the initial wave of binucleation peaks. Hence, we hypothesized that the PIDDosome must be active in most CM, which aligns well with the increased mRNA levels of all of its components (Figure 3). Interestingly, it seems that its action is tightly regulated, as mRNA of PIDDosome components drop on P10, suggesting PIDDosome shut-down or downregulation. Similar findings have been noted in the liver (PMID: *31983631). Alkass and colleagues also show that very few CMs enter another round of DNA synthesis between P7 and P14, and hence possible transcriptome changes in the absence of the PIDDosome will be strongly diluted. *

      Please note that on P1, there is no difference between genotypes to be expected as all CM are mononucleated diploids and cytokinesis competent, as previously demonstrated (PMID: *26544945). Moreover, PIDDosome expression levels are extremely low (Fig. 3A). As such, no difference between genotypes are expected on P1. In addition, on P14 the ploidy phenotype observed in PIDDosome knockout mice reaches the maximum and ploidy increases are comparable to adult tissue. Thus, at this time the trigger for PIDDosome activation (cytokinesis failure) is no longer observed as the majority of CMs are post-mitotic, (PMID: 26247711). As such the impact of PIDDosome activation on the P14 transcriptome is most likely negligible. However, if desired, we can expand our bioinformatics analysis summarizing findings made related to DEGs over time in wt animals by comparing genotypes also on day 1 and day 14. In light of the above, analysis between genotypes on P7 holds still appears as the one most meaningful. *

      Some validation of the RNAseq and/or proteomics results would be an important addition to this study

      We agree with this notion and propose to validate key candidates related to cardiomyocyte proliferation and polyploidization, some of which we found to be differentially expressed at the mRNA level on day 7in the RNAseq data (e.g., p21, Foxm1, Kif18a, Lin37 and others)

      Regarding the proteomics results, we face the challenge that we can only try to confirm if candidate proteins are likely caspase substrates in silico using DeepCleave*, and potentially pick one or two candidates linked to CM differentiation for further analysis in vitro and in heterologous cell based assays (e.g. 293T cells), as no bona-fide ventricular cardiomyocyte cell lines exist. Primary postnatal CMs are extremely difficult to transfect, nor they proliferate without drug-treatment, or fail cytokinesis ex vivo. *

      Figure 4D: the authors make the conclusion that p21 is downstream of PIDD (et p53 independent). However, this is not supported by the data because the increase in 4N cells/decrease in 2N cells, although statistically significant, is nowhere near that of caspase-2 KO and caspase-2/p21 KO. Statistics should also compare p32KO with c2KO. In the absence of any other data, the more likely conclusion is that p21 is not involved.

      *We agree that the findings related to the impact seen upon loss of p21 suggest that it is not the only effector involved in ploidy control and it may not even be an effector engaged by caspase-2, as C2/p21 DKO mice have an even higher ploidy increase, albeit not statistically significant. However, it is important to highlight that p21 (Cdkn1a) was found to be downregulated in our transcriptomic analysis suggesting an involvement in the caspace2-cascade. We are happy to highlight this when presenting the results and in the discussion. *

      *We assume that this referee refers to p73 KO data that should be compared to Casp2 KO data (could be read as p73 or p53, but the latter we compare side by side with Casp2 in Fig. 4 already). As p73 KO mice were not found to be viable beyond day 7 (our attempt to find animals on day 10 failed, in line with published literature (PMID: 24500610, 10716451)), we can only offer to compare this data set to the data presented in Figure 3C, where we have analyzed ploidy increases on day 7 from wt and PIDDosome mutant mice. This re-analysis will show that only Caspase-2 mutant mice display a significant ploidy increase on P7, when compared to wt or p73 mutant animals, while no difference are noted between wt and p73 mutant mice (to be included in new Suppl. Fig. 3C) *

      Minor comments: Suggest moving Figure 4A to Figure 3 as it seems to fit better there based on the citation of this figure in the text

      *We can see some benefit in this recommendation and included panel 4A now in an updated version of Figure 3. *

      Recommend enhancing the brightness of microscopy images in Figure 1E and 2D

      We will try to improve image quality, may have been due to PDF conversion

      Significance

      This study provides interesting information for the role of the PIDDosome in protecting from polyploidy and adds to the body of work by this same group studying this pathway in the liver.

      The main weakness in terms of significance is the lack of a phenotype in the hearts of these animals. Therefore, it is clear that ploidy (or at least PIDDosome dependent ploidy) has minimal impact on cardiac development.

      We respectfully disagree with the comment that the lack of impact on cardiac function constitutes a weakness of our findings. Several studies on ploidy control in the liver (PMID 34228992) but importantly also heart (PMID: 36622904) have failed to document a clear impact of increased ploidy on organ function. This does not infer insignificance, but maybe rather that the context where this becomes relevant has not been identified. We are happy expand on this in our discussion

      The authors mention that they have not tried giving these mice an myocardial infarct (MI) or inducing any other type of cardiac damage. Although it is understood that these experiments are likely outside of the scope of the present study, without this information the impact of this study is moderate. I recommend expanding the discussion to provide a more in-depth possible rationale as to why ploidy perturbations do not lead to structural changes like in the liver.

      Despite this, the insights to the pathway itself are interesting to investigators in the caspase-2 field if a little underdeveloped, especially concerning the role of p21.

      My expertise is in cell death and caspase biology (especially caspase-2). I have sufficient expertise to evaluate all parts of this paper.

      *As mentioned above, we will amend our conclusions on p21, in light of potential findings made when validating DEG candidates, as stated above. *

      *We hope that the changes and amendments proposed here will be satisfactory to this referee to recommend publication of a revised manuscript. *

      Reviewer #2

      __Evidence, reproducibility and clarity: __

      __Summary: __

      In this study, the authors investigated the role of the PIDDosome during cardiomyocyte polyploidization. PIDDosome is a multi-protein complex activating the endopeptidase Caspase-2, and shown to be involved in eliminating cells with extra centrosomes or in response to genotoxic stress (Burigotto & Fava, 2021, Sladky and Villunger, 2020). In both cases, the PIDDosome is recruited in a ANKRD26-dependent manner at the centrosomes leading to p53 stabilization and cell death (Burigotto & Fava, 2021; Evans et al., 2020; Burigotto et al., 2021).

      Here, by studying mouse cardiomyocyte differentiation, the authors showed that PIDDosome is imposing ploidy restriction during cardiomyocyte differentiation. Importantly, in contrast to a previous report in the liver (Sladky et al., 2020), they showed that PIDDosome acts in a p53-independent manner in cardiomyocytes. Indeed, they suggested that PIDDosome controls ploidy in cardiomyocytes through p21 activation.

      We want to thank this reviewer for the time taken to evaluate our work and provide critical feedback that will help to improve our revised manuscript.

      __Major comments: __

      In general the conclusions of the authors are well supported by the experiments. However, I would suggest the following experiments/analysis to strengthen the paper:

      The authors should improve the Figure 1 to help the readers who are not familiar with cardiomyocyte polyploidization. For instance, I would suggest to add a scheme to summarize cardiomyocyte polyploidization (in terms of nuclear size, mono vs multi and so on).

      We agree that a visual summary of the postnatal timing of CM polyploidization will be helpful for the generalist not familiar with the topic and have added a scheme, adapted from a study by Alkass et al. (PMID: *26544945), who elegantly defined the timing of this process during postnatal mice life (now Fig. 1A). *

      Based on the images they presented in 1B, the authors should also measure the nuclear area or volume in the different conditions in which components of the PIDDosome were depleted. Indeed, these two parameters should be easier to conceptualize for the readers (instead of the fluorescence nuclear intensity). This could help to understand if the nuclear size is maintained between the different conditions and if this is comparable between mono, bi or multinucleated cardiomyocytes.

      We have acquired this data and it can be used to provide additional information on nuclear area and/or volume. We propose to focus on re-analyzing data from wt, Casp2 and XMLC2CRE/Casp2f/f mice. The additional information can be included in Figures 1 & 2, respectively.

      • In Figure 2A, the authors presented cross section of heart from animals showing that PIDDosome depletion has no effect on heart size. This is a surprising result since cardiomyocytes have higher ploidy levels and this could have an effect on their function. Since the importance of this observation, the authors should present a quantification of the heart size in the different conditions shown in Figure 2A.

      We agree with this comment. We can measure the heart vs. body weight ratio or tibia length in adult Casp2-/- vs. WT (3 month old) in order to indirectly evaluate possible increases in CM size linked to increased ploidy.

      Also, the percentage of cardiomyocytes presenting higher levels of ploidy seems quite low. The authors should discuss this point. In particular because this could explain the absence of consequences on heart size and function at steady state.

      We agree with this conclusion and will expand on this in our discussion. It is important to note that as opposed to findings made in liver (PMID: *31983631), genetic manipulation of ploidy regulators such as E2f7/8 (PMID: 36622904), only led to modest changes in CM ploidy, suggesting that either a small band-width compatible with normal heart function exists, or that additional mechanisms exist that take control when these thresholds set by the PIDDosome or E2f7/8 are exceeded. These mechanisms could involve Cyclin G (PMID: 20360255), or TNNI3K (PMID: 31589606). Importantly, a recent publication has shown that overexpression of Plk1(T210D) and Ect2 from birth causes increased heart weight coupled with a minor decrease in CM size. These mice undergo to premature death (PMID: 39912233) suggesting that CM polyploidization is a tight regulated process regulated by several independent mechanisms during heart development. *

      In Figure 2D, the authors measured the cardiomyocyte cross-sectional area and concluded that removing PIDDosome components have no effect on cardiomyocyte cell size. Since it has been shown that ploidy increase is normally associated with an increase in cell area, the authors should measure cell area of cardiomyocytes analyzed in Figure 1B. It could be then interesting to establish a correlation with nuclear area and the mono, bi or multinucleated status. This will strengthen the results showing that ploidy increases without affecting cell area.

      Indeed, studies in PIDDosome deficient livers suggest that tissue is containing fewer but bigger cells (PMID: *31983631). As opposed to the liver the percentage of cardiomyocytes presenting higher levels of ploidy is relatively low. Thus, a possible increase in CM size in PIDDosome deficient mice may be masked in heart cross-sections. In order to better correlate the ploidy with cell size, we propose to reanalyze our microscopy images used to extract the data displayed in Fig. 1D. We may run into the problem though that the number of cells acquired may become limiting to achieve sufficient statistical power. In this case we could pool data from different PIDDosome mutant CM to increase statistical power. Again, we propose to initially prioritize wt vs. Casp2 vs. XMLC2/Casp2f/f mice. In addition, we can offer to quantify heart to body weight ratio or tibia length as an additional read-out (see answer to a previous reviewer comment). *

      The authors should discuss the fact that PIDDosome depletion lead only to a mild increase in ploidy levels (4N) in a small percentage of cardiomyocyte. If the PIDDosome is controlling ploidy, one could expect that removing it should lead to a drastic increase in the ploidy levels. Is PIDDosome depletion leading to cell death in some cardiomyocyte? The authors should discuss this point in the discussion or if relevant show a staining with an apoptosis marker. Is another mechanism compensating to prevent higher ploidy levels in cardiomyocytes?

      These are valid thoughts, some of which we contemplated before. In part, we have addressed them in our response to Reviewer#1, above, discussing similar findings made in E2f7/8 deficient hearts (PMID: 36622904), or Cyclin G overexpressing hearts (PMID: 20360255), where also only modest changes in ploidy were achieved. Together these observations are suggesting alternative control mechanism able to act, or limited tolerance towards larger shifts in ploidy, incompatible with proper cell function and survival. Towards this end, we can offer to test if we find increased signs of cell death in PIDDosome mutant hearts by TUNEL staining of histological sections. Of note, we did not find evidence for such a phenomenon in the liver (PMID: 31983631).

      Even if the authors presented RNAseq data suggesting that the PIDDosome is activated during cardiomyocyte differentiation, they should clearly demonstrate this point to strengthen the message of the paper. Indeed, the conclusions are based on the absence of PIDDosome components triggering higher ploidy in cardiomyocytes. However, we don't know whether (and when) the PIDDosome is activated during cardiomyocyte differentiation to control their ploidy levels. I would suggest to analyze PIDDosome activation markers by immunofluorescence in *cardiomyocytes at different developmental stages. *

      *We agree with this referee that direct proof of PIDDosome activation would be helpful and that we only infer back from loss of function phenotypes when and where the PIDDosome becomes activated. However, several technical issues prevent us from collecting more direct evidence of PIDDosome activation in the developing heart. 1) Polyploidization in heart CM appears to happen gradually in CM from day 3 on with a peak at day 7 (PMID: 26544945). Hence, this is not a synchronous process, where we could pinpoint simultaneous activation of the PIDDosome in all cells at the same time, which would facilitate biochemical analysis, e.g., by western blotting for signs of Caspase-2 activation (i.e. the loss of its pro-form, PMID: 28130345). 2) Our most reliable readout, MDM2 cleavage by caspase-2 giving rise to specific fragments detectable in western, is not applicable to mouse tissue, as the antibody we use only detects human MDM2 (PMID: 28130345) and no other MDM2 Ab we tested gave satisfactory results. Independent of that, 3) we do not see involvement of p53 in CM ploidy control (arguing against a role of MDM2). *

      *As such, we can only offer to look at extra centrosome clustering in postnatal binucleated CM (as also suggested further below), as a putative trigger for PIDDosome activation. However, this has been published by the first author of this study before (PMID 31301302). Given that we have made the significant effort to time resolve the increase in ploidy in postnatal mice (please note that several hearts needed to be pooled for each time point, analyzed in multiple biological replicates), we think that our conclusions are well-justified based on the genetic data provided. *

      Concerning the methods, the authors must add the references for each product they used and not only the origin. When relevant, the RRID should be indicated. Without this information the method and the data cannot be reproduced.

      We will update this information where relevant to reproduce our results

      Minor comments:

      In general, the text and the figures are clear. Nevertheless, I would suggest the following changes:

      • Figures 1B, 2B and 2C: the y-axis must start at 0.

      We will adopt axes accordingly

      Figure 4A: The authors should stain centrosomes in cardiomyocytes. This should strengthen the conclusion taken by the authors based on the results obtained in mice depleted for ANKRD26. Indeed, for the moment they are insufficient to conclude about the role of the centrosomes. The authors should show that centrosomes cluster in cardiomyocytes (a condition necessary for PIDDosome activation in polyploid cells) and if possible that component of the PIDDosome are recruited here.

      *This point is well taken and addressed in part above. Clustering of extra centrosomes has been documented and published by the first author of this study in rat polyploid cardiomyocytes (PIMID; cited). We can offer to show clustering of centrosomes in mouse CM isolated from day 7 hearts, but while PIDD1 can be detected well in MEF, we repeatedly failed to stain fro PIDD1 in primary CMs. *

      Figure 4F: I would suggest to modify the working model to emphasize more the differences between WT and PIDDosome KO.

      We will aim to improve this cartoon/graphical abstract

      The prior studies are referenced appropriately.

      Reviewer #2 (Significance (Required)):

      How polyploid cells control their ploidy levels during differentiation remains poorly understood. The data presented here represent thus an advance concerning this question. The actual model concerning PIDDosome activation relies on the presence of extra centrosomes that drives the ANKDR26-dependent recruitment of the PIDDosome. Then, Caspase 2 is activated leading to a p53-p21 dependent cell cycle arrest (Burigotto & Fava, 2021, Sladky and Villunger, 2020; Janssens & Tinel, 2012; Evans et al., 2020; Burigotto et al., 2021). In this study, the authors showed that similar pathway takes place during cardiomyocyte differentiation to control ploidy levels. These data are reminiscent of previous work showing PIDDosome involvement during hepatocyte polyploidization (Sladky et al. 2020). Together, these data highlight the prominent role of the PIDDosome complex in controlling ploidy levels in physiological context. Importantly, this study identified that the classical p53-dependent cell cycle arrest described after PIDDosome activation is not involved here. Instead, the data established that independently of p53, p21 contribute to control cardiomyocyte ploidy. In consequence, this study extends the initial pathway associated with PIDDosome activation and suggest that other mechanisms could take place to restrain cell proliferation upon PIDDosome activation. Overall, this makes this paper significant and of interest for the following fields: polyploidy, heart/cardiomyocyte development and PIDDosome.

      My field of expertise includes polyploidy, cell cycle and genetic instability.

      We thank this reviewer for the time taken and the positive feedback provided.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      N/A

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

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *As outlined above, limited tools are available to validate putative caspase-2 substrates, identified in proteomics analysis, in an impactful manner. *
      • *Also, as discussed above, we deem myocardial infarction experiments in mice as unsuitable to improve our work, as with all likely-hood, they will yield negative results. *
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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors investigated the role of the PIDDosome during cardiomyocyte polyploidization. PIDDosome is a multi-protein complex activating the endopeptidase Caspase-2, and shown to be involved in eliminating cells with extra centrosomes or in response to genotoxic stress (Burigotto & Fava, 2021, Sladky and Villunger, 2020). In both cases, the PIDDosome is recruited in a ANKRD26-dependent manner at the centrosomes leading to p53 stabilization and cell death (Burigotto & Fava, 2021; Evans et al., 2020; Burigotto et al., 2021).

      Here, by studying mouse cardiomyocyte differentiation, the authors showed that PIDDosome is imposing ploidy restriction during cardiomyocyte differentiation. Importantly, in contrast to a previous report in the liver (Sladky et al., 2020), they showed that PIDDosome acts in a p53-independent manner in cardiomyocytes. Indeed, they suggested that PIDDosome controls ploidy in cardiomyocytes through p21 activation.

      Major comments:

      In general the conclusions of the authors are well supported by the experiments. However, I would suggest the following experiments/analysis to strengthen the paper:

      • The authors should improve the Figure 1 to help the readers who are not familiar with cardiomyocyte polyploidization. For instance, I would suggest to add a scheme to summarize cardiomyocyte polyploidization (in terms of nuclear size, mono vs multi and so on). Based on the images they presented in 1B, the authors should also measure the nuclear area or volume in the different conditions in which components of the PIDDosome were depleted. Indeed, these two parameters should be easier to conceptualize for the readers (instead of the fluorescence nuclear intensity). This could help to understand if the nuclear size is maintained between the different conditions and if this is comparable between mono, bi or multinucleated cardiomyocytes.
      • In Figure 2A, the authors presented cross section of heart from animals showing that PIDDosome depletion has no effect on heart size. This is a surprising results since cardiomyocytes have higher ploidy levels and this could have an effect on their function. Since the importance of this observation, the authors should present a quantification of the heart size in the different conditions shown in Figure 2A. Also, the percentage of cardiomyocytes presenting higher levels of ploidy seems quiet low. The authors should discuss this point. In particular because this could explain the absence of consequences on heart size and function at steady state.
      • In Figure 2D, the authors measured the cardiomyocyte cross-sectional area and concluded that removing PIDDosome components have no effect on cardiomyocyte cell size. Since it has been shown that ploidy increase is normally associated with an increase in cell area, the authors should measure cell area of cardiomyocytes analyzed in Figure 1B. It could be then interesting to establish a correlation with nuclear area and the mono, bi or multinucleated status. This will strengthen the results showing that ploidy increases without affecting cell area.
      • The authors should discuss the fact that PIDDosome depletion lead only to a mild increase in ploidy levels (4N) in a small percentage of cardiomyocyte. If the PIDDosome is controlling ploidy, one could expect that removing it should lead to a drastic increase in the ploidy levels. Is PIDDosome depletion leading to cell death in some cardiomyocyte? The authors should discuss this point in the discussion or if relevant show a staining with an apoptosis marker. Is another mechanism compensating to prevent higher ploidy levels in cardiomyocytes?
      • Even if the authors presented RNAseq data suggesting that the PIDDosome is activated during cardiomyocyte differentiation, they should clearly demonstrate this point to strengthen the message of the paper. Indeed, the conclusions are based on the absence of PIDDosome components triggering higher ploidy in cardiomyocytes. However, we don't know whether (and when) the PIDDosome is activated during cardiomyocyte differentiation to control their ploidy levels. I would suggest to analyze PIDDosome activation markers by immunofluorescence in cardiomyocytes at different developmental stages.

      Concerning the methods, the authors must add the references for each product they used and not only the origin. When relevant, the RRID should be indicated. Without this information the method and the data cannot be reproduced.

      The statistics are well indicated in the figures and in the figure legends.

      Minor comments:

      In general, the text and the figures are clear. Nevertheless, I would suggest the following changes:

      • Figures 1B, 2B and 2C: the y-axis must start at 0.
      • Figure 4A: The authors should stain centrosomes in cardiomyocytes. This should strengthen the conclusion taken by the authors based on the results obtained in mice depleted for ANKRD26. Indeed, for the moment they are insufficient to conclude about the role of the centrosomes. The authors should show that centrosomes cluster in cardiomyocytes (a condition necessary for PIDDosome activation in polyploid cells) and if possible that component of the PIDDosome are recruited here.
      • Figure 4F: I would suggest to modify the working model to emphasize more the differences between WT and PIDDosome KO.

      The prior studies are referenced appropriately.

      Significance

      How polyploid cells control their ploidy levels during differentiation remains poorly understood. The data presented here represent thus an advance concerning this question.

      The actual model concerning PIDDosome activation relies on the presence of extra centrosomes that drives the ANKDR26-dependent recruitment of the PIDDosome. Then, Caspase 2 is activated leading to a p53-p21 dependent cell cycle arrest (Burigotto & Fava, 2021, Sladky and Villunger, 2020; Janssens & Tinel, 2012; Evans et al., 2020; Burigotto et al., 2021). In this study, the authors showed that similar pathway takes place during cardiomyocyte differentiation to control ploidy levels. These data are reminiscent of previous work showing PIDDosome involvement during hepatocyte polyploidization (Sladky et al. 2020). Together, these data highlight the prominent role of the PIDDosome complex in controlling ploidy levels in physiological context.

      Importantly, this study identified that the classical p53-dependent cell cycle arrest described after PIDDosome activation is not involved here. Instead, the data established that independently of p53, p21 contribute to control cardiomyocyte ploidy. In consequence, this study extend the initial pathway associated with PIDDosome activation and suggest that other mechanisms could take place to restrain cell proliferation upon PIDDosome activation.

      Overall, this makes this paper significant and of interest for the following fields: polyploidy, heart/cardiomyocyte development and PIDDosome.

      My field of expertise includes polyploidy, cell cycle and genetic instability.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This manuscript by Leone et al describes the role of the PIDDosome in cardiomyocytes. Using a series of whole body and cardiomyocyte specific knockouts, the authors show that the PIDDosome maintains correct ploidy in these cells. It achieves this through inducing cell cycle arrest in cardiomyocytes in a p53 dependent manner. Despite this effect on ploidy, PIDDosome-deficient hearts show no structural or functional defects. Statistics and rigor appears to be adequate.

      Major comments:

      1. Figure 1 uses fluorescent intensity of a nuclear stain to determine ploidy per nucleus and they further separate the results into mononucleated, binucleated or multinucleated cells. It is hard to know how to interpret these results without further information or controls. Is there a good positive control that can be used to help to show whether this assay is quantitative. The differences are larger with the Raidd and caspase-2 knockouts than with the Pidd knockouts but this is not addressed.
      2. On line 459 the authors state that the increase in polyploidy in PIDDosome knockouts occurs in adult hood but this is not directly tested. In fact in the next section the polyploidy is assessed in early postnatal development. This statement should be explained or removed
      3. In Figure 4. The authors obtained RNAseq data for P1, P7 and P14 but only show the differences with and without caspase-2 at P7. Given that the differences in ploidy are more significant at P14 (Fig 3D), all the comparisons should be shown along with analysis of whether the same genes/gene families are altered in the absence of caspase-2
      4. Some validation of the RNAseq and/or proteomics results would be an important addition to this study
      5. Figure 4D: the authors make the conclusion that p21 is downstream of PIDD (et p53 independent). However, this is not supported by the data because the increase in 4N cells/decrease in 2N cells, although statistically significant, is nowhere near that of caspase-2 KO and caspase-2/p21 KO. Statistics should also compare p32KO with c2KO. In the absence of any other data, the more likely conclusion is that p21 is not involved.

      Minor comments:

      Suggest moving Figure 4A to Figure 3 as it seems to fit better there based on the citation of this figure in the text Recommend enhancing the brightness of microscopy images in Figure 1E and 2D

      Significance

      This study provides interesting information for the role of the PIDDosome in protecting from polyploidy and adds to the body of work by this same group studying this pathway in the liver. The main weakness in terms of significance is the lack of a phenotype in the hearts of these animals. Therefore, it is clear that ploidy (or at least PIDDosome dependent ploidy) has minimal impact on cardiac development. The authors mention that they have not tried giving these mice an MI or inducing any other type of cardiac damage. Although it is understood that these experiments are likely outside of the scope of the present study, without this information the impact of this study is moderate. I recommend expanding the discussion to provide a more indepth possible rationale as to why ploidy perturbations do not lead to structural changes like in the liver. Despite this, the insights to the pathway itself are interesting to investigators in the caspase-2 field if a little underdeveloped, especially concerning the role of p21.

      My expertise is in cell death and caspase biology (especially caspase-2). I havesufficient expertise to evaluate all parts of this paper.

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      Reply to the reviewers

      The response appears in a PDF document, which will be easier to read than plain text

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      Referee #3

      Evidence, reproducibility and clarity

      This article investigates the phenomenon of intracellular protein agglomeration. The authors distinguish between agglomeration and aggregation, both physically characterising them and developing a simple but elegant assay to differentiate the two. Using microscopy and structural analysis, this research demonstrates that unlike aggregates, agglomerates retain their folded structures (and are not misfolded), and do not colocalise with chaperones or interact with the proteostasis machinery which targets and breaks down misfolded proteins. The inert nature of agglomerates was further confirmed in fitness assays, though they were observed to disrupt the yeast proteome. Overall, agglomerated proteins were described and characterised, and shown to be largely neutral in vivo.

      The claims and conclusions were well supported by the data. Microscopy and CD spectra (previously published) were used to confirm the nature of agglomerates and to rule out colocalistion with proteostasis machinery. This was confirmed by testing ubiquitination.

      The fitness of yeast cells carrying enzymatically-inactive agglomerates was assayed by generating growth curves over 24 hours. The growth rate and doubling time were taken from these growth curves as a proxy for relative fitness. The authors mention not wanting to mask differences in lag, log or stationary phases between mutants. This could be achieved by using the area under each growth curve, rather than growth rate or doubling time alone. No further experimentation would be needed, and area under the curve may provide a more holistic metric to measure fitness by.

      The results indicate that agglomerates confer a slight fitness advantage. The authors do not speculate on a reason for this. I would be interested to know why they thought this might be.

      Referees cross-commenting

      I have read the reports from the other reviewers and agree with their comments.

      Significance

      Protein filamentation is observed across the tree of life, and contributes greatly to cell structure and organisation (Wagstaff, J., Löwe, J. Prokaryotic cytoskeletons: protein filaments organizing small cells. Nat Rev Microbiol 16, 187-201 (2018).). Recent work in this field has shown that self-assembly is also important for enzyme function (S. Lim, G. A. Jung, D. J. Glover, D. S. Clark, Enhanced Enzyme Activity through Scaffolding on Customizable Self-Assembling Protein Filaments. Small 2019, 15, 1805558.). Previous work from several of these authors demonstrated that the ability of a protein to filament is subject to selection (Garcia-Seisdedos H, Empereur-Mot C, Elad N, Levy ED. Proteins evolve on the edge of supramolecular self-assembly. Nature. 2017 Aug 10;548(7666):244-247. doi: 10.1038/nature23320. Epub 2017 Aug 2. PMID: 28783726.). It has become increasingly clear that protein assemblies are ubiquitous, evolvable and perhaps overlooked in research.

      This research explores a specific type of filamentation, named agglomeration, unique in that the protein which assemble are not misfolded (Romero-Romero ML, Garcia-Seisdedos H. Agglomeration: when folded proteins clump together. Biophys Rev. 2023;15: 1987-2003.). This is particularly of biomedical interest due to its role in disease, such as sickle cell anaemia (J. Hofrichter, P.D. Ross, & W.A. Eaton, Kinetics and Mechanism of Deoxyhemoglobin S Gelation: A New Approach to Understanding Sickle Cell Disease*, Proc. Natl. Acad. Sci. U.S.A. 71 (12) 4864-4868, https://doi.org/10.1073/pnas.71.12.4864 (1974).) The current research adds to the field by specifically exploring agglomerates in the most detailed methodology to date.

      The novelty of this research lies especially in two areas; (1) establishing a method for distinguishing between aggregation and agglomeration, and (2) the finding that agglomerates are largely innocuous in vivo. The method established for defining agglomerates is simple, elegant and well-described in this paper's methods. The authors then probe cellular responses to agglomeration via both proteostasis machinery and cellular fitness. They noted no disruption to fitness and observed little targeting of agglomerates by chaperones. The experiments were thorough, conclusive, and resulted in interesting findings.

      The inertia of this type of protein filament is unexpected; agglomerates are large and have been associated with disease. The results of this study, however, indicate that agglomerates are non-toxic and well-tolerated in vivo. The authors speculate that agglomerates may have evolved in a non-adaptive process, which is evolutionary very interesting. They also posit that these results could lead to synthetic biology applications such as a tracking expression or as a molecular sensor. This work is of great interest and impact both in cell biology, biomedicine and in-vivo biology.

      Personal note: I come from a background of enzyme evolution and have viewed the work in this light.

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      Referee #2

      Evidence, reproducibility and clarity

      This is a very interesting paper investigating the fitness and cellular effects of mutations that drive dihedral protein complex into forming filaments. The Levy group have previously shown that this can happen relatively easily in such complexes and this paper now investigates the cellular consequences of this phenomenon. The study is very rigorous biophysically and very surprisingly comes up empty in terms of an effect: apparently this kind of self-assembly can easily be tolerated in yeast, which was certainly not my expectation. This is a very interesting result, because it implies that such assemblies may evolve neutrally because they fulfill the two key requirements for such a trajectory: They are genetically easily accessible (in as little as a single mutation), and they have perhaps no detrimental effect on fitness. This immediately poses two very interesting questions: Are some natural proteins that are known to form filaments in the cell perhaps examples of such neutral trajectories? And if this trait is truly neutral (as long as it doesn't affect the base biochemical function of the protein in question), why don't we observe more proteins form these kinds of ordered assemblies.

      I have no major comments about the experiments as I find that in general very carefully carried out. I have two more general comments:

      1. The fitness effect of these assemblies, if one exists, seems very small. I think it's worth remembering that even very small fitness effects beyond even what competition experiments can reveal could in principle be enough to keep assembly-inducing alleles at very low frequencies in natural populations. Perhaps this could be acknowledged in the paper somewhere.
      2. The proteins used in this study I think were chosen such that they do not have an important function in yeast that could be disrupted by assembly This allows the effect of the large scale assemblies to be measured in isolation. If I deduced this correctly, this should probably be pointed out agin in this paper (I apologise if I missed this).
      3. The model system in which these effects were tested for is yeast. This organism has a rigid cell wall and I was wondering if this makes it more tolerant to large scale assemblages than wall-less eukaryotes. Could the authors comment on this?

      Minor points:

      In Figure 2D, what are the fits? And is there any analysis that rules out expression effects on the mutant caused by higher levels of the wild-type? The error bars in Figure 2E are not defined.

      Significance

      This is a remarkably rigours paper that investigates whether self-assembly into large structures has any fitness effect on a single celled organism. This is very relevant, because a landmark paper from the Levy group showed that many proteins are very close in genetic terms to forming such assemblies. The general expectation I think would have been that this phenomenon is pretty harmful. This would have explained why such filaments are relatively rare as far as we know. This paper now does a large number of highly rigours experiments to first prove beyond doubt that a range of model proteins really can be coaxed into forming such filaments in yeast cells through a very small number of mutations. Its perhaps most surprising result is that this does not negatively affect yeast cells.

      From an evolutionary perspective, this is a very interesting and highly surprising result. It forces us to rethink why such filaments are not more common in Nature. Two possible answers come to mind: First, it's possible that filamentation is not directly harmful to the cell, but that assembling proteins into filaments can interfere with their basic biochemical function (which was not tested for here).

      Second, perhaps assembly does cause a fitness defect, but one so small that it is hard to measure experimentally. Natural selection is very powerful, and even fitness coefficients we struggle to measure in the laboratory can have significant effects in the wild. If this is true, we might expect such filaments to be more common in organisms with small effective population sizes, in which selection is less effective.

      A third possibility is of course that the prevalence of such self-assembly is under-appreciated. Perhaps more proteins than we currently know assemble into these structures under some conditions without any benefit or detriment to the organism.

      These are all fascinating implications of this work that straddle the fields of evolutionary genetics and biochemistry and are therefore relevant to a very wide audience. My own expertise is in these two fields. I also think that this work will be exciting for synthetic biologists, because it proves that these kinds of assemblies are well tolerated inside cells.

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      Referee #1

      Evidence, reproducibility and clarity

      In this work, the authors used yeast cell as a model system to study the abovementioned question. They established a model protein system using fluorescently labeled proteins that can form both agglomerates and aggregates. Using imaging experiments, they arguably showed that agglomerates do not colocalize with the proteostasis machinery, echoing what was observed by proteomics results. The proteomics results after pull down assay to study the interactome revealed that agglomerate-size-dependent changes were dependent on the cell-wall and plasma-membrane proteins. On the other hand, as expected, the misfolded proteins (aggregates) showed heavy involvement of proteostasis network components.

      Although the experiments still lack some controls and failed to support some of the conclusions, I found this work is a nice complement of the field to emphasize the point that "aggregates" and "agglomerates" are two different states, which is often mistaken by lots of researchers in recent years, in particular with the membraneless organelles (LLPS). I support its publication after the authors may consider the following suggestions and make necessary improvement.

      Major concerns:

      My major concern was raised by the lack of evidence to support the model system's folding state in the cell. 1. In Figure 1 and 2, I found the evidence to distinguish the folded state of proteins in the cells was limited. The concept of using hybrid imaging technique to prove the folding state is not a common experiment. The description of Figure 2 was very limited. I am sure the general audience can be convinced that the model proteins were actually folded and form agglomeration. 2. In addition, for mutants formed aggregates, the authors may consider to perform fractionation or crosslinking or native page experiment to show the evidence of protein misfolding and aggregation. 3. Have the authors considered to use FRAP assay to distinguish "aggregates" and "agglomerates" states in the cell? Does each of the state display different dynamics in the cell?

      Minor concerns:

      1. In Figure 3, it is very interesting to see such patten. I wonder why some of the chaperones were not responsive to misfolded proteins but some were very addicted to proteostasis. Could you elaborate more on this point? Are they chaperone sensitive, namely selective to 60/10, 70/40 or 90 system?
      2. In Figure 6, I suggest to add GO analysis and KEGG analysis to distinguish pathways and functional mismatch between "aggregates" and "agglomerates" interactomics.
      3. The quantity control for the proteomics studies is needed, namely the reproducibility of 3 repeats?
      4. This may beyond the scope of this work. I am interested whether the authors could point out whether similar works can be done in mammalian cells. What is the model system for mammalian cell that can form "agglomerates".

      Referees cross-commenting

      I read through the other two reviewers' comments, which I found reasonable. It seems like all reviewers agreed that this work is of enough significance for the field only with several technical concerns.

      Significance

      The submitted manuscript emphasized on a very important but often misleading concept: "aggregates" and "agglomerates" are two different states of protein structures in the cell with distinct physiological roles. However, these two states are of very similar phenotype: punctate structure in the cell. While the proteostasis network has been well-established for its central role of protein quality control and coping with misfolded and aggregated proteome, the authors attempted to profile the mechanism and physiological impact of mutation-induced folded-state protein filamentation, namely a model of "agglomerates". Such overarching goal of this work clearly pointed out the novelty of this work. Clearly, this is a new angle and aspect remained to be clarified for the field.

  2. May 2025
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      Reply to the reviewers

      Manuscript number: RC-2025-02887

      Corresponding author(s): Philippe Bastin

      1. General Statements [optional]

      • *

      We thank the reviewers for their constructive suggestions. We are delighted to see that they appreciated our work and its interest for the broad cell biology community, as well as the potential impact of the inducible expression of tagged tubulin as a new tool to investigate microtubule assembly at large.

      We are now providing a full revision that contains two major modifications and that addresses all the minor points detailed below. The two major modifications are:

      • A simplification and a shortening of the text as requested by reviewers 1 and 3
      • The addition of a new experiment evaluating the role of the locking protein CEP164C to gain insight into the mechanism, as suggested by reviewers 1 and 2 Briefly, CEP164C is a protein localised to the transition fibres (structures that dock the basal body of the flagellum to the membrane) of only the old flagellum. Its depletion leads to an excessive elongation of the old flagellum and the production of a shorter new flagellum, suggesting competition between the two flagella for tubulin incorporation (Atkins et al., 2021). In the new figure 5, we have expressed tagged tubulin in the CEP164CRNAi cell line and formally demonstrated simultaneous incorporation in both flagella. Unexpectedly, the new flagellum incorporated more tubulin than the old one, suggesting a bias of tubulin targeting in favour of the new flagellum and the existence of additional contributors to the Grow-and-Lock model.

      2. Point-by-point description of the revisions

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

      • *

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript by Daniel Abbühl on "A novel approach to tagging tubulin reveals MT assembly dynamics of the axoneme in Trypanosoma brucei" uses an innnovative approach to label tubulin, which allows the authors to unveil new mechanisms in flagellar length regulation.

      The manuscript is very nice and will be very interesting for the cell biology community and therefore should be accepted. In some parts it becames a bit complex with all the models and complex phrasing, I wonder whether the text could be simplified to be more appealing. I have a few minor comments:

      We agree that some of the explanations are lengthy and complex. We have simplified the explanations and hopefully made the models more accessible. Complexity comes from the fact that trypanosomes do not have a synchronized cell cycle.

      -From the model the authors show in Figure 8- there should be a way of pulsing the cells in G1 for a short amount of time -2 hours- and getting both flagella tips labelled. But the authors seem to require longer labelling to get that result. This should be better explained.

      We are not quite sure what is meant here with both flagella as in G1-phase, all cells are mono-flagellated. We do see mono-flagellated cells with a labelled tip after 2 hours, both with the HALO-tag or the Ty-1-tubulin system.

      In regard to bi-flagellate cells, we believe that incorporation in the OF happened at the beginning of G1-phase when the cell was mono-flagellated. If tubulin is present at that point, it will be incorporated at the tip. This cell then approaches the end of G1-phase and starts to initiate NF assembly. Since tagged tubulin is already present it will be incorporated along the whole length of the NF.

      A short induction of 2h would not suffice as it wouldn't cover the duration of the G1-phase and the initiation of a NF (duration of G1-phase is ~4h). We attempted to explain this in Fig. 4 and reworked the text to make this clearer.

      -Why do some cells not express the construct? Weren´t they all selected?

      We never managed to get a cell line where inducible expression is present in 100% of cells. Here, around 95% of cells were positive for Ty-1-tubulin after 24h of induction. Non-expression is not a phenomenon restricted to this tubulin cell line but also observed with other ectopically expressed proteins (e.g. Sunter et al. JCS 2015, Bastin et al. MCB 1999). All these cell lines represent clonal populations and are resistant to antibiotic treatment, however not all cells express the respective protein. For each experiment where we believed the number of expressing cells matter (for example the washout), we quantified in how many cells Ty-1-tubulin was present in the cell body microtubules.

      -"The linear regression line in Fig. 3C was corrected by subtracting 45 minutes from each timepoint due to the previously reported delay between addition of tetracycline and the expression of the respective protein". However, in the authors data the delay may amount to one hour (western analysis- S4). Shouldn´t they use their data.

      Indeed, the western blot shows expression after 1-hour, however we did not take a 45-minute timepoint, so we don't know if the protein was detectable at that time. In addition, IFA is more sensitive than western blot. We cannot say exactly when the average cell starts to express the induced protein.

      -Fig 3: To measure the timepoints of flagella growth, wouldn´t it be better to do it with NF that started to grow before induction, rather than starting to grow after induction, to be sure that the timing of incorporation is fully accounted for?

      We indeed did consider only NFs, which started to grow before induction, as suggested by the reviewer. In the revised version the description of the experiment can be found on page 9 line 22 - 28.

      -Although it is not the focus of the manuscript it would have been very interesting to use the CEP164C mutant to see whether it would change the dynamics of incorporation and fully test their model and discussion.

      This is a great suggestion, so we performed some experiments to address this issue. When CEP164C was knocked down before Ty-1-tubulin expression, integration is seen at the distal tip of both NF and OF. This is coherent with the idea of removal of the locking protein from the OF. However, lengths of the green segments in NF and OF do not have the same length (NF ~6 µm, OF ~2 µm), which indicates that CEP164C might not be the only protein involved in regulating flagellum length. A new figure explaining this experiment was added (Fig. 5, Fig. S6). We believe this data provides novel insight on the locking mechanism and strengthens the manuscript.

      -In some parts of the manuscript/supplemental material the authors say they insert the Ty-1- tag one aminoacid after the acetylated lysine- other parts they say two aminoacids after- this should be consistent.

      We thank the reviewer for spotting these mistakes, we have changed the text accordingly.

      -Fig. S1: 'Binding epitope of the TAT-1 antibody is highlighted in red'. There is no highlighting in red in this figure?

      This sentence was removed.

      -Fig. S2: Western blots are not very clear. What is the 'X' present in the C (first lane)? Weight of markers should be shown also in S4.

      Molecular weight markers have been added. X is an empty lane, we have now indicated this in the figure legend.

      -Fig 5: 'C: Frequency of bi-flagellated cells grouped by the different types of' The authors didn't finish the sentence.

      Previous Fig. 5 is now Fig. 6. Sentence has been completed. "Frequency of bi-flagellated cells grouped by different types of old flagella"

      -Fig. S7: The 'B' is missing in both picture and legend.

      This has been added


      Significance

      This study advances our knowledge of flagellar length regulation and maintenance. Moreover, the tools designed in this work will be very useful for the cell biology community in general.


      Reviewer #2

      Evidence, reproducibility and clarity

      Summary: The length of the old flagellum of Trypanosome is constant during G1 phase as well as during cell cycle progression when the new flagellum is assembled. The authors have previously proposed a "Grow and Lock" model for the flagellar length control in which no flagellar building blocks are incorporated. To test this hypothesis, the authors used a tagging strategy for alpha-tubulin and tracking its incorporation. The authors showed that the new flagellum incorporates new tubulins, as is expected. For the mature flagellum, tubulins are incorporated at the flagellar tip and only when the cells start to assemble the new flagellum. Thus, it shows that old flagellum is stable but not completely locked for the incorporation of tubulins.

      Major comments: The study is methodologically rigorous, integrating fluorescence microscopy, biochemical approaches, and proteomic analyses to validate the functionality of the tagged tubulin. The use of both inducible expression and endogenous protein tagging (HaloTag) strengthens the conclusions. This study has supported the "Grow-and-Lock" model" that the authors previously proposed. In addition, they have revealed that the stability of the old flagellum is temporally controlled.

      The data showed that brief incorporation of tubulins at the tip of the old flagellum occurs when the cells start to form the new flagellum. I thought the assembly of the new flagellum occurs during the cell division. However, in the abstract, it says that "The restriction is lifted briefly after the bi-flagellated cell has divided." Is my understanding wrong?

      We believe incorporation at the tip of the "OF" occurred after the cell has divided, when the OF daughter is mono-flagellated. It happens before this daughter cells starts assembling its new flagellum is formed. Of course, when looking at biflagellated cells, the NF as well as the tip of the OF will be green, but our data supports that incorporation happened in G1-phase and not during the biflagellated stage as the lock seals the OF before the NF emerges. To clarify on terminology: The bi-flagellate stage begins when basal bodies are duplicated, shortly after the beginning of S-phase and ends with cytokinesis. This means G1-phase and the mono-flagellated stage are nearly the same (Woodward and Gull, JCS1990) and occupy ~40% of the cell cycle.

      P12, "The cartoon in Fig. 5A illustrates the progression of the cells in scenario 2 (Fig. 4A) over the duration of one cell cycle (~9 hours)" I thought that one cell cycle should start with cell with only one flagellum, followed by assembly of a new flagellum during cell division, the cell then divides when the new flagellum is almost completely assembled. If my understanding is correct, perhaps the cartoon should be modified accordingly.

      Indeed, the cell cycle starts with a cell in G1-phase. Here, we have chosen the initiation of a NF assembly as our starting point because we focused the investigation on bi-flagellated cells. We have now illustrated the cell cycle (adapted from Woodward and Gull 1990) and when cells are biflagellated in Fig. 6A (revised version).

      Minor comments:

      1) Several references are not correctly formatted. P3: (Flavin and Slaughter, 1974) (Rosenbaum 1969). P10, (Sherwin et al., 1987)(Sheriff et al., 2014) 2) In several places there are no space between the number and the unit. For eample, P3, 9 - 24µm/h. 7, 1μg/m; P8, 50kDa; P9, 1M; 8-9h; P11, 2.9µm/h and etc. 3) P11, Flagella were extracted. I thought the cells were extracted.

      Thank you for pointing these out, we have changed these in the text.


      Significance

      Cilia and eukaryotic flagella are considered dynamic structures in which the flagellar components especially tubulins under constant turnovers even in steady state. This work demonstrates that in Trypanosome the stable old flagellum is temporally controlled for tubulin turnovers, suggesting a tight regulation of microtubule dynamics. Future elucidation of the regulatory mechanism will be more interesting. This work will be interesting to the field of cilia and microtubules. In addition, the new technique used for tracking tubulins will also be interesting.

      I am an expert on ciliary biology.

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

      Summary:

      This study seeks to investigate the mechanism by which the length of an eukaryotic cilium is set and maintained in a constant state. The flagellated protist Trypanosoma brucei serves as the study model and the authors take advantage of the genetic tools that allow precise modification and tagging of flagellar proteins and they build on prior knowledge about the well-characterised flagellar assembly cycle, which allows tracking the assembly of a new flagellum alongside an existing old one in the course of one cell cycle. The group of Bastin has previously reported a very interesting "Grow-and-Lock Model for the Control of Flagellum Length in Trypanosomes" and this current manuscript provides a test of this model, and a refinement. Key to this is an advance in technique, reported here, namely expression of an epitope tagged version of alpha tubulin. The epitope is inserted in an internal loop, which apparently for the first time provides a traceable tubulin that is reliably incorporated into the cytoskeleton (subpellicular array, spindle and cilium). Expressing an inducible version of this Ty-1-tubulin allows for a set of experiments that measure the place and timing of tubulin incorporation into cilia. The results are largely confirmatory of previous findings (incorporation exclusively into the new flagellum, at the distal end, linear growth rate that matches previous estimates). Examination of tubulin incorporation patterns then reveal additional information about the old flagellum: evidence from Ty-1-tubulin labelling, corroborated by incorporation patterns of another ciliary protein (RSP 4/6) suggest that the "lock" on the old flagellum is relieved for short periods after cell division, leading to a refined model presented in Figure 8.

      Major comments:

      This study provides an elegant test of the grow-and-lock model and the major conclusions are supported by the data. I have no major concerns.

      Minor comments:

      There are several minor points that could be addressed to make the manuscript easier to follow (and adding line numbers to the manuscript would help with reviewing).

      The introduction is quite long. Some of the well-established background information on the T. brucei cell cycle could be shortened. If the paper is intended for a broader audience, it would be valuable instead to cite studies that have succeeded in tagging tubulin and tracing its incorporation in other cilia. Could the Ty-1-tubulin approach be relevant more broadly or are simpler methods already established?

      The introduction has been shortened, we now also cite two published studies that tracked tubulin integration in Chlamydomonas and C. elegans respectively.

      On p.6 the rationale for endogenous tagging was to "reduce the risk of artifacts portentially due to untimely expression or unnatural protein levels". However most of the experiments were done with ectopically expressed inducible Ty-1-tubulin. For the experiments it is crucial to use an inducible system but the authors may wish to comment why the risk of artifacts was no longer a concern.

      The reasoning here was that in case the Ty-1-tubulin would not have been incorporated into MTs, we could have attributed it solely to the presence of the tag and no other factors, but this was not the case. This therefore allowed us to move to the inducible expression system.

      On p.7 / Fig S2A-B there appears to be a mistake in the presentation. Spindles are mentioned in the text - I can't see any in the figure. Fig S2A and B both show cytoskeletons, but the text suggests only B is about cytoskeletons. None of the blot shows BB2 staining of different cell fractions, contrary to statements in the text. The letter codes in the panel (T, C, D) don't match the codes in the legend (T, P, S).

      We thank the reviewer for spotting the mistakes. A panel with the spindle was added in Fig. S2. We did not stain fraction blots of the in-situ tagged cell lines with BB2. However, this was done with the inducible cell line and is shown in Fig. 1D. Letter code in the legend was adapted to match the figure.

      Figure 1. The evidence for incorporation into spindles is not strong. The structure indicated by the arrive could be a spindle but it's not very clear. There is a great example of a labelled spindle only in figure S5A. Here, at the start, it would be good to show a panel of cells in successive cell cycle stages (best, whole cells and cytoskeletons) to clearly show the structures that are labelled with Ty-1-tubulin.

      The current Fig. 1B (Fig. 1A before) depicts whole cells of an induced and a non-induced culture; we show whole cells to provide a complete picture of tubulin integration. A panel with detergent extracted cytoskeletons from the in situ tagged cell line has been added to Fig. 1A. We chose to show cytoskeletons or isolated flagella instead of whole cells because (1) the flagella are easier to see and (2) it formally demonstrates that tagged tubulin is incorporated in MTs.

      In general, tubulin labelling of the spindle was more consistently observed in whole cells as we did not use spindle preserving extraction buffers when preparing cytoskeletons. However, we did observe clear spindles in cytoskeletons as well (see Fig. S5 for example). The same was observed for the beta-tubulin specific KMX1 antibody in the past which is the gold standard to visualize the spindle (Sasse and Gull JCS1988). Regardless, a panel depicting spindle progression through mitosis using staining of Ty-1-tubulin has been added in Fig. S2 (The panel is a mix of whole cells and cytoskeletons).

      On p.8 (end of first paragraph) there is reference to cell cycle analyses, but no data is shown. Also on p.8, please clarify what the evidence is that "a fraction of cells did not respond to tetracycline". The fact that they remain unstained by Ty-1-tubulin is not in itself evidence they did not respond to tetracycline.

      We did not show the cell cycle data as it was similar to non-induced and does not provide any new information in our opinion. Hence, the sentence has been removed.

      The reviewer is correct that we do not have evidence that these cells did not respond to tetracycline. Some cells remained completely devoid of Ty-1-tubulin even after multiple days of induction. This was typically between 5-10% of cells. In experiments where the exact number is important, we counted the amount of "non-expressers" in whole cells.

      Figure S4A. The blot for the soluble fraction is not of great quality. I don't see how the conclusion was reached that the Ty-1-tubulin bands were faint.

      The blot of the soluble fraction that was stained with BB2 had to be exposed a lot longer compared to the blot stained with TAT-1. The soluble blots were repeated with the same result (lots of background noise when using BB2, a clear blot with TAT-1). In the TAT-1 blot only the endogenous tubulin band is clearly visible, with some very faint signal above corresponding to the Ty-1-tubulin. Soluble Ty-1-tubulin with BB2 or TAT-1 is visible in Fig. 1D after longer inductions.

      On p.11, it would be interesting to compare measured elongation rates with previously measured estimates for flagellum growth, comparing the growth rates, and relating them to cell cycle times in the corresponding experiments (which vary slightly between labs and studies).

      We attempted to address this in the discussion by comparing our experiments to the assembly rate measured with the PFR as reporter (Bastin et al. 1999). We could mention the corresponding doubling times in correlation to how many cells are bi-flagellated, but this was only done with the Ty-1-tubulin cell line and not with the PFR. In our experiments the average doubling time was ~9 hours with 52% of cells being bi-flagellated. This was measured with FTZC (marker of the transition zone at the base of the flagellum) and Mab25 (marker of the axoneme of the flagellum) which will lead to a slight underestimate of the real number of bi-flagellated cells, as the NF is initially very close which makes it difficult to notice/differentiate from the old one.

      Figure S6. I find the presentation of this figure confusing. It should be revised with clearer labelling of "cell cycle 1", "cell cycle 2", and the precise meaning of "type 3" should be clarified. There are two instances of "type 1" in the drawing, but one of these seems to fulfil the criteria of "type 3" (OF 1-4µm).

      We agree with the reviewer and therefore decided to remove this figure. We also considered the comments of the other two reviewers about complexity of the manuscript and changed the text of figure 5 to make it more approachable. This includes a simpler explanation for the expected amounts of flagella.

      Figure 7. In panel A, the absence of label at the NF distal end is not total, a purple line is still visible. Was any quantitation attempted (signal intensity, changes in length of labelled fragments over time?). Minimally, say how many cells were analysed for the numbers in panels D and E, and how many times this experiment was done.

      We agree with the reviewer that the decrease in the TMR signal in the NF of the cell in the original Fig. 7A (currently Fig. 8A) is gradual and not abrupt. Similarly to the Ty-1-tubulin experiments where the tagged protein becomes progressively more available (increasing intensity), the intensity of TMR-ligand becomes progressively less abundant (gradually decreasing intensity) as new (not TMR labelled) protein gets synthesized during the period of NF construction, progressively diluting the initially fully labeled population of RSP4/6. The slope of the gradient may differ between axonemal constituents, as it reflects the kinetics of protein synthesis, degradation, its incorporation into the axoneme, as well as the size of the soluble protein pool in the cytosol. We classify this type of signal as gradients, as opposed to the sharp decrease. At initial times after TMR-ligand washout (e.g. 4 hours in Fig. 8C), this long gradient is observed at the distal end of NFs and in some uniflagellated cells (NF-inheriting daughters). The distal ends of OFs in these experiments (if not fully labelled) display a sharp decrease, as do frequent uniflagellated cells, likely OF-inheriting daughters. The existence of these two different patterns demonstrates that two different mechanisms are responsible for incorporation of fresh RSP4/6 into the NF and OF axoneme, respectively. While incorporation into the NF is gradual, incorporation into the distal region of the OF is stepwise (restricted in time). Numbers of cells quantified for the table in Fig. 8 have been added. The NFs and OFs displaying the patterns of the gradient and sharp decrease, respectively, were observed in multiple experiments.

      Reviewer #3 (Significance (Required)):

      • General assessment: strengths and limitations

      Strengths: Trypanosoma brucei is a powerful model system in which to ask detailed questions about the assembly dynamics and hierarchy of microtubule-based cytoskeletal structures in general and cilia in particular. This elegant and well-designed study overcomes a previous technical limitation by allowing for the direct labelling of alpha tubulin, one of the main building blocks of the ciliary axoneme. The study sets out to test a specific hypothesis (grow-and-lock model) and provides evidence in support, leading to a refined model for cilia length regulation in trypanosomes.

      Limitations: With this system, visualisation of new tubulin incorporation requires de novo synthesis. There is a time lag between inducing expression of Ty-1-tubulin with tetracycline and being able to visualize the tagged proteins that needs to be taken into consideration. This time lag was estimated based on previous studies and the relatively quick appearance of Ty-1-tubulin on Western blots (within hours). This inevitably creates a situation where levels of tagged tubulin change rapidly, creating gradients of signal intensity (and variations in levels) that lead to some uncertainty in estimations of length of labelled microtubule fragments. Furhtermore, the epitope label is not compatible with live cell imaging, restricting analyses to fixed cells. The Ty-1-tubulin data is well ducmented; the RSP4/6 data appear to corroborate these findings but are less extensively documented.

      • Advance: The results succeed in integrating several recent findings from different research groups into a refined coherent model about cilia length regulation in trypanosomes. The tubulin tagging method could be gainfully transferred to other systems (although the state of the field in tubulin tagging in other systems is not clearly laid out in the paper).

      This paper could be of interest to a broad cell biology community interested in cilia and cytoskeletal dynamics.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This study seeks to investigate the mechanism by which the length of an eukaryotic cilium is set and maintained in a constant state. The flagellated protist Trypanosoma brucei serves as the study model and the authors take advantage of the genetic tools that allow precise modification and tagging of flagellar proteins and they build on prior knowledge about the well-characterised flagellar assembly cycle, which allows tracking the assembly of a new flagellum alongside an existing old one in the course of one cell cycle. The group of Bastin has previously reported a very interesting "Grow-and-Lock Model for the Control of Flagellum Length in Trypanosomes" and this current manuscript provides a test of this model, and a refinement. Key to this is an advance in technique, reported here, namely expression of an epitope tagged version of alpha tubulin. The epitope is inserted in an internal loop, which apparently for the first time provides a traceable tubulin that is reliably incorporated into the cytoskeleton (subpellicular array, spindle and cilium). Expressing an inducible version of this Ty-1-tubulin allows for a set of experiments that measure the place and timing of tubulin incorporation into cilia. The results are largely confirmatory of previous findings (incorporation exclusively into the new flagellum, at the distal end, linear growth rate that matches previous estimates). Examination of tubulin incorporation patterns then reveal additional information about the old flagellum: evidence from Ty-1-tubulin labelling, corroborated by incorporation patterns of another ciliary protein (RSP 4/6) suggest that the "lock" on the old flagellum is relieved for short periods after cell division, leading to a refined model presented in Figure 8.

      Major comments:

      This study provides an elegant test of the grow-and-lock model and the major conclusions are supported by the data. I have no major concerns.

      Minor comments:

      There are several minor points that could be addressed to make the manuscript easier to follow (and adding line numbers to the manuscript would help with reviewing).

      The introduction is quite long. Some of the well-established background information on the T. brucei cell cycle could be shortened. If the paper is intended for a broader audience, it would be valuable instead to cite studies that have succeeded in tagging tubulin and tracing its incorporation in other cilia. Could the Ty-1-tubulin approach be relevant more broadly or are simpler methods already established?

      On p.6 the rationale for endogenous tagging was to "reduce the risk of artifacts portentially due to untimely expression or unnatural protein levels". However most of the experiments were done with ectopically expressed inducible Ty-1-tubulin. For the experiments it is crucial to use an inducible system but the authors may wish to comment why the risk of artifacts was no longer a concern.

      On p.7 / Fig S2A-B there appears to be a mistake in the presentation. Spindles are mentioned in the text - I can't see any in the figure. Fig S2A and B both show cytoskeletons, but the text suggests only B is about cytoskeletons. None of the blot shows BB2 staining of different cell fractions, contrary to statements in the text. The letter codes in the panel (T, C, D) don't match the codes in the legend (T, P, S).

      Figure 1. The evidence for incorporation into spindles is not strong. The structure indicated by the arrive could be a spindle but it's not very clear. There is a great example of a labelled spindle only in figure S5A. Here, at the start, it would be good to show a panel of cells in successive cell cycle stages (best, whole cells and cytoskeletons) to clearly show the structures that are labelled with Ty-1-tubulin.

      On p.8 (end of first paragraph) there is reference to cell cycle analyses, but no data is shown. Also on p.8, please clarify what the evidence is that "a fraction of cells did not respond to tetracycline". The fact that they remain unstained by Ty-1-tubulin is not in itself evidence they did not respond to tetracycline.

      Figure S4A. The blot for the soluble fraction is not of great quality. I don't see how the conclusion was reached that the Ty-1-tubulin bands were faint.

      On p.11, it would be interesting to compare measured elongation rates with previously measured estimates for flagellum growth, comparing the growth rates, and relating them to cell cycle times in the corresponding experiments (which vary slightly between labs and studies).

      Figure S6. I find the presentation of this figure confusing. It should be revised with clearer labelling of "cell cycle 1", "cell cycle 2", and the precise meaning of "type 3" should be clarified. There are two instances of "type 1" in the drawing, but one of these seems to fulfil the criteria of "type 3" (OF 1-4µm).

      Figure 7. In panel A, the absence of label at the NF distal end is not total, a purple line is still visible. Was any quantitation attempted (signal intensity, changes in length of labelled fragments over time?). Minimally, say how many cells were analysed for the numbers in panels D and E, and how many times this experiment was done.

      Significance

      General assessment: strengths and limitations

      Strengths: Trypanosoma brucei is a powerful model system in which to ask detailed questions about the assembly dynamics and hierarchy of microtubule-based cytoskeletal structures in general and cilia in particular. This elegant and well-designed study overcomes a previous technical limitation by allowing for the direct labelling of alpha tubulin, one of the main building blocks of the ciliary axoneme. The study sets out to test a specific hypothesis (grow-and-lock model) and provides evidence in support, leading to a refined model for cilia length regulation in trypanosomes.

      Limitations: With this system, visualisation of new tubulin incorporation requires de novo synthesis. There is a time lag between inducing expression of Ty-1-tubulin with tetracycline and being able to visualize the tagged proteins that needs to be taken into consideration. This time lag was estimated based on previous studies and the relatively quick appearance of Ty-1-tubulin on Western blots (within hours). This inevitably creates a situation where levels of tagged tubulin change rapidly, creating gradients of signal intensity (and variations in levels) that lead to some uncertainty in estimations of length of labelled microtubule fragments. Furhtermore, the epitope label is not compatible with live cell imaging, restricting analyses to fixed cells. The Ty-1-tubulin data is well ducmented; the RSP4/6 data appear to corroborate these findings but are less extensively documented.

      Advance: The results succeed in integrating several recent findings from different research groups into a refined coherent model about cilia length regulation in trypanosomes. The tubulin tagging method could be gainfully transferred to other systems (although he state of the field in tubulin tagging in other systems is not clearly laid out in the paper).

      This paper could be of interest to a broad cell biology community interested in cilia and cytoskeletal dynamics.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The length of the old flagellum of Trypanosome is constant during G1 phase as well as during cell cycle progression when the new flagellum is assembled. The authors have previously proposed a "Grow and Lock" model for the flagellar length control in which no flagellar building blocks are incorporated. To test this hypothesis, the authors used a tagging strategy for alpha-tubulin and tracking its incorporation. The authors showed that the new flagellum incorporates new tubulins, as is expected. For the mature flagellum, tubulins are incorporated at the flagellar tip and only when the cells start to assemble the new flagellum. Thus, it shows that old flagellum is stable but not completely locked for the incorporation of tubulins.

      Major comments:

      The study is methodologically rigorous, integrating fluorescence microscopy, biochemical approaches, and proteomic analyses to validate the functionality of the tagged tubulin. The use of both inducible expression and endogenous protein tagging (HaloTag) strengthens the conclusions. This study has supported the "Grow-and-Lock" model" that the authors previously proposed. In addition, they have revealed that the stability of the old flagellum is temporally controlled.

      The data showed that brief incorporation of tubulins at the tip of the old flagellum occurs when the cells start to form the new flagellum. I thought the assembly of the new flagellum occurs during the cell division. However, in the abstract, it says that "The restriction is lifted briefly after the bi-flagellated cell has divided." Is my understanding wrong?

      P12, "The cartoon in Fig. 5A illustrates the progression of the cells in scenario 2 (Fig. 4A) over the duration of one cell cycle (~9 hours)" I thought that one cell cycle should start with cell with only one flagellum, followed by assembly of a new flagellum during cell division, the cell then divides when the new flagellum is almost completely assembled. If my understanding is correct, perhaps the cartoon should be modified accordingly.

      Minor comments:

      1. Several references are not correctly formatted. P3: (Flavin and Slaughter, 1974) (Rosenbaum 1969). P10, (Sherwin et al., 1987)(Sheriff et al., 2014)
      2. In several places there are no space between the number and the unit. For eample, P3, 9 - 24µm/h. 7, 1μg/m; P8, 50kDa; P9, 1M; 8-9h; P11, 2.9µm/h and etc.
      3. P11, Flagella were extracted. I thought the cells were extracted.

      Significance

      Cilia and eukaryotic flagella are considered dynamic structures in which the flagellar components especially tubulins under constant turnovers even in steady state. This work demonstrates that in Trypanosome the stable old flagellum is temporally controlled for tubulin turnovers, suggesting a tight regulation of microtubule dynamics. Future elucidation of the regulatory mechanism will be more interesting. This work will be interesting to the field of cilia and microtubules. In addition, the new technique used for tracking tubulins will also be interesting.

      I am an expert on ciliary biology.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Daniel Abbühl on "A novel approach to tagging tubulin reveals MT assembly dynamics of the axoneme in Trypanosoma brucei" uses an innnovative approach to label tubulin, which allows the authors to unveil new mechanisms in flagellar length regulation.

      The manuscript is very nice and will be very interesting for the cell biology community and therefore should be accepted. In some parts it becames a bit complex with all the models and complex phrasing, I wonder whether the text could be simplified to be more appealing. I have a few minor comments:

      • From the model the authors show in Figure 8- there should be a way of pulsing the cells in G1 for a short amount of time -2 hours- and getting both flagella tips labelled. But the authors seem to require longer labelling to get that result. This should be better explained.
      • Why do some cells not express the construct? Weren´t they all selected?
      • "The linear regression line in Fig. 3C was corrected by subtracting 45 minutes from each timepoint due to the previously reported delay between addition of tetracycline and the expression of the respective protein". However, in the authors data the delay may amount to one hour (western analysis- S4). Shouldn´t they use their data.
      • Fig 3: To measure the timepoints of flagella growth, wouldn´t it be better to do it with NF that started to grow before induction, rather than starting to grow after induction, to be sure that the timing of incorporation is fully accounted for?
      • Although it is not the focus of the manuscript it would have been very interesting to use the CEP164C mutant to see whether it would change the dynamics of incorporation and fully test their model and discussion.
      • In some parts of the manuscript/supplemental material the authors say they insert the Ty-1- tag one aminoacid after the acetylated lysine- other parts they say two aminoacids after- this should be consistent.
      • Fig. S1: 'Binding epitope of the TAT-1 antibody is highlighted in red'. There is no highlighting in red in this figure?
      • Fig. S2: Western blots are not very clear. What is the 'X' present in the C (first lane)? Weight of markers should be shown also in S4.
      • Fig 5: 'C: Frequency of bi-flagellated cells grouped by the different types of' The authors didn't finish the sentence.
      • Fig. S7: The 'B' is missing in both picture and legend.

      Significance

      This study advances our knowledge of flagellar length regulation and maintenance. Moreover the tools designed in this work will be very useful for the cell biology community in general.

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

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      Reply to the reviewers

      We thank the reviewers for providing valuable comments and suggestions for improving the manuscript.

      Response to reviewer comments:

      Reviewer-1

      Comment 1: Major concern is the study lacks rigor in several areas where n=2, results are not quantified with statistics. They need to run power analysis and increase their samples sizes. Please include statistics on all measurements. Filamentous actin staining and alpha-sma is used to visualize mechanosensing but also in other cell activities such as cell contractility for movement, cell to substrate adhesion, cell division, etc. They need to query more mechanosensing related pathways (Piezo1/2, Yap/taz-Hippo, integrin-Focal Adhesion Kinase, etc) to show that mechanosensing changed.

      Response: We have increased the sample size to a minimum of n=3 in most cases. However, a few experiments will require more time to increase sample size, as mentioned below.

      Our data emphasized the role of Rac1 and SRF. We understand that other molecular players may also be involved in sensing or responding to mechanical forces, but surveying multiple families of candidates without a specific hypothesis or functional experiment is beyond the scope of this study.

      __Comment 2: __Fig. 1: In panel E, the cranial bone area measurement is not normalized to mitigate the possible effect of individual differences.

      Response: We have re-quantified the data with normalization to the length of the skull.

      __Comment 3: __In Fig. 2 the authors mentioned many phenotypical changes (bone length changes, gap thickness change, apex thickness change, etc.) based on histology stain, none of them are quantified to show a significant difference between Rac1-WT and Rac1-KO.

      Response: In Fig. 2A, we present the gross morphology of the Rac1-KO embryos and only discuss the tissue defects like edema, hematoma, and hypoplasia, confirmed through H&E as shown in Fig. 2C. We also show the apical limits of the intact calvaria in Fig. 2D, consistent with the calvaria defects observed at birth. In fact, we do not discuss any “bone length changes, gap thickness, or apex thickness change” in this section as suggested by the reviewer. To address the request for more quantification we have added measurement of the edematous area of the apical mesenchyme at E14.5 (Fig. 2C), and this is now shown in Suppl. Fig. 1E. We also added quantification of embryo genotypes and Chi-square tests, now shown in Suppl. Fig. 1D.

      Comment 4: Fig. 2 In panel D, with only 2 embryos per group is not enough for quantitation

      Response: We plan to increase the number of embryos during the revision period.

      Comment 5: Fig. 2 In panel D, the two arrows in the Rac1-KO mutants are not easy to catch.

      Response: We made the arrows bigger and bolder.

      Comment 6: Fig. 3 The thickness quantification is not performed.

      Response: We added quantification in Fig. 3D.

      Comment 7: Fig. 3 The images show an obvious curve change of the apex between the control and mutant. Such change is not discussed in the results. Is it due to histology issue?

      Response: We do not think it is due to technical issues but reflects a real change in the shape of the apex of the head. We modified the graphical representation in Figure 3E to reflect this change in curvature. We also added the following sentence to the results on page 7: “We also noted a loss of curvature in the apex of the Rac1-KO head at E13.5, which correlated with loss of aSMA+ mesenchymal cells and thinning of the EMM (Fig. 3E).”

      __Comment 8: __The merged layer did not show S100a6. While the authors are showing apical expansion of the mesenchyme toward the dermis and meninges, it is hard to track where they are without a merged image.

      Response: We added merged images.

      Comment 9: Fig. 4 In panel B, 2 biological replicates per genotype are very low.

      __Response: __The effect of Rac1-KO on cell cycle is already known (Moore et al. 1997; Nikolova et al. 2007; Gahankari et al. 2021), and our result is supported by in vivo quantification of Tom+Edu+ cells in different regions of the embryonic head shown in Fig. 4A. We prefer not to repeat this assay.

      Comment 10: Fig. 4 There is no cell death data.

      Response: We will generate data on cell death during the revision period.

      __Comment 11: __Fig. 5 In panel B, the GAPDH western plot bands in the mutants seem to be thinner than those of controls.

      Response: We verified equal loading with a Ponceau stain, so this minor change in the GAPDH level could be due to biological differences in the protein level. Nevertheless, by our estimation this minor difference does not explain away the major difference in Rac1 and Srf levels.

      __Comment 12: __Though the immunostain showed a decrease in signal intensity, it is hard to know whether the decrease is significant enough across all Rac1-KO mutants. They need to measure the fluorescence intensity and perform statistics.

      Response: We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the revision period.

      Comment 13: Fig. 6: Similar as Fig. 2, there is no quantification and n=1 per genotype is not enough

      Response: During the revision period we will increase the number of E12.5 Srf-KO and Srf-WT embryos to n=3 for Figure 6G. All other panels currently have n=7 or greater.

      Comment 14: Fig. 7: Need quantification between Srf-KO and Rac1-KO with statistics to show they are not different, but both significantly different from WTs

      Response: In Figure 7D we have added quantification of aSMA area in Srf-KO and Rac1-KO. These results show that both mutants have a similar phenotype with reduced aSMA expression compared to their respective WT littermates, which supports the conclusion that they work in the same pathway. We do not agree with the reviewer that the two mutants should show no statistical difference, because Rac1 and Srf are different genes with overlapping but also non-overlapping functions. During the revision period we will add more Srf-KO embryos and repeat the statistical analysis.

      Comment 15: Supplement Fig.2: No image showing the time point before E11.5.

      Response: We will add an E10.5 time point during the revision period.

      Comment 16: Supplement Fig.3: The ventral view of Rac1-WT does not have the same angle as it shows in Rac1-KO. Makes harder to see the difference between control and mutant.

      Response: We adjusted the brightness/contrast to make the difference clearer.

      Comment 17: Supplement Fig.4 &7: The alkaline phosphatase stained area needs to be normalized to some other metric because the embryos could be different size.

      Response: We normalized to the width of the eye and is now represented in Suppl. Fig. 4 and 7.

      Comment 18: Supplement Fig 6 A: The legend and figure don't match. Is it E13.5 or 14.5. Panel 6B needs better images without curling of the tissue.

      Response: This has been fixed. The immunostaining images in Suppl. Fig. 6A is E14.5. Panel B is now replaced with better images in the revised manuscript.


      Reviewer-2

      __Comment 1.1: __In Fig. 5, links between Rac1, SRF, αSMA, and contractility in mesenchymal cells are shown. Molecular analyses (Western blot and qPCR) were performed using primary cultured mesenchymal cells (prepared after freed from the epidermal population). Although use of cells prepared from E18.5 embryos may have been chosen by the authors for the safe isolation of the mesenchymal population without contamination of epidermal cells, this reviewer finds that anti-SRF immunoreactivity is weaker at E13.5 than at E12.5 (throughout the section including the mesencephalic wall) and therefore wonder whether SRF expression changes in a stage-dependent manner. So, simply borrowing results obtained from E18.5-derived cells for describing the scenario around E12.5 and E13.5 is a little disappointing point found only here in this study.

      Response: In fact, the reason we chose E18.5 was to get enough cells to do the experiments in Figure 5A-D without extensive passaging and/or immortalization, which would undoubtedly cause the cells to deviate from their in vivo character as they become adapted to growing on plastic with 10% serum. Therefore, we prefer not to change the cells as suggested by the reviewer.

      __Comment 1.2: __In Fig. 5F, it is difficult to clearly see "reduction" of SRF immunoreactivity in Rac1-KO. Therefore, quantification of %SRF+/totalTomato+ would be desired.

      Response: __We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the __revision period.

      __Comment 1.3: __Separately, direct comparison of spontaneous centripetal shrinkage of the apical/dorsal scalp tissues, which will occur in 30 min when prepared at E12.5 or E13.5 (Tsujikawa et al., 2022), between WT and Rac1-KO would strengthen the results in Fig. 5D. As KO is specific to the mesenchyme, the authors do not have to worry about removal of the epidermal layer (which would be much more difficult at E12.5-13.5 than E18.5). If the degree of centripetal shrinkage of the "epidermis plus mesenchyme" layers were smaller in Rac1-KO, it would be interpreted to be mainly due to poorer recoiling activity and contractility of the Rac1-KO mesenchymal tissue.

      Response: __We will try to perform the centripetal shrinkage assays as shown by Tsujikawa et al., during the __revision period.

      Comment 2: The authors favor "apical" vs. "basolateral" to tell the relative positions in the embryonic head, not only in the adult head. But "apical" vs. "basolateral" should be accompanied with dorsal vs. ventral at least at the first appearance. Apical-to-basal axis or apex vs. basolateral by itself can provide, in many contexts, impressions that epithelial layers/cells are being discussed. Please note that the authors also use "caudal" (in the embryonic head). Usually, a universally defined anatomical axis perpendicular to the rostral-to-caudal axis is the dorsal-to-ventral axis.

      Response: Apologies for confusing terminology. The terminology is now defined uniformly according to the anatomical axis.

      Comment 3: One of the authors' statements in ABSTRACT "In control embryos, α-smooth muscle actin (αSMA) expression was spatially restricted to the apical mesenchyme, suggesting a mechanical interaction between the growing brain and the overlying mesenchyme" and a similar one in RESULTS "αSMA was not detected in the basolateral mesenchyme of either genotype from E12.5-E14.5 (Suppl. Fig. 4A), suggesting restriction of the mechanosensitive cell state to the apical mesenchyme" need to be at least partly revised, taking previous publication about the normal αSMA pattern in the embryonic head into account more carefully. Tsujikawa et al. (2022) described "Low-magnification observations showed superficial immunoreactivity for alpha smooth muscle actin (αSMA), which has been suggested to function in cells playing force-generating and/or constricting roles; this immunoreactivity was continuously strong throughout the dorsal (calvarial) side of the head but not ventrally toward the face, producing a staining pattern similar to a cap (Figure 2A)" . Therefore, in this new paper, descriptions like "we observed ...., consistent with ....(2022)" or "we confirmed .... (2022)" would be more accurate and appropriate regarding this specific point. Such a minor change does not reduce this study's overall novelty at all.

      Response: Thank you for the correction. We have replaced the terminology and cited the article (Tsujikawa et al., 2022) appropriately, crediting their finding.

      Comment 4: It would be very helpful if the authors provide a schematic illustration in which physiological and pathological scenarios (at the molecular, cellular, and tissue levels found or suggested by this study) are shown.

      Response: We have added a schematic representation of the molecular changes happening in the apical head development because of Rac1- and Srf-KO, and it is represented in Suppl. Fig. 7C.


      Comment 5: Despite being put in the title, "mechanosensing" by mesenchymal cells is not directly assessed in this study. If appropriate, something like "mechano-functioning" would be closer to what the authors demonstrated.

      __Response: __We changed the title to refer to “mechano-responsive mesenchyme”. We think this is appropriate because the cells of interest have reduced aSMA and reduced proliferation, both of which are known to occur, at least in part, as responses to mechanical inputs.

      Reviewer-3

      Comment 1: Prrx1-Cre targets calvarial mesenchyme and Suzuki et al., 2009 showed that Prrx1-Cre mediated loss of Rac1 lead to calvarial bone phenotype due to incomplete fusion of the skull. While this phenotype was not studied in detail, the statement in the intro and discussion that the calvarial phenotype has not been recapitulated in mice is incorrect.

      Response: Suzuki et al showed incomplete fusion of the skull. Although the skull is a tissue that is affected in AOS, it is not akin to the scalp and calvaria aplasia that typifies AOS. Our result stands apart from this. We clarified our position as such:

      Introduction (page 4): “Nevertheless, the calvaria phenotype seen in AOS individuals has not been explored in detail or fully recapitulated in mice.”

      Discussion (page 11): Previous studies have demonstrated the role of Rac1 in mesenchyme-derived tissues, but they did not recapitulate AOS phenotypes.”

      Comment 2: The authors show that Pdgfra-Cre induced knockout of Rac1 leads to lower-than-expected numbers of Rac1-cKO embryos at E18.5 and P1. Phenotypic analysis shows that the earliest phenotype is blebbing and hematoma in the nasal region at E11.5/12.5. It is stated that this was resolved at E18.5. It is unclear if this is truly a resolution of the phenotype or that these embryos fail to survive until E18.5. Do 100% of the Rac1-cKO embryos exhibit the blebbing/hematoma at E11.5/12.5? What is the observed number/percentage of Rac1-cKO embryos at E11.5/12.5? If the observed percentage of Rac1-cKO is similar to that at E18.5 (lower than the expected 25%), this would support resolution. If the observed ratio is as expected at E11.5/12.5, then this would support embryonic loss before E18.5 rather than phenotypic resolution.

      Response: Please note that 100% (n=12) of E12.5 Rac1-KO embryos displayed nasal and mild caudal edema as exhibited in Fig. 2A, but none (n=16) had blebbing/hematoma by E18.5. We added tables for the number of embryos recovered at E12.5 and E18.5 to Supplemental Figure 1. These results show that the percentage of mutants at E12.5 was 21.42%, not significantly different from the expected frequency (p = 0.5371). At E18.5, the percentage dropped slightly to 18.3%, but still not significantly different from expected (p = 0.1545). The significant change in frequency of blebbing/hematoma from E12.5 to E18.5, without any significant change in the frequency of mutants, supports phenotypic resolution of the early blebbing/hematoma.

      Comment 3: It is stated that brain shape is altered in Rac1-cKO embryos at E14.5 and E18.5 and concluded that these shape differences are secondary to the cranial defects. Pdgfra+ cells gives rise to the meninges and if the Pdgfra-Cre line recapitulates this expression, then loss of the ubiquitously expressed Rac1 in the meninges could lead to a primary defect in the brain, which may lead to secondary defects in the calvarium and scalp. Their conclusion should recognize other possibilities.

      Response: We agree it is possible that there are meninges defects that secondarily change the shape of the brain, and we added a mention of this possibility. It is highly unlikely that scalp defects are only secondary to brain changes because the first observable phenotypes are in the EMM that gives rise to the scalp.

      Comment 4: The TdTom staining in wholemount at E13.5 (Supplemental Figure 2B) is difficult to appreciate in the image shown.

      Response: At E11.5 there is good contrast between labeled cranial structures and non-labeled body. At E13.5, Tomato appears in most of the mesenchymal cells in the embryo, so there is not as much contrast. The lack of contrast at E13.5 may cause the reviewer think there is something wrong with the image.

      Comment 5: The idea that the EMM laminates into the meninges and scalp layers is not new and should be properly cited (Vu et al., 2021, Scientific Reports). The following paper should also be cited on the use of alpha-SMA (Acta2) as a marker of the anterior calvaria mesenchyme: Holms et al., 2020 Cell Reports.

      Response: Thank you. We are happy to add those citations.

      Comment 6: It is concluded that meningeal development is maintained in the cKO; however, this conclusion was based on a single marker (S100a6) that is both expressed in the presumptive meninges and dermis and greatly reduced overall in the cKO. This conclusion should be softened or other markers used to show that the meninges is indeed normal.

      Response: We softened the conclusion on the meninges in the revised manuscript, as this part of the phenotype is was not our focus but it would be a good thing to look at in the future.

      Comment 7: The overlap of S100a6 and alpha-SMA is difficult to appreciate in the images shown in Figure 3. Since this is important to the conclusion, co-staining should be done. If co-staining cannot be done due to the primary antibodies' origins, then ISH should be done.

      Response: We added merged images.

      Comment 8: It is concluded that reduced alpha-SMA suggests an early failure of Rac-cKO cells to respond to the mechanical environment. While this is one possibility, the reduction of alpha-SMA may simply be due to a reduction of these cells resulting from failed differentiation, decreased proliferation, or increased apoptosis.

      Response: We think the fact that aSMA is downregulated in cultured cells strongly argues against it being a trivial consequence of reduce proliferation etc. Nevertheless, we softened our conclusion to allow for some of these things to also contribute to the reduced aSMA expression. We will check apoptosis during the revision period.

      Comment 9: The conclusion that alpha-SMA is a transient population only present in apical cranial mesenchyme between E12.5-14.5 is not consistent with prior studies: Holms et al., 2020 Cell Reports; Holms et al., 2021 Nature Communications; Farmer et al., 2021 Nature Communications; Takeshita et al., 2016 JBMR.

      Response: There is no contradiction. Our statements are based on antibody staining where it is very evident that a-SMA-expressing cells are detectable throughout the apical mesenchyme between E12.5 and E14.5. But at E18.5 we do not see this kind of broad aSMA expression the apical head, suggesting a transient and spatially restricted population of cells in the apical mesenchyme. This is consistent with the studies from Tsujikawa et al., 2022 and Angelozzi et al., 2022. The papers mentioned by the reviewer are only focused on the suture mesenchyme. They do not claim there is broad aSMA/Acta2 expression in the apical head, but only in a spatially restricted subpopulation of suture mesenchymal cells.

      Comment 10: In the SRF immunostaining results in control and Rac1-cKO embryos, it is difficult to appreciate the nuclear localization at E12.5 in Figure 5E, as the DAPI is over saturated, and the image quality is poor. The image quality is also poor in Figure 5F.

      Response: We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the revision period.

      Comment 11: To what extent is the expression/localization of MRTF, the transcriptional co-activator of SRF, altered in the calvarial mesenchyme of Rac1-cKO embryos? Changes in MRTF would strengthen the link between Rac1 and SRF.

      Response: We do not know how MRTF expression/localization changes in the embryo tissue, but western blot data on Rac1-KO fibroblasts revealed a reduction in expression/nuclear localization of MRTF-A/B that mirrored the changes in SRF. We added these blots to Figure 5A. However, as noted at the end of the discussion, MRTF is not always required for SRF function in vivo ( Dinsmore, Elife 2022). The MRTFA/B-KO is a possibility for future work.

      Comment 12: Hypoplasia of the apical mesenchyme (Figure 6G, inset 1) in Srf-cKO is difficult to see.

      Response: During the revision period we will increase the number of E12.5 Srf-KO and Srf-WT embryos to n=3 for Figure 6G and replace the picture with a better one.

      Comment 13: Generally, the organization of the data into many main and supplemental Figures makes the flow difficult to follow.

      __Response____: __We understand the concern, but we have tried our best to organize the most important data into main figures and the relevant but less essential data into supplemental figures.

      Comment 14: SFR interacts with Pdgfra interacts genetically with Srf in neural crest cells in craniofacial development, with Srf being a target of PDGFRa signaling (Vasudevan and Soriano, 2015, Dev Cell). Since the Pdgfra-Cre line used here is hemizygous, is important that the control used to look at SRF expression in the Rac1-cKO is Pdgfra-Cre+.

      Response: It is standard practice to include some Cre+ mice in the control set to reveal whether Cre has toxic effects in the cells of interest. To the reviewer’s concern about genetic interactions between the Pdgfra gene and Srf, this should not be relevant here because the Pdgfra-Cre used in our study is a transgene and does not affect the endogenous Pdgfra gene.

      Comment 15: The text size in all figures is too small and varies throughout, making it difficult to read.

      Response: To fit the panel in the Word document, the figure is resized. This should not be an issue in the final manuscript.

      Comment 16: Details about the pulse-chase timing of the EdU experiments should be included in the results. Also, does n = 3 for each stage and each genotype? I would be helpful to include a representative section for a control and cKO littermate pair.

      Response: The details are now included in the methods section. Yes, n=3 in each stage and genotype (Fig. 4A). The representative images are also included.

      Comment 17: The relative sizing of the panels within and between figures is haphazard. Some are very large and others very small (Figure 2, 6, Supplemental Figure 1, 2, 6, 7).

      Response: The image panels are fixed in the revised manuscript.

      Comment 18: In Figure 5A and F, the titles "E12.5" and "E13.5" are in italics.

      Response: The fonts for the figures are fixed in the revised manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: This manuscript by Rathnakar et al. examines the role of the small GTPase Rac1 in apical closure of the scalp and skull. Rac1 activity is regulated the guanine nucleotide exchange factor DOCK6 and the GTPase AHGAP31. Loss of function variants in DOCK6 and gain of function variants in AHGAP31 lead to sustained inactivation of Rac1 in Adams-Oliver syndrome (AOS), which is characterized by aplasia cutis congenita, underlying calvarial defects, and limb abnormalities. While Rac1 is thought to be a key in the pathogenesis of AOS, how decreased in Rac1 activity impact development of the head is not well-understood. The authors find that conditional loss of Rac1 in cranial mesenchyme (using Pdgfra-Cre), leads to AOS-like abnormalities in the scalp and skull. They go on to show that these abnormalities are linked to reduced alpha-SMA expression in the early migrating mesenchyme (EMM), decreased osteoprogenitor cells in the supraorbital mesenchyme (SOM), decreased proliferation, and the contractile function of fibroblasts. They also find that Rac1 cKO leads to reduced expression of the mechanosensitive transcription factor SRF. Finally, they show that loss of SRF in cranial mesenchyme (using Pdgfra-Cre) leads to an AOS-like scalp and skull phenotype that has mechanistic overlap with their findings in the Rac1 cKO.

      Major:

      1. Prrx1-Cre targets calvarial mesenchyme and Suzuki et al., 2009 showed that Prrx1-Cre mediated loss of Rac1 lead to calvarial bone phenotype due to incomplete fusion of the skull. While this phenotype was not studied in detail, the statement in the intro and discussion that the calvarial phenotype has not been recapitulated in mice is incorrect.
      2. The authors show that Pdgfra-Cre induced knockout of Rac1 leads to lower-than-expected numbers of Rac1-cKO embryos at E18.5 and P1. Phenotypic analysis shows that the earliest phenotype is blebbing and hematoma in the nasal region at E11.5/12.5. It is stated that this was resolved at E18.5. It is unclear if this is truly a resolution of the phenotype or that these embryos fail to survive until E18.5. Do 100% of the Rac1-cKO embryos exhibit the blebbing/hematoma at E11.5/12.5? What is the observed number/percentage of Rac1-cKO embryos at E11.5/12.5? If the observed percentage of Rac1-cKO is similar to that at E18.5 (lower than the expected 25%), this would support resolution. If the observed ratio is as expected at E11.5/12.5, then this would support embryonic loss before E18.5 rather than phenotypic resolution.
      3. It is stated that brain shape is altered in Rac1-cKO embryos at E14.5 and E18.5 and concluded that these shape differences are secondary to the cranial defects. Pdgfra+ cells gives rise to the meninges and if the Pdgfra-Cre line recapitulates this expression, then loss of the ubiquitously expressed Rac1 in the meninges could lead to a primary defect in the brain, which may lead to secondary defects in the calvarium and scalp. Their conclusion should recognize other possibilities.
      4. The TdTom staining in wholemount at E13.5 (Supplemental Figure 2B) is difficult to appreciate in the image shown.
      5. The idea that the EMM laminates into the meninges and scalp layers is not new and should be properly cited (Vu et al., 2021, Scientific Reports). The following paper should also be cited on the use of alpha-SMA (Acta2) as a marker of the anterior calvaria mesenchyme: Holms et al., 2020 Cell Reports.
      6. It is concluded that meningeal development is maintained in the cKO; however, this conclusion was based on a single marker (S100a6) that is both expressed in the presumptive meninges and dermis and greatly reduced overall in the cKO. This conclusion should be softened or other markers used to show that the meninges is indeed normal.
      7. The overlap of S100a6 and alpha-SMA is difficult to appreciate in the images shown in Figure 3. Since this is important to the conclusion, co-staining should be done. If co-staining cannot be done due to the primary antibodies' origins, then ISH should be done.
      8. It is concluded that reduced alpha-SMA suggests an early failure of Rac-cKO cells to respond to the mechanical environment. While this is one possibility, the reduction of alpha-SMA may simply be due to a reduction of these cells resulting from failed differentiation, decreased proliferation, or increased apoptosis.
      9. The conclusion that alpha-SMA is a transient population only present in apical cranial mesenchyme between E12.5-14.5 is not consistent with prior studies: Holms et al., 2020 Cell Reports; Holms et al., 2021 Nature Communications; Farmer et al., 2021 Nature Communications; Takeshita et al., 2016 JBMR.
      10. In the SRF immunostaining results in control and Rac1-cKO embryos, it is difficult to appreciate the nuclear localization at E12.5 in Figure 5E, as the DAPI is over saturated, and the image quality is poor. The image quality is also poor in Figure 5F.
      11. To what extent is the expression/localization of MRTF, the transcriptional co-activator of SRF, altered in the calvarial mesenchyme of Rac1-cKO embryos? Changes in MRTF would strengthen the link between Rac1 and SRF.
      12. Hypoplasia of the apical mesenchyme (Figure 6G, inset 1) in Srf-cKO is difficult to see.
      13. Generally, the organization of the data into many main and supplemental Figures makes the flow difficult to follow.
      14. SFR interacts with Pdgfra interacts genetically with Srf in neural crest cells in craniofacial development, with Srf being a target of PDGFRa signaling (Vasudevan and Soriano, 2015, Dev Cell). Since the Pdgfra-Cre line used here is hemizygous, is important that the control used to look at SRF expression in the Rac1-cKO is Pdgfra-Cre+.

      Minor:

      1. The text size in all figures is too small and varies throughout, making it difficult to read.
      2. Details about the pulse-chase timing of the EdU experiments should be included in the results. Also, does n = 3 for each stage and each genotype? I would be helpful to include a representative section for a control and cKO littermate pair.
      3. The relative sizing of the panels within and between figures is haphazard. Some are very large and others very small (Figure 2, 6, Supplemental Figure 1, 2, 6, 7).
      4. In Figure 5A and F, the titles "E12.5" and "E13.5" are in italics.

      Significance

      Overall, this is an interesting study that shares mechanistic insight into the scalp and skull deformities in AOS. The overall presentation of the work, particularly the figures, should be improved and streamlined to enhance clarity and better emphasize the novelty of the study. In addition, the conclusions are not always well-supported by the results and the interpretation of the results do not fully consider and cite previous studies.

      Audience: Developmental Biologists

      Expertise: Craniofacial development and disease

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In mice lacking Rac1 in the PDGFRa+ mesenchymal cell lineage, the authors found Adams-Oliver syndrome (AOS)-like defects of the apical/dorsal scalp and calvaria, which was accompanied by the secondary brain protrusion by E18.5. The primary phenotype emerged at E11.5 and worsened from E12.5 to E14.5 in the apical/dorsal region of the embryonic head, with limited lateral expansion as well as reduced thickening/stratification of the mesenchymal layer expressing α-smooth muscle actin (αSMA). Very similar in vivo abnormalities were obtained when serum response factor (SRF), known as a mechanotransducing factor, was removed in PDGFRα+ mesenchymal cells. Rac1-lacking mesenchymal cells proliferated poorly in vivo and contracted weakly in culture, with reduced expression of SRF and αSMA. Based on these results and previously obtained understanding that the developing apical/dorsal mesenchyme is mechanically stretched by the underlying brain, the authors conclude that the mechanosensing-triggered morphogenetic behaviors of the apical/dorsal mesenchymal cells (i.e., proliferation, stratification, and contraction, which all lead to physical stability or mechanical resilience of that layer) is mediated by Rac1 and SRF. The authors also suggest that this molecular mechanism for the physiological maturation of the apical/dorsal mesenchyme may underlie the ventral-to-dorsal progression of osteogenesis, absence of which explains AOS pathogenesis.

      Major comments:

      In Fig. 5, links between Rac1, SRF, αSMA, and contractility in mesenchymal cells are shown. Molecular analyses (Western blot and qPCR) were performed using primary cultured mesenchymal cells (prepared after freed from the epidermal population). Although use of cells prepared from E18.5 embryos may have been chosen by the authors for the safe isolation of the mesenchymal population without contamination of epidermal cells, this reviewer finds that anti-SRF immunoreactivity is weaker at E13.5 than at E12.5 (throughout the section including the mesencephalic wall) and therefore wonder whether SRF expression changes in a stage-dependent manner. So, simply borrowing results obtained from E18.5-derived cells for describing the scenario around E12.5 and E13.5 is a little disappointing point found only here in this study. In Fig. 5F, it is difficult to clearly see "reduction" of SRF immunoreactivity in Rac1-KO. Therefore, quantification of %SRF+/totalTomato+ would be desired. Separately, direct comparison of spontaneous centripetal shrinkage of the apical/dorsal scalp tissues, which will occur in 30 min when prepared at E12.5 or E13.5 (Tsujikawa et al., 2022), between WT and Rac1-KO would strengthen the results in Fig. 5D. As KO is specific to the mesenchyme, the authors do not have to worry about removal of the epidermal layer (which would be much more difficult at E12.5-13.5 than E18.5). If the degree of centripetal shrinkage of the "epidermis plus mesenchyme" layers were smaller in Rac1-KO, it would be interpreted to be mainly due to poorer recoiling activity and contractility of the Rac1-KO mesenchymal tissue.

      Minor comments:

      1. The authors favor "apical" vs. "basolateral" to tell the relative positions in the embryonic head, not only in the adult head. But "apical" vs. "basolateral" should be accompanied with dorsal vs. ventral at least at the first appearance. Apical-to-basal axis or apex vs. basolateral by itself can provide, in many contexts, impressions that epithelial layers/cells are being discussed. Please note that the authors also use "caudal" (in the embryonic head). Usually, a universally defined anatomical axis perpendicular to the rostral-to-caudal axis is the dorsal-to-ventral axis.
      2. One of the authors' statements in ABSTRACT "In control embryos, α-smooth muscle actin (αSMA) expression was spatially restricted to the apical mesenchyme, suggesting a mechanical interaction between the growing brain and the overlying mesenchyme" and a similar one in RESULTS "αSMA was not detected in the basolateral mesenchyme of either genotype from E12.5-E14.5 (Suppl. Fig. 4A), suggesting restriction of the mechanosensitive cell state to the apical mesenchyme" need to be at least partly revised, taking previous publication about the normal αSMA pattern in the embryonic head into account more carefully. Tsujikawa et al. (2022) described "Low-magnification observations showed superficial immunoreactivity for alpha smooth muscle actin (αSMA), which has been suggested to function in cells playing force-generating and/or constricting roles; this immunoreactivity was continuously strong throughout the dorsal (calvarial) side of the head but not ventrally toward the face, producing a staining pattern similar to a cap (Figure 2A)" . Therefore, in this new paper, descriptions like "we observed ...., consistent with ....(2022)" or "we confirmed .... (2022)" would be more accurate and appropriate regarding this specific point. Such a minor change does not reduce this study's overall novelty at all.
      3. It would be very helpful if the authors provide a schematic illustration in which physiological and pathological scenarios (at the molecular, cellular, and tissue levels found or suggested by this study) are shown.
      4. Despite being put in the title, "mechanosensing" by mesenchymal cells is not directly assessed in this study. If appropriate, something like "mechano-functioning" would be closer to what the authors demonstrated.

      Significance

      This study advances understanding of a key aspect of the molecular mechanisms underlying the normal mammalian craniofacial development, unveiling the role of Rac1 and SRF in the apical/dorsal mesenchymal layer which has inter-tissue mechanical relationships with the embryonic brain underneath. This study also advances understanding of Adams-Oliver Syndrome pathogenesis, demonstrating the biological significance of the normal inter-tissue mechanical relationships in the developing mammalian head. This study may have opened a door for the genetic/molecular dissection toward the tissue-level mechano-engineering, which would stimulate development of next-generation organoids or assembloids. Broad audience including developmental biologists/neuroscientists, molecular/cellular biologists, pathologists, clinical geneticists, and pediatricians would be interested in this work.

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper "Mouse scalp development requires Rac1 and SRF for the maintenance of mechanosensing mesenchyme", the authors demonstrated that deletion of Rac1 (Rac1-KO) with a PDGFRαCreTG mouse model led to absence of skull apex and a blebbing formation while the limbs were not impacted. Rac1-KO mice showed the Rac1 regulated expansion of the apical mesenchyme toward the very apex meningeal and dermis layer and the osteogenic differentiation of supra orbital arch mesenchyme. Rac1 also regulates the proliferation of apical mesenchyme, dermis differentiation, and mechanosensing of the cranial mesenchyme cells. The authors also indicated Rac1 was a regulator of Srf by showing the deletion of Rac1 lead to lower Srf mRNA level and SRF protein expression. Deletion of Srf showed similar phenotypes as Rac1-KO mice.

      Major concern is the study lacks rigor in several areas where n=2, results are not quantified with statistics. They need to run power analysis and increase their samples sizes. Please include statistics on all measurements. Filamentous actin staining and alpha-sma is used to visualize mechanosensing but also in other cell activities such as cell contractility for movement, cell to substrate adhesion, cell division, etc. They need to query more mechanosensing related pathways (Piezo1/2, Yap/taz-Hippo, integrin-Focal Adhesion Kinase, etc) to show that mechanosensing changed.

      Comments by figure.

      Fig. 1: In panel E, the cranial bone area measurement is not normalized to mitigate possible effect of individual differences.

      Fig. 2:

      1. While the authors mentioned many phenotypical changes(bone length changes, gap thickness change, apex thickness change, etc) based on histology stain, none of them are quantified to show a siginificant difference between Rac1-WT and Rac1-KO.
      2. In panel D, with only 2 embryos per group is not enough for quantitation.
      3. In panel D, the two arrows in the Rac1-KO mutants are not easy to catch.

      Fig. 3:

      1. The thickness quantification is not performed.
      2. The images show an obvious curve change of the apex between the control and mutant. Such change is not discussed in the results. Is it due to histology issue?
      3. The merged layer did not show S100a6. While the authors are showing apical expansion of the mesenchyme toward the dermis and meninges, it is hard to track where they are without a merged image.

      Fig.4:

      1. In panel B, 2 biological replicates per genotype are very low
      2. There is no cell death data.

      Fig. 5:

      1. In panel B, the GPDH western plot bands in the mutants seem to be thinner than those of controls.
      2. Though the immunostain showed a decrease in signal intensity, it is hard to know whether the decrease is significant enough across all Rac1-KO mutants. They need to measure the fluorescence intensity and perform statistics.

      Fig. 6: Similar as Fig. 2, there is no quantification and n=1 per genotype is not enough.

      Fig. 7: Need quantification between Srf-KO and Rac1-KO with statistics to show they are not different but both significantly different with WTs.

      Supplement Fig.2: No image showing the time point before E11.5.

      Supplement Fig.3: The ventral view of Rac1-WT does not have the same angle as it shows in Rac1-KO. Makes harder to see the difference between control and mutant.

      Supplement Fig.4 &7: The alkaline phosphatase stained area needs to be normalized to some other metric because the embryos could be different size.

      Supplement Fig 6 A: The legend and figure don't match. Is it E13.5 or 14.5. Panel 6B needs better images without curling of the tissue.

      Significance

      Please see my comments above. This work is broadly of interest to developmental biologist, fracture healing, and human genetics fields.

      The paper is easy to understand and follow. The massive amount of histology and immunostaining images make it easy to identify the point the authors want to show. All the figures are well-labeled and visually informative. The experiment sequence is logic. The gene deletion models provide solid and direct evidence on the necessity of their function during early head development. The discussion is thoughtfully written and clear. The authors discuss the connection of Rac1 and SRF with other signaling pathways, which makes them promising target toward Adams-Oliver syndrome.

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      Reply to the reviewers

      We thank the reviewers for their comments

      __Reviewer 1 __

      This is review of the manuscript „A simple method to efficiently generate structural variation in plants" by Bechen et al. The manuscript presents a very interesting and innovative approach to generate structural variant mutations (including large ones) in the genome of Arabidopsis thaliana using a simple chemical treatment with TOPII inhibitor etoposide. Authors show that unlike chemical mutagens commonly used for induction of SNPs (EMS, sodium azide...), etoposide-treatment caused structural variants like DNA deletions, insertions, inversions and translocations. These mutations were identified by the whole genome short and long read sequencing that also indicated a WT-like frequency of SNPs. This finding can potentially help inducing mutations similar to high energy radiation in potentially any plant. First, the manuscript provides description of the unusual phenotypes found after etoposide treatment and their Mendelistic inheritance. Based on this, authors performed whole genome sequencing and mutation detection, validation. The experimental part ends by transcriptome analysis that authors use as the approach to identify the causal mutations. This part is, in my opinion, the weakest part of the manuscript and would benefit from further clarification or even additional experiments (see below). Overall the manuscript is very clear and contains all necessary information. The only part that was confusing to me, was the section focusing on the transcriptome analysis.

      __Response: __Thank you for your appreciation of the study. As detailed below, we have changed our presentation of the RNA-seq results to better describe their purpose.

      Major points: Line 222: In the section „RNA-Seq identifies genes that are associated with structural variation and mutant phenotypes", authors suggest that the changes in the transcript amount were used to identify causal mutations. I got confused by this section. Exach of the examples represents unique situation and thus only single cases are presented which makes it hard to estimate robustness of the presented approach. Also, the presented mutations have prominent phenotypes that were already heavily studied in the past and therefore the possible causal genes are mostly known. Therefore, I am not sure how this approach would stand in case of traits with unknown underlying genes.

      __R____esponse: __Our intent was not to present a new method for mapping causative mutations. Like any other induced genetic mutation, there are many possible strategies for identifying the causative locus (loci), such as mapping-by-sequencing via a segregating F2 population (as mentioned below). Organization of the manuscript’s results has been changed to reflect that mRNA-seq was conducted to learn more about the phenotypes, not to definitively identify causal genes/mutations. We have also added additional text to the discussion to clarify candidate mutation mapping approaches on line 376: “How do we identify genetic changes that are causative for phenotypes of interest? Future studies will accelerate candidate-gene discovery by employing structural-variant callers, de novo genome assembly-based approaches, and RNA-Seq based mapping (Mahmoud et al 2019). Although our study did not aim to use RNA-Seq to identify mutations, it provides an example of how RNA-Seq data in tandem with genome sequencing can help shortlist potential causal mutations. In cases where a potential causative variant is not obvious, these strategies can be combined with traditional genetic mapping approaches. However, mapping-by-sequencing approaches might not be easily applicable to some classes of mutants. For example, inversions or translocations can suppress recombination and reduce the efficacy of mapping-by-sequencing.”

      When refering to the case with the chromosomal inversion, I do not see how one will be able to map a candidate based on the relatively mild expression (but maybe I am missing something here). Similarly, the „mapping" approach applied to the variegated line would not be possible on a trait that is less studied and the candidates are not well known. I wonder why authors did not perform association mapping on a bulk of phenotypically mutant plants collected from a segregating F2 backcross population. This might be a more robust way of linking the phenotype with a mutation.

      Response: Our primary goal with the manuscript was to demonstrate that etoposide-treatment induces mutant phenotypes and structural variation. Identifying the causal mutation for every phenotype is outside the scope of the present study. As described above, we have added additional text in the discussion to briefly describe candidate mutation mapping approaches that researchers can use.

      Discussion section: I am missing discussion on how etoposide could be causing such structural variants.

      Response: Etoposide’s mode of action is well-studied in animal systems and has been described in text that has been moved from results to introduction. Starting on line 90 it reads “Topo II relaxes torsional stress from DNA supercoiling generated during DNA replication or transcription by transiently breaking both strands and then ligating them after passing a DNA segment through the break. Between strand breakage and ligation, Topo II is covalently linked to DNA via a tyrosine residue, forming a topoisomerase cleavage complex [37]. This complex is stabilized by the inhibitor etoposide. A collision between covalently-linked Topo II and DNA polymerases during DNA replication, or with RNA polymerases during transcription, leads to removal of the Topo II enzyme, which results in the generation of double-stranded breaks (DSBs) [38–41]. The imprecise repair of DSBs leads to genomic rearrangements and structural variation in mouse spermatocytes, fibroblasts, and in human cells [42–44]. Previously, it was shown that treatment with etoposide inhibits plant growth[45,46] and causes fragmentation of chromosomes during meiosis in Arabidopsis [45]. However, its potential as a mutagen that can induce structural variation has not been investigated"

      Minor points:

      Line 70: Possibly add sodium azide. It is frequently used as mutagen for some plant species.

      __Response: __Added sodium azide to line 76.

      Line 122: „...etoposide is an excellent mutagen for efficiently creating large-effect mutations." This cannot be claimed at this point because the sequence analysis data were not shown yet. Please reformulate.

      Response: We changed this line (now line 133) to: “The large proportion of plants showing visible phenotypes suggested that etoposide could be an excellent mutagen for efficiently creating large-effect mutations.”

      **Referees cross-commenting** My main issue was the mapping protocol using transcriptomic changes. It is hard to believe that this approach would work well on unknown/less studied traits. What is your opinion?

      Response: Identifying the causal mutation for every phenotype is outside the scope of the present study. Organization of the manuscript’s results has been changed to reflect that RNA-seq was conducted to learn more about the phenotypes, not to definitively identify causal genes/mutations. In some instances (BR-like dwarf), the RNA-seq data, combined with prior knowledge, suggested a causal variant (AS1), which was further bolstered by the identification of structural variants. Note that it is only for the variegated mutant that we definitively identified the causal mutation (IM) by genetic complementation.

      Reviewer #1 (Significance (Required)):

      Strengths - innovative way on how to induce structural variant mutations in plants.

      Limitations - The approach on how to map the mutations needs more development. At this point i tis not clear how well the approach will work in other plant species.

      Audience - basic and applied plant scientists

      Response: We have adjusted our discussion of the role of mRNA-seq in the study, and added comments on approaches for mapping causative mutations. We hope this now clarifies the overall strategy. Topoisomerase’s sensitivity to etoposide inhibition is conserved amongst tested plants and animal species. We have changed the sentence and added references to introduction to show that etoposide acts on other plant species (line 100): “Previously, it was shown that treatment with etoposide impacts genome stability and inhibits plant growth in Arabidopsis thaliana, Allium cepa, and Lathyrus sativus [46-49] and causes fragmentation of chromosomes during meiosis in Arabidopsis [48]. “ In addition, preliminary (unpublished) work in our labs shows that etoposide has mutagenic impacts on legumes and Brassicaceae crop species. We therefore believe that this protocol should be widely applicable to other plant species.

      ===

      Reviewer 2

      • The manuscript describes mutagenesis of Arabidopsis by a topoisomerase II inhibitor. The method is effective, resulting in good density of SV and no detectable SNV. The authors provide a full characterization of the mutants, their phenotypes, and their genomes. The 34-sample selected for genomic analysis is sufficient to make firm conclusions.

      • The manuscript is clearly written and illustrated.

      • The manuscript does a very good job at covering the phenotypic and molecular analysis for this type of mutagenesis. For example, they highlight the difference between short and long reads in the identification of SV.

      • The figures are very clear, with the exception of Fig. 3, which I found harder to follow. It would be enhanced by describing the candidate lesion(s) in the first panel of each mutant series. This would clarify the expectation. For example, larger indels (not examined here) should be associated with higher (insertion) or lower (deletion) expression of the affected genes. In the cases presented in Fig.3, the structural changes do not suggest obvious hypotheses. The authors examine the regions near breakpoint of inversions or near small indels. It makes sense, but it does not make the figure very digestible. The connected text in the results, on the other hand, is very clear. Perhaps, making the conclusions in the figure legend as well? As a connected thought, it would have been useful to provide expression data for a large indel exemplifying the cis/trans nature of regulatory changes.

      __Response: __Thank you for your helpful comments. The RNA-seq data is now presented before the DNA sequencing data to clarify the role of the RNA-seq data in this study. The previous Figure 3 is now Figure 2. We have clarified the presentation by combining original Fig. 3C and 2E into a single panel (Fig. 4F); moving 3J to Figure 4E; removing what was 3I; and moving the original 3B, 3E-G to Figure S9. In Figure S9, a label for the type of SV was added to the scatterplots of expression of genes surrounding the SVs. For further clarity, the panel describing the inversion in BR-like dwarf is also now plotted in the same way as those of short-internode dwarf. We agree it would be informative to provide expression data for a large indel to determine the extent of cis or trans effects, but we did not find any large indels in the samples we sequenced with long-reads.

      We agree with the reviewer that some of the mutants we have generated would be great material for further studying cis/trans nature of regulatory changes. We are strongly interested in this question; it is certainly a subject for a future publication.

      • Deletions and other rearrangements may affect meiosis as noted by the authors. In addition, they can display gametophytic phenotypes and a deficit in transmission. The likelihood increases with the size of the indel. Large indels are not transmitted. Accordingly, for indels above a certain size, it is not possible to determine the number of causal loci from F2 ratios.

      __Response: __We agree that the impact of very large SVs on meiosis alters segregation ratios and prevents determination of causal loci from F2 ratios. Impacts on meiosis will also likely impact our ability to use techniques based on bulk segregant analysis to finely map causal mutations. It is important to note that such mutations comprise only a fraction of all detected SVs.

      Identification of multiple loci causing a phenotype in plants carrying large SVs will therefore require other approaches. For example, structural variation callers can be used to identify the boundaries of SVs like duplications or deletions. RNA-Seq can be used to identify genes at the SV whose expression is highly effected by cis-regulatory changes. To test if those SVs are responsible for the phenotype, genes at SV boundaries along with the novel promoters can then be reintroduced as transgenes. This should enable one to identify combinations of mutated genes that are responsible for the phenotypes.

      • Although the use of topo II inhibitors for mutagenesis in plants is novel, the mutagenic effects described here are well documented in animals. This should be acknowledged (e.g. Heisig, Mutagen. 2009; Ferguson, Env Mol Mutagen. 1994)

      __Response: __We have acknowledged prior work demonstrating the mutagenic impact of etoposide using primary literature as well as more recent references. These include references # 43-45.

      **Referees cross-commenting** I also found the expression analysis confusing and in need of revision. One reason is that a set of clear expectations were not provided. I believe that the RNAseq analysis is expected to help identify the gene(s) that underlie a trait. For example, genes located on an indel are likely to display expression proportional to copy number. Also, a new junction or translocation could influence expression of the gene next to the break point. The authors should make this clear in the figure and the text.

      Response: Organization of manuscript’s results has been changed to reflect that mRNA-seq was conducted to learn more about the phenotypes, not to definitively identify causal genes/mutations.

      Reviewer #2 (Significance (Required)):

      • I appreciated the description of the method. It should be widely applicable. In arabidopsis, it requires sustained growth in the presence of the inhibitor. This could limit its applicability. For example, it may not be effective with pollen because exposure by a short soaking period may not be sufficient. Culturing of large seeded species is possible, but adds complexity. In this context, radiations have advantages. I do agree with the authors on the difficulty in identifying a source. However, once one is found, radiation treatment is very simple and convenient.

      • The manuscript describes a useful tool and the connected spectrum of mutations. It has the novelty, quality, and relevance to represent a significant contribution to plant biology and to be of broad interest.

      __Response: __Thank you for your feedback. For Arabidopsis, we germinated and grew seeds on media containing etoposide for about two weeks (see Methods). In work that is not yet ready for publication, we have taken the same approach with a legume species and other Brassicaceae that have substantially larger seeds. We find that we need to use a higher dose of etoposide to induce phenotypes, but that it is easy to germinate and grow large-seeded plants for a couple of weeks on media containing etoposide. We don’t anticipate that seed size will be limiting for this method.

      We agree that this technique will not work for pollen; it will only work for tissues with significant levels of DNA replication. However, this technique alleviates the need for collecting pollen. For example, pollen irradiation has been used to create poplar with structural variants. However, if using etoposide-based mutagenesis, one could grow poplar seeds, cuttings, explants, embryos, or calli on etoposide-containing media.

      Reviewer 3


      Summary:

      The manuscript entitled "A simple method to efficiently generate structural variation in plants" by Bechen et al. investigated an efficient mutagen for inducing large structural variations in plants, replacing traditional irradiation methods with a chemical mutagenesis strategy. The study examined the effects of etoposide, a DNA topoisomerase II inhibitor, on structural variations and demonstrated that etoposide treatment induces a wide range of phenotypic and genome changes, including inversions, duplications, and deletions. Additionally, the authors analyzed the relationship between gene expression changes and genomic alterations to identify potential causal genes underlying specific phenotypes. While their findings provide clear and reliable evidence of structural variations induced by etoposide, I have several suggestions to enhance the clarity of their results, as detailed below.

      __Response: __We thank the reviewer for their feedback. It has helped improve the presentation and clarity of our results.

      Major comments:

      1. Lines 166-169: My understanding is that you selected etoposide-treated M1 plants based on specific phenotypes, and observed their M2 and M3 progeny, categorizing them as either phenotype-positive or phenotype-negative. In Table S3, phenotypes other than BR-like dwarf, virescent, and short internode dwarf are not mentioned. Does this indicate that these other lines did not exhibit heritable phenotypic traits? If other lines showed some phenotype changes, could you incorporate progeny relationships along with phenotype information into Table S3? Additionally, in Figures S2 and S3, you reference 26A lines. Did they exhibit similar phenotypic changes among them?

      __Response:____ __Unfortunately, we do not have detailed phenotypes of each chosen M1 line. Most lines had one or more of the phenotypes we mention in the results – “Those exposed to 160 µM of etoposide exhibited significantly more abnormal phenotypes than DMSO only or 80 µM etoposide plants, including loss of apical dominance, gnarled leaves, reduced plant size, seed abortion, and lower seed number at maturity (Figure 1A).” It is important to note that M2 phenotypes were not observed in M1.

      Table S3 is now Table S7. Only some mutants lines or lineages that were sequenced had one of the scored phenotypes in M2 (10B,13B, 1A, 1B, 21A, 24B, 26A, 34C, 5A,9A). Of these, we have formally tracked inheritance of only 13B, 1A, 34C, and 5A over multiple generations. We do not have similar data for other sequenced lines . We also sequenced some lines (17B, 21B, 26C) without any mutant phenotypes to assess if plants lacking visible phenotypes still carried SVs. Indeed, as described in Table S9, all three of these lines carried small SVs, which might not affect genes that create an obvious visible phenotype under our growth or observation conditions.

      Yes. 26A siblings all exhibited the same flat leaf phenotype.

      Overall, our data suggests that mutant phenotypes and their causal SVs can be stably transmitted through multiple rounds of meiosis.

      Lines 187-189 and Figure S4: The assessment of repeat copy number variation provides valuable insights. However, based on the figure, the conclusion that "etoposide treatment likely did not trigger genomic instability in repetitive DNA" is difficult to interpret. Could you modify the figure into a box plot with raw data points and include a statistical analysis to support this conclusion?

      __Response: __The figure has been modified to a box plot and is now presented as main Figure 3. Wilcox test has been performed and shows no significant difference in read depth over NOR2, NOR4, and telomere regions between control and etoposide-treated lines.

      Line 200 and Figure S8A: You state that SNV analysis identified a similar number of SNVs in treated and control plants. However, this is not easily interpretable from the figure. Could you include a statistical comparison between etoposide-treated and control plants? For example, EMS mutagenesis is known to induce specific G/C → A/T transitions. Did etoposide-treated and control plants exhibit the same types of nucleotide changes, or were there differences in the mutation spectrum?

      Response: This figure has been modified to assess the entire mutational spectrum of SNVs, including statistical comparisons, and is now part of Figure 3. We have also added the following text on line 244: “However, SNV analysis identified a comparable spectrum and number of SNVs in etoposide-treated and control lines (Figure 3), suggesting that etoposide did not induce excess SNVs.”

      Lines 219-220: Your conclusion clearly demonstrates the detection of numerous structural variations using both short- and long-read sequencing technologies. Could you provide a summary table listing the detected mutation positions? Since short-read sequencing is generally less effective in detecting large structural variations, I am particularly interested in evaluating the accuracy of Lumpy Express in identifying mutations.

      Response: Short-read sequencing and Lumpy Express are unsurprisingly less effective in detecting large structural variations when compared with long-read based approaches. SVs detected by Nanopore were missed by short-read sequencing and Lumpy Express. However, it is hard to benchmark the efficacy of Lumpy Express as only a few lines were sequenced by both Nanopore long-read sequencing and short-read sequencing. After removing SVs that were also present in control lines, we could identify only one SV detected by both Lumpy Express and Nanopore sequencing; this SV is a deletion. In plant 1A_4_5, which was sequenced by short reads, Lumpy Express called a 70 bp deletion at Chr 5: 5776579. SV calling using Nanopore-generated sequence of a sibling plant, 1A_4_11, identified a 70 bp deletion at Chr 5:5776578. The SVs identified by Lumpy Express are presented in Table S9. Those identified from long-read data are in Table S11.

      1. Figures 3E-G: To facilitate a clearer comparison of the effects of structural variations on gene expression between BR-like dwarf and short internode dwarf, could you add an average trend line to the figures, similar to Figure 3B?

      Response: An average trend line has been added to these plots. These data are now presented in Figure S9.

      Minor comments:

      Line 105 and Figure 1A: In the manuscript, etoposide concentrations are stated as 0, 40, 80, and 160 μM, whereas Figure 1A labels the concentrations as 0, 80, 160, and 320 μM. Should the figure be updated to 0, 40, 80, and 160 μM for consistency?

      __Response: __Thank you for the comment. We updated the results section to make concentrations listed consistent with methods (0, 20, 40, 80,160, 320, and 640 µM) and added additional description of seedling growth such that all concentrations are described. We also updated references to the figure such that only sentences regarding concentrations with photos reference Fig 1A.

      Figure 1B legend: Typographical error: "roundsof" → "rounds of".

      Response: Corrected.

      Line 109: Do you have a summary table for the M1 generation? If so, could you provide it as a supplementary table?

      Response: We regretfully do not have the data to populate a summary table for the M1 generation. As described above, most M1 plants have one or more of the following phenotypes mentioned in text: “loss of apical dominance, gnarled leaves, reduced plant size, seed abortion, and lower seed number at maturity “

      Line 119: Figure 1B only defines developmental stages. To improve clarity, consider revising "Figure 1B" to "Figure 1B-F", allowing readers to easily understand the corresponding figures.

      __Response: __We updated the Figure reference to Fig. 1B-F.

      Line 121: The citation "(Figure S1, Table S1)" would be clearer if placed at the end of the sentence.

      __Response: __The citation was moved to the end of the sentence.

      Lines 137, 148, 167: To maintain consistency with Figure 1C-F and the manuscript's logical flow, could you standardize the order of phenotypes as "virescent, short internode dwarf, and BR-like dwarf" instead of the current variation?

      __Response: __We have standardized the order of the figures and the discussions of phenotypes in the text as: BR-like dwarf, short-internode dwarf, virescent, then variegated.

      Line 139: Why is "Figure 1B" referenced at this position? Would it be more appropriate to remove this reference?

      Response: Figure 1B shows that the phenotypes were able to be transmitted at least until the M5 generation, thus the reference.

      Figure S7 legend: Typographical error: "to to" → "to".

      __Response: __Corrected.

      Figure S8B (Chromosome 5 labels): Could you adjust the position labels to maintain a consistent format with other chromosomes?

      Response: This figure has been modified on request of another reviewer such that this is no longer applicable.

      Lines 262, 277, 279: "Figure S11" should be corrected to "Figure S10".


      __Response: __Corrected.

      Line 269: "Figure S10" should be corrected to "Figure S11B-H".


      __Response: __Corrected.

      Reviewer #3 (Significance (Required)):

      In mutation studies aimed at inducing large-scale genomic variations, irradiation has traditionally been the primary method for mutagenesis. However, this study proposes a more efficient and accessible alternative using chemical mutagenesis with a DNA topoisomerase II inhibitor. Genomic analysis of mutants generated through this treatment revealed extensive genomic alterations, with a mutation frequency exceeding that of gamma irradiation-induced mutants. These findings suggest that this approach has the potential to advance mutation research for plant biologists and breeders seeking efficient methods for trait improvement. Furthermore, the authors integrate RNA-seq analysis for selected traits, demonstrating a systematic workflow for candidate gene identification and facilitating the determination of causal genes.


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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The manuscript entitled "A simple method to efficiently generate structural variation in plants" by Bechen et al. investigated an efficient mutagen for inducing large structural variations in plants, replacing traditional irradiation methods with a chemical mutagenesis strategy. The study examined the effects of etoposide, a DNA topoisomerase II inhibitor, on structural variations and demonstrated that etoposide treatment induces a wide range of phenotypic and genome changes, including inversions, duplications, and deletions. Additionally, the authors analyzed the relationship between gene expression changes and genomic alterations to identify potential causal genes underlying specific phenotypes. While their findings provide clear and reliable evidence of structural variations induced by etoposide, I have several suggestions to enhance the clarity of their results, as detailed below.

      Major comments:

      1. Lines 166-169: My understanding is that you selected etoposide-treated M1 plants based on specific phenotypes, and observed their M2 and M3 progeny, categorizing them as either phenotype-positive or phenotype-negative. In Table S3, phenotypes other than BR-like dwarf, virescent, and short internode dwarf are not mentioned. Does this indicate that these other lines did not exhibit heritable phenotypic traits? If other lines showed some phenotype changes, could you incorporate progeny relationships along with phenotype information into Table S3? Additionally, in Figures S2 and S3, you reference 26A lines. Did they exhibit similar phenotypic changes among them?
      2. Lines 187-189 and Figure S4: The assessment of repeat copy number variation provides valuable insights. However, based on the figure, the conclusion that "etoposide treatment likely did not trigger genomic instability in repetitive DNA" is difficult to interpret. Could you modify the figure into a box plot with raw data points and include a statistical analysis to support this conclusion?
      3. Line 200 and Figure S8A: You state that SNV analysis identified a similar number of SNVs in treated and control plants. However, this is not easily interpretable from the figure. Could you include a statistical comparison between etoposide-treated and control plants? For example, EMS mutagenesis is known to induce specific G/C → A/T transitions. Did etoposide-treated and control plants exhibit the same types of nucleotide changes, or were there differences in the mutation spectrum?
      4. Lines 219-220: Your conclusion clearly demonstrates the detection of numerous structural variations using both short- and long-read sequencing technologies. Could you provide a summary table listing the detected mutation positions? Since short-read sequencing is generally less effective in detecting large structural variations, I am particularly interested in evaluating the accuracy of Lumpy Express in identifying mutations.
      5. Figures 3E-G: To facilitate a clearer comparison of the effects of structural variations on gene expression between BR-like dwarf and short internode dwarf, could you add an average trend line to the figures, similar to Figure 3B?

      Minor comments:

      Line 105 and Figure 1A: In the manuscript, etoposide concentrations are stated as 0, 40, 80, and 160 μM, whereas Figure 1A labels the concentrations as 0, 80, 160, and 320 μM. Should the figure be updated to 0, 40, 80, and 160 μM for consistency?

      Figure 1B legend: Typographical error: "roundsof" → "rounds of".

      Line 109: Do you have a summary table for the M1 generation? If so, could you provide it as a supplementary table?

      Line 119: Figure 1B only defines developmental stages. To improve clarity, consider revising "Figure 1B" to "Figure 1B-F", allowing readers to easily understand the corresponding figures.

      Line 121: The citation "(Figure S1, Table S1)" would be clearer if placed at the end of the sentence.

      Lines 137, 148, 167: To maintain consistency with Figure 1C-F and the manuscript's logical flow, could you standardize the order of phenotypes as "virescent, short internode dwarf, and BR-like dwarf" instead of the current variation?

      Line 139: Why is "Figure 1B" referenced at this position? Would it be more appropriate to remove this reference?

      Figure S7 legend: Typographical error: "to to" → "to".

      Figure S8B (Chromosome 5 labels): Could you adjust the position labels to maintain a consistent format with other chromosomes?

      Lines 262, 277, 279: "Figure S11" should be corrected to "Figure S10".

      Line 269: "Figure S10" should be corrected to "Figure S11B-H".

      Significance

      In mutation studies aimed at inducing large-scale genomic variations, irradiation has traditionally been the primary method for mutagenesis. However, this study proposes a more efficient and accessible alternative using chemical mutagenesis with a DNA topoisomerase II inhibitor. Genomic analysis of mutants generated through this treatment revealed extensive genomic alterations, with a mutation frequency exceeding that of gamma irradiation-induced mutants. These findings suggest that this approach has the potential to advance mutation research for plant biologists and breeders seeking efficient methods for trait improvement. Furthermore, the authors integrate RNA-seq analysis for selected traits, demonstrating a systematic workflow for candidate gene identification and facilitating the determination of causal genes.

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      Referee #2

      Evidence, reproducibility and clarity

      • The manuscript describes mutagenesis of Arabidopsis by a topoisomerase II inhibitor. The method is effective, resulting in good density of SV and no detectable SNV. The authors provide a full characterization of the mutants, their phenotypes, and their genomes. The 34-sample selected for genomic analysis is sufficient to make firm conclusions.
      • The manuscript is clearly written and illustrated.
      • The manuscript does a very good job at covering the phenotypic and molecular analysis for this type of mutagenesis. For example, they highlight the difference between short and long reads in the identification of SV.
      • The figures are very clear, with the exception of Fig. 3, which I found harder to follow. It would be enhanced by describing the candidate lesion(s) in the first panel of each mutant series. This would clarify the expectation. For example, larger indels (not examined here) should be associated with higher (insertion) or lower (deletion) expression of the affected genes. In the cases presented in Fig.3, the structural changes do not suggest obvious hypotheses. The authors examine the regions near breakpoint of inversions or near small indels. It makes sense, but it does not make the figure very digestible. The connected text in the results, on the other hand, is very clear. Perhaps, making the conclusions in the figure legend as well? As a connected thought, it would have been useful to provide expression data for a large indel exemplifying the cis/trans nature of regulatory changes.
      • Deletions and other rearrangements may affect meiosis as noted by the authors. In addition, they can display gametophytic phenotypes and a deficit in transmission. The likelihood increases with the size of the indel. Large indels are not transmitted. Accordingly, for indels above a certain size, it is not possible to determine the number of causal loci from F2 ratios.
      • Although the use of topo II inhibitors for mutagenesis in plants is novel, the mutagenic effects described here are well documented in animals. This should be acknowledged (e.g. Heisig, Mutagen. 2009; Ferguson, Env Mol Mutagen. 1994)

      Referees cross-commenting

      I also found the expression analysis confusing and in need of revision. One reason is that a set of clear expectations were not provided. I believe that the RNAseq analysis is expected to help identify the gene(s) that underlie a trait. For example, genes located on an indel are likely to display expression proportional to copy number. Also, a new junction or translocation could influence expression of the gene next to the break point. The authors should make this clear in the figure and the text.

      Significance

      • I appreciated the description of the method. It should be widely applicable. In arabidopsis, it requires sustained growth in the presence of the inhibitor. This could limit its applicability. For example, it may not be effective with pollen because exposure by a short soaking period may not be sufficient. Culturing of large seeded species is possible, but adds complexity. In this context, radiations have advantages. I do agree with the authors on the difficulty in identifying a source. However, once one is found, radiation treatment is very simple and convenient.
      • The manuscript describes a useful tool and the connected spectrum of mutations. It has the novelty, quality, and relevance to represent a significant contribution to plant biology and to be of broad interest.
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      Referee #1

      Evidence, reproducibility and clarity

      This is review of the manuscript „A simple method to efficiently generate structural variation in plants" by Bechen et al. The manuscript presents a very interesting and innovative approach to generate structural variant mutations (including large ones) in the genome of Arabidopsis thaliana using a simple chemical treatment with TOPII inhibitor etoposide. Authors show that unlike chemical mutagens commonly used for induction of SNPs (EMS, sodium azide...), etoposide-treatment caused structural variants like DNA deletions, insertions, inversions and translocations. These mutations were identified by the whole genome short and long read sequencing that also indicated a WT-like frequency of SNPs. This finding can potentially help inducing mutations similar to high energy radiation in potentially any plant. First, the manuscript provides description of the unusual phenotypes found after etoposide treatment and their Mendelistic inheritance. Based on this, authors performed whole genome sequencing and mutation detection, validation. The experimental part ends by transcriptome analysis that authors use as the approach to identify the causal mutations. This part is, in my opinion, the weakest part of the manuscript and would benefit from further clarification or even additional experiments (see below).

      Overall the manuscript is very clear and contains all necessary information. The only part that was confusing to me, was the section focusing on the transcriptome analysis.

      Major points:

      Line 222: In the section „RNA-Seq identifies genes that are associated with structural variation and mutant phenotypes", authors suggest that the changes in the transcript amount were used to identify causal mutations. I got confused by this section. Exach of the examples represents unique situation and thus only single cases are presented which makes it hard to estimate robustness of the presented approach. Also, the presented mutations have prominent phenotypes that were already heavily studied in the past and therefore the possible causal genes are mostly known. Therefore, I am not sure how this approach would stand in case of traits with unknown underlying genes. When refering to the case with the chromosomal inversion, I do not see how one will be able to map a candidate based on the relatively mild expression (but maybe I am missing something here). Similarly, the „mapping" approach applied to the variegated line would not be possible on a trait that is less studied and the candidates are not well known. I wond why authors did not perform association mapping on a bulk of phenotypically mutant plants collected from a segregating F2 backcross population. This might be a more robust way of linking the phenotype with a mutation.

      Discussion section: I am missing discussion on how etoposide could be causing such structural variants.

      Minor points:

      Line 70: Possibly add sodium azide. It is frequently used as mutagen for some plant species. Line 122: „...etoposide is an excellent mutagen for efficiently creating large-effect mutations." This cannot be claimed at this point because the sequence analysis data were not shown yet. Please reformulate.

      Referees cross-commenting

      My main issue was the mapping protocol using transcriptomic changes. It is hard to believe that this approach would work well on unknown/less studied traits. What is your opinion?

      Significance

      Strengths Innovative way on how to induce structural variant mutations in plants.

      Limitations The approach on how to map the mutations needs more development. At this point i tis not clear how well the approach will work in other plant species.

      Audience basic and applied plant scientists

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      Reply to the reviewers

      Manuscript number: RC-2025-02888

      Corresponding author(s): Christian, Fankhauser

      General Statements

      We were pleased to see that the three reviewers found our work interesting and provided supportive and constructive comments.

      Our answers to their comments and/or how we propose to address them in a revised manuscript are included in bold.

      1. Description of the planned revisions

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

      Summary: Plant systems sense shading by neighbors via the phytochrome signaling system. In the shade, PHYTOCHROME-INTERACTING FACTORS (PIFs) accumulate and are responsible for transcriptional reprogramming that enable plants to mobilize the "shade-avoidance response". Here, the authors have sought to examine the role of chromatin in modulating this response, specifically by examining whether "open" or "closed" chromatin regions spanning PIF target genes might explain the transcriptional output of these genes. They used a combination of ATAC-seq/CoP-qPCR (to detect open regions of chromatin), ChIP (to assay PIF binding) and RNA-seq (to measure transcript abundance) to understand how these processes may be mechanistically linked in Arabidopsis wild-type and pif mutant lines. They found that some chromatin accessibility changes do occur after LRFR (shade) treatment (32 regions after 1h and 61 after 25 h). While some of these overlap with PIF-binding sites, the authors found no correlation between open chromatin states and high levels of transcription. Because auxin is an important component of the shade-avoidance response and has been shown to control chromatin accessibility in other contexts, they examined whether auxin might be required for opening these regions of chromatin. They find that in an auxin biosynthesis mutant, there is a small subset of PIF target genes whose chromatin accessibility seems altered relative to the wild-type. Likewise, they found that chromatin accessibility for certain PIF targets is altered in phyB and pif mutant and propose that PIFs are necessary for changing the accessibility of chromatin in these genes. The authors thus propose that PIF occupancy of already open regions, rather than increased accessibility, underly the increase in transcript of abundance of these target genes in response to shade.

      Major comments: *• I find that the data generally support the hypothesis presented in the manuscript that chromatin accessibility alone does not predict transcription of PIF target genes in the shade. That said, I think that a paragraph from the discussion (lines 321-332) would benefit from some careful rephrasing. I think it is perfectly reasonable to propose that PIF occupancy is more predictive of shade-induced transcriptional output than chromatin accessibility, but I think that calling PIF occupancy "the key drivers" (line 323) or "the main driving force" (line 76) risks ignoring the observation that levels of PIF occupancy specifically do not predict expression levels of PIF target genes (Pfeiffer et al., 2014, Mol Plant). For PIL1 and HFR1, the authors have shown that PIF promoter occupancy and transcript levels are correlated, but the central finding of Pfeiffer et al. was that this pattern does not apply to the majority of PIF direct target genes. Finding factors (i.e. histone marks) that convert PIF-binding information into transcriptional output appears to have been the impetus for the experiments devised in Willige et al., 2021 and Calderon et al., 2022. It is great that the authors have outlined in the discussion that there are a number of factors that modulate PIF transcriptional activating activity but I think that the emphasis on PIF-binding explaining transcript abundance should be moderated in the text. *

      We appreciate the reviewers’ comments and will address it by introducing appropriate changes to the discussion. One element that should be pointed out is that the study of Willige et al., 2021 allows us to look at sites where PIF7 is recruited in response to the shade stimulus (a low R/FR treatment) and relate this to higher transcript abundance of the nearby genes. The study of Pfeiffer et al., 2014 which analyses PIF ChIP studies from several labs does not include this dynamic view of PIF recruitment in response to a stimulus. For example, this study re-analyses data from our lab, Hornitschek et al., 2012, in which we did PIF5 ChIP in low R/FR, but we did not compare that to high R/FR to enable an analysis of sites where we see recruitment of PIF5 in response to a shade cue. In the revised manuscript we will also include a new figure comparing PIF7 recruitment and changes in gene expression at direct PIF target genes.

      • I think that the hypothesis could be further supported by incorporating the previously published ChIP-seq data on PIF1, PIF3 and PIF5 binding. Given these data are published/publicly available, I think it would be helpful to note which of the 72 DARs are bound by PIF1, PIF3 and/or PIF5. Especially so given that PIF5 (Lorrain et al., 2008, Plant J) and PIF1/PIF3 (Leivar et al., 2012, Plant Cell) contribute at least in some capacity to transcriptional regulation in response to shade. At the very least, it might help explain some of the observed increases in nucleosome accessibility observed for genes that don't have PIF4 or PIF7-binding.* This is a thoughtful suggestion. Our choice to focus on PIF7 target genes is dictated by two reasons. First, the finding that amongst all tested PIFs, PIF7 is the major contributor to the control of low R/FR (neighbor proximity) induced responses in seedlings (e.g. Li et al., 2012; de Wit et al., 2016; Willige et al., 2021). In addition, the PIF7 ChIP-seq and gene expression data from the Willige et al., 2021 paper was obtained using growth conditions very similar to the ones we used, hence allowing us to compare it to our data. As the reviewer suggests, other PIFs also contribute to the low R/FR response and hence looking at ChIP-seq for those PIFs in publicly available data is also informative. One limitation of this data is that ChIP-seq was not always done in seedlings grown in conditions directly comparable to the conditions we used (except for PIF5, see above). Nevertheless, we have performed this analysis with the available data suggested by the reviewer and intend to include the results in the revised version of the manuscript, presumably updated Figure 4B.

      • In the manuscript, there are several instances where separate col-0 (wild type) controls have been used for identical experiments. Specifically, qPCR (Fig 3C, Fig S7C/D and Fig S8C/D), CoP-qPCR (Fig 5B/5C and Fig S8E/F) and hypocotyl measurements (Fig S7A/B and Fig S8A/B). In the cases of the hypocotyl measurements, there appear to be hardly any differences between col-0 controls indicating the measurements can be confidently compared between genotypes.

      We appreciate this comment but to be comprehensive, we like to include a Col-0 control for each experiment (whenever possible) and hence also include the data when available.

      • In some cases of qPCR and CoP-qPCR experiments however, the differences in values obtained from col-0 samples that underwent identical experimental treatments appear to vary significantly. In Figure 3C for example, the overall trend for PIL1 expression in col-0 is the same (e.g. HRFR levels are low, LRFR1 levels are much higher and LRFR25 levels drop down to some intermediate level) but the expression levels themselves appear to differ almost two-fold for the LRFR 1h timepoint (~110 on the left panel vs ~60 for the right panel). Given the size of the error bars, it appears that these data represent the mean from only one biological replicate. PIL1 expression levels at LRFR 1h as reported in Fig S7C and D also show similar ~2-fold differences. __This is a good comment. Having looked at PIL1 gene induction by low R/FR in dozens of similar experiments made us realize that indeed while the PIL1 induction is always massive, the extent is somewhat variable. Based on the data that we have (including from RNA-seq) we are convinced that this is due to the very low level of expression of PIL1 in high R/FR conditions. Given that induction by low R/FR is expressed as fold increase relative to baseline high R/FR expression, small changes in the lowly expressed PIL1* in high R/FR leads to seemingly significant differences in its induction by low R/FR across experiments.__

      All qPCR data is represented by three biological replicates, and the variation between them per experiment is low, which is reflected in the size of the SD error bars. Data on technical and biological replicates in each panel will be clearly indicated in the revised figure legends.

      • I would recommend that the authors explicitly describe the number of biological replicates used for each experiment in the methods section. If indeed these experiments were only performed once, I think the authors should be very careful in the language used in describing their conclusions and in assigning statistical significance. One possibility that could also be helpful would be normalizing LRFR 1h and LRFR 25h values to HRFR values and plotting these data somewhere in the supplemental data. If, for example, the relative levels of PIL1 are different between replicates but the fold-induction between HRFR and LRFR 1h are the same, this would at least allay any concerns that the experimental treatments were not the same. I understand that doing so precludes comparison between genotypes, but I do think it's important to show that at least the control data are comparable between experiments.

      * All qPCR and CoP-qPCR experiments have been performed with three 3 biological replicates as described in Materials and Methods section, and these are represented in the Figures. Relative gene expression in the qPCR experiments was normalized to two housekeeping genes YLS8 and UBC21 and afterwards to one biological replicate of Col-0 control in HRFR. As indicated for the previous comment information about replicates will be included in the updated figure legends.

      • Similarly, for the CoP-qPCR experiments presented in Fig 5B and 5C, the col-0 values for region P2 between Fig 5B and 5C shows that while HRFR and LRFR 1h look comparable, the values presented for LRFR 25h are quite different.

      * This comment of the reviewer prompted us to propose a different way of representing the data that is clearer (new Figure 5B and 5C). We believe that this facilitates the comparison between the genotypes. Enrichment over the input was calculated for the chromatin accessibility of each region. Chromatin accessibility was further normalized against two open control regions on the promoters of ACT2 (AT3G18780, region chr3:6474579: 6474676) and RNA polymerase II transcription elongation factor (AT1G71080 region chr1:26811833:26811945). The difference between previous representation is that the regions are not additionally subtracted to Col-0 in HRFR. We will update the Materials and Methods and figure legend sections with this information.

      Minor comments: • Presentation of Supplemental Figure 7A/7B and Supplemental Figure 8A/8B could be changed to make the data more clear (i.e. side-by-side rather than superimposed).

      We propose changing the presentation of the hypocotyl length data to show the values for days side-by-side as the Reviewer suggests.

      • I think that the paragraph introducing auxin (lines 25-37) could be reduced to 1-2 sentences and merged into a separate introductory paragraph given that the SAV3 work makes up a relatively minor component of the manuscript.

      * We agree with the reviewer and will reduce the paragraph about auxin and merge it with the previous paragraph about transcription.

        • For Figure 3A, I would strongly encourage the authors to show some of the raw western blot data for PIF4, PIF5 and PIF7 protein abundance (and loading control), not just the normalized values. This could be put into supplemental data, but I think it should accompany the manuscript.

      * We agree that presenting the raw data that was used for quantification is important. We will include the western blots used for quantifying PIF4, PIF5 and PIF7 protein abundance (and loading control DET3). This information will presumably be included to the Supplementary Figure 3C (figure number to be confirmed once we decide on all new data to be presented).

      • Lines 145-147 "we observed a strong correlation between PIF4 protein levels (Figure 3A) and PIL1 promoter occupancy (Figure 3B), and a similar behavior was seen with PIF7 (Figure 3B)." According to Fig 3A, there is no statistically significant increase in PIF7 abundance after 1h shade. There is an apparent increase in PIF7 promoter occupancy, but the variation appears too large for it to be statistically significant. I agree that qualitatively there is a correlation for PIF4 but I think the description of the behavior of PIF7 should be rephrased.

      * __As suggested by the reviewer, we will rephrase this paragraph to more accurately account for our data and also what was reported by others (e.g. Willige et al, 2021, in Li et al, 2012) regarding the regulation PIF7 levels and phosphorylation in response to a low R/FR treatment. __

      • There appear to be issues in the coloring of the labels (light blue dots vs dark blue dots) for the PIF7 panels of Fig 3B and Supplemental Fig 3B.*

      We thank the reviewer for pointing this out. This will be clarified by appropriate changes in the figure to avoid confusion in the revised version of Figure 3B.

      Reviewer #1 (Significance (Required)):

      This authors here have sought to examine the possibility that the transcriptional responses to shade mediated by the phy-PIF system might involve large-scale opening or closing of chromatin regions. This is an important and unanswered question in the field despite several studies that have looked at the role of histone variants (H2A.Z) and modifications (H3K4me3 and H3K9ac) in modulating PIF transcriptional activating activity. The authors have shown that, at least in the case of the transcriptional response to shade mediated by PIF7 (and to an extent PIF4), large-scale changes in chromatin accessibility are not occurring in response to shade treatment.

      The results presented in this study support the hypothesis that large-scale changes in chromatin accessibility may have already occurred before plants see shade. This opens the possibility that perhaps the initial perception of light by etiolated (dark-grown seedlings) might trigger changes in chromatin accessibility, opening up chromatin in regions encoding "shade-specific" genes and/or closing chromatin in regions encoding "dark-specific" genes.

      The audience for this particular manuscript encompasses a fairly broad group of biologists interested in understanding how environmental stimuli can trigger changes in chromatin reorganization and transcription. The results here are important in that they rule out chromatin accessibility changes as underlying the changes in transcription between the short-term and long-term shade responses. They also reveal that there are a few cases in which chromatin accessibility does change in a statistically-significant manner in response to shade. These regions, and the molecular players which regulate their accessibility, merit further exploration.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The study by Paulisic et al. explores the variations in chromatin accessibility landscape induced by plant exposure to light with low red/far-red ratios (LRFR), which mimicks neighbor shade perception. The authors further compare these changes with the genome association of PIF4 and PIF7 transcription factors - two major actors of gene expression regulation in response to LRFR. While this is not highlighted in the main text, the analyses of chromatin accessibility are performed on INTACT-mediated nucleus sorting, presumably to ensure proper and clean isolation of nuclei.

      Major comments

      • Why is the experimental setup exposing plants to darkness overnight? Does this affect the response to LRFR, by a kind of reset of phytochrome signaling? I guess this choice was made to maintain a strong circadian rhythm. Yet, given that PIF genes are clock-regulated, I am afraid that this choice complicates data interpretation concerning the specific effects of LRFR exposure.

      There appears to be some confusion which prompts us to better explain our protocol both by changing Figure 1A (that outlines the experimental conditions) and in the text.

      Seedlings are grown in long day conditions because this is more physiologically relevant than growing them in constant light, which is a rather unnatural condition.

      The reviewer is correct that PIF transcription is under circadian control and the shade avoidance response is gated by the circadian clock (e.g. Salter et al., 2003). To prevent conflating circadian and light quality effects, all samples that are compared are harvested at the same ZT (circadian time – hours after dawn). This allows us to focus our analysis on light quality effects specifically. We are therefore convinced that our protocol does not complicate the interpretation of the LRFR effects reported here.

      • As a result of this setup, the 1h exposure to LRFR immediately follows HRFR while the 3h final LRFR exposure of the « 25h LRFR » samples immediately follows a long period of darkness. Can this explain why in several instances (e.g., at the ATHB2 gene) 1h LRFR seems to have stronger effects than 25h LRFR on chromatin accessibility?* Please check the explanation above. Both samples are harvested at the same ZT (ZT3, meaning 3 hours after dawn). The 1h LRFR seedlings went through the night, had 2 hours of HRFR then 1h of LRFR. The 25h are harvested at the very same ZT, meaning 3h after dawn. Importantly, the HRFR control was also harvested at ZT3, meaning 3h after dawn. As indicated above this protocol allows us to focus on the light quality effects by comparing samples that are all harvested at the same ZT.

      We expect that the changes in Fig. 1A and associated text changes will clarify this issue.

      • Lane 42 cites the work by Calderon et al 2022 as « Transcript levels of these genes increase before the H3K4me3 levels, implying that H3K4me3 increases as a consequence of active transcription ». Despite this previous study being reviewed and published, such a strong conclusion should be taken cautiously, and I disagree with it. The study by Calderon et al compares RNA-seq with ChIP-seq data, two methodologies with very different sensitivity, especially when employing bulk cells/whole seedlings as starting materials. For example, a gene strongly induced in a few cells will give a good Log2FC in RNA-seq data analysis (as new transcripts are produced after a low level of transcripts before shade) but, even though its chromatin variations would follow the same temporality or would even precede gene induction, this would be invisible in bulk ChIP-seq data analysis (which averages the signal of all cells together). I understand the rationale for relying on the conclusions made in an excellent lab with strong expertise in light signaling, but I recommend being cautious when relying on these conclusions to interpret new data.* We agree with this comment, and we will change the text to reflect this.

      • The problem is that the same issue holds true when comparing ATAC-seq and RNA-seq data. ATAC signals reflect average levels over all cells while RNA-seq data can be influenced by a few cell highly expressing a given gene. Even though authors carefully sorted nuclei using an INTACT approach, this should be discussed, in particular when gene clusters (such as cluster C-D) show no match between chromatin accessibility and transcript level variations. In this regard, is PIF7 expressed in many cells or a small niche of cells upon LRFR exposure? The conclusions on its role in chromatin accessibility, analyzed here as mean levels of many different seedling cells, could be affected by PIF7 activity pattern (e.g., at lane 293). __This is a good comment. PIF7 is expressed in the cotyledons and leaves in LD conditions (Kidokoro et al, 2009, Galvao et al, 2019), and few available scRNA-seq datasets indicate an enrichment of PIF7 in the epidermis (Kim et al, 2021, Lopez-Anido et al, 2021). LRFR exposure only mildly represses PIF7* expression as seen in Figure 3A and also in our bulk RNA-seq study (Table S4). We will discuss this potential limitation to our study in a revised version of the manuscript.__

      • Lane 89, the conclusion linking DNA methylation and DNA accessibility is unclear to me, this may be rephrased. Also, it should be noted that in gene-rich regions, most DNA methylation is located along the body of moderately to highly transcribing genes (gene-body methylation) while promoters of active and inactive genes are most frequently un-methylated.* We will rephrase to better reflect the presence or absence of DNA methylation on promoter regions of shade regulated genes that contain accessible sites.

      • Figure 3B shows a few ChIP-qPCR results with important conclusions. Why not sequencing the ChIPped DNA to obtain a genome-wide view of the PIF4-PIF7 relationships at chromatin, and also consequently a more robust genome-wide normalization?

      * Several studies have shown that in the conditions that we studied here: transfer of seedlings from high R/FR (simulated sun) to low R/FR (neighbor proximity), amongst all PIFs, PIF7 is the one that plays the most dominant function (e.g. Li et al., 2012; de Wit et al., 2016; Willige et al., 2021). PIF4 and PIF5 also contribute but to a lesser extent. Given that Willige et al., 2021 did extensive ChIP-seq studies for PIF7 using similar conditions to the ones we used, we decided to rely on their data (that we re-analyzed), rather than performing our own PIF7 ChIP-seq analysis. While also performing a ChIP-seq analysis for PIF4 in similar conditions might be useful (this data is not available as far as we know), we are not convinced that doing that experiment would substantially modify the message. In the revised version we will also include analysis of the data from Pfeiffer et al., 2014, which comprises a ChIP-seq. dataset for PIF5 (the closest paralog of PIF4) initially performed by Hornitschek et al., in 2012 in low R/FR conditions (see comment to reviewer 1 above). For new ChIP-seq, we would have to make this experiment from scratch with substantially more material than what we used for the targeted ChIP-qPCR analyses. We thus do not feel that such an investment (time and money) is warranted.

        • Given the known functional interaction between PIF7 and INO80, it would be relevant to test whether changes in chromatin accessibility at ATHB2 and other genes are affected in ino80 mutant seedlings. __We agree with the reviewer that this is potentially an interesting experiment. This will allow us to determine whether the nucleosome histone composition has an influence on nucleosome positioning at selected shade-regulated genes (e.g. ATHB2). We note that according to available data, the effect of INO80 would be expected once PIF7 started transcribing shade-induced genes. We therefore propose comparing the WT with an ino80 mutant for their seedling growth phenotype, expression of selected shade marker gene (e.g. ATHB2*) and chromatin accessibility before (high R/FR) and after low R/FR treatment at selected shade marker genes. This will allow us to determine whether INO80 influences chromatin accessibility prior to a low R/FR treatment and/or once the treatment started. Our plan is to include this data in a revised version of the manuscript. __
      • On the same line, it would be interesting to test whether PIF7 target regions with pre-existing accessible chromatin would exist in ino80 mutant plants. In other words, testing a model in which chromatin remodeling by INO80 defines accessibility under HRFR to enable rapid PIF recruitment and DNA binding upon LRFR exposure.*

      See our answer just above.

      Minor comments

      *• In Figure 1C, it seems that PIF7 target genes do not match the set of LRFR-downregulated genes (even less than at random). Why not exclude these 4 genes from the analyses? *

      This is correct. There are indeed only 4 downregulated PIF7 target genes as we define them. Removing these genes from the analyses does not change our interpretation of the data and hence for completeness we propose keeping them in a revised version of the manuscript

      • Figure 3A shows the quantification of protein blots, but I did not find the corresponding images. These should be shown in the figure or as a supplementary figure with proper controls.

      * We will include the raw Westen blots used for quantification of PIF4, PIF5 and PIF7 in the revised version of the manuscript

        • Lane 102, it is unclear why PIF7 target genes were defined as the -3kb/TSS domains while Arabidopsis intergenic regions are on average much shorter. Gene regulatory regions, or promoters, are typically called within -1kb/TSS regions to avoid annotating a ChIP peak to the upstream gene or TE. A better proxy of PIF7 typical binding sites in gene regulatory regions could be determined by analysing the mean distance between PIF7 peak coordinates and the closest TSS. Typically, a gene meta-plot would give this information. __We agree that the majority of PIF7 binding peaks are close to the 5’ of the TSS based on the PIF7 binding distribution meta-plot. But several known PIF binding sites are actually further upstream than 1kb 5’ of the TSS (e.g. ATHB2 and HFR1). However, we re-analyzed the data using your suggestion with -2kb/TSS and -1kb/TSS and while the number of target genes is reduced, it does not change our conclusions about PIF7 binding sites being located on accessible chromatin regions. Importantly, some well characterized LRFR induced genes such as HFR1* would not be annotated correctly if only peaks closest to the gene TSS were taken into account, without flanking genes. In this case only the neighboring AT1G02350 would be annotated, hence missing some important PIF7 target genes. Taking this into consideration we will not modify this part of the analysis in a revised manuscript.__
      • Figure 4B, what's represented in the ATAC-seq heatmap: does a positive z-score represent high accessibility?*

      On the ATAC-seq heatmap we have represented z-scores of the average CPM (counts per million) for accessible chromatin regions. Z-scores are calculated by subtracting the average CPM from the median of averaged CPMs for each accessible chromatin region and then divided by the standard deviation (SD) of those averaged CPMs across all groups per accessible region (in our case a group is an average of three biological replicates for either HRFR, 1h or 25h of LRFR). In that sense, z-score indicates a change in accessibility, where higher z-score indicates opening of the region and lower z-score indicates a region becoming more closed when compared among the three light treatments (HRFR, 1h or 25h of LRFR). We will make sure that this is clear in the revised manuscript. Reviewer #2 (Significance (Required)):

      Contradicting the naive hypothesis that PIFs may target shade-inducible genes to « open » chromatin of shade-inducible genes with the help of chromatin remodelers, such as INO80, the study highlights that PIF7 typically associates with pre-existing accessible chromatin states. Thus, even though this is not stated, results from this study indicate that PIF7 is not a pioneer transcription factor. The data seem very robust, and while some conclusions need clarification, it should be of great interest to the community of scientists studying plant light signaling and shade responses.

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

      In their manuscript, Paulisic et al. investigate whether the transcriptional response of Arabidopsis seedlings to shade depends on chromatin accessibility, with a specific focus on PIF7-regulated genes. To this end, they perform ATAC-seq and RNA-seq, along with other experiments, on seedlings exposed to short and long shade and correlate the results with previously reported PIF7 and PIF4 ChIP-seq data. Based on their findings, they propose that shade-mediated transcriptional regulation may not require extensive remodeling of DNA accessibility. Specifically, they suggest that the open chromatin conformation allows PIFs to easily access and recognize their binding motifs, rapidly initiating gene expression in response to shade. This transcriptional response primarily depends on a transient increase in PIF stability and gene occupancy, with changes in chromatin accessibility occurring in only a small number of genes.

      Major comments: * • I have one issue that, in my opinion, requires more attention. To define the PIF7 target genes, which were later used to estimate whether PIF7 binds to open or closed chromatin and affects DNA accessibility after its binding, the authors compared the 4h LRFR data point from Willige et al. (2021) ChIP-seq with their 1h RNA-seq data point. This comparison might have missed early genes where PIF7 binds before the 1h time point but is no longer present on DNA at 4h. I understand the decision to choose the 4h Willige et al. ChIP-seq data point, performed under LD conditions, as it matches the data in this study, rather than the 5min-30min data points, which were conducted in constant light. However, if possible, it would be interesting to also compare the RNA-seq data with the early PIF7 binding genes to assess how many additional PIF7 target genes could be identified based on that comparison and whether this might alter the conclusions. If the authors do not agree with this point, it should at least be emphasized that the ChIP-seq data and the RNA-seq/ATAC-seq data were performed under different LRFR conditions (R/FR 0.6 vs. 0.1), which may lead to the misidentification of PIF7 target genes in the manuscript.*

      1) This is an interesting suggestion, we therefore reanalyzed 5, 10 and 30 min ChIP-seq timepoints from Willige et al, 2021 and compared them to 4h of LRFR (ZT4). We have crossed these lists of potential PIF7 targets with our 1h LRFR PIF457 dependent genes based on our RNA-seq. While some PIF7 targets appear only in early time points 5-10 min of LRFR exposure, overall, the number and composition of PIF7 target genes is rather constant across these timepoints. We propose to include these additional analyses in a revised version of the manuscript as a supplemental figure. However, these additional analyses do not influence our general conclusions.

      2) The comment regarding the R/FR ratio is important. We will point this out although the conditions used by Willige et al., 2021 and the ones we used are similar, they are not exactly the same in terms of R/FR ratio. Importantly, in both studies the early transcriptional response largely depends on the same PIFs, many of the same response genes are induced (e.g. PIL1, AtHB2, HFR1, YUC8, YUC9 and many others) and the physiological response (hypocotyl elongation) is similar. This shows that this low R/FR response yields robust responses.

      Minor comments: • In Fig. 1D, please describe the meaning of the blue shaded areas and the blue lines under the ATAC-seq peaks, as they do not always correlate.

      The shaded areas and the bars define the extension of the ATAC-seq accessible chromatin peaks. We will add the meaning of the shaded areas and the blue bars in the Figure legend and correct the colors in a revised manuscript

      • In Fig. 1E, it could be helpful to note that the 257 peaks in the right bar correspond to the peaks associated with the 177 genes in the left bar.* We will update Figure 1E and Figure legends for better understanding as the Reviewer suggested.

      • In lines 116, 119, and 122, I believe it should read "Fig. 2" instead of "Fig. 2A."* We thank the Reviewer for noticing the error that we will correct.

      • Lines 138-139: "PIF7 total protein levels were overall more stable, and only a mild and non-significant increase of PIF7 levels was seen at 1 h of LRFR." Since PIF7 usually appears as two bands in HRFR and only one band in LRFR, how was the protein level of PIF7 quantified in Fig. 3A? Additionally, I was wondering about the authors' thoughts on the discrepancy with Willige et al. (2021, Extended Data Fig. 1d), where PIF7 abundance seems to increased after 30 min and 2 h of LRFR.* PIF7 protein levels were quantified by considering both the upper and the lower band in HRFR (total PIF7) and normalizing its levels to DET3 loading control. We still observe an increase in the total PIF7 protein levels at 1h of LRFR, however this change was not statistically significant in these experiments. In our conditions as in Willige et al, 2021, the increase in PIF7 protein levels to short term shade seems consistent as is the pronounced shift or disappearance of the upper band (phosphorylated form) on the Western blots (raw data will be available in the revised manuscript). We will introduce text changes referring to the phosphorylation status of PIF7 in our conditions.

      • Line 150: "... many early PIF target genes (Figure 3C)." Since only PIL1 is shown in Fig. 3C, I would recommend revising this sentence. Alternatively, the data could be presented, as in Fig. 2, for all the PIF7 target genes with transient expression patterns.

      * We will introduce changes in the text to reflect that we only show PIL1 in the main Figure 3C.

      • Line 204: I'm not sure if Supplementary Fig. 7C-D is correct here. If it is, could the order of the figures be changed so that Supplementary Fig. 7C-D becomes Supplementary Fig. 7A-B?*

      The order of the panels A-B in the Supplementary Figure 7 follows the order of the text in the manuscript and is mentioned before panels C-D. It refers to the sentence “Overexpression of phyB resulted in a strong repression of hypocotyl elongation in both HRFR and LRFR, while the absence of phyB promoted hypocotyl elongation (Supplementary Figure 7A-B).”

        • Line 208: "In all three cases...". Please clarify what the three cases refer to. __We will change the text to more explicitly refer to the differentially accessible regions (DARs) of the genes ATHB2 and HFR1* shown in Figure 5A.__
      • Line 231: Should Fig. 5C also be cited here in addition to Supplementary Fig. 7?* We will add the reference to Figure 5C that was missing.

      *• In Supplementary Table 3, more information is needed. For example, it could mention: "This data is presented in Fig. 3 and is based on datasets from ChIP-seq, RNA-seq, etc."

      *

      The table will be updated with more information as suggested by the Reviewer.

      • In the figure legend of Fig. 4B, please check the use of "( )".*

      We will correct the error and include the references inside the parenthesis.

      Reviewer #3 (Significance (Required)):

      Paulisic et al. present novel discoveries in the field of light signaling and shade avoidance. Their findings extend our understanding of how DNA organization, prior to shade, affects PIF binding and how PIF binding remodels DNA accessibility. The data presented support the conclusions well and are backed by sufficient experimental evidence.

      2. Description of the revisions that have already been incorporated in the transferred manuscript

      The manuscript has not been modified yet.

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

      • *

      Reviewer 2 asked for new ChIP-seq analyses for PIF7 and PIF4. For reasons that we outlined above, we believe that such analyses are not required, and we currently do not intend performing these experiments.

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      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript, Paulisic et al. investigate whether the transcriptional response of Arabidopsis seedlings to shade depends on chromatin accessibility, with a specific focus on PIF7-regulated genes. To this end, they perform ATAC-seq and RNA-seq, along with other experiments, on seedlings exposed to short and long shade and correlate the results with previously reported PIF7 and PIF4 ChIP-seq data. Based on their findings, they propose that shade-mediated transcriptional regulation may not require extensive remodeling of DNA accessibility. Specifically, they suggest that the open chromatin conformation allows PIFs to easily access and recognize their binding motifs, rapidly initiating gene expression in response to shade. This transcriptional response primarily depends on a transient increase in PIF stability and gene occupancy, with changes in chromatin accessibility occurring in only a small number of genes.

      Major comments:

      I have one issue that, in my opinion, requires more attention. To define the PIF7 target genes, which were later used to estimate whether PIF7 binds to open or closed chromatin and affects DNA accessibility after its binding, the authors compared the 4h LRFR data point from Willige et al. (2021) ChIP-seq with their 1h RNA-seq data point. This comparison might have missed early genes where PIF7 binds before the 1h time point but is no longer present on DNA at 4h. I understand the decision to choose the 4h Willige et al. ChIP-seq data point, performed under LD conditions, as it matches the data in this study, rather than the 5min-30min data points, which were conducted in constant light. However, if possible, it would be interesting to also compare the RNA-seq data with the early PIF7 binding genes to assess how many additional PIF7 target genes could be identified based on that comparison and whether this might alter the conclusions. If the authors do not agree with this point, it should at least be emphasized that the ChIP-seq data and the RNA-seq/ATAC-seq data were performed under different LRFR conditions (R/FR 0.6 vs. 0.1), which may lead to the misidentification of PIF7 target genes in the manuscript.

      Minor comments:

      - In Fig. 1D, please describe the meaning of the blue shaded areas and the blue lines under the ATAC-seq peaks, as they do not always correlate.

      - In Fig. 1E, it could be helpful to note that the 257 peaks in the right bar correspond to the peaks associated with the 177 genes in the left bar.

      - In lines 116, 119, and 122, I believe it should read "Fig. 2" instead of "Fig. 2A."

      Lines 138-139: "PIF7 total protein levels were overall more stable, and only a mild and non-significant increase of PIF7 levels was seen at 1 h of LRFR."

      Since PIF7 usually appears as two bands in HRFR and only one band in LRFR, how was the protein level of PIF7 quantified in Fig. 3A? Additionally, I was wondering about the authors' thoughts on the discrepancy with Willige et al. (2021, Extended Data Fig. 1d), where PIF7 abundance seems to increased after 30 min and 2 h of LRFR. Line 150: "... many early PIF target genes (Figure 3C)." Since only PIL1 is shown in Fig. 3C, I would recommend revising this sentence. Alternatively, the data could be presented, as in Fig. 2, for all the PIF7 target genes with transient expression patterns. - Line 204: I'm not sure if Supplementary Fig. 7C-D is correct here. If it is, could the order of the figures be changed so that Supplementary Fig. 7C-D becomes Supplementary Fig. 7A-B? - <br /> - Line 208: "In all three cases...". Please clarify what the three cases refer to. - <br /> - Line 231: Should Fig. 5C also be cited here in addition to Supplementary Fig. 7? - <br /> - In Supplementary Table 3, more information is needed. For example, it could mention: "This data is presented in Fig. 3 and is based on datasets from ChIP-seq, RNA-seq, etc." - <br /> - In the figure legend of Fig. 4B, please check the use of "( )".

      Significance

      Paulisic et al. present novel discoveries in the field of light signaling and shade avoidance. Their findings extend our understanding of how DNA organization, prior to shade, affects PIF binding and how PIF binding remodels DNA accessibility. The data presented support the conclusions well and are backed by sufficient experimental evidence.

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      Referee #2

      Evidence, reproducibility and clarity

      The study by Paulisic et al. explores the variations in chromatin accessibility landscape induced by plant exposure to light with low red/far-red ratios (LRFR), which mimicks neighbor shade perception. The authors further compare these changes with the genome association of PIF4 and PIF7 transcription factors - two major actors of gene expression regulation in response to LRFR. While this is not highlighted in the main text, the analyses of chromatin accessibility are performed on INTACT-mediated nucleus sorting, presumably to ensure proper and clean isolation of nuclei.

      Major comments

      • Why is the experimental setup exposing plants to darkness overnight? Does this affect the response to LRFR, by a kind of reset of phytochrome signaling? I guess this choice was made to maintain a strong circadian rhythm. Yet, given that PIF genes are clock-regulated, I am afraid that this choice complicates data interpretation concerning the specific effects of LRFR exposure.
      • As a result of this setup, the 1h exposure to LRFR immediately follows HRFR while the 3h final LRFR exposure of the « 25h LRFR » samples immediately follows a long period of darkness. Can this explain why in several instances (e.g., at the ATHB2 gene) 1h LRFR seems to have stronger effects than 25h LRFR on chromatin accessibility?
      • Lane 42 cites the work by Calderon et al 2022 as « Transcript levels of these genes increase before the H3K4me3 levels, implying that H3K4me3 increases as a consequence of active transcription ». Despite this previous study being reviewed and published, such a strong conclusion should be taken cautiously, and I disagree with it. The study by Calderon et al compares RNA-seq with ChIP-seq data, two methodologies with very different sensitivity, especially when employing bulk cells/whole seedlings as starting materials. For example, a gene strongly induced in a few cells will give a good Log2FC in RNA-seq data analysis (as new transcripts are produced after a low level of transcripts before shade) but, even though its chromatin variations would follow the same temporality or would even precede gene induction, this would be invisible in bulk ChIP-seq data analysis (which averages the signal of all cells together). I understand the rationale for relying on the conclusions made in an excellent lab with strong expertise in light signaling, but I recommend being cautious when relying on these conclusions to interpret new data.
      • The problem is that the same issue holds true when comparing ATAC-seq and RNA-seq data. ATAC signals reflect average levels over all cells while RNA-seq data can be influenced by a few cell highly expressing a given gene. Even though authors carefully sorted nuclei using an INTACT approach, this should be discussed, in particular when gene clusters (such as cluster C-D) show no match between chromatin accessibility and transcript level variations. In this regard, is PIF7 expressed in many cells or a small niche of cells upon LRFR exposure? The conclusions on its role in chromatin accessibility, analyzed here as mean levels of many different seedling cells, could be affected by PIF7 activity pattern (e.g., at lane 293).
      • Lane 89, the conclusion linking DNA methylation and DNA accessibility is unclear to me, this may be rephrased. Also, it should be noted that in gene-rich regions, most DNA methylation is located along the body of moderately to highly transcribing genes (gene-body methylation) while promoters of active and inactive genes are most frequently un-methylated.
      • Figure 3B shows a few ChIP-qPCR results with important conclusions. Why not sequencing the ChIPped DNA to obtain a genome-wide view of the PIF4-PIF7 relationships at chromatin, and also consequently a more robust genome-wide normalization?
      • Given the known functional interaction between PIF7 and INO80, it would be relevant to test whether changes in chromatin accessibility at ATHB2 and other genes are affected in ino80 mutant seedlings.
      • On the same line, it would be interesting to test whether PIF7 target regions with pre-existing accessible chromatin would exist in ino80 mutant plants. In other words, testing a model in which chromatin remodeling by INO80 defines accessibility under HRFR to enable rapid PIF recruitment and DNA binding upon LRFR exposure.

      Minor comments

      • In Figure 1C, it seems that PIF7 target genes do not match the set of LRFR-downregulated genes (even less than at random). Why not exclude these 4 genes from the analyses?
      • Figure 3A shows the quantification of protein blots, but I did not find the corresponding images. These should be shown in the figure or as a supplementary figure with proper controls.
      • Lane 102, it is unclear why PIF7 target genes were defined as the -3kb/TSS domains while Arabidopsis intergenic regions are on average much shorter. Gene regulatory regions, or promoters, are typically called within -1kb/TSS regions to avoid annotating a ChIP peak to the upstream gene or TE. A better proxy of PIF7 typical binding sites in gene regulatory regions could be determined by analysing the mean distance between PIF7 peak coordinates and the closest TSS. Typically, a gene meta-plot would give this information.
      • Figure 4B, what's represented in the ATAC-seq heatmap: does a positive z-score represent high accessibility?

      Significance

      Contradicting the naive hypothesis that PIFs may target shade-inducible genes to « open » chromatin of shade-inducible genes with the help of chromatin remodelers, such as INO80, the study highlights that PIF7 typically associates with pre-existing accessible chromatin states. Thus, even though this is not stated, results from this study indicate that PIF7 is not a pioneer transcription factor. The data seem very robust, and while some conclusions need clarification, it should be of great interest to the community of scientists studying plant light signaling and shade responses.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Plant systems sense shading by neighbors via the phytochrome signaling system. In the shade, PHYTOCHROME-INTERACTING FACTORS (PIFs) accumulate and are responsible for transcriptional reprogramming that enable plants to mobilize the "shade-avoidance response". Here, the authors have sought to examine the role of chromatin in modulating this response, specifically by examining whether "open" or "closed" chromatin regions spanning PIF target genes might explain the transcriptional output of these genes. They used a combination of ATAC-seq/CoP-qPCR (to detect open regions of chromatin), ChIP (to assay PIF binding) and RNA-seq (to measure transcript abundance) to understand how these processes may be mechanistically linked in Arabidopsis wild-type and pif mutant lines. They found that some chromatin accessibility changes do occur after LRFR (shade) treatment (32 regions after 1h and 61 after 25 h). While some of these overlap with PIF-binding sites, the authors found no correlation between open chromatin states and high levels of transcription. Because auxin is an important component of the shade-avoidance response and has been shown to control chromatin accessibility in other contexts, they examined whether auxin might be required for opening these regions of chromatin. They find that in an auxin biosynthesis mutant, there is a small subset of PIF target genes whose chromatin accessibility seems altered relative to the wild-type. Likewise, they found that chromatin accessibility for certain PIF targets is altered in phyB and pif mutant and propose that PIFs are necessary for changing the accessibility of chromatin in these genes. The authors thus propose that PIF occupancy of already open regions, rather than increased accessibility, underly the increase in transcript of abundance of these target genes in response to shade.

      Major comments:

      I find that the data generally support the hypothesis presented in the manuscript that chromatin accessibility alone does not predict transcription of PIF target genes in the shade. That said, I think that a paragraph from the discussion (lines 321-332) would benefit from some careful rephrasing. I think it is perfectly reasonable to propose that PIF occupancy is more predictive of shade-induced transcriptional output than chromatin accessibility, but I think that calling PIF occupancy "the key drivers" (line 323) or "the main driving force" (line 76) risks ignoring the observation that levels of PIF occupancy specifically do not predict expression levels of PIF target genes (Pfeiffer et al., 2014, Mol Plant). For PIL1 and HFR1, the authors have shown that PIF promoter occupancy and transcript levels are correlated, but the central finding of Pfeiffer et al. was that this pattern does not apply to the majority of PIF direct target genes. Finding factors (i.e. histone marks) that convert PIF-binding information into transcriptional output appears to have been the impetus for the experiments devised in Willige et al., 2021 and Calderon et al., 2022. It is great that the authors have outlined in the discussion that there are a number of factors that modulate PIF transcriptional activating activity but I think that the emphasis on PIF-binding explaining transcript abundance should be moderated in the text.

      I think that the hypothesis could be further supported by incorporating the previously published ChIP-seq data on PIF1, PIF3 and PIF5 binding. Given these data are published/publicly available, I think it would be helpful to note which of the 72 DARs are bound by PIF1, PIF3 and/or PIF5. Especially so given that PIF5 (Lorrain et al., 2008, Plant J) and PIF1/PIF3 (Leivar et al., 2012, Plant Cell) contribute at least in some capacity to transcriptional regulation in response to shade. At the very least, it might help explain some of the observed increases in nucleosome accessibility observed for genes that don't have PIF4 or PIF7-binding.

      In the manuscript, there are several instances where separate col-0 (wild type) controls have been used for identical experiments. Specifically, qPCR (Fig 3C, Fig S7C/D and Fig S8C/D), CoP-qPCR (Fig 5B/5C and Fig S8E/F) and hypocotyl measurements (Fig S7A/B and Fig S8A/B). In the cases of the hypocotyl measurements, there appear to be hardly any differences between col-0 controls indicating the measurements can be confidently compared between genotypes.

      In some cases of qPCR and CoP-qPCR experiments however, the differences in values obtained from col-0 samples that underwent identical experimental treatments appear to vary significantly. In Figure 3C for example, the overall trend for PIL1 expression in col-0 is the same (e.g. HRFR levels are low, LRFR1 levels are much higher and LRFR25 levels drop down to some intermediate level) but the expression levels themselves appear to differ almost two-fold for the LRFR 1h timepoint (~110 on the left panel vs ~60 for the right panel). Given the size of the error bars, it appears that these data represent the mean from only one biological replicate. PIL1 expression levels at LRFR 1h as reported in Fig S7C and D also show similar ~2-fold differences.

      I would recommend that the authors explicitly describe the number of biological replicates used for each experiment in the methods section. If indeed these experiments were only performed once, I think the authors should be very careful in the language used in describing their conclusions and in assigning statistical significance. One possibility that could also be helpful would be normalizing LRFR 1h and LRFR 25h values to HRFR values and plotting these data somewhere in the supplemental data. If, for example, the relative levels of PIL1 are different between replicates but the fold-induction between HRFR and LRFR 1h are the same, this would at least allay any concerns that the experimental treatments were not the same. I understand that doing so precludes comparison between genotypes, but I do think it's important to show that at least the control data are comparable between experiments.

      Similarly, for the CoP-qPCR experiments presented in Fig 5B and 5C, the col-0 values for region P2 between Fig 5B and 5C shows that while HRFR and LRFR 1h look comparable, the values presented for LRFR 25h are quite different.

      Minor comments:

      Presentation of Supplemental Figure 7A/7B and Supplemental Figure 8A/8B could be changed to make the data more clear (i.e. side-by-side rather than superimposed).

      I think that the paragraph introducing auxin (lines 25-37) could be reduced to 1-2 sentences and merged into a separate introductory paragraph given that the SAV3 work makes up a relatively minor component of the manuscript.

      For Figure 3A, I would strongly encourage the authors to show some of the raw western blot data for PIF4, PIF5 and PIF7 protein abundance (and loading control), not just the normalized values. This could be put into supplemental data, but I think it should accompany the manuscript.

      Lines 145-147 "we observed a strong correlation between PIF4 protein levels (Figure 3A) and PIL1 promoter occupancy (Figure 3B), and a similar behavior was seen with PIF7 (Figure 3B)." According to Fig 3A, there is no statistically significant increase in PIF7 abundance after 1h shade. There is an apparent increase in PIF7 promoter occupancy, but the variation appears too large for it to be statistically significant. I agree that qualitatively there is a correlation for PIF4 but I think the description of the behavior of PIF7 should be rephrased.

      There appear to be issues in the coloring of the labels (light blue dots vs dark blue dots) for the PIF7 panels of Fig 3B and Supplemental Fig 3B.

      Significance

      This authors here have sought to examine the possibility that the transcriptional responses to shade mediated by the phy-PIF system might involve large-scale opening or closing of chromatin regions. This is an important and unanswered question in the field despite several studies that have looked at the role of histone variants (H2A.Z) and modifications (H3K4me3 and H3K9ac) in modulating PIF transcriptional activating activity. The authors have shown that, at least in the case of the transcriptional response to shade mediated by PIF7 (and to an extent PIF4), large-scale changes in chromatin accessibility are not occurring in response to shade treatment.

      The results presented in this study support the hypothesis that large-scale changes in chromatin accessibility may have already occurred before plants see shade. This opens the possibility that perhaps the initial perception of light by etiolated (dark-grown seedlings) might trigger changes in chromatin accessibility, opening up chromatin in regions encoding "shade-specific" genes and/or closing chromatin in regions encoding "dark-specific" genes.

      The audience for this particular manuscript encompasses a fairly broad group of biologists interested in understanding how environmental stimuli can trigger changes in chromatin reorganization and transcription. The results here are important in that they rule out chromatin accessibility changes as underlying the changes in transcription between the short-term and long-term shade responses. They also reveal that there are a few cases in which chromatin accessibility does change in a statistically-significant manner in response to shade. These regions, and the molecular players which regulate their accessibility, merit further exploration.

      My fields of expertise are photobiology, photosynthesis and early seedling development.

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      Reply to the reviewers

      Manuscript number: RC-2025-02953

      Corresponding author(s): Andreas, Villunger

      [The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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      *We would like to thank the reviewers for their constructive input and overall support. We appreciate to provide a provisional revision plan, as outlined here, and are happy to engage in additional communication with journal editors via video call, in case further clarifications are needed. *

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      Reviewer #1

      __Evidence, reproducibility and clarity __

      Summary: This manuscript by Leone et al describes the role of the PIDDosome in cardiomyocytes. Using a series of whole body and cardiomyocyte specific knockouts, the authors show that the PIDDosome maintains correct ploidy in these cells. It achieves this through inducing cell cycle arrest in cardiomyocytes in a p53 dependent manner. Despite this effect on ploidy, PIDDosome-deficient hearts show no structural or functional defects. Statistics and rigor appear to be adequate.

      We thank this referee for taking the time to evaluate our work and their valuable comments. We assume that this reviewer by mistake indicates that the phenomenon we describe, depends on p53. As outlined in the abstract and throughout the manuscript, the effect is independent of p53, but may additionally still involve p21, acting along or parallel to the PIDDosome.

      Major comments: 1. Figure 1 uses fluorescent intensity of a nuclear stain to determine ploidy per nucleus and they further separate the results into mononucleated, binucleated or multinucleated cells. It is hard to know how to interpret these results without further information or controls. Is there a good positive control that can be used to help to show whether this assay is quantitative? The differences are larger with the Raidd and caspase-2 knockouts than with the Pidd knockouts but this is not addressed.

      *We appreciate this concern. Regarding a “good positive control” we can say that we follow state-of the art in the cardiomyocyte field and studies by the Evans (PMID: 36622904), Kuhn (PMID: 32109383), Bergmann (PMID: 26544945) and Patterson labs (PMID: 28783163, 36912240) all use the identical approach to discriminate 2n from 4n nuclei in microscopy images at the cellular level. The fact that the majority of rodent CM nuclei is indeed diploid (PMID: 31175264, 31585517 and 32078450) and a large number of nuclei has been evaluated to assess their mean fluorescence intensity (MFI) reduces the risk of a systematic bias in our analysis. Moreover, we have used an orthogonal approach that is indeed quantitative to define DNA content, i.e,. flow-cytometry based evaluation of DNA content in isolated CM nuclei (Fig. 1C). We hence are confident our assays are quantitative. *

      Regarding the fact that loss of Pidd1 causes a more saddle phenotype, we can offer to discuss this in light of the fact that Pidd1 has additional functions, outside the PIDDosome (PMID: 35343572), and that we made similar observations when analyzing ploidy in hepatocytes (PMID: *31983631). Given the fact that all components of the PIDDosome show a similar phenotype, and that this phenotype is mimicked by loss of the protein that connects PIDD1 and centrosomes, ANKRD26 (Fig. 4a), we are confident that this biological variation in our analysis is not affecting our conclusions. *

      On line 459 the authors state that the increase in polyploidy in PIDDosome knockouts occurs in adult hood but this is not directly tested. In fact, in the next section the polyploidy is assessed in early postnatal development. This statement should be explained or removed.

      We see that we have made an unclear statement here. In fact, we first noted increases in ploidy in adult heart and then define the time window in development when this happens. This sentence will be rephrased.

      In Figure 4. The authors obtained RNAseq data for P1, P7 and P14 but only show the differences with and without caspase-2 at P7. Given that the differences in ploidy are more significant at P14 (Fig 3D), all the comparisons should be shown along with analysis of whether the same genes/gene families are altered in the absence of caspase-2.

      The reason why we focus on postnatal day 7 (P7) is that data from Alkass et al (PMID: 26544945) and other labs (PMID: 31175264 ) document that on this day the initial wave of binucleation peaks. Hence, we hypothesized that the PIDDosome must be active in most CM, which aligns well with the increased mRNA levels of all of its components (Figure 3). Interestingly, it seems that its action is tightly regulated, as mRNA of PIDDosome components drop on P10, suggesting PIDDosome shut-down or downregulation. Similar findings have been noted in the liver (PMID: *31983631). Alkass and colleagues also show that very few CMs enter another round of DNA synthesis between P7 and P14, and hence possible transcriptome changes in the absence of the PIDDosome will be strongly diluted. *

      Please note that on P1, there is no difference between genotypes to be expected as all CM are mononucleated diploids and cytokinesis competent, as previously demonstrated (PMID: *26544945). Moreover, PIDDosome expression levels are extremely low (Fig. 3A). As such, no difference between genotypes are expected on P1. In addition, on P14 the ploidy phenotype observed in PIDDosome knockout mice reaches the maximum and ploidy increases are comparable to adult tissue. Thus, at this time the trigger for PIDDosome activation (cytokinesis failure) is no longer observed as the majority of CMs are post-mitotic, (PMID: 26247711). As such the impact of PIDDosome activation on the P14 transcriptome is most likely negligible. However, if desired, we can expand our bioinformatics analysis summarizing findings made related to DEGs over time in wt animals by comparing genotypes also on day 1 and day 14. In light of the above, analysis between genotypes on P7 holds still appears as the one most meaningful. *

      Some validation of the RNAseq and/or proteomics results would be an important addition to this study

      We agree with this notion and propose to validate key candidates related to cardiomyocyte proliferation and polyploidization, some of which we found to be differentially expressed at the mRNA level on day 7in the RNAseq data (e.g., p21, Foxm1, Kif18a, Lin37 and others)

      Regarding the proteomics results, we face the challenge that we can only try to confirm if candidate proteins are likely caspase substrates in silico using DeepCleave*, and potentially pick one or two candidates linked to CM differentiation for further analysis in vitro and in heterologous cell based assays (e.g. 293T cells), as no bona-fide ventricular cardiomyocyte cell lines exist. Primary postnatal CMs are extremely difficult to transfect, nor they proliferate without drug-treatment, or fail cytokinesis ex vivo. *

      Figure 4D: the authors make the conclusion that p21 is downstream of PIDD (et p53 independent). However, this is not supported by the data because the increase in 4N cells/decrease in 2N cells, although statistically significant, is nowhere near that of caspase-2 KO and caspase-2/p21 KO. Statistics should also compare p32KO with c2KO. In the absence of any other data, the more likely conclusion is that p21 is not involved.

      *We agree that the findings related to the impact seen upon loss of p21 suggest that it is not the only effector involved in ploidy control and it may not even be an effector engaged by caspase-2, as C2/p21 DKO mice have an even higher ploidy increase, albeit not statistically significant. However, it is important to highlight that p21 (Cdkn1a) was found to be downregulated in our transcriptomic analysis suggesting an involvement in the caspace2-cascade. We are happy to highlight this when presenting the results and in the discussion. *

      *We assume that this referee refers to p73 KO data that should be compared to Casp2 KO data (could be read as p73 or p53, but the latter we compare side by side with Casp2 in Fig. 4 already). As p73 KO mice were not found to be viable beyond day 7 (our attempt to find animals on day 10 failed, in line with published literature (PMID: 24500610, 10716451)), we can only offer to compare this data set to the data presented in Figure 3C, where we have analyzed ploidy increases on day 7 from wt and PIDDosome mutant mice. This re-analysis will show that only Caspase-2 mutant mice display a significant ploidy increase on P7, when compared to wt or p73 mutant animals, while no difference are noted between wt and p73 mutant mice (to be included in new Suppl. Fig. 3C) *

      Minor comments: Suggest moving Figure 4A to Figure 3 as it seems to fit better there based on the citation of this figure in the text

      *We can see some benefit in this recommendation and included panel 4A now in an updated version of Figure 3. *

      Recommend enhancing the brightness of microscopy images in Figure 1E and 2D

      We will try to improve image quality, may have been due to PDF conversion


      Significance

      This study provides interesting information for the role of the PIDDosome in protecting from polyploidy and adds to the body of work by this same group studying this pathway in the liver.

      The main weakness in terms of significance is the lack of a phenotype in the hearts of these animals. Therefore, it is clear that ploidy (or at least PIDDosome dependent ploidy) has minimal impact on cardiac development.

      We respectfully disagree with the comment that the lack of impact on cardiac function constitutes a weakness of our findings. Several studies on ploidy control in the liver (PMID 34228992) but importantly also heart (PMID: 36622904) have failed to document a clear impact of increased ploidy on organ function. This does not infer insignificance, but maybe rather that the context where this becomes relevant has not been identified. We are happy expand on this in our discussion

      • *

      The authors mention that they have not tried giving these mice an myocardial infarct (MI) or inducing any other type of cardiac damage. Although it is understood that these experiments are likely outside of the scope of the present study, without this information the impact of this study is moderate. I recommend expanding the discussion to provide a more in-depth possible rationale as to why ploidy perturbations do not lead to structural changes like in the liver.

      Despite this, the insights to the pathway itself are interesting to investigators in the caspase-2 field if a little underdeveloped, especially concerning the role of p21.

      My expertise is in cell death and caspase biology (especially caspase-2). I have sufficient expertise to evaluate all parts of this paper.

      *As mentioned above, we will amend our conclusions on p21, in light of potential findings made when validating DEG candidates, as stated above. *

      *We hope that the changes and amendments proposed here will be satisfactory to this referee to recommend publication of a revised manuscript. *

      • *


      Reviewer #2

      __Evidence, reproducibility and clarity: __

      __Summary: __

      In this study, the authors investigated the role of the PIDDosome during cardiomyocyte polyploidization. PIDDosome is a multi-protein complex activating the endopeptidase Caspase-2, and shown to be involved in eliminating cells with extra centrosomes or in response to genotoxic stress (Burigotto & Fava, 2021, Sladky and Villunger, 2020). In both cases, the PIDDosome is recruited in a ANKRD26-dependent manner at the centrosomes leading to p53 stabilization and cell death (Burigotto & Fava, 2021; Evans et al., 2020; Burigotto et al., 2021).

      Here, by studying mouse cardiomyocyte differentiation, the authors showed that PIDDosome is imposing ploidy restriction during cardiomyocyte differentiation. Importantly, in contrast to a previous report in the liver (Sladky et al., 2020), they showed that PIDDosome acts in a p53-independent manner in cardiomyocytes. Indeed, they suggested that PIDDosome controls ploidy in cardiomyocytes through p21 activation.

      We want to thank this reviewer for the time taken to evaluate our work and provide critical feedback that will help to improve our revised manuscript.

      __Major comments: __

      In general the conclusions of the authors are well supported by the experiments. However, I would suggest the following experiments/analysis to strengthen the paper:

      The authors should improve the Figure 1 to help the readers who are not familiar with cardiomyocyte polyploidization. For instance, I would suggest to add a scheme to summarize cardiomyocyte polyploidization (in terms of nuclear size, mono vs multi and so on).

      We agree that a visual summary of the postnatal timing of CM polyploidization will be helpful for the generalist not familiar with the topic and have added a scheme, adapted from a study by Alkass et al. (PMID: *26544945), who elegantly defined the timing of this process during postnatal mice life (now Fig. 1A). *

      Based on the images they presented in 1B, the authors should also measure the nuclear area or volume in the different conditions in which components of the PIDDosome were depleted. Indeed, these two parameters should be easier to conceptualize for the readers (instead of the fluorescence nuclear intensity). This could help to understand if the nuclear size is maintained between the different conditions and if this is comparable between mono, bi or multinucleated cardiomyocytes.

      We have acquired this data and it can be used to provide additional information on nuclear area and/or volume. We propose to focus on re-analyzing data from wt, Casp2 and XMLC2CRE/Casp2f/f mice. The additional information can be included in Figures 1 & 2, respectively.

      • In Figure 2A, the authors presented cross section of heart from animals showing that PIDDosome depletion has no effect on heart size. This is a surprising result since cardiomyocytes have higher ploidy levels and this could have an effect on their function. Since the importance of this observation, the authors should present a quantification of the heart size in the different conditions shown in Figure 2A.

      We agree with this comment. We can measure the heart vs. body weight ratio or tibia length in adult Casp2-/- vs. WT (3 month old) in order to indirectly evaluate possible increases in CM size linked to increased ploidy.

      Also, the percentage of cardiomyocytes presenting higher levels of ploidy seems quite low. The authors should discuss this point. In particular because this could explain the absence of consequences on heart size and function at steady state.

      We agree with this conclusion and will expand on this in our discussion. It is important to note that as opposed to findings made in liver (PMID: *31983631), genetic manipulation of ploidy regulators such as E2f7/8 (PMID: 36622904), only led to modest changes in CM ploidy, suggesting that either a small band-width compatible with normal heart function exists, or that additional mechanisms exist that take control when these thresholds set by the PIDDosome or E2f7/8 are exceeded. These mechanisms could involve Cyclin G (PMID: 20360255), or TNNI3K (PMID: 31589606). Importantly, a recent publication has shown that overexpression of Plk1(T210D) and Ect2 from birth causes increased heart weight coupled with a minor decrease in CM size. These mice undergo to premature death (PMID: 39912233) suggesting that CM polyploidization is a tight regulated process regulated by several independent mechanisms during heart development. *

      • *

      In Figure 2D, the authors measured the cardiomyocyte cross-sectional area and concluded that removing PIDDosome components have no effect on cardiomyocyte cell size. Since it has been shown that ploidy increase is normally associated with an increase in cell area, the authors should measure cell area of cardiomyocytes analyzed in Figure 1B. It could be then interesting to establish a correlation with nuclear area and the mono, bi or multinucleated status. This will strengthen the results showing that ploidy increases without affecting cell area.

      Indeed, studies in PIDDosome deficient livers suggest that tissue is containing fewer but bigger cells (PMID: *31983631). As opposed to the liver the percentage of cardiomyocytes presenting higher levels of ploidy is relatively low. Thus, a possible increase in CM size in PIDDosome deficient mice may be masked in heart cross-sections. In order to better correlate the ploidy with cell size, we propose to reanalyze our microscopy images used to extract the data displayed in Fig. 1D. We may run into the problem though that the number of cells acquired may become limiting to achieve sufficient statistical power. In this case we could pool data from different PIDDosome mutant CM to increase statistical power. Again, we propose to initially prioritize wt vs. Casp2 vs. XMLC2/Casp2f/f mice. In addition, we can offer to quantify heart to body weight ratio or tibia length as an additional read-out (see answer to a previous reviewer comment). *

      The authors should discuss the fact that PIDDosome depletion lead only to a mild increase in ploidy levels (4N) in a small percentage of cardiomyocyte. If the PIDDosome is controlling ploidy, one could expect that removing it should lead to a drastic increase in the ploidy levels. Is PIDDosome depletion leading to cell death in some cardiomyocyte? The authors should discuss this point in the discussion or if relevant show a staining with an apoptosis marker. Is another mechanism compensating to prevent higher ploidy levels in cardiomyocytes?

      These are valid thoughts, some of which we contemplated before. In part, we have addressed them in our response to Reviewer#1, above, discussing similar findings made in E2f7/8 deficient hearts (PMID: 36622904), or Cyclin G overexpressing hearts (PMID: 20360255), where also only modest changes in ploidy were achieved. Together these observations are suggesting alternative control mechanism able to act, or limited tolerance towards larger shifts in ploidy, incompatible with proper cell function and survival. Towards this end, we can offer to test if we find increased signs of cell death in PIDDosome mutant hearts by TUNEL staining of histological sections. Of note, we did not find evidence for such a phenomenon in the liver (PMID: 31983631).

      Even if the authors presented RNAseq data suggesting that the PIDDosome is activated during cardiomyocyte differentiation, they should clearly demonstrate this point to strengthen the message of the paper. Indeed, the conclusions are based on the absence of PIDDosome components triggering higher ploidy in cardiomyocytes. However, we don't know whether (and when) the PIDDosome is activated during cardiomyocyte differentiation to control their ploidy levels. I would suggest to analyze PIDDosome activation markers by immunofluorescence in *cardiomyocytes at different developmental stages. *

      *We agree with this referee that direct proof of PIDDosome activation would be helpful and that we only infer back from loss of function phenotypes when and where the PIDDosome becomes activated. However, several technical issues prevent us from collecting more direct evidence of PIDDosome activation in the developing heart. 1) Polyploidization in heart CM appears to happen gradually in CM from day 3 on with a peak at day 7 (PMID: 26544945). Hence, this is not a synchronous process, where we could pinpoint simultaneous activation of the PIDDosome in all cells at the same time, which would facilitate biochemical analysis, e.g., by western blotting for signs of Caspase-2 activation (i.e. the loss of its pro-form, PMID: 28130345). 2) Our most reliable readout, MDM2 cleavage by caspase-2 giving rise to specific fragments detectable in western, is not applicable to mouse tissue, as the antibody we use only detects human MDM2 (PMID: 28130345) and no other MDM2 Ab we tested gave satisfactory results. Independent of that, 3) we do not see involvement of p53 in CM ploidy control (arguing against a role of MDM2). *

      *As such, we can only offer to look at extra centrosome clustering in postnatal binucleated CM (as also suggested further below), as a putative trigger for PIDDosome activation. However, this has been published by the first author of this study before (PMID 31301302). Given that we have made the significant effort to time resolve the increase in ploidy in postnatal mice (please note that several hearts needed to be pooled for each time point, analyzed in multiple biological replicates), we think that our conclusions are well-justified based on the genetic data provided. *

      Concerning the methods, the authors must add the references for each product they used and not only the origin. When relevant, the RRID should be indicated. Without this information the method and the data cannot be reproduced.

      We will update this information where relevant to reproduce our results

      Minor comments:

      In general, the text and the figures are clear. Nevertheless, I would suggest the following changes:

      • Figures 1B, 2B and 2C: the y-axis must start at 0.

      We will adopt axes accordingly

      Figure 4A: The authors should stain centrosomes in cardiomyocytes. This should strengthen the conclusion taken by the authors based on the results obtained in mice depleted for ANKRD26. Indeed, for the moment they are insufficient to conclude about the role of the centrosomes. The authors should show that centrosomes cluster in cardiomyocytes (a condition necessary for PIDDosome activation in polyploid cells) and if possible that component of the PIDDosome are recruited here.

      *This point is well taken and addressed in part above. Clustering of extra centrosomes has been documented and published by the first author of this study in rat polyploid cardiomyocytes (PIMID; cited). We can offer to show clustering of centrosomes in mouse CM isolated from day 7 hearts, but while PIDD1 can be detected well in MEF, we repeatedly failed to stain fro PIDD1 in primary CMs. *

      Figure 4F: I would suggest to modify the working model to emphasize more the differences between WT and PIDDosome KO.

      We will aim to improve this cartoon/graphical abstract

      The prior studies are referenced appropriately.

      Reviewer #2 (Significance (Required)):

      How polyploid cells control their ploidy levels during differentiation remains poorly understood. The data presented here represent thus an advance concerning this question. The actual model concerning PIDDosome activation relies on the presence of extra centrosomes that drives the ANKDR26-dependent recruitment of the PIDDosome. Then, Caspase 2 is activated leading to a p53-p21 dependent cell cycle arrest (Burigotto & Fava, 2021, Sladky and Villunger, 2020; Janssens & Tinel, 2012; Evans et al., 2020; Burigotto et al., 2021). In this study, the authors showed that similar pathway takes place during cardiomyocyte differentiation to control ploidy levels. These data are reminiscent of previous work showing PIDDosome involvement during hepatocyte polyploidization (Sladky et al. 2020). Together, these data highlight the prominent role of the PIDDosome complex in controlling ploidy levels in physiological context. Importantly, this study identified that the classical p53-dependent cell cycle arrest described after PIDDosome activation is not involved here. Instead, the data established that independently of p53, p21 contribute to control cardiomyocyte ploidy. In consequence, this study extends the initial pathway associated with PIDDosome activation and suggest that other mechanisms could take place to restrain cell proliferation upon PIDDosome activation. Overall, this makes this paper significant and of interest for the following fields: polyploidy, heart/cardiomyocyte development and PIDDosome.

      My field of expertise includes polyploidy, cell cycle and genetic instability.

      We thank this reviewer for the time taken and the positive feedback provided.

      • *

      • *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • *

      N/A

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

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      • *As outlined above, limited tools are available to validate putative caspase-2 substrates, identified in proteomics analysis, in an impactful manner. *

      • *Also, as discussed above, we deem myocardial infarction experiments in mice as unsuitable to improve our work, as with all likely-hood, they will yield negative results. *
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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors investigated the role of the PIDDosome during cardiomyocyte polyploidization. PIDDosome is a multi-protein complex activating the endopeptidase Caspase-2, and shown to be involved in eliminating cells with extra centrosomes or in response to genotoxic stress (Burigotto & Fava, 2021, Sladky and Villunger, 2020). In both cases, the PIDDosome is recruited in a ANKRD26-dependent manner at the centrosomes leading to p53 stabilization and cell death (Burigotto & Fava, 2021; Evans et al., 2020; Burigotto et al., 2021).

      Here, by studying mouse cardiomyocyte differentiation, the authors showed that PIDDosome is imposing ploidy restriction during cardiomyocyte differentiation. Importantly, in contrast to a previous report in the liver (Sladky et al., 2020), they showed that PIDDosome acts in a p53-independent manner in cardiomyocytes. Indeed, they suggested that PIDDosome controls ploidy in cardiomyocytes through p21 activation.

      Major comments:

      In general the conclusions of the authors are well supported by the experiments. However, I would suggest the following experiments/analysis to strengthen the paper:

      • The authors should improve the Figure 1 to help the readers who are not familiar with cardiomyocyte polyploidization. For instance, I would suggest to add a scheme to summarize cardiomyocyte polyploidization (in terms of nuclear size, mono vs multi and so on). Based on the images they presented in 1B, the authors should also measure the nuclear area or volume in the different conditions in which components of the PIDDosome were depleted. Indeed, these two parameters should be easier to conceptualize for the readers (instead of the fluorescence nuclear intensity). This could help to understand if the nuclear size is maintained between the different conditions and if this is comparable between mono, bi or multinucleated cardiomyocytes.
      • In Figure 2A, the authors presented cross section of heart from animals showing that PIDDosome depletion has no effect on heart size. This is a surprising results since cardiomyocytes have higher ploidy levels and this could have an effect on their function. Since the importance of this observation, the authors should present a quantification of the heart size in the different conditions shown in Figure 2A. Also, the percentage of cardiomyocytes presenting higher levels of ploidy seems quiet low. The authors should discuss this point. In particular because this could explain the absence of consequences on heart size and function at steady state.
      • In Figure 2D, the authors measured the cardiomyocyte cross-sectional area and concluded that removing PIDDosome components have no effect on cardiomyocyte cell size. Since it has been shown that ploidy increase is normally associated with an increase in cell area, the authors should measure cell area of cardiomyocytes analyzed in Figure 1B. It could be then interesting to establish a correlation with nuclear area and the mono, bi or multinucleated status. This will strengthen the results showing that ploidy increases without affecting cell area.
      • The authors should discuss the fact that PIDDosome depletion lead only to a mild increase in ploidy levels (4N) in a small percentage of cardiomyocyte. If the PIDDosome is controlling ploidy, one could expect that removing it should lead to a drastic increase in the ploidy levels. Is PIDDosome depletion leading to cell death in some cardiomyocyte? The authors should discuss this point in the discussion or if relevant show a staining with an apoptosis marker. Is another mechanism compensating to prevent higher ploidy levels in cardiomyocytes?
      • Even if the authors presented RNAseq data suggesting that the PIDDosome is activated during cardiomyocyte differentiation, they should clearly demonstrate this point to strengthen the message of the paper. Indeed, the conclusions are based on the absence of PIDDosome components triggering higher ploidy in cardiomyocytes. However, we don't know whether (and when) the PIDDosome is activated during cardiomyocyte differentiation to control their ploidy levels. I would suggest to analyze PIDDosome activation markers by immunofluorescence in cardiomyocytes at different developmental stages.

      Concerning the methods, the authors must add the references for each product they used and not only the origin. When relevant, the RRID should be indicated. Without this information the method and the data cannot be reproduced.

      The statistics are well indicated in the figures and in the figure legends.

      Minor comments:

      In general, the text and the figures are clear. Nevertheless, I would suggest the following changes:

      • Figures 1B, 2B and 2C: the y-axis must start at 0.
      • Figure 4A: The authors should stain centrosomes in cardiomyocytes. This should strengthen the conclusion taken by the authors based on the results obtained in mice depleted for ANKRD26. Indeed, for the moment they are insufficient to conclude about the role of the centrosomes. The authors should show that centrosomes cluster in cardiomyocytes (a condition necessary for PIDDosome activation in polyploid cells) and if possible that component of the PIDDosome are recruited here.
      • Figure 4F: I would suggest to modify the working model to emphasize more the differences between WT and PIDDosome KO.

      The prior studies are referenced appropriately.

      Significance

      How polyploid cells control their ploidy levels during differentiation remains poorly understood. The data presented here represent thus an advance concerning this question.

      The actual model concerning PIDDosome activation relies on the presence of extra centrosomes that drives the ANKDR26-dependent recruitment of the PIDDosome. Then, Caspase 2 is activated leading to a p53-p21 dependent cell cycle arrest (Burigotto & Fava, 2021, Sladky and Villunger, 2020; Janssens & Tinel, 2012; Evans et al., 2020; Burigotto et al., 2021). In this study, the authors showed that similar pathway takes place during cardiomyocyte differentiation to control ploidy levels. These data are reminiscent of previous work showing PIDDosome involvement during hepatocyte polyploidization (Sladky et al. 2020). Together, these data highlight the prominent role of the PIDDosome complex in controlling ploidy levels in physiological context.

      Importantly, this study identified that the classical p53-dependent cell cycle arrest described after PIDDosome activation is not involved here. Instead, the data established that independently of p53, p21 contribute to control cardiomyocyte ploidy. In consequence, this study extend the initial pathway associated with PIDDosome activation and suggest that other mechanisms could take place to restrain cell proliferation upon PIDDosome activation.

      Overall, this makes this paper significant and of interest for the following fields: polyploidy, heart/cardiomyocyte development and PIDDosome.

      My field of expertise includes polyploidy, cell cycle and genetic instability.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This manuscript by Leone et al describes the role of the PIDDosome in cardiomyocytes. Using a series of whole body and cardiomyocyte specific knockouts, the authors show that the PIDDosome maintains correct ploidy in these cells. It achieves this through inducing cell cycle arrest in cardiomyocytes in a p53 dependent manner. Despite this effect on ploidy, PIDDosome-deficient hearts show no structural or functional defects. Statistics and rigor appears to be adequate.

      Major comments:

      1. Figure 1 uses fluorescent intensity of a nuclear stain to determine ploidy per nucleus and they further separate the results into mononucleated, binucleated or multinucleated cells. It is hard to know how to interpret these results without further information or controls. Is there a good positive control that can be used to help to show whether this assay is quantitative. The differences are larger with the Raidd and caspase-2 knockouts than with the Pidd knockouts but this is not addressed.
      2. On line 459 the authors state that the increase in polyploidy in PIDDosome knockouts occurs in adult hood but this is not directly tested. In fact in the next section the polyploidy is assessed in early postnatal development. This statement should be explained or removed
      3. In Figure 4. The authors obtained RNAseq data for P1, P7 and P14 but only show the differences with and without caspase-2 at P7. Given that the differences in ploidy are more significant at P14 (Fig 3D), all the comparisons should be shown along with analysis of whether the same genes/gene families are altered in the absence of caspase-2
      4. Some validation of the RNAseq and/or proteomics results would be an important addition to this study
      5. Figure 4D: the authors make the conclusion that p21 is downstream of PIDD (et p53 independent). However, this is not supported by the data because the increase in 4N cells/decrease in 2N cells, although statistically significant, is nowhere near that of caspase-2 KO and caspase-2/p21 KO. Statistics should also compare p32KO with c2KO. In the absence of any other data, the more likely conclusion is that p21 is not involved.

      Minor comments:

      Suggest moving Figure 4A to Figure 3 as it seems to fit better there based on the citation of this figure in the text Recommend enhancing the brightness of microscopy images in Figure 1E and 2D

      Significance

      This study provides interesting information for the role of the PIDDosome in protecting from polyploidy and adds to the body of work by this same group studying this pathway in the liver. The main weakness in terms of significance is the lack of a phenotype in the hearts of these animals. Therefore, it is clear that ploidy (or at least PIDDosome dependent ploidy) has minimal impact on cardiac development. The authors mention that they have not tried giving these mice an MI or inducing any other type of cardiac damage. Although it is understood that these experiments are likely outside of the scope of the present study, without this information the impact of this study is moderate. I recommend expanding the discussion to provide a more indepth possible rationale as to why ploidy perturbations do not lead to structural changes like in the liver. Despite this, the insights to the pathway itself are interesting to investigators in the caspase-2 field if a little underdeveloped, especially concerning the role of p21.

      My expertise is in cell death and caspase biology (especially caspase-2). I havesufficient expertise to evaluate all parts of this paper.

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      Reply to the reviewers

      General Statements [optional]

      There were several points that were raised by multiple reviewers, which we respond to as follows.

      1. The reviewers pointed to a lack of clear comparison with experimental data. Perhaps this was insufficiently clear in the first submission, but the analysis of ECT-2 localization during cytokinesis was intended as a validation of the model, parameterized based on polarization and applied without further modification to cytokinesis. These situations differ in numerous respects: centrosome number, centrosome size, and we used several experimental conditions to control centrosome positioning. To address this more extensively, in the revised submission we analyzed our data further (Longhini and Glotzer, 2022) to extract profiles of ECT-2 and myosin. We used these profiles both to constrain model parameters (Appendix B.3) and to compare with model predictions for both polarization and cytokinesis (Figs. 3 and 5).
      2. All of the reviewers pointed to our assumption that myosin indirectly recruits ECT-2. We apologize for a lack of clarity in the original draft about this. We had intended to convey the hypothesis that ECT-2 is recruited by a species that is advected with myosin, but for the sake of the minimal model we do not introduce any extra equations for this species and instead assume it colocalizes with myosin. In the revised manuscript, we address this by clearly listing the assumption (#2 on p. 7), and by comparing to an alternative model (Eq. (S4) and Fig. S7) that accounts directly for a third advected species. We also document specifically (second panel from left in Fig. 4) why the short residence time of ECT-2 makes patterning by pure advection impossible. That said, we still do not know the identity of this factor.
      3. The reviewers pointed out that our use of the M4 term to limit contractility was dubious. This was a (probably misguided) attempt to use previously-published models to constrain our model. In the revised submission, we replaced this term with a more general nonlinear term Mk, where we first demonstrate that k = 1 is insufficient to match the data (p. 32), then consider k = 2,3. We present results in the main text for k = 2, while Fig. S5 shows that the corresponding results for k = 3 are not very different. Put another way, we empirically demonstrate that the specific form of this nonlinear term is not important, as long as it prevents contractile instabilities (as pointed out by one of the reviewers).
      4. Apparently, our extension of the model to cytokinesis, and the evidence for validation of the model, was not clear in the original draft. Because of this, we reformulated the section (3.4) and figure (5) on cytokinesis. We identified four representative examples of centrosome positions, then compared the experimental profile of ECT-2 accumulation to the model result. For simplicity, we also eliminated the simulations of the non-phosphorylatable inactive copy of ECT-2 (“ECT-2 6A”). A more detailed analysis of that data revealed that the pattern of accumulation of ECT-2 6A at cleavage furrowing was more similar to the end of polarization, indicating that this copy of ECT-2 appears to have much slower turnover than the endogenous copy (as would expected from phosphorylation-dependent membrane displacement).
      5. Fundamentally, our study addresses a similar question to (Illukkumbura et al., 2023), in the sense that we seek to understand how cortical flows could pattern ECT-2 and myosin, even though the residence time of ECT-2 is very low. Despite the similarities, it differs from the cited study in that ECT-2 is not an inert component that is asymmetrically distributed, but rather a component which regulates myosin levels and cortical flows, ultimately feeding back on its own accumulation. Due to these similarities and differences, we added an expository section in the discussion (p. 18) comparing our results to those of that study.

      Point-by-point description of the revisions

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

      Reviewer 1

      In this article, Maxian et al. propose a model combining 1-d simulations of ECT-2 and Myosin concentration at the cortex through binding/unbinding and advection at the cortex, with an input for AIR-1 cortical concentration based on the spatial localisation of the centrosomes in the cytoplasm. The objective of the authors is to recapitulate the role of (1) AIR-1, (2) its effector ECT-2 and (3) the downstream effector, driver of cortical flows, the molecular motors Myosin, in two key physiological processes, polarization and cell division. This is important as work over the last 10 years have emphasized the role of AIR-1 in embryo polarization. Previous biochemical-mechanical models have focused on RhoA/Myosin interactions (Nishikawa et al, 2017), the importance of a negative feedback and excitable RhoA dynamics (Michaux et al, 2018), or anterior PARs/posterior PARs/Myosin (Gross et al, 2019). The authors thus attempt to provide a new descriptive model in which RhoA is implicit, instead focusing on the role of centrosome localization on AIR-1 localization, and providing a framework to explore polarity establishment and cell division based on these 3 simple players. The first part of the model is very reminiscent of previously published models, while the second instead provides a link between the initial polarizing cue AIR-1 and polarization. Based on this description, the model is precisely tuned to achieve polarization while matching experimental observations of flow speed and ECT-2 A/P enrichment shape. The results are therefore certainly new and interesting.

      Thank you for the positive assessment!

      Major comments:

      1. The authors use the position of the centrosomes as a static entry, resulting in a static AIR1 input. Is this true, or are the positions of the centrosomes dynamically modulated over the course of the different processes simulated here (for example as a consequence of cortical flows?), and if so, is the assumption of immobile position?

      We assume that the centrosomes are fixed on the timescale of the cortical dynamics, and study how the cortex responds to a static AIR-1 signal (see clarifying comment on p. 4). In Fig. S4, we show that the cortex responds rapidly to changes in the existence or position of the AIR-1 signal. As such, slower dynamics might be the result of slowly moving centrosomes, as we show in supplementary simulations (Fig. S8).

      1. While in its principle the model is quite simple and elegant, the detailed form of the equations describing the interactions between the players is more complex. Are all these required? If they are crucially important for the behavior of the model, these should be described more thoroughly, and if possible rooted more directly in experimental results:

      Thank you for this comment. We agree that there were several non-trivial terms in our “minimal” model. Our guiding principle for the revision was to reduce complexity and better justify the terms that are included.

      (a) kMEMEc _(Linear enhancement term): why would myosin impact E concentration? The authors state, p.7, ”There is a modest increase in the recruitment rate of ECT-2 due to cortical myosin (directly or indirectly), in a myosin concentration-dependent manner (Longhini and Glotzer, 2022).” I could not find the data supporting this assumption Longhini and Glotzer apparently rather point to a modulation of cortical flows. (”During anaphase, asymmetric ECT-2 accumulation is also myosin-dependent, presumably due to its role in generating cortical flows.”). Embedding this effect in the recruitment rate instead of expecting it from the model thus appears awkward. Could the authors specify how they came to this conclusion, which the authors might have derived from observations made in their previous work, but maybe did not fully document there?

      This is an important issue. Since it was raised by all of the reviewers, we addressed it in our general comments. Throughout the manuscript (Figs. 4 and S4), we tried to highlight that cortical flows are insufficient to localize ECT-2, while the recruitment hypothesis provides a better match to the experimental data. The recruitment by an advected species was speculated upon in Longhini and Glotzer: ”Rather, we favor a model in which the association of ECT-2 with the cortex involves interactions with cortical component(s) that are concentrated by cortical flows.”

      (b) kEME2Mc (ECT-2 non-linear impact on Myosin): does the specific form of the value to convey the enhancement (square form) have an impact on the results?

      The specific form does not have an impact. In fact, in the revised version, our experimental data shows an asymmetry in myosin that is actually lower than ECT-2. As such, a nonlinear term here lacks justification, and we switched to a linear term of the form kEMEMc (see model equations on p. 6).

      (c) KfbM4 ”The form of this term is a coarse-grained version of previously-published work (Michaux et al., 2018).” Myosin feedback on myosin localization proportionally to_ M4 _does not seem to directly derive from Michaux et al. Please detail this points more extensively and detail the derivation, in the supplements if not in the main text.

      Based on this comment and that of reviewer 2, we decided to switch to a more general term for nonlinear negative feedback, as discussed in point 3 in general comments.

      (d) P23. Parameter values: ”This is 1.5 times longer than the estimate for single molecules (Nishikawa et al., 2017; Gross et al., 2019) to reflect the more long-lived nature of myosin foci during establishment phase (Munro et al., 2004).” Not sure what the authors mean by more long-lived duration of foci during establishment phase. Seems rather arbitrary.

      This was a misstatement on our part. A closer look at Gross et al. revealed that, under conditions similar to those we simulate (initial polarity establishment), the residence time of myosin is about 15 s (off rate 0.06 s−1). We modified our justification (p. 30) to include this. We also looked at the effect of longer myosin residence time on polarity establishment (Fig. S8).

      1. It would be very helpful (and indeed more convincing) to include a direct comparison between modeling results and experimental counterpart whenever possible. This might not be possible for some data (e.g. Fig. 3d from Cowan et al), but should be possible for other, in particular Fig. 3c and Fig. 5b, for the flow speed and ECT-2 profiles. In Fig. 5b in particular, previously published experimental data could be produced to give the reader to compare model with experiments (possibly provided as an inset, at least for the wild type conditions).

      We tried to bring in more data based on what was available from previous work (Longhini and Glotzer, 2022). Frame intervals of 10 s prohibited a PIV analysis for flow speeds, and punctate myosin profiles often made it difficult to measure myosin concentration. We were, however, able to extract the ECT-2 concentration from our previous movies and compare it to the model results. We included these comparisons in Figs. 3 and 5, with accompanying discussion in the text.

      Minor comments: 1. Fig. 5b: ECT-2 C 6A(dhc-1) do not seem to be referenced or discussed in the main text.

      Also, why present the results for the flow for 2 conditions and the ECT-2 localisation for 4? Or does the variation of ECT-2 not impact the flow profile?

      As discussed in general comments, we decided to reformulate the cytokinesis figure to incorporate more experimental data. Since we have detailed data on ECT-2 localization, we presented these in Fig. 5 for four experimental conditions, comparing each to the model.

      1. p.6: Given that the non-normalized data is used in the main text, and the normalized only appears in the supplemental, maybe star the dimensionless and remove all hats from the main for greater legibility?

      We changed the notation to make the main text variables (dimensional) unadorned, while the dimensionless variables in the SI now have hats.

      1. p.6: Eqn 1a: carrot missing on 3rd E?

      This is now a moot point because of the previous comment.

      1. p.14: replace_“embryo treatment” with ”experimental conditions”?

      We changed “embryo treatment” to “experimental conditions” globally.

      1. p.21, S4a: add_ A = A/A(Tot)

      We added it in the last display on p. 28.

      1. p.22: ”L = 134.6_ µ_m” - please write 134_ µ_m to retain the precision of original measurements

      We made this change.

      1. p.22: Please provide formula for all dimensionless values as a table at the end of the supplemental for the eager but less-mathematically proficient reader.

      We added Table 1 to list the relationship between dimensional and dimensionless parameters.

      Reviewer 2

      The manuscript by Maxian, Longhini and Glotzer presents purely modeling work performed by the first author in conjunction with the already published experimental work by Longhini and Glotzer (eLife, 2022). The aim of the manuscript is to provide a mathematical model that connects the actomyosin contractility of the cell cortex in C. elegans zygote with the activity of the centrosomal kinase AurA (AIR-1 in C. elegans). The major claim of the authors is that their model, fitted to the experimental data pertaining to the zygote polarization, also describes dynamics during the zygote cytokinesis. In the model, the authors provide a heuristic approach to the biochemical dynamics, reducing their treatment to two variables: myosin and Ect2 Rho GEF. The biochemical model is integrated with a simple 1D active gel-type model for the cortical flow. The model uses static diffusive field of activity of AurA kinase in the cytoplasm as an input to their chemo-mechanical model.

      Major concerns:

      1. The biochemical model is highly heuristic and several major assumptions are poorly justified. Thus, the authors explicitly introduce recruitment of Ect2 by myosin, something apparently based on the experimental observations by Longhini and Glotzer in 2022, which had not been biochemically confirmed since with a clear molecular mechanism.

      This is an important issue, and we appreciate your concern which was shared by the other reviewers. As discussed above on p. 1, we tried to justify this assumption better by (a) clearly stating it on p. 7, and (b) demonstrating that the dynamics we observe in live embryos are impossible without it. The model confirms what was pointed out by Longhini and Glotzer, that the short residence time of ECT-2, combined with in vivo flow speeds on the order of 10 µm/min, make it impossible for cortical flows alone to redistribute ECT-2.

      1. The contribution of AurA is introduced highly schematically as a term based on enzyme inhibition biochemistry that increases the off rate of Ect2. The major assumption of the model is that AurA phosphorylates Ect2 strictly on the membrane (cortex) of the cell. Why? No molecular justification is given. If the authors cannot provide clear justification, this major assumption has to be clearly declared as such. The phosphorylation/dephosphorylation dynamics of Ect2 is not considered at all.

      We clarified that the species we consider in the model (E) is unphosphorylated ECT-2, so that the negative flux comes from either unbinding or phosphorylation. Of course, AIR-1 phosphorylates ECT-2 in the cytoplasm as well, but our model only tracks the binding of unphosphorylated ECT-2 to the cortex. We clarified this on p. 6.

      1. In the equation for myosin, the authors introduce disassembly/ inactivation term proportional to the fourth order of concentration of myosin. Why? This is a major assumption, which appears to be derived from the work by Michaux et al. 2018. There the authors (Michaux et al.) postulated that the rate of inactivation of RhoA GTPase was somehow proportional to the fourth power of RhoA concentration. It appears that Maxian et al. further assume that the myosin concentration is fast variable enslaved by Rho, so that_ M ∼ _[RhoA]. They then presumably assume that if the rate of degradation/ inactivation of Rho is proportional to the fourth power of Rho concentration, so is true for myosin (M). This is a logical error and is not justified. An important question, why do the current authors need this unusual assumption with such a high power of M disassembly/inactivation? Perhaps, this is because without this rather dubious term the cortex flow produces a blow-up of myosin concentration? This would be expected in their mechanical model - the continuous flow of actomyosin not compensated by cortex disassembly generally causes blow-up of biochemical concentrations transported by the flow, this is a known problem of the “simple” active gel model used by the authors. Maxian et al. have to provide clear derivation of the term −KfbM4 _and also demonstrate why they need this exotic assumption.

      As mentioned above in general comments, this was a misguided attempt on our part to use previous literature to directly assign values to model parameters. In the revised manuscript, we considered a more general term for the nonlinear feedback. The fitting occurs in Fig. S3, where we impose the ECT-2 profile during pseudo-cleavage and try to fit the myosin profile. k = 1 is eliminated because the ECT-2 and myosin have different asymmetries. Higher order nonlinearities (k = 2,3) are successful in fitting the experimental data. In the main text, we present results from k = 2, then use Fig. S5 to present results on the k = 3 case.

      1. The equation for myosin M has a membrane-binding term, which is second order in concentration of Ect2~E2, without which the model will not show the instability that the authors need. The only justification given is that ”some nonlinearity is required”. A proper derivation should be given here.

      Our experimental data shows an asymmetry in myosin that is actually lower than ECT-2. As such, a nonlinear term in the binding rate lacks justification, and we switched to a linear term of the form kEMEMc (see model equations on p. 6).

      1. The diffusion coefficients for Ect2 and myosin are chosen to be the same. Why? Clearly these molecules so different in size - myosin being a gigantic cluster monster of size_ 300nm _believed to be bound to actin, should have a much smaller diffusion coefficient?

      Thank you for raising this point. We used the same diffusion coefficient for simplicity; because its dimensionless value is less than 10−4, diffusion is relatively unimportant in shaping the concentration fields. If we assume instead, for instance, that myosin cannot diffuse in the membrane, while ECT-2 has a ten-fold larger diffusion coefficient, the steady state profiles of ECT-2 and myosin are changed by at most 5% (see Fig. S6).

      1. There are confusing statements regarding the role of actomyosin flows. In the beginning of the manuscript, the authors seem to state that since Ect2 has a high off rate, the effect of the flow on Ect2 localization is negligible in comparison with direct binding to myosin. Later, the authors state that flows are absolutely essential for the patterning. The authors need to clearly explain where and how the flows are important or not.

      Thank you for pointing out this confusion. In the revised manuscript, we tried to be explicit that the combination of recruitment and flows is essential for patterning ECT-2. We did this in Figs. 4 and 5 by showing the results of simulations without recruitment (Fig. 4) and without recruitment and flows (Fig. 5).

      Minor points:

      1. page 9. Why is the rate of dephosphorylation of AurA is named Koff?

      We changed the notation to kinac to reflect inactivation.

      1. page 10. “Note that the model is calibrated to predict... which matches experimental observations” - this sentence needs changing. You want to say that you fit the model to experiments in the Longhini and Glotzer paper. There is no prediction here.

      We removed this sentence.

      1. page 14. “A plot of Ect-2 accumulation as a function of distance from the nearest cortex...” - clearly the word ”centrosome” is meant here instead of ”cortex”.

      What was meant by this sentence was the distance from the centrosome to the nearest cortex pole (anterior or posterior). We modified it to make this more clear (p. 15).

      1. page 16. ”Inactive, non-phosphorylatable version of Ect-2...” - non-phosphorylatable is clear, but why inactive?

      As discussed in general comments we decided to simplify the cytokinesis figure and remove the simulations with non-phosphorylatable ECT-2. While it is not relevant, the ECT-2 6A variant represents a fragment of the protein that lacks the catalytic domain. Our original goal was to use these data to track the ECT-2 localization without perturbing the system biochemistry, but the data gave the hint of longer exchange kinetics, which confounded our analysis.

      Reviewer 3

      _Maxian et al. developed a mathematical model to explain the essential elements and interactions necessary and sufficient for the polarisation of the C. elegans zygote. The initiation of zygote polarisation has been extensively studied in recent years, highlighting the role of the centrosomal kinase Aurora-A (AIR-1) in controlling the cortical distribution of RhoGEF (ECT-2) and actomyosin contractility during polarisation. Although genetic experiments have demonstrated their function in this process, it remains to be tested whether these factors and their interactions are sufficient to induce polarisation.

      This work has provided a theoretical framework to predict the activity of AIR-1 in the cytoplasm and at the cell cortex, and the cortical distribution of ECT-2 and myosin-II (NMY-2). This framework can recapitulate the dynamic rearrangement of ECT-2 and myosin-II during polarisation, with centrosomes positioned at the posterior pole of the zygote. This model can explain, at least in part, the asymmetric distribution of ECT-2 and myosin-II in the zygote undergoing cytokinesis, suggesting that the mechanism of AIR-1-mediated control of ECT-2 and myosin-II would regulate patterning during polarisation and cytokinesis. This theoretical framework is developed with reasonable assumptions based on previous genetic experiments (except for the myosin-dependent regulation of ECT-2; see comments below).

      Thank you for the positive assessment!

      Major issues:

      1. The authors insist that this model correctly predicts the spatio-temporal dynamics of ECT-2 and myosin-II during polarisation and cytokinesis. However, the predicted results do not reproduce the in vivo pattern of ECT-2 in both phases. ECT-2 is cleared from the posterior cortex and establishes a graded pattern across the antero-posterior axis during polarisation (see their previous publication in eLife 2022, 11, e83992, Fig1A -480s) and cytokinesis (see eLife 2022, 11, e83992, Fig1C 60s and 120s). During both stages, ECT-2 does not show local enrichment at the boundary between the anterior and posterior cortical domains in vivo. In fact, when comparing the predicted results with the in vivo pattern of ECT-2 and cortical flow, the authors used non-quantitative descriptions such as ’in good agreement’, ’a realistic magnitude’,, ’resemble’. These vague descriptions should be revised and a quantitative assessment of ECT-2 distribution between in silico and in vivo should be included in a revised manuscript.

      As mentioned on p. 1, in the revised manuscript we interacted with the data in a much stronger way. We first used data during pseudo-cleavage to infer the ECT-2/myosin relationship. We then examined (Fig. 3) quantitatively how the ECT-2 accumulation during polarization matches the experimental data (it matches early but not later stages). We repeated this for cytokinesis in Fig. 5, where we compared the ECT-2 profile across four experimental conditions to the model prediction.

      1. I assume that the strange local enrichment of ECT-2 at the anteroposterior boundary is due to their assumption that the binding rate of ECT-2 is increased by a linear increase via cortical myosin-II (page 6). This assumption is not directly supported by experimental evidence. A previous study by the same group (eLife 2022, 11, e83992) showed that a progressive increase in ECT-2 concentration at the anterior cortex is partially accompanied by an increase in cortical flow and transport of myosin-II from the posterior pole to the anterior cortex. This observation supports the idea that ECT-2 may associate with cortical components transported by myosin-II based cortical flow. This unrealistic assumption makes the predicted distribution pattern of ECT-2 almost identical to that of cortical myosin-II, resulting in an increase in the concentration of ECT-2 at the anteroposterior boundary where myosin-II forms pseudocleavages and cleavage furrows. The authors should clarify why their mathematical model used this assumption and provide a comprehensive analysis and evaluation of the parameter value for an ECT-2-myosin-II interaction.

      In the revised manuscript, we outlined the justification for this assumption after presenting the model equations. In the Appendix, we were able to constrain all parameters except the recruitment term. Then, we provided an analysis of how polarization changes when the recruitment term is increased. We show that the ECT-2 asymmetries with myosin flows are the same as those simply due to AIR-1 inhibition (since the lifetime of ECT2 is small). Adding indirect recruitment gives asymmetries that resemble experimental data from early establishment of polarity. We showed this both by assuming “myosin” (a species which colocalizes with myosin) recruits ECT-2 (Fig. S4) and by simulating an alternative model (Eq. (S4)) where an explicit species that is advected with cortical flows recruits myosin (Fig. S7).

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      Reply to the reviewers

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

      Summary: In this paper, the authors perform a screen by feeding C. elegans different E. coli genetic mutants and examining the effect on the expression of fat-7, a stearoyl-CoA 9-desturase, which has been associated with longevity. They identify 26 E. coli strains that decrease fat-7 expression, all of which slow development and increase lifespan. RNA sequencing of worms treated with 4 of these strains identified genes involved in defense against oxidative stress among those genes that are commonly upregulated. Feeding C. elegans these 4 bacterial strains results in increased ROS and activation of the mitochondrial unfolded protein response, which appears to contribute to lifespan extension as these bacterial strains do not increase lifespan when the mitochondrial unfolded protein response transcription factor ATFS-1 is disrupted. Finally, the authors demonstrate a role for iron levels in mediating these phenotypes: iron supplementation inhibits the phenotypes caused by the identified bacterial strains, while iron chelation mimics these phenotypes. Response: We thank the reviewer for an excellent summary of our work.

      Major comments: The proposed model involves an increase in ROS levels activating the UPRmt and then leading to lifespan extension. If the elevation is ROS levels is contributing then treatment with antioxidants should prevent UPRmt activation and lifespan extension. Response: This is an excellent point. We will treat the FAT-7-suppressing diets with antioxidants and observe the effect on C. elegans UPRmt activation and lifespan.

      The authors suggest that iron depletion may disrupt iron-sulfur cluster proteins. The Rieske iron-sulfur protein ISP-1 from mitochondrial electron transport chain complex III has previously been associated with lifespan. Point mutations affecting the function of ISP-1 or RNAi decreasing the levels of ISP-1 both result in increased lifespan (PMID 20346072, 11709184). Thus, iron depletion may be increasing ROS, activating UPRmt and increasing lifespan through decreasing ISP-1 levels.

      Response: The reviewer has raised an intriguing possibility that the increased lifespan on the FAT-7-suppressing diets could be because of perturbation of ISP-1 function. While ISP-1 levels may not be directly affected by the mutant diets, ISP-1 function might be perturbed on these diets as ISP-1 function requires iron-sulfur clusters. Therefore, we will study the lifespan of isp-1(qm150) mutant on the FAT-7-suppressing diets to explore whether the lifespan extension on these diets is ISP-1 dependent.

      All of the Kaplan-meier survival plots are missing statistical analyses. Please add p-values.

      Response: The p-values for all the survival plots are included in the respective figure legends.

      It would be helpful to include a model diagram of the proposed mechanisms in the main figures.

      Response: We will make a model diagram after completing the experiments suggested by the reviewers.

      Minor comments: Rather than "mutant diets" it would be more informative to call these "FAT-7-decreasing diets"

      Response: We have changed “mutant diets” to “FAT-7-suppressing diets” throughout the manuscript.

      Is it surprising that none of the bacterial strains increased FAT-7 levels? Why do you think this is?

      Response: Yes, it was indeed surprising to find only bacterial strains that reduced FAT-7 levels and none that increased them. One possible explanation is that these bacterial mutants may not directly regulate fat-7 expression. Instead, they might alter the overall dietary composition, which is known to influence fat-7 levels. It appears that none of the tested mutants modified the diet in a manner that would lead to fat-7 upregulation.

      Page 5. "We hypothesized that diets reducing FAT-7 might elevate oleic acid levels". Since FAT-7 converts stearic acid to oleic acid, wouldn't deceasing FAT-7 levels decrease oleic acid levels and increase stearic acid levels?

      Response: FAT-7 expression is regulated by a feedback mechanism and is sensitive to the fatty acid composition within host cells; elevated levels of unsaturated fatty acids, such as oleic acid, suppress FAT-7 expression. There are two possible ways bacterial mutants could lead to reduced FAT-7 levels: (1) by directly inhibiting FAT-7 expression, which would be expected to result in increased stearic acid levels; or (2) by supplying higher amounts of oleic acid through their composition, thereby suppressing FAT-7 expression via feedback regulation. We focused on the second possibility, as elevated oleic acid levels—like those seen with FAT-7-suppressing diets—are known to promote C. elegans lifespan. To avoid confusion, we have revised the statement to: “We hypothesized that bacterial diets might reduce FAT-7 expression because they have elevated levels of oleic acid”.

      Page 6. The authors cite Bennett et al. 2014 for the statement that "Activation of the UPRmt has been associated with lifespan extension". This paper reaches the opposite conclusion "Activation of the mitochondrial unfolded protein response does not predict longevity in Caenorhabditis elegans". Also, in the Bennett paper and PMID 34585931, it is shown that constitutive activation of ATFS-1 decreases lifespan. Thus, the relationship between the UPRmt and lifespan is not straightforward. These points should be mentioned.

      Response: The reviewer has raised an important point. We have now included a paragraph in the discussion to highlight these points. The revised manuscript reads: “All 26 FAT-7-suppressing diets identified in our study elevated hsp-6p::GFP expression and extended C. elegans lifespan. Although UPRmt activation and lifespan extension were consistently observed across these diets, there was no strong correlation between hsp-6p::GFP levels and the degree of lifespan extension. The role of the UPRmt in promoting longevity remains controversial (Bennett et al., 2014; Soo et al., 2021; Wu et al., 2018). For instance, gain-of-function mutations in atfs-1 have been shown to reduce lifespan (Bennett et al., 2014; Soo et al., 2021). However, a recent study demonstrated that mild UPRmt activation can extend lifespan, whereas strong activation has the opposite effect (Di Pede et al., 2025). These findings suggest that UPRmt contributes to longevity only under specific conditions and at specific activation levels. In our study, lifespan extension on FAT-7-suppressing diets was dependent on ATFS-1, indicating that UPRmt activation was necessary for this effect.

      Page 6. "Our transcriptomic analysis suggested elevated ROS". Rather than refer to gene expression, it would be better to refer to the ROS measurements that were performed.

      Response: We have changed it to the following sentence: “Our ROS measurement analysis suggested elevated ROS levels in worms fed FAT-7-suppressing diets.

      The long-lived mitochondrial mutants isp-1 and nuo-6 have increased ROS, UPRmt activation and increased lifespan. Multiple studies have examined gene expression in these long-lived mutant strains. How does gene expression in these mutants compare to worms treated with the FAT-7-decreasing E. coli mutants? While not necessary for this publication, it would be interesting to see whether the FAT-7-decreasing E. coli strains can increase isp-1 and nuo-6 lifespan.

      Response: We will compare the gene expression changes observed in isp-1 and nuo-6 mutants with the gene expression changes observed in worms exposed to FAT-7-suppressing diets. Additionally, we will examine the lifespan of isp-1 mutants on the mutant diets. These data will be included in the revised manuscript.

      SEK-1 is also involved in the p38-mediated innate immune signaling pathway, which has been shown to contribute to longevity in C. elegans. In fact, disruption of sek-1 using RNAi decreased the lifespan of several long-lived mutant strains PMID 36514863.

      Response: We thank the reviewer for highlighting this point. We have now added that the role of SEK-1 in regulating lifespan on FAT-7-suppressing diets could also be because of its role in innate immunity. The revised manuscript reads: “Notably, SEK-1 also regulates innate immunity and is essential for the extended lifespan observed in several long-lived C. elegans mutants (Soo et al., 2023). Therefore, its effect on lifespan in response to FAT-7-suppressing diets may also stem from its role in innate immune regulation.

      Figure 2. Why were cyoA and ycbk chosen to show the full Kaplan-meier survival plot?

      Response: These were selected randomly to show the range of the lifespan phenotype observed.

      Figure 2, panel D. A better title may be "Mean Survival (Percent increase from control)"

      Response: We have made this change.

      While not necessary for this paper, it would be interesting to determine whether the FAT-7-decreasing E. coli strains alter resistance to oxidative stress.

      Response: We will study the survival of worms on these diets upon supplementation with paraquat.

      Figure 4. It may be interesting to include a correlation plot comparing hsp-6::GFP fluorescence and lifespan. It looks like the magnitudes of increase for each phenotype are not correlated.

      Response: We have added a new Figure (Figure S4) to show the correlation between hsp-6::GFP fluorescence levels and percent change in mean lifespan. Indeed, there is no correlation between these phenotypes.

      Reviewer #1 (Significance (Required)):

      Overall, this is an interesting paper and the experiments are rigorously performed. The bacterial screen was comprehensive and was followed up by careful mechanistic experiments. This paper will be of interest to researchers studying the biology of aging. A diagram of the working model of the underlying mechanisms would enhance the paper. Response: We thank the reviewer for highlighting the significance of the study. We will include a model in the revised manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Das et al. investigate how different bacterial mutants affect the lifespan of C. elegans. The authors screened a library of E. coli mutants using a fat-7 reporter and identified 26 strains that reduce fat-7 expression, cause developmental delay, induce the mitochondrial unfolded protein response (using hsp-6 reporter), and increase worm lifespan. Among these, they focused on four strains and demonstrated that the effects of these mutants on developmental delay, fat-7 expression, and hsp-6 induction could be suppressed by iron supplementation. Furthermore, they showed that iron depletion alone is sufficient to induce fat-7 expression in worms. The lifespan extension observed in worms fed these mutant bacterial strains depends on SKN-1, SEK-1, and HLH-30. Overall, this is a well-written manuscript that highlights the role of iron in regulating fat-7 expression. However, the findings from the initial screen do not significantly expand upon what is already known in the literature. Many of the identified hits overlap with those reported by Zhang et al. (2019), which also highlighted the role of iron in developmental delay and hsp-6 induction. While the lifespan data and the role of fat-7 are novel aspects of this study, the authors have not conducted detailed mechanistic investigations to address key questions, such as: 1) How does the deletion of these bacterial genes alter the metabolic state of the diet? 2) How do these metabolic changes influence fat-7 expression in worms? 3) How does the downregulation of fat-7 contribute to longevity? Addressing these points would strengthen the mechanistic insights of the study.

      Response: We thank the reviewer for a thoughtful summary of our work and for the valuable feedback provided to improve the manuscript. We would like to emphasize that the screening conditions and objectives of our study were fundamentally different from those of Zhang et al. (2019). Furthermore, Zhang et al. (2019) did not investigate the effects of the bacterial mutants identified in their screens on C. elegans lifespan. Notably, the 26 bacterial mutants identified in our screen do not overlap with those reported in previous studies that examined bacterial strains promoting C. elegans longevity. As detailed below, we will address the points raised by the reviewer that will certainly strengthen the mechanistic insights of the study.

      Here are my detailed comments: 1. Suppressing FAT-7 levels in C. elegans does not inherently increase lifespan. To directly attribute this effect to FAT-7, it would be important to attempt a rescue experiment to restore FAT-7 expression and assess whether the lifespan extension persists. Additionally, measuring oleic acid levels in these mutants would help determine whether a high-oleic-acid diet is suppressing FAT-7 expression. The role of oleic acid cannot be ruled out using fat-2 mutants (Fig. 3B), as fat-2 mutants accumulate oleic acid when fed WT bacteria, but this may not translate to endogenous oleic acid accumulation in conditions where FAT-7 is suppressed.

      Response: We thank the reviewer for these useful suggestions. We will overexpress FAT-7 under a pan-tissue promoter (eft-3) and study lifespan on FAT-7-suppressing diets. Moreover, to explore whether oleic acid has any role in enhancing lifespan on FAT-7-suppressing diets, we will study the lifespan of worms on these diets upon supplementing with oleic acid along with wild-type bacterium control.

      To understand the host-microbe interaction in this study, it is important to determine what specific changes in the bacteria contribute to the observed phenotypes in worms. Identifying these bacterial factors will provide a clearer picture of their role in influencing worms stress signaling and lifespan.

      Response: The phenotypes observed in C. elegans across all the identified bacterial mutants are remarkably consistent, including increased UPRmt activation, reduced FAT-7 levels, delayed development, and extended lifespan. This consistency suggests that a common underlying factor is driving these effects. Although the bacterial mutants appear genetically diverse, gene expression data from C. elegans, along with comparisons to the findings of Zhang et al. (2019), indicate that elevated levels of reactive oxygen species (ROS) may represent this shared factor. These results suggest that bacterial ROS play a central role in mediating the host-microbe interactions underlying the observed phenotypes. To further support this hypothesis, we will directly measure ROS levels in the identified bacterial mutants. Additionally, we will test whether antioxidant treatment can suppress the C. elegans phenotypes, thereby establishing a causal role for bacterial ROS.

      It is important to rule out any changes in food consumption in worms fed these bacterial mutants, as differences in feeding amount could attribute to the observed lifespan effects.

      Response: We will carry out pharyngeal pumping rate measurements to study whether there is any difference in food consumption in worms fed these bacterial mutants.

      In figure 5A to 5G, please include the same-day controls to help clarify how iron supplementation effects these phenotypes relative to the control. For example, in Fig. 5F, it appears that iron extends the lifespan of worms fed the control diet. It would be clearer if appropriate controls were included in all of these figures or summarized in a table to help understand the impact of iron.

      Response: We will include these controls in the revised manuscript.

      How does iron depletion affect the levels of fat-7, and how does this contribute to the activation of the longevity pathways discussed in the manuscript.

      Response: This is an intriguing question. There are at least two possible explanations: (1) oxidative stress may directly downregulate fat-7 expression, and (2) iron depletion could reduce ferroptosis, which in turn may influence fatty acid metabolism. In the revised manuscript, we will include data on how oxidative stress affects FAT-7 expression.

      Minor comments 1. Please include a detailed table of the lifespan data for all replicates as a supplementary table.

      Response: We have included the details of survival curves for all the data in the new Table S2.

      In the Methods section, specify at what stage the worms were exposed to iron and the iron chelator for the lifespan experiments.

      Response: The L1-synchronized worms were exposed to iron and iron chelator plates and allowed to develop till the late L4 stage before being transferred to lifespan assay plates that also contained the respective supplements. This information is now included in the Methods section.

      Please clarify whether equal optical density (O.D.) of cells was seeded for both the WT and mutant strains, and mention if the mutants exhibit any growth defects.

      Response: We have examined the growth of the bacterial mutants and found that they do not exhibit growth defects. Therefore, for all the assays, NGM plates were seeded with saturated cultures of all the bacterial strains. We have now included the growth curves data in the manuscript (Figure S4).

      Reviewer #2 (Significance (Required)):

      Significance General Assessment: This study by Das et al. explores the impact of bacterial mutants on C. elegans lifespan. A key strength of the study is the identification of bacterial mutants that influence the expression of the gene encoding fatty acid desaturase (fat-7) and lifespan in C. elegans. Furthermore, the study highlights the role of iron in regulating fat-7 expression, suggesting that iron imbalance may play a crucial role in modulating fatty acid metabolism. However, the study's main limitation is that it does not significantly extend the current understanding of the microbial modulation of host metabolism and aging, as many of the identified bacterial hits overlap with those previously reported in Zhang et al. (2019). The manuscript would benefit from more in-depth mechanistic exploration, especially with regard to how specific bacterial factors influence the metabolic state of the worms and how these changes ultimately modulate fat-7 expression and longevity.

      Response: We thank the reviewer for highlighting the significance of our study. Once again, we would like to emphasize that the screening conditions and objectives of our study differed fundamentally from those of Zhang et al. (2019). Furthermore, Zhang et al. did not investigate the impact of the bacterial mutants identified in their screen on C. elegans lifespan. As outlined above, we will address the reviewer’s comments, which will undoubtedly strengthen the mechanistic insights of our study.

      Advance: This study presents a conceptual advance by exploring the iron-dependent regulation of fat-7 expression and lifespan in C. elegans, linking bacterial mutations with key longevity pathways (SKN-1, SEK-1, and HLH-30). The novelty lies in the direct investigation of the bacterial-induced changes in fat-7 expression, though the role of iron in these mutants for development and induction of mito-UPR was previously shown in the literature. This study also adds to the growing body of work on C. elegans as a model for studying aging and host-microbe interactions, particularly in understanding how diet and microbial exposure affect metabolic processes and lifespan.

      Response: We thank the reviewer for highlighting the advancement made by our study.

      Audience: This research will primarily interest specialized audiences in aging research, microbiology, and metabolism, especially those focused on host-microbe interactions. Keywords of my expertise: Host-microbe interactions, metabolism, system biology, C. elegans, aging.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Das et al. investigate how different bacterial mutants affect the lifespan of C. elegans. The authors screened a library of E. coli mutants using a fat-7 reporter and identified 26 strains that reduce fat-7 expression, cause developmental delay, induce the mitochondrial unfolded protein response (using hsp-6 reporter), and increase worm lifespan. Among these, they focused on four strains and demonstrated that the effects of these mutants on developmental delay, fat-7 expression, and hsp-6 induction could be suppressed by iron supplementation. Furthermore, they showed that iron depletion alone is sufficient to induce fat-7 expression in worms. The lifespan extension observed in worms fed these mutant bacterial strains depends on SKN-1, SEK-1, and HLH-30.

      Overall, this is a well-written manuscript that highlights the role of iron in regulating fat-7 expression. However, the findings from the initial screen do not significantly expand upon what is already known in the literature. Many of the identified hits overlap with those reported by Zhang et al. (2019), which also highlighted the role of iron in developmental delay and hsp-6 induction. While the lifespan data and the role of fat-7 are novel aspects of this study, the authors have not conducted detailed mechanistic investigations to address key questions, such as: 1) How does the deletion of these bacterial genes alter the metabolic state of the diet? 2) How do these metabolic changes influence fat-7 expression in worms? 3) How does the downregulation of fat-7 contribute to longevity? Addressing these points would strengthen the mechanistic insights of the study.

      Here are my detailed comments:

      1. Suppressing FAT-7 levels in C. elegans does not inherently increase lifespan. To directly attribute this effect to FAT-7, it would be important to attempt a rescue experiment to restore FAT-7 expression and assess whether the lifespan extension persists. Additionally, measuring oleic acid levels in these mutants would help determine whether a high-oleic-acid diet is suppressing FAT-7 expression. The role of oleic acid cannot be ruled out using fat-2 mutants (Fig. 3B), as fat-2 mutants accumulate oleic acid when fed WT bacteria, but this may not translate to endogenous oleic acid accumulation in conditions where FAT-7 is suppressed.
      2. To understand the host-microbe interaction in this study, it is important to determine what specific changes in the bacteria contribute to the observed phenotypes in worms. Identifying these bacterial factors will provide a clearer picture of their role in influencing worms stress signaling and lifespan.
      3. It is important to rule out any changes in food consumption in worms fed these bacterial mutants, as differences in feeding amount could attribute to the observed lifespan effects.
      4. In figure 5A to 5G, please include the same-day controls to help clarify how iron supplementation effects these phenotypes relative to the control. For example, in Fig. 5F, it appears that iron extends the lifespan of worms fed the control diet. It would be clearer if appropriate controls were included in all of these figures or summarized in a table to help understand the impact of iron.
      5. How does iron depletion affect the levels of fat-7, and how does this contribute to the activation of the longevity pathways discussed in the manuscript.

      Minor comments

      1. Please include a detailed table of the lifespan data for all replicates as a supplementary table.
      2. In the Methods section, specify at what stage the worms were exposed to iron and the iron chelator for the lifespan experiments.
      3. Please clarify whether equal optical density (O.D.) of cells was seeded for both the WT and mutant strains, and mention if the mutants exhibit any growth defects.

      Significance

      General Assessment: This study by Das et al. explores the impact of bacterial mutants on C. elegans lifespan. A key strength of the study is the identification of bacterial mutants that influence the expression of the gene encoding fatty acid desaturase (fat-7) and lifespan in C. elegans. Furthermore, the study highlights the role of iron in regulating fat-7 expression, suggesting that iron imbalance may play a crucial role in modulating fatty acid metabolism. However, the study's main limitation is that it does not significantly extend the current understanding of the microbial modulation of host metabolism and aging, as many of the identified bacterial hits overlap with those previously reported in Zhang et al. (2019). The manuscript would benefit from more in-depth mechanistic exploration, especially with regard to how specific bacterial factors influence the metabolic state of the worms and how these changes ultimately modulate fat-7 expression and longevity.

      Advance: This study presents a conceptual advance by exploring the iron-dependent regulation of fat-7 expression and lifespan in C. elegans, linking bacterial mutations with key longevity pathways (SKN-1, SEK-1, and HLH-30). The novelty lies in the direct investigation of the bacterial-induced changes in fat-7 expression, though the role of iron in these mutants for development and induction of mito-UPR was previously shown in the literature. This study also adds to the growing body of work on C. elegans as a model for studying aging and host-microbe interactions, particularly in understanding how diet and microbial exposure affect metabolic processes and lifespan.

      Audience: This research will primarily interest specialized audiences in aging research, microbiology, and metabolism, especially those focused on host-microbe interactions.

      Keywords of my expertise: Host-microbe interactions, metabolism, system biology, C. elegans, aging.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this paper, the authors perform a screen by feeding C. elegans different E. coli genetic mutants and examining the effect on the expression of fat-7, a stearoyl-CoA 9-desturase, which has been associated with longevity. They identify 26 E. coli strains that decrease fat-7 expression, all of which slow development and increase lifespan. RNA sequencing of worms treated with 4 of these strains identified genes involved in defense against oxidative stress among those genes that are commonly upregulated. Feeding C. elegans these 4 bacterial strains results in increased ROS and activation of the mitochondrial unfolded protein response, which appears to contribute to lifespan extension as these bacterial strains do not increase lifespan when the mitochondrial unfolded protein response transcription factor ATFS-1 is disrupted. Finally, the authors demonstrate a role for iron levels in mediating these phenotypes: iron supplementation inhibits the phenotypes caused by the identified bacterial strains, while iron chelation mimics these phenotypes.

      Major comments:

      The proposed model involves an increase in ROS levels activating the UPRmt and then leading to lifespan extension. If the elevation is ROS levels is contributing then treatment with antioxidants should prevent UPRmt activation and lifespan extension.

      The authors suggest that iron depletion may disrupt iron-sulfur cluster proteins. The Rieske iron-sulfur protein ISP-1 from mitochondrial electron transport chain complex III has previously been associated with lifespan. Point mutations affecting the function of ISP-1 or RNAi decreasing the levels of ISP-1 both result in increased lifespan (PMID 20346072, 11709184). Thus, iron depletion may be increasing ROS, activating UPRmt and increasing lifespan through decreasing ISP-1 levels.

      All of the Kaplan-meier survival plots are missing statistical analyses. Please add p-values.

      It would be helpful to include a model diagram of the proposed mechanisms in the main figures.

      Minor comments:

      Rather than "mutant diets" it would be more informative to call these "FAT-7-decreasing diets"

      Is it surprising that none of the bacterial strains increased FAT-7 levels? Why do you think this is?

      Page 5. "We hypothesized that diets reducing FAT-7 might elevate oleic acid levels". Since FAT-7 converts stearic acid to oleic acid, wouldn't deceasing FAT-7 levels decrease oleic acid levels and increase stearic acid levels?

      Page 6. The authors cite Bennett et al. 2014 for the statement that "Activation of the UPRmt has been associated with lifespan extension". This paper reaches the opposite conclusion "Activation of the mitochondrial unfolded protein response does not predict longevity in Caenorhabditis elegans". Also, in the Bennett paper and PMID 34585931, it is shown that constitutive activation of ATFS-1 decreases lifespan. Thus, the relationship between the UPRmt and lifespan is not straightforward. These points should be mentioned.

      Page 6. "Our transcriptomic analysis suggested elevated ROS". Rather than refer to gene expression, it would be better to refer to the ROS measurements that were performed.

      The long-lived mitochondrial mutants isp-1 and nuo-6 have increased ROS, UPRmt activation and increased lifespan. Multiple studies have examined gene expression in these long-lived mutant strains. How does gene expression in these mutants compare to worms treated with the FAT-7-decreasing E. coli mutants? While not necessary for this publication, it would be interesting to see whether the FAT-7-decreasing E. coli strains can increase isp-1 and nuo-6 lifespan.

      SEK-1 is also involved in the p38-mediated innate immune signaling pathway, which has been shown to contribute to longevity in C. elegans. In fact, disruption of sek-1 using RNAi decreased the lifespan of several long-lived mutant strains PMID 36514863.

      Figure 2. Why were cyoA and ycbk chosen to show the full Kaplan-meier survival plot?

      Figure 2, panel D. A better title may be "Mean Survival (Percent increase from control)"

      While not necessary for this paper, it would be interesting to determine whether the FAT-7-decreasing E. coli strains alter resistance to oxidative stress.

      Figure 4. It may be interesting to include a correlation plot comparing hsp-6::GFP fluorescence and lifespan. It looks like the magnitudes of increase for each phenotype are not correlated.

      Significance

      Overall, this is an interesting paper and the experiments are rigorously performed. The bacterial screen was comprehensive and was followed up by careful mechanistic experiments. This paper will be of interest to researchers studying the biology of aging. A diagram of the working model of the underlying mechanisms would enhance the paper.

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      Reply to the reviewers

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

      Summary: Chitin is a critical component of the extracellular matrix of arthropods and plays an essential role in the development and protection of insects. There are two chitin synthases in insects: Type A (exoskeletons) and Type B (for the peritrophic matrix in the gut). The study aims to investigate the specificity and mechanisms of the two chitin synthases in D. melanogaster and to clarify whether they are functionally interchangeable. Various genetic manipulations and fluorescence-based labeling were used to analyze the expression, localization, and function of Kkv and Chs2 in different tissues. Chs2 is expressed in the PR cells of the proventriculus and is required for chitin deposition in the peritrophic matrix. Kkv can deposit chitin in ectodermal tissues but not in the peritrophic matrix, whereas Chs2 can deposit chitin in the peritrophic matrix but not in ectodermal tissues. The subcellular localization of chitin synthases is specific to the tissues in which they are expressed. Kkv localizes apically in ectodermal tissues, whereas Chs2 localizes apically in the PR cells of the proventriculus. Altogether, Kkv and Chs2 cannot replace each other. The specificity of chitin synthases in D. melanogaster relies on distinct cellular and molecular mechanisms, including intracellular transport pathways and the specific molecular machinery for chitin deposition.*

      • *

      Congratulations on this incredible story and manuscript, which is straightforward and well-written. However, I have some comments that may help to improve it.

      We thank the reviewer for this very positive comment. We have addressed all comments to clarify and improve our manuscript.

      Major comments: 1.) Funny thing: the Chs2 mutant larva shows a magenta staining below the chitin accumulation of the esophagus, which looks like a question mark in 1H but cannot be found in control. Is that trachea reaching the pv?

      We assume that the reviewer refers to Fig 1N. As the reviewer suspects, this corresponds to a piece of trachea. Figure 1N shows a single section, making it difficult to identify what this staining corresponds to. We are providing below a projection of several sections where it is easier to identify the staining as tracheal tissue (arrow).

      We are now marking this pattern as trachea (tr) in the manuscript Figure 1N

      2.) Also, though it is evident that the PM chitin is lost in Ch2 mutants, could it be that the region is disturbed and cells express somewhere else chitin? There are papers by Fuß and Hoch (e.g., Mech of Dev, 79, 1998; Josten, Fuß et al., Dev. Biol.267, 2004) using markers such as Dve, Fkh, Wg, Delta, and Notch, etc. for precisely marking the endodermal/ectodermal region in the embryonic foregut/proventriculus. It would be beneficial to show, along with chitin and Chs expression patterns, the ectoderm/endoderm cells. This is particularly important as the authors report endodermal expression of Chs2 in embryos but don't use co-markers of the endodermal cells.

      We agree with the reviewer that this is an important issue and we note that Reviewer 2 also raised the same point. Therefore, we have addressed this issue.

      We obtained an antibody against Dve, kindly provided by Dr. Hideki Nakagoshi. Dve marks the endodermal region in the proventriculus (Fuss and Hoch, 1998, Fuss et al., 2004, Nakagoshi et al., 1998).This antibody worked nicely in our dissected L3 digestive tracts and allowed us to mark the endodermal region. We also obtained an antibody against Fkh, kindly provided by Dr. Pilar Carrera. Fkh marks the ectodermal foregut cells (Fuss and Hoch, 1998, Fuss et al., 2004). While, in our hands, this antibody performed well in embryonic tissues, we observed no staining in our dissected L3 digestive tracts. The reason for this is unclear, but we suspect technical limitations may be responsible (the ectodermal region of the proventriculus is very internal, potentially hindering antibody penetration). To circumvent this inconvenience, we tested a FkhGFP tagged allele available in Bloomington Stock Center. Fortunately, we were able to detect GFP in ectodermal cells of L3 carrying this allele. Using this approach, we conducted experiments to detect Fkh and Dve in the wild type or in Df(Chs2) conditions (Fig S1). In addition, we used these markers to map the expression of Kkv and Chs2 in the proventriculus (Fig 4).

      Altogether the results using these endodermal/ectodermal markers confirmed the presence of a cuticle adjacent to the FkhGFP-positive cells and a PM adjacent to the PR cells, marked by Dve. This PM is absent in Df(Chs2) L3 escapers, however, the general pattern of Fkh/Dve expression is not affected. Finally, we show that Chs2-expressing cells are positive for Dve while Kkv-expressing cells are not. We were unable to conduct an experiment demonstrating Kkv and Fkh co-expression due to technical incompatibilities, as both genes require the use of GFP-tagged alleles to visualise their expression. However, we believe that our imaging of Dve/Kkv clearly shows that Kkv expressing cells lack Dve expression and are localised in the internal (ectodermal) region of the proventriculus (Fig 4E).

      3.) The origin of midgut chitin accumulation is unclear. Chitin can come from yeast paster. Can the authors check kkv and chs2 mutants for food passage and test starving L1 larvae to detect chitin accumulation in the midgut without feeding them?

      This is a very interesting point that has also intrigued us.

      We observed that, in addition to the PM layer lining the midgut epithelium, CBP staining also revealed a distinct luminal pattern. Our initial hypothesis was that this pattern corresponded to the PM. However, its presence in Df(Chs2) larval escapers clearly indicates that this is not the case. Unfortunately, we cannot assess this pattern in kkv mutants, as these die at eclosion and do not proceed to larva stages.

      As the reviewer suggests, a likely possibility is that the luminal pattern originates from components in the food. These could correspond to yeast, as suggested by the reviewer, or possibly remnants of dead larvae present in the media (although Drosophila is considered herbivore in absence of nutritional stress).

      To assess whether the luminal pattern originates from the food we conducted two independent experiments. In experiment 1, we collected larvae reared under normal food conditions. Newly emerged L3 larvae were transferred in small numbers to minimise cannibalism (Ahmad et al., 2015) to new Petri plates containing moist paper. Larvae were starved for 3,4 or 5 days. Larvae starved for more than 5 days did not survive. We then dissected the guts and analysed CBP staining. We observed the presence of luminal CBP staining in these larvae, along with the typical PM signal in the proventriculus and along the midgut. In experiment 2, we collected larvae directly on agar plates containing only agar (without yeast or any other nutrients). We allowed the larvae to develop. These larvae showed minimal growth. We dissected the guts of these small larvae (which were challenging to dissect) and analysed CBP staining. Again, we detected presence of luminal CBP staining.

      These experiments indicate that, despite starvation, a luminal chitin pattern is still detected, suggesting that it is unlikely to originate from food. However, we cannot unequivocally rule out the possibility that the cannibalistic, detrivorous or carnivorous behavior of the nutrionally stressed larvae (Ahmad et al., 2015) in our experiments may influence the results. Therefore, more experiments would be required to address this point.

      In summary, while we cannot provide a definitive answer to the reviewer's question, nor fully satisfy our own curiosity, we would like to note that this specific observation is unrelated to the main focus of our study, as we have confirmed that the luminal pattern is not dependent on Chs2 function.

      Portions of midgut of starved larvae under the regimes indicated, stained for chitin (CBP, magenta). Note the presence of the luminal chitin pattern in the midgut

      4.) Subcellular localization assays require improved analysis, such as a co-marker for the apical membrane and statistical analysis with co-localization tools, showing the overlap at the membrane and intracellularly with membrane co-markers and KDEL.

      We have addressed the point raised by the reviewer. To analyse and quantify Chs2 subcellular localisation, particularly considering the observed pattern, we decided to use both a membrane and an ER marker. As a membrane marker we used srcGFP expressed in tracheal cells (see answer to point 7 of Reviewer 1) and as an ER marker we used KDEL. In this analysis, tracheal cells also expressed Chs2, which was visualised using the Chs2 antibody generated in the lab.

      To assess the colocalisation of Chs2 with each marker we used the JaCop pluggin in Fiji. We analysed individual cells from different embryos stained for membrane/ER/Chs2 using single confocal sections (to avoid artificial colocalisation). Images were processed as described in Materials and Methods. We obtained the Pearson's correlation coefficient (r), which measures the degree of colocalisation, for Chs2/srcGFP and Chs2/KDEL, n=36 cells from 9 different embryos. The average r value for Chs2/srcGFP was 0,064, while the average for Chs2/KDEL was around 0,7. r ranges between -1 and 1, where 1 indicates perfect correlation, 0 no correlation, and -1 perfect anti-correlation. Typically, an r value of 0.7 and above is considered a strong positive correlation, whereas a value below 0,1 is regarded as very weak or no correlation. Thus, our colocalisation analysis supports the hypothesis that Chs2 is primarily retained in the ER when expressed in non-endogenous tissues, likely unable to reach the membrane.

      We have reorganised the figures and now present an example of Chs2/srcGFP/KDEL subcellular localisation in tracheal cells and the colocalisation analysis in Fig 5H. The colocalisation analysis is described in the Materials and Methods section.

      Minor comments:

      5.) The authors used "L3 larval escapers." It would be interesting to know if the lack of Chs2 and the peritrophic matrix cause any physiological defects or lethality.

      The point raised by the reviewer is very interesting and relevant. The peritrophic matrix is proposed to play several important physiological roles, including the spatial organisation of the digestive process, increasing digestive efficiency, protection against toxins and pathogens, and serving as a mechanical barrier. Therefore, it is expected that the absence of chitin in the PM of the Df(Chs2) larval escapers may cause various physiological effects.

      Analysing these effects is a complex task, and it constitutes an entire research project on its own. In addressing the physiological requirements of the PM, we aim to analyse adult flies and assess various parameters, including viability, digestive transit dynamics, gut integrity, resistance to infections, fitness and fertility.

      A critical initial challenge in conducting a comprehensive analysis of the physiological requirements of the PM is identifying a suitable condition to evaluate the absence of Chs2. In this work we are using a combination of two overlapping deficiencies that uncover Chs2, along with a few additional genes (as indicated in Fig S1F). This deficiency condition presents two major inconveniences: first, the observed defects could be caused or influenced by the absence of genes other than Chs2, preventing us from conclusively attributing the defects to Chs2 loss (unless we rescued the defects by adding Chs2 back as we did in the manuscript). Second, the larva escapers, which are rare, do not survive to adulthood (indicating lethality but preventing us from analysing specific physiological aspects).

      To overcome these limitations, we are currently working to identify a genetic condition in which we can specifically analyse the absence of Chs2. We have identified several available RNAi lines and we are testing their efficiency in preventing chitin deposition in the PM. Additionally, we are characterising a putative null Chs2 allele, Chs2CR60212-TG4.0. This stock contains a Trojan-GAL4 gene trap sequence in the third intron, inserted via CRISPR/Cas9. As described in Flybase (https://flybase.org/), the inserted cassette contains a 'Trojan GAL4' gene trap element composed of a splice acceptor site followed by the T2A peptide, the GAL4 coding sequence and an SV40 polyadenylation signal. When inserted in a coding intron in the correct orientation, the cassette should result in truncation of the trapped gene product and expression of GAL4 under the control of the regulatory sequences of the trapped gene. We already know that, when crossed to a reporter line (e.g. UAS-GFP or UAS-nlsCherry) this line reproduces the Chs2 expression pattern, suggesting that the insertion may generate a truncated Chs2 protein. This line would represent an ideal tool to assess the absence of Chs2, and we are currently characterising it for further analysis

      In summary, we fully agree with the reviewer that investigating the physiological requirements of the PM is a compelling area of research, and we are actively addressing this question. However, this investigation constitutes a substantial and independent research effort that we believe is beyond the scope of the current manuscript at this stage.

      6.) The order identifiers are missing for materials and antibodies, e.g., anti-GFP (Abcam), but Abcam provides several ant-GFP; which was used? Please provide order numbers that guarantee the repeatability for others.

      We have now added all identifiers for materials and reagents used, in the materials and methods section.

      7.) Figure S5C, C', what marks GFP (blue) in the trachea? Maybe I have overlooked the description. What is UASsrcGFP? What is the origin of this line?

      We apologise for not providing a more detailed description of the UASsrcGFP line. This line corresponds to RRID BDSC#5432, as now indicated in Materials and Methods section.

      In this transgene, the UAS regulatory sequences drive the expression of GFP fused to Tag:Myr(v-src). As described in Flybase (https://flybase.org/), the P(UAS-srcEGFP) construct contains the 14 aa myristylation domain of v-src fused to EGFP. This tag is commonly used to target proteins of interest to the plasma membrane. The construct was generated by Eric Spana and is available in Drosophila stock centers.

      We typically use this transgene as a plasma membrane marker to outline cell membrane contours. In our experiments, srcGFP, under the control of the btlGal4 promoter, was used to visualise the membrane of tracheal cells in relation to Chs2 accumulation. As indicated in point 4, we have now transferred the images of srcGFP/Chs2/KDEL to the main Figures and used it for colocalisation analyses.

      8.) The authors claim that they validated the anti-Chs2 antibody. However, they show only that it recognizes a Cht2 epitope via ectopic expression. For more profound validation, immune staining is required in deletion mutants, upon knockdown, or upon expression of recombinant proteins, which is not shown.

      We generated an antibody against Chs2. We found that the antibody does not reliably detect the endogenous Chs2 protein, and so we find no pattern in the proventriculus or any other tissue in our immunostainings. It is very possible that the combination of low endogenous levels of Chs2 with a sub-optimal antibody (or low titer) leads to this result. In any case, as the antibody does not detect endogenous Chs2, it cannot be validated by analysing the expression upon Chs2 knockdown. In contrast, our antibody clearly detects specific staining in various tissues (e.g. trachea, salivary glands, gut) when Chs2 is expressed using the Gal4/UAS system, confirming its specificity for Chs2. It is worth to point that it is not unusual to find antibodies that are not sensitive enough to detect endogenous proteins but can detect overexpressed proteins (e.g

      (Lebreton and Casanova, 2016)).

      As an additional way to validate the specificity of our antibody, we have used the chimeras generated, as suggested by the reviewer. As indicated in the Materials and Methods section, the Anti-Chs2 was generated against a region comprising 1222-1383 aa in Chs2, with low homology to Kkv. This region is present in the kkv-Chs2GFP chimera but absent in Chs2-KkvGFP (see Fig 7A). Accordingly, our antibody recognises kkv-Chs2GFP but does not recognise Chs2-KkvGFP (Fig S7).

      We have revised the text in chapter 6 (6. Subcellular localisation of Chs2 in endogenous and ectopic tissues) to clarify these points and we have added the validation of the antibody using the chimeras in chapter 8 (8. Analysis of Chs2-Kkv chimeras) and Fig S7

      9) The legend and text explaining Fig. 4 D-E' can be improved. The authors used the Crimic line, which is integrated into the third ("coding") intron. This orientation can lead to the expression of Gal4 and cause a truncated version of the protein (according to Flybase). Is Chs2 expression reduced in the crimic mutant? If the mutation causes expression of a truncated version, the Chs2 antibody may not be able to detect it as it recognizes a fragment between 1222 and 1383 aa? Also, I'm unsure whether the Chs2 antibody or GFP was used to detect expression in PR cells. The authors describe using Ch2CR60212>SrcGFP together with Chs2+ specific antibodies.

      We apologise for the confusion.

      As the reviewer points, Chs2CR60212-TG4.0 contains a Trojan-GAL4 gene trap sequence in the third intron, inserted via CRISPR/Cas9. As described in Flybase (https://flybase.org/), the inserted cassette contains a 'Trojan GAL4' gene trap element composed of a splice acceptor site followed by the T2A peptide, the GAL4 coding sequence and an SV40 polyadenylation signal. When inserted in a coding intron in the correct orientation, the cassette should result in truncation of the trapped gene product and expression of GAL4 under the control of the regulatory sequences of the trapped gene.

      We found that when crossed to UAS-GFP or UAS-nlsCherry, this line reproduces a expression pattern that must correspond to Chs2. As the antibody that we generated is not suitable for detecting Chs2 endogenous expression, we resorted to using this combination, Chs2CR60212-TG4.0 crossed to a reporter line (such asUAS-GFP or UAS-nlsCherry), to visualise Chs2 expression by staining for GFP/Cherry in the intestinal tract and in the embryo (Figures 4 and S4).

      We realise that the Figure labelling we used in our original submission is very misleading, and we apologise for this. In the original figures we had labelled the staining combination with Kkv, Chs2, Exp as if we had used these antibodies. However, in all cases, we used GFP to visualise the pattern of these proteins in the genetic combinations indicated in the figures. We have corrected this in our revised version. We have also updated the text (Chapter 5), figures and figure legends.

      As the reviewer points, the insertion in Chs2CR60212-TG4.0 is likely to generate a truncated Chs2 protein. We cannot confirm this using the Chs2 antibody we generated because it does not recognise the endogenous Chs2 pattern. Nevertheless, as indicated in point 5, we are currently characterising this line. Our preliminary results indicate a high complexity of effects from this allele that require thorough analysis, as it may be acting as a dominant negative.

      Reviewer #1 (Significance (Required)):

      Significance: The manuscript's strength and most important aspects are the genetic analysis, expression, and localization studies of the two Chitin synthases in Drosophila embryos and larvae. However, beyond this manuscript, the development of mechanistic details, such as interaction partners that trigger secretion and action at the apical membranes and the role of the coiled-coil domain, will be interesting.

      The manuscript uses "first-class" genetics to describe the different roles of the two Chitin synthases in Drosophila, comparing ectodermal chitin (tracheal and epidermal chitin) with endodermal (midgut) chitin. Such a precise analysis has not been investigated before in insects. Therefore, the study deeply extends knowledge about the role of Chitin synthases in insects.

      The audience will specialize in basic research in zoology, developmental biology, and cell biology regarding - how the different Chitin synthases produce chitin. Nevertheless, as chitin is relevant to material research and medical and immunological aspects, the manuscript will be fascinating beyond the specific field and thus for a broader audience.

      I'm working on chitin in the tracheal system and epidermis in Drosophila.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ Drosophila have two different chitin synthase enzymes, Kkv and Chs2, and due to unique expression patterns and mutant phenotypes, it is relatively clear that they have different functions in producing either the cuticle-related chitin network (Kkv) or the chitin associated with the peritrophic matrix (PM). However, what is unknown is whether the different functions in making cuticle vs PM chitin is related to differences in cellular expression and/or enzyme properties within the cell. The authors exploit the genetic tractability of Drosophila and their ability to image cuticle vs PM chitin production to examine whether these 2 enzymes can substitute each other. They conclude that these two proteins are not equivalent in their capacity to generate chitin. The data are convincing; however, it is currently presented in a subjective fashion, which makes it difficult to interpret. Additionally, in my opinion there is some interpretation that requires softening or alternatively interpreted.

      We are pleased that the reviewer finds our data convincing. However, we acknowledge the reviewer's concern that our data was presented in a subjective manner, and we apologise for this. In response, we have carefully reviewed the entire manuscript and revised our data presentation to ensure a more objective tone. Numerous changes (including additional quantifications, new experiments and clarifications) have been incorporated throughout the text. These revisions are highlighted in the marked-up version. We hope that this revision provides a more accurate and objective presentation of our work.

      Major Comments:

      1- While the imaging is lovely, there are some things that are difficult to see in the figures. For example, the "continuous, thin and faint 'chitin' layer that lined the gut epithelium" is very difficult to visualise in the control images. Can they increase the contrast to help the reader appreciate this layer? This is particularly important as we are asked to appreciate a loss of this layer in the absence of Chs2.

      We have tried to improve the figures so that the PM layer in the midgut region is more clearly visible. We have added magnifications of small sections at the midgut lumen/epithelium border in grey to help visualise the PM. These improvements have been made in Figures 1,2,S1,S2,S3 and we believe that they better illustrate our results.

      2- All the mutant analysis is presented subjectively. For example, the authors state that they "found a consistent difference of CBP staining when they compared the 'Chs2' escapers to the controls". How consistent is consistent? Can this be quantified? What is the penetrance of this phenotype? They say that the thin layer is absent in the midgut and the guts are thinner. Could they provide more concrete data?

      As indicated above, we have reviewed the text to provide a more objective description of the phenotypes.

      We have quantified the defects in the Df(Chs2) mutant conditions. For this quantification we dissected intestinal tracts of control and Df(Chs2) larva escapers. We fixed, stained and mounted them together. The control guts expressed GFP in the midgut region as a way to distinguish control from mutants. We analysed the presence or absence of chitin in the PM. We found absence of chitin in the proventricular lumen and in the midgut in all Df(Chs2) guts and presence of chitin there in all control ones (n=12 Df(Chs2) guts, n=9 control guts, from 5 independent experiments). The results indicate a fully penetrant phenotype of lack of chitin in Df(Chs2) larva escapers (100% penetrance). We have added this quantification in the text, chapter 2 (2. Chs2 deposits chitin in the PM).

      To quantify the thickness of the guts, we took measurements of the diameter in control and Df(Chs2) guts at two comparable distance positions from the proventriculus (position 1, position 2, see image). Our quantifications indicated thinner tubes in mutant conditions.

      Image shows the anterior part of the intestinal tract, with the proventriculus encircled in white. Positions 1 and 2 indicate where the diameter quantifications were taken. Scatter plots quantifying the diameter at the two different positions in control and Chs2 larval escapers. Bars show mean {plus minus} SD. p=p value of unpaired t test two-tailed with Welch's correction.

      However, we are aware that our analysis of the thickness of the gut is not accurate, because we have not used markers to precisely measure at the same position in all guts and because we have not normalised the measurement position in relation to the whole intestinal tract (mainly due to technical issues).

      In relation to the fragility, we noticed that the guts of Chs2 larval escapers tended to break more easily during dissection than control guts, however, we have not been able to quantify this parameter in a reliable and objective manner.

      Since we consider that the requirement of Chs2 for PM deposition is sufficiently demonstrated, and that aspects such as gut morphology or fragility relate to the physiological requirements of the PM, which we are beginning to address as a new independent project (see our response to point 5 of Reviewer 1), we have decided to remove the sentence 'We also noticed that the guts of L3 escapers were thinner and more fragile at dissection." from the manuscript to avoid subjectivity.

      3- They state that Chs2 was able to restore accumulation of chitin in the PM of the proventriculus and the midgut. Please quantify. Additionally, does this restore the morphology of the guts (related to the comment above on the thinner guts in the absence of Chs2)?

      We have quantified the rescue of chitin deposition in the PM when Chs2 is expressed in PR cells in a Df(Chs2) mutant background. For this quantification we used the following genetic cross: PRGal4/Cyo; Df(Chs2)/TM6dfdYFP (females) crossed to UASChs2GFP or UASChs2/Cyo; Df(Chs2)/TM6dfdYFP. We selected Df(Chs2) larval escapers by the absence of TM6 (recognisable by the body shape). Among these larval escapers, we identified the presence of Chs2 in PR cells by the expression of GFP or Chs2. We found absence of chitin in the proventriculus and in the midgut in all Df(Chs2) guts that did not express Chs2 in PR cells (n=8/8 Df(Chs2)). In contrast, chitin was present in those intestinal tracts where Chs2 expression was detected in PR cells (n=8/8 PRGal4-UASChs2; Df(Chs2) guts, from 5 independent experiments). The results indicate a full rescue of chitin deposition by Chs2 expression in PR cells in Df(Chs2) mutant larvae. We have added this quantification in the text, chapter 2 (2. Chs2 deposits chitin in the PM).

      As requested by the reviewer, we have also conducted measurements to quantify gut thickness. We performed an analysis similar to the one described in point 2, this time comparing the diameter of Df(Chs2) and PRGal4-UASChs2;Df(Chs2) guts at positions 1 and 2 (see image in point 2 of Reviewer 2). Our quantifications indicated that guts were thicker when Chs2 is expressed in the PR region in Df(Chs2) larval escapers.

      As discussed in point 2, we have decided not to include these results in the manuscript, as this type of analysis requires a more comprehensive investigation.

      Scatter plots quantifying the diameter at the two different positions in Chs2 larval escapers and Chs2 larval escapers expressing Chs2 in PR cells. Bars show mean {plus minus} SD. p=p value of unpaired t test two-tailed with Welch's correction.

      4- This may be beyond the scope of this paper, but I find it interesting that the PM chitin is deposited in the proventricular lumen. Yet it forms a thin layer that lines the entire midgut? Any idea how this presumably dense chitin network gets transported throughout the midgut to line the epithelium? I imagine that this is unlikely due to diffusion, especially if they see an even distribution across the midgut. Do they see any evidence of a graded lining (i.e. is it denser in the midgut towards the proventriculus and does this progressively decrease as you look through the midgut?)?

      Insect peritrophic matrices have been classified into Type I and II (with some variations) depending on their origin (extensively reviewed in (Peters, 1992, Hegedus et al., 2019). Type I PMs are typically produced by delamination as concentric lamellae along the length of the midgut. Type II PMs, in contrast, are produced in a specialised region of the midgut that corresponds to the proventriculus and are typically more organised than Type I. In Type II PMs, distinct layers originate from distinct cell clusters in the proventriculus. It has been proposed that as food passes, it becomes encased by the extruded PM, which then slides down to ensheath the midgut. Drosophila larvae have been proposed to secrete a type II PM: through PM implantation experiments, Rizki proposed that the proventriculus is required to generate the PM in Drosophila larvae (Rizki, 1956). Our experiments confirmed this hypothesis: we show that expressing Chs2 exclusively in PR cells is sufficient to produce a PM along the midgut. Furthermore, we also show that expressing Chs2 in the midgut is not sufficient to produce a PM layer lining the midgut, at least at larval stages.

      The type II PM in Drosophila is proposed to be fully organised into four layers in the proventricular region (also referred as PM formation zone) before reaching the midgut (Peters, 1992, King, 1988, Rizki, 1956, Zhu et al., 2024). However, the mechanism by which the PM is subsequently transported into the midgut remains unclear. PM movement posteriorly is thought to depend on to the pressure exerted by continuous secretion of PM material (Peters, 1992). Early work by Wigglesworth (1929, 1930) proposed that the PM is secreted into the proventricular lumen, becomes fully organised, and is then pushed down by a press mechanism involving the aposed ectodermal/endodermal walls of the proventriculus. Rizki suggested that muscular contractions of the proventriculus walls may play a role, and that peristaltic movements of the gut add a pulling force to push the PM into the midgut (Rizki, 1956). Nevertheless, to our knowledge, the exact mechanism is still not fully understood.

      In response to the reviewer's question, the level of resolution of our analysis does not allow us to determine whether there is a graded PM lining along the midgut. However, available data using electron microscopy approaches suggest that the PM is a fully organised structure composed of four layers that is secreted and transported to line the midgut (King, 1988, Zhu et al., 2024).

      5- The authors state that expression of kkv in tracheal cells of kkv mutants perfectly restores accumulation of chitin in the luminal filaments. Is this really 100% restoration? They also reference a paper here, which may have quantified this result.

      We previously reported that the expression of kkv in tracheal cells restores chitin deposition in kkv mutants (Moussian et al,2015). However, our previous study did not quantify this rescue. As requested by the reviewer, we have now quantified the extent of the rescue.

      To perform this quantification, we used the following genetic cross:

      btlGa4/(Cyo); kkv/TM6dfdYFP (females) crossed to +/+; kkv UASkkvGFP/TM6dfdYFP (males)

      We stained the resulting embryos with CBP (to detect chitin) and GFP. GFP staining allowed us to identify the kkv mutants (by the absence of dfdYFP marker) and to simultaneously identify the embryos that expressed kkvGFP in tracheal cells (through btlGal4-driven expression). Since btlGal4 is homozygous viable, most females carried two copies of btlGal4.

      We compared the following embryo populations across 4 independent experiments:

      1. Cyo/+; kkv/kkv UASkkvGFP (kkv mutants not expressing kkv in the trachea)
      2. btlGal4/+; kkv/kkv UASkkvGFP (kkv mutants expressing kkv in the trachea) Results:

      3. Cyo/+; kkv/kkv UASkkvGFP ---- 0/6 embryos deposited chitin in trachea

      4. btlGal4/+; kkv/kkv UASkkvGFP ---- 27/27 embryos deposited chitin in trachea These results indicate complete restauration of chitin deposition in kkv mutants when kkv is expressed in tracheal cells (100% rescue).

      To further investigate whether Chs2 can compensate for kkv function in ectodermal tissues, we performed a similar quantification using the following genetic cross:

      btlGa4/(Cyo); kkv/TM6dfdYFP (females) crossed to UASChs2GFP/UASChs2GFP; kkv UASkkvGFP/TM6dfdYFP (males)

      We compared the following embryo populations across 2 independent experiments:

      1. Cyo/UASChs2GFP; kkv/kkv (kkv mutants not expressing Chs2 in the trachea)
      2. btlGal4/ UASChs2GFP; kkv/kkv (kkv mutants expressing Chs2 in the trachea) Results:

      3. Cyo/UASChs2GFP; kkv/kkv ---- 0/4 embryos deposited chitin in trachea

      4. btlGal4/ UASChs2GFP; kkv/kkv ---- 0/16 embryos deposited chitin in trachea These results indicate no restauration of chitin deposition in kkv mutants expressing Chs2 in the trachea (0% rescue).

      We have now incorporated these quantifications in the text, chapter 4 (4. Chs2 cannot replace Kkv and deposit chitin in ectodermal tissues.)

      6- They ask whether Kkv overexpression in the proventriculus can rescue Chs2 mutants... and vice versa, whether Chs2 overexpression in ectodermal cells can rescue kkv mutants. They show that kkv overexpression leads to an intracellular accumulation of chitin in the proventriculus. However, Chs2 overexpression in the trachea did not lead to any accumulation of chitin in the cells. They tailored their experiments and the associated discussion to address the hypothesis that there is potentially some difference in trafficking of these components. However, another possibility, which they have not ruled out, is that the different ability of kkv and Chs2 to produce chitin inside cells of the proventriculus and ectoderm, respectively, is potentially related to different enzymatic activities and cofactors required for chitin formation in these different cell types. Is this another potential explanation for the differences that they observe?

      We note that Kkv overexpression in any cell type (e.g. ectoderm, endoderm) consistently leads to chitin polymerisation. In ectodermal tissues, Kkv expression, in combination with Exp/Reb activity, results in extracellular chitin deposition. In the absence of Exp/Reb, Kkv expression leads to the accumulation of intracellular chitin punctae (De Giorgio et al., 2023, Moussian et al., 2015); this work). This correlates with the accumulation of Kkv at the apical membrane and presence of Kkv-containing vesicles, regardless of the presence of Exp/Reb (De Giorgio et al., 2023, Moussian et al., 2015); Figure 6, S6). In endodermal tissues, regardless of the presence of Exp/Reb, Kkv cannot deposit chitin extracellularly and instead produces intracellular chitin punctae. This correlates with a diffuse accumulation of Kkv in the endodermal cells (PR cells, or gut cells in the embryo) but presence of Kkv-containing vesicles (Figure 6, S6).

      In previous work we showed that Kkv's ability to polymerise chitin is completely abolished when it is retained in the ER. Indeed, we found that a mutation in a conserved WGTRE region leads to ER retention, the absence of Kkv-containing vesicles in the cell, and absence of intracellular chitin punctae or chitin deposition (De Giorgio et al., 2023).

      These findings indicate a correlation between Kkv subcellular localisation and chitin polymerisation/extrusion. Therefore, we hypothesise that intracellular trafficking and subsequent subcellular localisation play a crucial role in regulating Kkv activity (De Giorgio et al., 2023; this work).

      We find that Chs2 is expressed in PR cells (Figure 4) and observe that only in these PR cells does Chs2 localise apically (Fig 5A-D, S5A,B). This localisation correlates with the ability of Chs2 to deposit chitin in the PM and the presence of intracellular chitin punctae in PR cells (Fig 1F). When Chs2 is expressed in other cells types, we detect it primarily in the ER and observed no Chs2-containing vesicles (vesicles are suggestive of trafficking). This localisation correlates with the inability of Chs2 to produce intracellular chitin punctae or extracellular chitin deposition.

      Again, these results suggest a correlation between Chs2 subcellular localisation and chitin polymerisation/extrusion, aligning with the results observed for Kkv. Therefore, we hypothesise in this work that the intracellular trafficking and subsequent subcellular localisation of Chs2 play a crucial role in regulating its activity.

      Our hypothesis is consistent with seminal work in yeast chitin synthases, which has demonstrated the critical role of intracellular trafficking, and particularly ER exit, in regulating chitin synthase activity (reviewed in (Sanchez and Roncero, 2022).

      That said, we cannot exclude other explanations that are also compatible with the observed results. As pointed out by the reviewer, it is possible that Chs2 and Kkv require different enzymatic activities and/or cofactors for chitin polymerisation/deposition, which may be specific to different cell types. Indeed, we know that the auxiliary proteins Exp/Reb are specifically expressed in certain ectodermal tissues (Moussian et al., 2015). These mechanisms could act jointly or in parallel with the regulation of intracellular trafficking, or could even regulate this intracellular trafficking itself.

      Identifying the exact mechanisms controlling Kkv and Chs2 intracellular trafficking would be necessary to determine whether additional mechanisms (specific cofactors or enzymatic activities) are also involved or even serve as the primary regulatory elements.

      We have introduced these additional possibilities in the discussion section.

      7- They co-express Chs2 and Reb and show that this does not lead to chitin production or secretion. In the discussion they conclude that Chs2 does not "seem to be dependent on 'Reb' activity". I think that this statement potentially needs softening. They show that Reb is not sufficient in to induce Chs2 chitin production in cells that do not normally make a PM. However, they do not show that it is not essential in cells that normally express Chs2 and make PM.

      We fully agree with the reviewer's observation and thank her/him for pointing it out.

      As indicated by the reviewer, we show that co-expression of Reb and Chs2 in different tissues does not lead to an effect distinct from that observed with Chs2 expression alone. In addition, in the discussion we mention that we could not detect expression of reb/exp in PR cells, which aligns with the findings from Zhu et al, 2024, indicating no expression of reb/exp in the midgut cells of the adult proventriculus, as assessed by scRNAseq. We found that exp is expressed in the ectodermal cells of the larval proventriculus (Fig S4D), correlating with kkv expression in this region and cuticle deposition. These findings led us to propose that Chs2 does not seem to be dependent on Exp/Reb activity.

      However, in our original manuscript, we did not directly address whether Exp/Reb are required in the cells that normally express Chs2. As a result, we could not conclude that Chs2 relies on a set of auxiliary proteins different from Exp/Reb, and therefore a different molecular mechanism to that of Kkv in regulating chitin deposition.

      To address this specific point, we have conducted a new experiment to test Exp/Reb requirement in PR cells. We co-expressed RNAi lines for Exp/Reb in these cells and found that chitin deposition in the PM was not prevented. This further supports the hypothesis that Exp/Reb activity is not necessary for Chs2 function. We have added this experiment to Chapter 4 and Fig S3I,J.

      8- They looked at the endogenous expression pattern of kkv and Chs2 and say that they found accumulation of Kkv in the proventriculus and no accumulation in the midgut. Siimilarly, they look at the expression of Chs2 and detect it in cells of the proventriculus. Are there markers of these different cell types that they could use to colocalize these enzymes?

      We agree with the reviewer that this is an important issue and we note that Reviewer 1 also raised the same point. Therefore, we have addressed this issue.

      We obtained an antibody against Dve, kindly provided by Dr. Hideki Nakagoshi. Dve marks the endodermal region in the proventriculus (Fuss and Hoch, 1998, Fuss et al., 2004, Nakagoshi et al., 1998).This antibody worked nicely in our dissected L3 digestive tracts and allowed us to mark the endodermal region. We also obtained an antibody against Fkh, kindly provided by Dr. Pilar Carrera. Fkh marks the ectodermal foregut cells (Fuss and Hoch, 1998, Fuss et al., 2004, Nakagoshi et al., 1998). While, in our hands, this antibody performed well in embryonic tissues, we observed no staining in our dissected L3 digestive tracts. The reason for this is unclear, but we suspect technical limitations may be responsible (the ectodermal region of the proventriculus is very internal, potentially hindering antibody penetration). To circumvent this inconvenience, we tested a FkhGFP tagged allele available in Bloomington Stock Center. Fortunately, we were able to detect GFP in ectodermal cells of L3 carrying this allele. Using this approach, we conducted experiments to detect Fkh and Dve in relation to chitin accumulation in the wild type (Fig S1). In addition, we used these markers to map the expression of Kkv and Chs2 in the proventriculus (Fig 4). Our results using these endodermal/ectodermal markers confirmed the presence of a cuticle adjacent to the FkhGFP-positive cells and a PM adjacent to the PR cells, marked by Dve. Additionally, we show that Chs2-expressing cells are positive for Dve while Kkv-expressing cells are not. We could not conduct an experiment showing Kkv and Fkh co-expression due to technical incompatibilities, as we have to use GFP tagged alleles for both Kkv and Fkh to reveal their expression. However, we believe that our imaging of Dve/Kkv clearly shows that Kkv expressing cells lack Dve expression and localise in the internal (ectodermal) region of the proventriculus (Fig 4E).

      9- They overexpress Chs2 in cells of the midgut and see that it colocalises with an ER marker. They conclude that it is retained in the ER, which again, for them suggests that it has a trafficking problem in these cells. However, they are overexpressing it in these cells and this strong accumulation that they observe in the ER could simply be due to the massive expression levels. Additionally, they cannot conclude that it doesn't get out of the ER at all. They could be correct in thinking that there may be a trafficking issue, but this experiment does not conclusively show that Chs2 is entirely retained in the ER when expressed in ectopic tissues. I wonder if their interpretation needs softening or whether they should potentially address alternative hypotheses.

      The reviewer raises two distinct issues: 1) the localisation of overexpressed proteins 2) Chs2 ER retention.

      We agree that massive overexpression can lead to artifactual subcellular localisation due to saturation of the secretory pathway, causing ER accumulation. In our experiments, we overexpressed Kkv and Chs2 in different tissues (trachea, salivary glands, embryonic gut, and larval proventriculus), inducing high levels of both chitin synthases.

      For Kkv, we observed distinct subcellular localisation patterns in ectodermal versus endodermal tissues (illustrated in new Fig S6). In ectodermal tissues such as the trachea, large amounts of KkvGFP were detected, most of it localising apically. We also detected a more general KkvGFP distribution throughout the cell, including the ER, particularly at early stages. Additionally, we observed many KkvGFP-positive vesicles, reflecting exocytic and endocytic trafficking, as described previously (De Giorgio et al., 2023). The presence of these vesicles (as well as the apical localisation) indicates that KkvGFP is able to exit the ER. Indeed, our previous work demonstrated that when Kkv is retained in the ER, it does not localise apically or appear in vesicles (De Giorgio et al, 2023). In endodermal tissues, as described in our manuscript, KkvGFP did not exhibit polarised apical localisation and instead showed a diffuse pattern with some cortical enrichment. However, the presence of KkvGFP-containing vesicles still suggests that the protein is capable of exiting the ER also in these endodermal tissues.

      We observed a different subcellular pattern when we overexpressed Chs2GFP. In tissues where Chs2 is not normally expressed (e.g., trachea, salivary gland, embryonic gut), we did not detect apical or membrane accumulation (see Fig. 5,S5, S6 and response to point 4 of Reviewer #1). Nor did we observe accumulation of Chs2GFP in intracellular vesicles. Instead, Chs2GFP showed strong colocalisation with an ER marker (see Fig. 5,S5, S6 and response to point 4 of Reviewer #1). In contrast, when overexpressed in PR cells, we detected apical enrichment (Fig 5A-D, S5A,B). This indicates that despite massive expression levels, Chs2 can exit the ER in particular tissues.

      Taken together, our results strongly suggest that overexpressed Kkv can exit the ER in the different tissues analysed, whereas most Chs2GFP is retained in the ER in tissues other than PR cells. This correlates with the ability of overexpressed KkvGFP to polymerise chitin (either in intracellular puncta or deposited extracellularly depending on the presence of Exp/Reb) in all analysed tissues. Conversely, Chs2 was unable to polymerise chitin (either in intracellular puncta or extracellularly regardless of Exp/Reb presence) in tissues other than PR cells.

      Nevertheless, we acknowledge that we cannot definitively conclude that all Chs2 protein is entirely retained in the ER. We have included this caveat in our revised manuscript (Chapter 6 and Discussion section).

      Minor Comments: - No mention of Fig 3I in the results section and the order discussed in the results does not match the order in the figure.

      We apologise for these inconsistencies. We have addressed this issue in the text, figure legend, and the image order in Figure 3 and Figure S3.

      • In the results please provide some information on what the CRIMIC collection is and how it allows you to see Chs2 expression for non-experts.

      We have addressed this point in chapter 5 in the revised version, and we now provide a more detailed explanation of the CRIMIC Chs2CR60212-TG4.0 allele.

      Further details of this allele are also provided in our responses to points 5 and 9 of Reviewer 1.

      Reviewer #2 (Significance (Required)):

      Drosophila produce different types of chitinous structures that are required for either the exoskeleton of the animal or for proper gut function (peritrophic matrix). Additionally, most insects have two enzymes involved in the production of chitin and current data suggests that they have unique roles in producing either the exoskeleton or the peritrophic matrix. However, it is unclear whether their different functions are due to differences in cell type expression or differences in physiological activity of the enzymes. The authors exploit Drosophila to drive these 2 enzymes in different cell types that are known to produce the exoskeleton or the peritrophic matrix to determine whether they can functionally substitute mutant backgrounds. Their results give us a hint that these enzymes are not equivalent. What the authors were unable to address is why they are not equivalent. They hypothesise that the different physiological functions of the enzymes may be related to trafficking differences within their respective cell types. While this is an interesting hypothesis, the date are not really clear yet to make this conclusion.

      This work will be of interest to anyone interested in chitinous structures in insects and the cell biology of chitin-related enzymes.

      Literature


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      DE GIORGIO, E., GIANNIOS, P., ESPINAS, M. L. & LLIMARGAS, M. 2023. A dynamic interplay between chitin synthase and the proteins Expansion/Rebuf reveals that chitin polymerisation and translocation are uncoupled in Drosophila. PLoS Biol, 21__,__ e3001978.

      FUSS, B. & HOCH, M. 1998. Drosophila endoderm development requires a novel homeobox gene which is a target of Wingless and Dpp signalling. Mech Dev, 79__,__ 83-97.

      FUSS, B., JOSTEN, F., FEIX, M. & HOCH, M. 2004. Cell movements controlled by the Notch signalling cascade during foregut development in Drosophila. Development, 131__,__ 1587-95.

      HEGEDUS, D. D., TOPRAK, U. & ERLANDSON, M. 2019. Peritrophic matrix formation. J Insect Physiol, 117__,__ 103898.

      KING, D. G. 1988. Cellular organization and peritrophic membrane formation in the cardia (proventriculus) of Drosophila melanogaster. J Morphol, 196__,__ 253-82.

      LEBRETON, G. & CASANOVA, J. 2016. Ligand-binding and constitutive FGF receptors in single Drosophila tracheal cells: Implications for the role of FGF in collective migration. Dev Dyn, 245__,__ 372-8.

      MOUSSIAN, B., LETIZIA, A., MARTINEZ-CORRALES, G., ROTSTEIN, B., CASALI, A. & LLIMARGAS, M. 2015. Deciphering the genetic programme triggering timely and spatially-regulated chitin deposition. PLoS Genet, 11__,__ e1004939.

      NAKAGOSHI, H., HOSHI, M., NABESHIMA, Y. & MATSUZAKI, F. 1998. A novel homeobox gene mediates the Dpp signal to establish functional specificity within target cells. Genes Dev, 12__,__ 2724-34.

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      RIZKI, M. T. M. 1956. The secretory activity of the proventriculus of Drosophila melanogaster. Journal of Experimental Zoology, 131__,__ 203-221.

      SANCHEZ, N. & RONCERO, C. 2022. Chitin Synthesis in Yeast: A Matter of Trafficking. Int J Mol Sci, 23.

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      Referee #2

      Evidence, reproducibility and clarity

      Drosophila have two different chitin synthase enzymes, Kkv and Chs2, and due to unique expression patterns and mutant phenotypes, it is relatively clear that they have different functions in producing either the cuticle-related chitin network (Kkv) or the chitin associated with the peritrophic matrix (PM). However, what is unknown is whether the different functions in making cuticle vs PM chitin is related to differences in cellular expression and/or enzyme properties within the cell. The authors exploit the genetic tractability of Drosophila and their ability to image cuticle vs PM chitin production to examine whether these 2 enzymes can substitute each other. They conclude that these two proteins are not equivalent in their capacity to generate chitin. The data are convincing; however, it is currently presented in a subjective fashion, which makes it difficult to interpret. Additionally, in my opinion there is some interpretation that requires softening or alternatively interpreted.

      Major Comments:

      • While the imaging is lovely, there are some things that are difficult to see in the figures. For example, the "continuous, thin and faint 'chitin' layer that lined the gut epithelium" is very difficult to visualise in the control images. Can they increase the contrast to help the reader appreciate this layer? This is particularly important as we are asked to appreciate a loss of this layer in the absence of Chs2.
      • All the mutant analysis is presented subjectively. For example, the authors state that they "found a consistent difference of CBP staining when they compared the 'Chs2' escapers to the controls". How consistent is consistent? Can this be quantified? What is the penetrance of this phenotype? They say that the thin layer is absent in the midgut and the guts are thinner. Could they provide more concrete data?
      • They state that Chs2 was able to restore accumulation of chitin in the PM of the proventriculus and the midgut. Please quantify. Additionally, does this restore the morphology of the guts (related to the comment above on the thinner guts in the absence of Chs2)?
      • This may be beyond the scope of this paper, but I find it interesting that the PM chitin is deposited in the proventricular lumen. Yet it forms a thin layer that lines the entire midgut? Any idea how this presumably dense chitin network gets transported throughout the midgut to line the epithelium? I imagine that this is unlikely due to diffusion, especially if they see an even distribution across the midgut. Do they see any evidence of a graded lining (i.e. is it denser in the midgut towards the proventriculus and does this progressively decrease as you look through the midgut?)?
      • The authors state that expression of kkv in tracheal cells of kkv mutants perfectly restores accumulation of chitin in the luminal filaments. Is this really 100% restoration? They also reference a paper here, which may have quantified this result.
      • They ask whether Kkv overexpression in the proventriculus can rescue Chs2 mutants... and vice versa, whether Chs2 overexpression in ectodermal cells can rescue kkv mutants. They show that kkv overexpression leads to an intracellular accumulation of chitin in the proventriculus. However, Chs2 overexpression in the trachea did not lead to any accumulation of chitin in the cells. They tailored their experiments and the associated discussion to address the hypothesis that there is potentially some difference in trafficking of these components. However, another possibility, which they have not ruled out, is that the different ability of kkv and Chs2 to produce chitin inside cells of the proventriculus and ectoderm, respectively, is potentially related to different enzymatic activities and cofactors required for chitin formation in these different cell types. Is this another potential explanation for the differences that they observe?
      • They co-express Chs2 and Reb and show that this does not lead to chitin production or secretion. In the discussion they conclude that Chs2 does not "seem to be dependent on 'Reb' activity". I think that this statement potentially needs softening. They show that Reb is not sufficient in to induce Chs2 chitin production in cells that do not normally make a PM. However, they do not show that it is not essential in cells that normally express Chs2 and make PM.
      • They looked at the endogenous expression pattern of kkv and Chs2 and say that they found accumulation of Kkv in the proventriculus and no accumulation in the midgut. Siimilarly, they look at the expression of Chs2 and detect it in cells of the proventriculus. Are there markers of these different cell types that they could use to colocalize these enzymes?
      • They overexpress Chs2 in cells of the midgut and see that it colocalises with an ER marker. They conclude that it is retained in the ER, which again, for them suggests that it has a trafficking problem in these cells. However, they are overexpressing it in these cells and this strong accumulation that they observe in the ER could simply be due to the massive expression levels. Additionally, they cannot conclude that it doesn't get out of the ER at all. They could be correct in thinking that there may be a trafficking issue, but this experiment does not conclusively show that Chs2 is entirely retained in the ER when expressed in ectopic tissues. I wonder if their interpretation needs softening or whether they should potentially address alternative hypotheses.

      Minor Comments:

      • No mention of Fig 3I in the results section and the order discussed in the results does not match the order in the figure.
      • In the results please provide some information on what the CRIMIC collection is and how it allows you to see Chs2 expression for non-experts.

      Significance

      Drosophila produce different types of chitinous structures that are required for either the exoskeleton of the animal or for proper gut function (peritrophic matrix). Additionally, most insects have two enzymes involved in the production of chitin and current data suggests that they have unique roles in producing either the exoskeleton or the peritrophic matrix. However, it is unclear whether their different functions are due to differences in cell type expression or differences in physiological activity of the enzymes. The authors exploit Drosophila to drive these 2 enzymes in different cell types that are known to produce the exoskeleton or the peritrophic matrix to determine whether they can functionally substitute mutant backgrounds. Their results give us a hint that these enzymes are not equivalent. What the authors were unable to address is why they are not equivalent. They hypothesise that the different physiological functions of the enzymes may be related to trafficking differences within their respective cell types. While this is an interesting hypothesis, the date are not really clear yet to make this conclusion.

      This work will be of interest to anyone interested in chitinous structures in insects and the cell biology of chitin-related enzymes.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Chitin is a critical component of the extracellular matrix of arthropods and plays an essential role in the development and protection of insects. There are two chitin synthases in insects: Type A (exoskeletons) and Type B (for the peritrophic matrix in the gut). The study aims to investigate the specificity and mechanisms of the two chitin synthases in D. melanogaster and to clarify whether they are functionally interchangeable. Various genetic manipulations and fluorescence-based labeling were used to analyze the expression, localization, and function of Kkv and Chs2 in different tissues.

      Chs2 is expressed in the PR cells of the proventriculus and is required for chitin deposition in the peritrophic matrix. Kkv can deposit chitin in ectodermal tissues but not in the peritrophic matrix, whereas Chs2 can deposit chitin in the peritrophic matrix but not in ectodermal tissues. The subcellular localization of chitin synthases is specific to the tissues in which they are expressed. Kkv localizes apically in ectodermal tissues, whereas Chs2 localizes apically in the PR cells of the proventriculus. Altogether, Kkv and Chs2 cannot replace each other. The specificity of chitin synthases in D. melanogaster relies on distinct cellular and molecular mechanisms, including intracellular transport pathways and the specific molecular machinery for chitin deposition. Congratulations on this incredible story and manuscript, which is straightforward and well-written. However, I have some comments that may help to improve it.

      Major comments:

      1. Funny thing: the Chs2 mutant larva shows a magenta staining below the chitin accumulation of the esophagus, which looks like a question mark in 1H but cannot be found in control. Is that trachea reaching the pv?
      2. Also, though it is evident that the PM chitin is lost in Ch2 mutants, could it be that the region is disturbed and cells express somewhere else chitin? There are papers by Fuß and Hoch (e.g., Mech of Dev, 79, 1998; Josten, Fuß et al., Dev. Biol.267, 2004) using markers such as Dve, Fkh, Wg, Delta, and Notch, etc. for precisely marking the endodermal/ectodermal region in the embryonic foregut/proventriculus. It would be beneficial to show, along with chitin and Chs expression patterns, the ectoderm/endoderm cells. This is particularly important as the authors report endodermal expression of Chs2 in embryos but don't use co-markers of the endodermal cells.
      3. The origin of midgut chitin accumulation is unclear. Chitin can come from yeast paster. Can the authors check kkv and chs2 mutants for food passage and test starving L1 larvae to detect chitin accumulation in the midgut without feeding them?
      4. Subcellular localization assays require improved analysis, such as a co-marker for the apical membrane and statistical analysis with co-localization tools, showing the overlap at the membrane and intracellularly with membrane co-markers and KDEL.

      Minor comments:

      1. The authors used "L3 larval escapers." It would be interesting to know if the lack of Chs2 and the peritrophic matrix cause any physiological defects or lethality.
      2. The order identifiers are missing for materials and antibodies, e.g., anti-GFP (Abcam), but Abcam provides several ant-GFP; which was used? Please provide order numbers that guarantee the repeatability for others.
      3. Figure S5C, C', what marks GFP (blue) in the trachea? Maybe I have overlooked the description. What is UASsrcGFP? What is the origin of this line?
      4. The authors claim that they validated the anti-Chs2 antibody. However, they show only that it recognizes a Cht2 epitope via ectopic expression. For more profound validation, immune staining is required in deletion mutants, upon knockdown, or upon expression of recombinant proteins, which is not shown.
      5. The legend and text explaining Fig. 4 D-E' can be improved. The authors used the Crimic line, which is integrated into the third ("coding") intron. This orientation can lead to the expression of Gal4 and cause a truncated version of the protein (according to Flybase). Is Chs2 expression reduced in the crimic mutant? If the mutation causes expression of a truncated version, the Chs2 antibody may not be able to detect it as it recognizes a fragment between 1222 and 1383 aa? Also, I'm unsure whether the Chs2 antibody or GFP was used to detect expression in PR cells. The authors describe using Ch2CR60212>SrcGFP together with Chs2+ specific antibodies.

      Referees cross-commenting I fully agree with the comments of Rev#2

      Significance

      The manuscript's strength and most important aspects are the genetic analysis, expression, and localization studies of the two Chitin synthases in Drosophila embryos and larvae. However, beyond this manuscript, the development of mechanistic details, such as interaction partners that trigger secretion and action at the apical membranes and the role of the coiled-coil domain, will be interesting.

      The manuscript uses "first-class" genetics to describe the different roles of the two Chitin synthases in Drosophila, comparing ectodermal chitin (tracheal and epidermal chitin) with endodermal (midgut) chitin. Such a precise analysis has not been investigated before in insects. Therefore, the study deeply extends knowledge about the role of Chitin synthases in insects.

      The audience will specialize in basic research in zoology, developmental biology, and cell biology regarding - how the different Chitin synthases produce chitin. Nevertheless, as chitin is relevant to material research and medical and immunological aspects, the manuscript will be fascinating beyond the specific field and thus for a broader audience.

      I'm working on chitin in the tracheal system and epidermis in Drosophila.

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      Reply to the reviewers

      Dear Editor,

      Thank you for reviewing our article. We are happy to see that the reviewers are positive on our manuscript. We have tried to address nearly all their comments. Find below a point-by-point answer.

      With best regards,

      Bruno Lemaitre and Asya Dolgikh

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

      This work defines NimB1 protein as a PS binding bridging molecule but with a negative regulatory role in efferocytosis. Specifically, the authors demonstrate via a variety of genetic, cell biological, and other approaches that loss of NimB1 leads to Drosophila macrophages being more adherent to apoptotic targets and engulf them more robustly. The authors also nicely demonstrate that the function of NimB1 differs from NimB4, and the double mutant demonstrating PS-binding yet, distinct roles. Further, the authors show that NimB1 does not affect bacterial phagocytosis.

      Overall, this is a well-done study. The authors have already done a very thorough job addressing the key points and I congratulate the authors.

      My only minor comment is that the authors could try to make the comment better about whether or not such a 'negative regulatory' bridging molecules may exist in other species, and particularly mammals. If so, this is quite novel. The authors refer to CD47 but this is a membrane protein. The other minor comment is whether the authors ever tried express the PS binding domains as a fusion protein - this would provide a more direct evidence for the binding to PS (although the authors do competitive inhibition with Annexin V). This could be commented upon although testing this is not necessary if they have not already done so.

      We greatly appreciate the reviewer’s positive feedback. In the revised manuscript, we have now included a more detailed discussion of mammalian proteins with analogous roles, specifically referencing Draper isoforms (I and II), the CD300 receptor family, and surfactant proteins A and B (see page 16).

      Reviewer #1 (Significance (Required)):

      The identification of the negative regulator bridging protein NimB1 is novel and could be broadly interesting to those studying efferocytosis.

      Regarding the suggestion to overexpress just the putative PS-binding domain of NimB1, we agree this could strengthen the evidence for its PS-binding function. However, generating a new transgenic fly line would require significant additional time. Moreover, the presence of a PS-binding motif was also proposed in the recent study on Orion (Ji et al., 2023), which we have cited in our manuscript. The Orion binds PS through a conserved RRY motif. This motif is critical for Orion’s ability to directly interact with PS and facilitate its secretion. Mutagenesis experiments disrupting the RRY motif—specifically substituting arginine residues with alanines—abolished Orion’s PS-binding capacity, demonstrating the essential role of this sequence. Functional assays also validated that Orion competes with Annexin V, a well-established PS-binding protein, for access to PS-exposing surfaces (Ji et al., 2023).

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      In this study, Dolgikh and colleagues propose a first investigation about the role of the drosophila Nimrod protein NimB1. Although the role of several members of the family in phagocytosis has been explored, the function of Nimrod type B proteins is less addressed. Within silico analysis, they first see a strong similarity between NimB1 and NimB4. They show that NimB1 is primarily expressed in phagocytes, and as NimB4 can bind phosphatidylserines (PS), leading to a possible shared role in efferocytosis. Using transgenic and null drosophila models, the authors then compare the impact of NimB1 overexpression or deficiency. They compare the effects shown to NimB4 and Draper deficient lines, as these two proteins were previously shown to play a role in efferocytosis. They propose that NimB1 is a secreted protein that binds apoptotic cells. They show that NimB1 deficiency changes the adhesion properties of macrophages. The major finding is that NimB1 delays the early stages of efferocytosis, contrary to NimB4 and Draper that on the contrary facilitate efferocytosis. In contrast, the authors propose that NimB1 increases the formation of phagosomes.

      We appreciate the reviewer’s acknowledgment that our key discovery centered around NimB1 functioning as a negative regulator of efferocytosis. This finding highlights NimB1’s distinct role compared to NimB4 and Draper, which instead promote the process.

      Major comments:

      One of the major technical challenges here was to generate models to allow the detection of the protein in cellulo and in vivo. Although the results are convincing in transgenic lines NimB1 expression is driven by the endogenous promoter, one could still argue that the GFP tags would lead to changes in the localization of the protein.

      We understand the concern regarding potential localization changes introduced by GFP tags. However, in previous studies, the same fosmid construct was applied to NimB4-sGFP, and produced a distinctly different expression pattern—NimB4-sGFP expression was more pronounced and clearly present in the glial cells in the brain (Petrignani et al, 2021: Figure EV1A). The fact that the NimB1-sGFP and NimB4-sGFP fosmids localized to different tissues suggests that possible any mis-localization changes due to the GFP tag do not override localization properties intrinsic to the proteins.

      In line with the previous comment, to show that NimB1 is a secreted protein, the authors use an overexpression model. How to be sure, that overexpression itself does not lead to increased secretion, or shedding from the membrane?

      The observation that uas-NimB1-RFP accumulates in the nephrocytes upon Lpp-Gal4 (fat body) expression, and the presence of a signal peptide suggests that this protein can be secreted.

      We cannot exclude that in endogenous condition, NimB1, remains attached to hemocytes. We have confirmed that the Lpp driver is not expressed in nephrocytes.

      Would an experiment with a control consisting in a known protein secreted by macrophages lead to the same staining pattern (positive control)? Could another methodology like a Western Blot on supernatants from hemocyte cell culture (over)expressing NimB1, with an anti-RFP staining, be envisaged?

      We have already performed similar experiment with other secreted proteins such as NimB4-GFP (Petrignani et al., 2021: Figure: 1B). In the revised article, we have added Viking-RFP as a positive control of a secreted protein (Figure S1F). Figure S2 shows a Western blot with hemolymph extract. We detected NimB1-RFP at its expected molecular weight of 44 kDa, verifying that is present into the hemolymph (Supplementary Document S2 C).

      It sems counterintuitive that phagocytes from Draper and NimB4 null mutants with defects in efferocytosis show increased load of apoptotic cells (Figure 6C and D in both unchallenged and injury condition). Do the authors have precedent data to cite going to the same direction? Are cell debris engulfed but not degraded efficiently?

      The observation that Draper and NimB4 null mutants have an increased load of apoptotic cells has already been reported in the literature. Several studies have now shown that Draper is not always required for the initial uptake of apoptotic corpses but is critical for phagosome maturation (Meehan et al., 2016; Serizier et al., 2022; Serizier & McCall, 2017). In our article on NimB4 (Petrignani et al., 2021), we have previously shown that the accumulation of immature phagosomes that are not properly eliminated indirectly impairs the uptake of new apoptotic corpses. This explains why efferocytosis is then impaired only at late time points, when unresolved phagosomes have accumulated to the threshold that prevents further phagocytosis.

      In Figure 6D it seems indeed that NimB4, NimB1/NimB4 and Draper mutants do not accumulate more apoptotic material upon injury. However, levels for NimB4 is close to the one obtained with NimB1 mutants. Is it statistically true? If yes, what could be the reason for this similarity? In any case, as some important conclusion relies on the comparison between UC and injury conditions, adequate statistics and representations could be proposed.

      We thank the reviewer for this pertinent observation and the opportunity to clarify. In the unchallenged (UC) condition, NimB4sk2 and draperΔ5 mutants indeed exhibit significantly elevated levels of apoptotic cell (AC) content in macrophages compared to wild-type and NimB1 mutant genotypes (****p crimic and NimB1229/NimB1crimic* mutants show significantly lower levels in the UC condition, consistent with a role for NimB1 in early recognition or regulation of phagocytic initiation, not in corpse degradation.

      In contrast, upon injury (90 minutes post-challenge) we observe a statistically significant increase in apoptotic material in NimB1 mutants compared to UC hemocytes of the same genotype (****p sk2 and draperΔ5* mutants between the UC and 90 min conditions (ns for NimB4). This is consistent with their known defect in corpse degradation, which results in saturation of phagocytic capacity at baseline, and an inability to respond further upon challenge with apoptotic cells.

      While the absolute levels of apoptotic material in injured NimB1 and UC NimB4 mutants appear similar at first glance, statistical testing confirms that they are significantly different. NimB4 mutant macrophages retain apoptotic debris due to defective degradation, whereas NimB1 mutants have an increase in newly acquired apoptotic content due to enhanced uptake.

      Additionally, NimB161, NimB4sk2 double mutants display a partial increase in apoptotic load upon injury (****p To directly address the reviewer’s suggestion, we have now recalculated and visualized key comparisons with appropriate statistical testing, as shown in Revision Figure 1. All statistical analyses were conducted using unpaired two-tailed Student’s t-tests. This additional figure allows clearer evaluation of genotype-specific differences at both baseline and post-injury conditions and supports our conclusions that NimB1 and NimB4 regulate distinct stages of phagocytosis. We have also clarified the text to better explain that both NimB4 and Draper mutants accumulate unresolved apoptotic material under baseline conditions, and do not accumulate further material upon challenge, due to a block in phagosome maturation.

      Revisions Figure 1.

      __Quantification of phagocytic events in wild-type and mutant macrophages under unchallenged and post-injury conditions __

      (A) Comparison of phagocytic events per frame in w1118 (wild-type), NimB1crimic, NimB1229/NimB1crimic, NimB4sk2, NimB161,NimB4 sk2, and draperΔ5 larvae under unchallenged conditions (UC) and 90 minutes after injury (90 min). Data are presented as individual data points with means. Statistical significance was determined using Student's t-test (*P (B) Direct comparison of phagocytic events between NimB1crimic (red) and NimB4sk2 (gray), and between NimB1229/crimic (dark red) and NimB4sk2 (gray) under both unchallenged (UC) and post-injury (90 min) conditions.

      The authors claim with analyses of Figure 8C and D, that NimB1 mutants show acidic vehicles normal in size and fluorescence intensity. However, statistical differences are still observed compared to control condition, which is also seen in representative images shown.

      In Figure 8C and D, we provide two quantitative measures to clarify the size and intensity of acidic vesicles. First, we show that mean fluorescence in hemocytes is elevated for all NimB and draper mutants compared to wild type, indicating an overall increase in internalized material. However, we also quantified the number of vesicles per hemocyte and found that NimB1 mutants exhibit significantly more vesicles. Despite this increase, the representative images do not show an obvious enlargement of individual vesicles, suggesting that while more material is being taken up, the vesicles themselves are not enlarged. The enlarged vesicles in case of NimB4 or draper mutant would result from the unresolved cargo (Petrignani et al., 2021). This distinction underscores that higher fluorescence values reflect increased cargo internalization, rather than the larger vesicular structures that result from impaired degradation as in NimB4 or draper mutants.

      Minor comments:

      In figure 2D, what allows to say the expression is restricted in macrophages? Is it the colocalization with SIMU being a macrophage-specific marker?

      In Figure 2D, we relied on SIMU as a macrophage-specific marker in Drosophila embryos to determine that NimB1 expression is restricted to macrophages. Previous research has demonstrated that SIMU is predominantly expressed in embryonic macrophages (where it is essential for apoptotic cell clearance) (Kurant et al., 2008; Roddie et al., 2019). Consequently, the colocalization of NimB1 signal with SIMU-positive cells strongly indicates that NimB1 is confined to macrophages during this developmental stage.

      In figure S3B and C, it appears that double NimB1/NimB4 mutants exhibit less spreading than single ones (especially NimB4). Is it the case (statistical significance). If yes what could be the explanation?

      Yes, the double NimB1, NimB4 mutants exhibit higher number of hemocytes and significantly reduced cell spreading compared to single mutants. The phenotype is similar to NimC1, eater double mutants (Melcarne et al., 2019) which also show higher number of hemocytes, reduced cell spreading and also diminished capacity to phagocytose apoptotic cells (and, in the case of NimC1, Eater, bacteria as well) (Melcarne et al., 2019). A likely explanation lies in impaired membrane remodeling critical for pseudopod extension and phagosome formation. Studies have shown that defects in actin polymerization or membrane tension can hinder pseudopod extension, reducing phagocytic efficiency (Lee et al., 2007; Masters et al., 2013). Same for the decreased ability of these mutants to form filopodium, a process essential for effective target engagement and engulfment. Filopodia play a significant role in capturing particles and directing them toward the macrophage body for engulfment (Horsthemke et al., 2017). Disruptions in these pathways lead to reduced phagocytic efficiency and a more rounded macrophage morphology, as the cells fail to spread properly (Horsthemke et al., 2017; Lillico et al., 2018). Other than these general observations, we do not have an explanation as to why NimB1, NimB4 double mutants specifically show a higher number of hemocytes and reduced cell spreading.

      Several graphs are identical between figure 4 and S4. It is probably not useful and complicates reading.

      We agree that duplicating these graphs complicates the presentation. Therefore, we have removed the redundant graphs in the supplementary materials, ensuring the data are shown only once to maintain clarity and ease of reading

      As TEM images shown in Figure 8B do not lead to quantitative data, I would put it as supplementary file.

      We agree that the TEM images in Figure 8B do not provide strictly quantitative data. To streamline the main manuscript, we have relocated these images to the supplementary section in the revised version

      Reviewer #2 (Significance (Required)):

      This study uses several approaches and models to address the role of NimB1 in efferocytosis. Both In Vitro and In Vivo approaches are proposed. They give insight into the role of this protein with unknown function so far. Some statistical analysis could be performed to improve the clarity of conclusions. One of the important aspects is the secreted nature of NimB1.However, additional approaches could be proposed to confirm this.

      Basic immunologists and cell biologists would be interested in reading this article that highlights the delicate equilibrium between pro and anti-efferocytosis molecules.

      I am an immunologist/cell biologist with expertise in lysosomal catabolism. As I work on mouse models or Human samples, my mastering of drosophila as a model is limited.

      We thank the reviewer for the positive evaluation of our work. In this revision, we have added further detail to clarify the properties of NimB1 as a secreted protein and strengthen our data presentation. By providing additional clarity on methods and interpretations, we hope immunologists and cell biologists—including those who do not routinely work with Drosophila—will find our findings more accessible.

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

      This paper investigates the role of NimB1, a secreted member of the Nimrod family in Drosophila, in the process of efferocytosis, the clearance of apoptotic cells by macrophages. Previous studies have identified NimB4, another secreted Nimrod protein, as a positive regulator of efferocytosis, enhancing both apoptotic cell binding and phagosome maturation. In contrast, the authors propose that NimB1 functions as a negative regulator, slowing down the early stages of apoptotic cell binding and internalization. This regulatory balance is suggested to fine-tune efferocytosis to maintain homeostasis.

      The primary aim of this study was to characterize the function of NimB1 to better understand the roles of proteins within the NimB family.

      This study identifies a novel function for NimB1 in modulating the early stages of efferocytosis, adding to our understanding of how Nimrod proteins fine-tune apoptotic cell clearance. The authors establish a clear phenotypic contrast between NimB1 and NimB4, which provides a compelling framework for understanding how positive and negative regulators coordinate phagocytosis. It also highlights the multiple roles of the secreted members of the Nimrod scavenger receptor family, which have remained so far poorly investigated.

      This is an interesting study that could be strengthened by additional validation and broader experimental support. As the authors point out in the discussion, it is known that PS bridging molecules contribute to phagocytosis and that the contribution of positive and negative players finely tune phagocytosis in flies and mammals. Clarifying the mode of action of NimB1 in those processes would higher the impact of this interesting piece of work. For example, does NimB1 interact with NimB4 and if so, what is the role of this interaction? How does NimB1 integrate in the signaling cascade that allows scavenger receptors to bind PS? Does it act similar to Orion by enhancing the PS binding of a scavenger receptor?

      Key Findings • NimB1 and NimB4 are structurally similar, as supported by AlphaFold2 modeling, suggesting functional relatedness. • NimB1 is expressed in macrophages, secreted into the hemolymph, and binds apoptotic cells in a phosphatidylserine (PS)-dependent manner. • NimB1 is induced by challenge. • NimB1 mutants display a hyper-phagocytic phenotype, with faster recognition and internalization of apoptotic cells. • NimB1 loss enhances macrophage adhesion and actin remodeling, while bacterial phagocytosis remains unaffected, suggesting a specific role in apoptotic clearance. • NimB1 acts early in the phagocytic process, while NimB4 functions at later stages, particularly in phagosome maturation.

      We thank the reviewer for their positive assessment and are pleased that our findings identify NimB1 as a novel secreted negative regulator of efferocytosis, underscoring a greater level of regulatory complexity in apoptotic cell clearance.

      Unfortunately, attempts to produce functional NimB1 protein were not successful, limiting our ability to address some of the reviewer’s suggestions experimentally. Despite these challenges, the evidence we present—particularly from our genetic assays—clearly indicates that NimB1 exerts an inhibitory influence during the early steps of apoptotic cell binding, distinguishing it from the late-stage promoting function of NimB4.

      Major comments:

      Figure 1: AlphaFold is a valuable tool for generating hypotheses, however predicted structures should not be presented as definitive evidence of similarity, particularly without complementary experimental validation. This section would be stronger if the structural predictions were explicitly framed as predictions. In the absence of such data, the interpretation should be toned down.

      We agree with the reviewer and we have now framed our observation as prediction and toned down our interpretation. We also note that the similarities between NimB4 and NimB1 are also underlined by the phylogenetic analysis and expression pattern.

      Figure 2DE: Given its basal level in homeostatic conditions, it would have been useful to look at the NimB1-GFP upon challenge. Also, the authors show only a single larval macrophage with no comparison point. To strengthen this result, the authors could include another protein quantification method, such as western blotting. Alternatively, labelling of NimB1>UASmRFP in embryo that present the highest expression levels would also strengthen this result.

      Unfortunately, we cannot currently perform additional experiments on embryos within the scope of this project because those experiments were performed by our collaborators in Haifa (Estee Kurant Lab). Repeating them would require sending the lines to their lab and accommodating their experimental schedule and manpower constraints.

      In supplementary Figure S1F: the authors overexpress NimB1-RFP using the fat body driver Lpp-Gal4 and show larvae with RFP in the nephrocyte. Could filet preparations be shown? Could the authors present evidence that the Lpp driver is not expressed in the nephrocytes (or refer to literature)?

      The Lpp-Gal80 driver is described as fat body-specific and has been used to manipulate gene expression in the fat body in many other studies. We have checked Lpp-Gal80>UAS-GFP expression in larvae and did not observe expression in larval nephrocytes. The whole live larvae were observed under the microscope with no prior filet preparations. To provide the evidence that Lpp is not expressed in the nephrocytes we are providing the images of the whole larvae expressing GPF from the Lpp, as per genotype: Lgg-Gal80>UAS-GFP (see below, Revisions Figure 2).


      Revisions Figure 2.

      __Expression pattern of Lpp-Gal80>UAS-GFP in Drosophila larvae __

      Representative fluorescence microscopy images showing GFP expression driven by the Lpp-Gal80 system in Drosophila larvae. The images display dorsal (top) and ventral (bottom) views of the same larva, demonstrating the pattern of expression throughout the fat body tissue. Green fluorescence indicates cells expressing the GFP reporter under the control of the Lpp promoter, which is predominantly active in the larval fat body.

      The results on the increased number of hemocytes observed in the double NimB1, NimB4 mutant animals (Figure S3A) remains not only disconnected from the rest of the data but also unexplained. Providing a mechanistic view may require a significant amount of work that may indicate an additional role of the two NimBs but will not add to our understanding of the role of NimB1 in phagocytosis. Nevertheless, it would be at least useful to know whether in the double mutant the lymph gland is still intact, as its precocious histolysis could account for the elevated number of hemocytes. If that were the case, that could indicate that lacking the two NimBs triggers an inflammatory state that affects the lymph gland, meaning that the pathway controlling phagocytosis also has a systemic impact on development. When checking the representative Figure S4D, it seems that very large cells are present in the double mutants, even larger than in the single mutants. These could be (pre)lamellocytes, which constitute activated hemocytes, known to impact the status of the lymph gland. If the enhanced number of hemocytes does not depend on lymph gland histolysis, a simple immunolabeling with the anti-PH3 antibody would assess the proliferative phenotype of the double mutant hemocytes. At least this piece of data would provide a better explanation for the observed phenotype.

      We thank the reviewer for this interesting comment. We cannot explain why NimB1, NimB4 double mutants have more hemocytes. It is unclear to us if this is a secondary consequence of defects in efferocytosis or linked to another function of these two NimBs, such as a role in adhesion. We did look at the lymph gland and our preliminary observations suggest that NimB1, NimB4 double mutants have an easily ruptured or fragile lymph gland, which could explain the higher number and the roundish shape of hemocytes in circulation as proposed by the reviewer. Lacking expertise on lymph gland, we prefer not to include this data, as they are not central to the main message of this article on role of NimB1 on efferocytosis. We have also noted the presence of lamellocytes in unchallenged NimB1, NimB4 double mutant larvae, as well as excessive lamellocyte production compared to controls upon clean injury (see below, Revisions Figure 3). We have mentioned the presence of lamellocytes in NimB1, NimB4 double mutants in the revised version. We prefer not include this new data directly in the article because this not central to the main message of the article.


      __Revisions Figure 3. __

      A.

      B.

      Lamellocyte recruitment following a clean injury in L3 Drosophila larvae:

      (A) Quantification of lamellocytes per 50 frames of x63 microscopy lens in w1118 (wild-type), NimB1crimic, NimB4sk2, NimB161, NimB4sk2, and draperΔ5 larvae under unchallenged conditions (UC) and 3 hours after clean injury (3h). Arrowheads indicate lamellocytes.

      (B) Representative confocal microscopy images of hemocytes isolated from challenged NimB161, NimB4sk2 larvae. Cells were fixed and stained with Phalloidin (green) to label F-actin and DAPI (blue) to visualize nuclei. The smaller inset (40x magnification) shows a detailed view of individual lamellocytes with characteristic morphology, while the larger field (20x magnification) displays the overall view on the hemocytes. Scale bar = 50 μm.

      Figure 6: The connection between the ex-vivo (Figure 5) and in vivo (Figure 6) assays should be clarified. In the first type of assay, the lack of NimB4 results in reduced internalization (while lack of NimB1 enhances it). In the in vivo assay, more fragments are seen within the cell (hence internalized), using the NimB4 mutant. Also, in the ex-vivo assay, the lack of NimB1 does not affect the first steps ('attachment' and 'membrane'), while NimB4 does, yet it is proposed that NimB1 acts in the early steps (page 11-12). In that case, wouldn't we expect the double mutant NimB1 NmB4 to have the NimB1 phenotype?

      The apparent discrepancy between our ex vivo and in vivo assays reflects the different methodologies and what each assay measures. In the ex vivo assay (Figure 4), we add exogenous fluorescently-labeled apoptotic cells to measure new engulfment events. Here, NimB4 mutant macrophages show reduced phagocytic index because they are already saturated with unresolved phagosomes, limiting their capacity to uptake additional corpses, as previously described by (Petrignani et al., 2021). This reduced uptake capacity is reflected in the decreased phagocytic index observed.

      In contrast, our in vivo assay (Figure 6) uses DAPI staining to visualize all internalized material, including previously engulfed debris. As expected, we observe accumulation of DAPI signals in NimB4 mutant macrophages under unchallenged conditions, reflecting their inability to process and clear phagosomes rather than enhanced engulfment. This phenotype highlights the role of NimB4 in phagosome maturation rather than initial uptake.

      Regarding the role of NimB1 in early phagocytic steps, while attachment and membrane measurements in the ex vivo assay don't show significant differences in NimBcrimic mutants individually, our other experiments demonstrate that NimB1 functions as a negative regulator during early recognition phases. The predominance of the NimB4 phenotype in the NimB1crimic, NimB4 double mutant parallels observations in draper mutants, where double mutants lacking both Draper I (positive regulator) and Draper II (negative regulator) display the Draper I phenotype (Logan et al., 2012). This suggests that phagosome maturation defects (the NimB4 phenotype) present a more severe bottleneck in the phagocytic process than enhanced early uptake (the NimB1crimic phenotype), explaining why the double mutant primarily exhibits accumulation of unresolved phagosomes rather than accelerated uptake. We have re-written this part of the article to clarify these points (see page 11).

      Figure 8A: a definition of the phagocytic cup mentioned in the text (page 12, 2nd paragraph) as well as the homogenization of the scale bars in Figure 8A would clarify the interpretation of Figure 8A. The structures shown for w1118 seem quite distant from the structures highlighted for NimB1crimic.

      According to reviewer 2, we have now moved this figure to the supplement. The reviewer is correct and we have modified the associated text to clarify the interpretation of the images (see page 12-13).

      The same scale should be used across different panels in Figure 8. This is particularly important since the authors mention the size of the lysotracker vesicles to conclude on their levels of maturity. This data and conclusions would be strengthened by a quantification of the vacuole sizes and the combination with markers of phagosome/lysosome maturation levels. It would help disentangling the complementary roles of NimB1 and NimB4.

      The scale bar has been homogenized.

      Minor comments:

      Figure 2BC: is there a particular reason to shift from Rp49 to Rpl32 as normalizing gene in Figure 2B and C? This prevents the comparison of NimB1 expression levels across the different graphs.

      We thank the reviewer for highlighting this point. We changed the housekeeping gene from Rp49 to RPL32 in Figure 2C to unify the normalization strategy across all experiments and allow comparisons throughout the manuscript.

      Page 9, 2nd paragraph and Figure S3C: the authors mention "Actin structure revealed an increased ratio of filopodia to lamellipodia across all mutants". A clear definition of the parameters defining filopodia and lamellipodia is required to fully appreciate the meaning of the ratio.

      We thank the reviewer for the comment. To address this comment, we have included a clear definition of the parameters used to distinguish filopodia and lamellipodia on page 9. In particular, in the revised version we now specify that filopodia were defined as thin, spike-like actin-rich protrusions, while lamellipodia were defined as broad, sheet-like structures at the cell periphery. These criteria were applied consistently for quantification.

      Figure S5B: a bar is missing in the right graph (% of cells containing AC, NimB1>UAS-NimB1-RFP). Page 10 2nd paragraph. The authors mention "draper mutants displayed impaired apoptotic cell binding and engulfment" referring to Figure 4. Figure S4 provide a more convincing illustration of this statement, since the decreased phagocytic index in Drpr KO is mostly due to less cells phagocytosing and not less material phagocytosed.

      We thank the reviewer for the careful examination. In Figure S5B, the missing bar was due to its color being too close to the background color, making it difficult to distinguish. We have now corrected this by adjusting the color to ensure it is clearly visible.

      Regarding the comment on page 10, we agree that Figure S4 more clearly illustrates the impaired apoptotic cell binding and engulfment observed in draper mutants, particularly through the reduced percentage of hemocytes engaging in phagocytosis. We have now clarified the statement in the text to ensure consistency and to guide the reader appropriately to Figure S4 (10).

      Figure 6: not easy to distinguish the DAPI labelling relative to the nucleus vs. that of apoptotic fragments.

      This is a good point. We have changed the images for clearer demonstration of the DAPI labelling. See Figure 6.

      Figure 7B: the number of cells used to generate the violin plot should be indicated in the legend or the method section.

      We have mentioned the number of cells used in the quantification (n-50 per genotype) in the figure legend.

      A schematic figure recapitulating the data would help

      We have added a schematic figure recapitulating the data. See Figure 9 with associated text.

      Page 11 last line: homeostatic rather than hemostatic.

      Thank you for this comment. We have changed it.

      Reviewer #3 (Significance (Required)):

      This study identifies a novel function for NimB1 in modulating the early stages of efferocytosis, adding to our understanding of how Nimrod proteins fine-tune apoptotic cell clearance. The authors establish a clear phenotypic contrast between NimB1 and NimB4, which provides a compelling framework for understanding how positive and negative regulators coordinate phagocytosis. It also highlights the multiple roles of the secreted members of the Nimrod scavenger receptor family, which have remained so far poorly investigated.

      This is an interesting study that could be strengthened by additional validation and broader experimental support. As the authors point out in the discussion, it is known that PS bridging molecules contribute to phagocytosis and that the contribution of positive and negative players finally tune phagocytosis in flies and mammals. Clarifying the mode of action of NimB1 in those processes would higher the impact of this interesting piece of work. For example, does NimB1 interact with NimB4 and if so, what is the role of this interaction? How does NimB1 integrate in the signaling cascade that allows scavenger receptors to bind PS? Does it act similar to Orion by enhancing the PS binding of a scavenger receptor?

      We thank the reviewer for the insightful comments and suggestions. Indeed, understanding the mode of action of NimB1 in the regulation of efferocytosis would significantly strengthen the impact of our findings. Our data, supported by structural and phylogenetic analyses, indicate that NimB1 and NimB4 share a conserved phosphatidylserine (PS)-binding motif, suggesting that these proteins may interact functionally. Preliminary biochemical observations, together with structural predictions, raise the possibility of a direct or indirect interaction between NimB1 and NimB4, although this remains to be experimentally confirmed.

      Our observations from NimB1 and NimB4 double mutants reveal that the phenotype closely resembles that of NimB4 single mutants, indicating that NimB4 plays a dominant role in the downstream maturation steps of phagosome processing. These findings are consistent with a model in which NimB1 may modulate early phagocytic uptake, possibly by competing with NimB4 for PS binding or by limiting NimB4 accessibility to apoptotic cells, thereby fine-tuning the rate of efferocytosis.

      Regarding the integration into the signaling cascade, while NimB1 and Orion both recognize PS, our data suggest that they function through distinct mechanisms. Orion enhances PS binding to Draper receptor isoforms to promote apoptotic corpse recognition. In contrast, NimB1 appears to act as an inhibitory modulator, potentially masking PS or limiting receptor engagement, thus slowing the phagocytic response. Further functional studies, including receptor-binding assays, will be important to determine whether NimB1 acts by altering receptor-ligand interactions or through a different regulatory pathway.

      Future experiments investigating the potential direct interactions between NimB1 and NimB4, their respective affinities for PS, and their influence on phagocytic receptor dynamics will be necessary to better understand NimB1’s precise mode of action. Such studies will help clarify how secreted regulators fine-tune efferocytosis in Drosophila and may offer broader insights into conserved principles of phagocytic regulation across species.

      __ __

      List of References:

      Horsthemke, M., Bachg, A. C., Groll, K., Moyzio, S., Müther, B., Hemkemeyer, S. A., Wedlich-Söldner, R., Sixt, M., Tacke, S., Bähler, M., & Hanley, P. J. (2017). Multiple roles of filopodial dynamics in particle capture and phagocytosis and phenotypes of Cdc42 and Myo10 deletion. The Journal of Biological Chemistry, 292(17), 7258–7273. https://doi.org/10.1074/jbc.M116.766923

      Ji, H., Wang, B., Shen, Y., Labib, D., Lei, J., Chen, X., Sapar, M., Boulanger, A., Dura, J.-M., & Han, C. (2023). The Drosophila chemokine–like Orion bridges phosphatidylserine and Draper in phagocytosis of neurons. Proceedings of the National Academy of Sciences, 120(24), e2303392120. https://doi.org/10.1073/pnas.2303392120

      Kurant, E., Axelrod, S., Leaman, D., & Gaul, U. (2008). Six-Microns-Under Acts Upstream of Draper in the Glial Phagocytosis of Apoptotic Neurons. Cell, 133(3), 498–509. https://doi.org/10.1016/j.cell.2008.02.052

      Lee, W. L., Mason, D., Schreiber, A. D., & Grinstein, S. (2007). Quantitative Analysis of Membrane Remodeling at the Phagocytic Cup. Molecular Biology of the Cell, 18(8), 2883–2892. https://doi.org/10.1091/mbc.E06-05-0450

      Lillico, D. M. E., Pemberton, J. G., & Stafford, J. L. (2018). Selective Regulation of Cytoskeletal Dynamics and Filopodia Formation by Teleost Leukocyte Immune-Type Receptors Differentially Contributes to Target Capture During the Phagocytic Process. Frontiers in Immunology, 9. https://doi.org/10.3389/fimmu.2018.01144

      Masters, T. A., Pontes, B., Viasnoff, V., Li, Y., & Gauthier, N. C. (2013). Plasma membrane tension orchestrates membrane trafficking, cytoskeletal remodeling, and biochemical signaling during phagocytosis. Proceedings of the National Academy of Sciences, 110(29), 11875–11880. https://doi.org/10.1073/pnas.1301766110

      Meehan, T. L., Joudi, T. F., Timmons, A. K., Taylor, J. D., Habib, C. S., Peterson, J. S., Emmanuel, S., Franc, N. C., & McCall, K. (2016). Components of the Engulfment Machinery Have Distinct Roles in Corpse Processing. PLOS ONE, 11(6), e0158217. https://doi.org/10.1371/journal.pone.0158217

      Melcarne, C., Ramond, E., Dudzic, J., Bretscher, A. J., Kurucz, É., Andó, I., & Lemaitre, B. (2019). Two Nimrod receptors, NimC1 and Eater, synergistically contribute to bacterial phagocytosis in Drosophila melanogaster. The FEBS Journal, 286(14), 2670–2691. https://doi.org/10.1111/febs.14857

      Petrignani, B., Rommelaere, S., Hakim-Mishnaevski, K., Masson, F., Ramond, E., Hilu-Dadia, R., Poidevin, M., Kondo, S., Kurant, E., & Lemaitre, B. (2021). A secreted factor NimrodB4 promotes the elimination of apoptotic corpses by phagocytes in Drosophila. EMBO Reports, 22(9), e52262. https://doi.org/10.15252/embr.202052262

      Roddie, H. G., Armitage, E. L., Coates, J. A., Johnston, S. A., & Evans, I. R. (2019). Simu-dependent clearance of dying cells regulates macrophage function and inflammation resolution. PLoS Biology, 17(5), e2006741. https://doi.org/10.1371/journal.pbio.2006741

      Serizier, S. B., & McCall, K. (2017). Scrambled Eggs: Apoptotic Cell Clearance by Non-Professional Phagocytes in the Drosophila Ovary. Frontiers in Immunology, 8, 1642. https://doi.org/10.3389/fimmu.2017.01642

      Serizier, S. B., Peterson, J. S., & McCall, K. (2022). Non-autonomous cell death induced by the Draper phagocytosis receptor requires signaling through the JNK and SRC pathways. Journal of Cell Science, 135(20), jcs250134. https://doi.org/10.1242/jcs.250134

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      Referee #3

      Evidence, reproducibility and clarity

      This paper investigates the role of NimB1, a secreted member of the Nimrod family in Drosophila, in the process of efferocytosis, the clearance of apoptotic cells by macrophages. Previous studies have identified NimB4, another secreted Nimrod protein, as a positive regulator of efferocytosis, enhancing both apoptotic cell binding and phagosome maturation. In contrast, the authors propose that NimB1 functions as a negative regulator, slowing down the early stages of apoptotic cell binding and internalization. This regulatory balance is suggested to fine-tune efferocytosis to maintain homeostasis.

      The primary aim of this study was to characterize the function of NimB1 to better understand the roles of proteins within the NimB family.

      This study identifies a novel function for NimB1 in modulating the early stages of efferocytosis, adding to our understanding of how Nimrod proteins fine-tune apoptotic cell clearance. The authors establish a clear phenotypic contrast between NimB1 and NimB4, which provides a compelling framework for understanding how positive and negative regulators coordinate phagocytosis. It also highlights the multiple roles of the secreted members of the Nimrod scavenger receptor family, which have remained so far poorly investigated.

      This is an interesting study that could be strengthened by additional validation and broader experimental support. As the authors point out in the discussion, it is known that PS bridging molecules contribute to phagocytosis and that the contribution of positive and negative players finely tune phagocytosis in flies and mammals. Clarifying the mode of action of NimB1 in those processes would higher the impact of this interesting piece of work. For example, does NimB1 interact with NimB4 and if so, what is the role of this interaction? How does NimB1 integrate in the signaling cascade that allows scavenger receptors to bind PS? Does it act similar to Orion by enhancing the PS binding of a scavenger receptor ?

      Key Findings

      • NimB1 and NimB4 are structurally similar, as supported by AlphaFold2 modeling, suggesting functional relatedness.
      • NimB1 is expressed in macrophages, secreted into the hemolymph, and binds apoptotic cells in a phosphatidylserine (PS)-dependent manner.
      • NimB1 is induced by challenge.
      • NimB1 mutants display a hyper-phagocytic phenotype, with faster recognition and internalization of apoptotic cells.
      • NimB1 loss enhances macrophage adhesion and actin remodeling, while bacterial phagocytosis remains unaffected, suggesting a specific role in apoptotic clearance.
      • NimB1 acts early in the phagocytic process, while NimB4 functions at later stages, particularly in phagosome maturation.

      Major comments

      1. Figure 1: AlphaFold is a valuable tool for generating hypotheses, however predicted structures should not be presented as definitive evidence of similarity, particularly without complementary experimental validation. This section would be stronger if the structural predictions were explicitly framed as predictions. In the absence of such data, the interpretation should be toned down.
      2. Figure 2DE : Given its basal level in homeostatic conditions, it would have been useful to look at the NimB1-GFP upon challenge. Also, the authors show only a single larval macrophage with no comparison point. To strengthen this result, the authors could include another protein quantification method, such as western blotting. Alternatively, labelling of NimB1>UASmRFP in embryo that present the highest expression levels would also strengthen this result.
      3. In supplementary Figure S1F : the authors overexpress NimB1-RFP using the fat body driver Lpp-Gal4 and show larvae with RFP in the nephrocyte. Could filet preparations be shown? Could the authors present evidence that the Lpp driver is not expressed in the nephrocytes (or refer to literature)?
      4. The results on the increased number of hemocytes observed in the double NimB1, NimB4 mutant animals (Figure S3A) remains not only disconnected from the rest of the data but also unexplained. Providing a mechanistic view may require a significant amount of work that may indicate an additional role of the two NimBs but will not add to our understanding of the role of NimB1 in phagocytosis. Nevertheless, it would be at least useful to know whether in the double mutant the lymph gland is still intact, as its precocious histolysis could account for the elevated number of hemocytes. If that were the case, that could indicate that lacking the two NimBs triggers an inflammatory state that affects the lymph gland, meaning that the pathway controlling phagocytosis also has a systemic impact on development. When checking the representative Figure S4D, it seems that very large cells are present in the double mutants, even larger than in the single mutants. These could be (pre)lamellocytes, which constitute activated hemocytes, known to impact the status of the lymph gland. If the enhanced number of hemocytes does not depend on lymph gland histolysis, a simple immunolabeling with the anti-PH3 antibody would assess the proliferative phenotype of the double mutant hemocytes. At least this piece of data would provide a better explanation for the observed phenotype.
      5. Figure 6: The connection between the ex-vivo (Figure 5) and in vivo (Figure 6) assays should be clarified. In the first type of assay, the lack of NimB4 results in reduced internalization (while lack of NimB1 enhances it). In the in vivo assay, more fragments are seen within the cell (hence internalized), using the NimB4 mutant. Also, in the ex-vivo assay, the lack of NimB1 does not affect the first steps ('attachment' and 'membrane'), while NimB4 does, yet it is proposed that NimB1 acts in the early steps (page 11-12). In that case, wouldn't we expect the double mutant NimB1 NmB4 to have the NimB1 phenotype?
      6. Figure 8A : a definition of the phagocytic cup mentioned in the text (page 12, 2nd paragraph) as well as the homogenization of the scale bars in Figure 8A would clarify the interpretation of Figure 8A. The structures shown for w1118 seem quite distant from the structures highlighted for NimB1crimic.
      7. The same scale should be used across different panels in Figure 8. This is particularly important since the authors mention the size of the lysotracker vesicles to conclude on their levels of maturity. This data and conclusions would be strengthened by a quantification of the vacuole sizes and the combination with markers of phagosome/lysosome maturation levels. It would help disentangling the complementary roles of NimB1 and NimB4.

      Minor comments:

      Figure 2BC : is there a particular reason to shift from Rp49 to Rpl32 as normalizing gene in Figure 2B and C? This prevents the comparison of NimB1 expression levels across the different graphs. Page 9, 2nd paragraph and Figure S3C: the authors mention "Actin structure revealed an increased ratio of filopodia to lamellipodia across all mutants". A clear definition of the parameters defining filopodia and lamellipodia is required to fully appreciate the meaning of the ratio. Figure S5B: a bar is missing in the right graph (% of cells containing AC, NimB1>UAS-NimB1-RFP). Page 10 2nd paragraph. The authors mention "draper mutants displayed impaired apoptotic cell binding and engulfment" referring to Figure 4. Figure S4 provide a more convincing illustration of this statement, since the decreased phagocytic index in Drpr KO is mostly due to less cells phagocytosing and not less material phagocytosed. Figure 6: not easy to distinguish the DAPI labelling relative to the nucleus vs. that of apoptotic fragments. Figure 7B: the number of cells used to generate the violin plot should be indicated in the legend or the method section. A schematic figure recapitulating the data would help. Page 11 last line: homeostatic rather than hemostatic.

      Significance

      This study identifies a novel function for NimB1 in modulating the early stages of efferocytosis, adding to our understanding of how Nimrod proteins fine-tune apoptotic cell clearance. The authors establish a clear phenotypic contrast between NimB1 and NimB4, which provides a compelling framework for understanding how positive and negative regulators coordinate phagocytosis. It also highlights the multiple roles of the secreted members of the Nimrod scavenger receptor family, which have remained so far poorly investigated.

      This is an interesting study that could be strengthened by additional validation and broader experimental support. As the authors point out in the discussion, it is known that PS bridging molecules contribute to phagocytosis and that the contribution of positive and negative players finely tune phagocytosis in flies and mammals. Clarifying the mode of action of NimB1 in those processes would higher the impact of this interesting piece of work. For example, does NimB1 interact with NimB4 and if so, what is the role of this interaction? How does NimB1 integrate in the signaling cascade that allows scavenger receptors to bind PS? Does it act similar to Orion by enhancing the PS binding of a scavenger receptor ?

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, Dolgikh and colleagues propose a first investigation about the role of the drosophila Nimrod protein NimB1. Although the role of several members of the family in phagocytosis has been explored, the function of Nimrod type B proteins is less addressed. With in silico analysis, they first see a strong similarity between NimB1 and NimB4. They show that NimB1 is primarily expressed in phagocytes, and as NimB4 can bind phosphatidylserines (PS), leading to a possible shared role in efferocytosis. Using transgenic and null drosophila models, the authors then compare the impact of NimB1 overexpression or deficiency. They compare the effects shown to NimB4 and Draper deficient lines, as these two proteins were previously shown to play a role in efferocytosis. They propose that NimB1 is a secreted protein that binds apoptotic cells. They show that NimB1 deficiency changes the adhesion properties of macrophages. The major finding is that NimB1 delays the early stages of efferocytosis, contrary to NimB4 and Draper that on the contrary facilitate efferocytosis. In contrast, the authors propose that NimB1 increases the formation of phagosomes.

      Major comments:

      • One of the major technical challenges here was to generate models to allow the detection of the protein in cellulo and in vivo. Although the results are convincing in transgenic lines NimB1 expression is driven by the endogenous promoter, one could still argue that the GFP tags would lead to changes in the localization of the protein.
      • In line with the previous comment, to show that NimB1 is a secreted protein, the authors use an overexpression model. How to be sure, that overexpression itself does not lead to increased secretion, or shedding from the membrane ?
      • Would an experiment with a control consisting in a known protein secreted by macrophages lead to the same staining pattern (positive control)? Could another methodology like a Western Blot on supernatants from hemocyte cell culture (over)expressing NimB1, with an anti-RFP staining, be envisaged?
      • It sems counterintuitive that phagocytes from Draper and NimB4 null mutants with defects in efferocytosis show increased load of apoptotic cells (Figure 6C and D in both unchallenged and injury condition). Do the authors have precedent data to cite going to the same direction? Are cell debris engulfed but not degraded efficiently?
      • In Figure 6D it seems indeed that NimB4, NimB1/NimB4 and Draper mutants do not accumulate more apoptotic material upon injury. However, levels for NimB4 is close to the one obtained with NimB1 mutants. Is it statistically true? If yes, what could be the reason for this similarity ? In any case, as some important conclusion relies on the comparison between UC and injury conditions, adequate statistics and representations could be proposed.
      • The authors claim with analyses of Figure 8C and D, that NimB1 mutants show acidic vehicles normal in size and fluorescence intensity. However, statistical differences are still observed compared to control condition, which is also seen in representative images shown.

      Minor comments:

      • In figure 2D, what allows to say the expression is restricted in macrophages ? Is it the colocalization with SIMU being a macrophage-specific marker?
      • In figure S3B and C, it appears that double NimB1/NimB4 mutants exhibit less spreading than single ones (especially NimB4). Is it the case (statistic significance). If yes what could be the explanation?
      • Several graphs are identical between figure 4 ans S4. It is probably not useful and complicates reading.
      • As TEM images shown in Figure 8B do not lead to quantitative data, I would put it as supplementary file.

      Significance

      This study uses several approaches and models to address the role of NimB1 in efferocytosis. Both In Vitro and In Vivo approaches are proposed. They give insight into the role of this protein with unknown function so far. Some statistical analysis could be performed to improve the clarity of conclusions. One of the important aspects is the secreted nature of NimB1.However, additional approaches could be proposed to confirm this.

      Basic immunologists and cell biologists would be interested in reading this article that highlights the delicate equilibrium between pro and anti-efferocytosis molecules.

      I am an immunologist/cell biologist with expertise in lysosomal catabolism. As I work on mouse models or Human samples, my mastering of drosophila as a model is limited.

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      Referee #1

      Evidence, reproducibility and clarity

      This work defines NimB1 protein as a PS binding bridging molecule but with a negative regulatory role in efferocytosis. Specifically, the authors demonstrate via a variety of genetic, cell biological, and other approaches that loss of NimB1 leads to Drosophila macrophages being more adherent to apoptotic targets and engulf them more robustly. The authors also nicely demonstrate that the function of NimB1 differs from NimB4, and the double mutant demonstrating PS-binding yet, distinct roles. Further, the authors show that NimB1 does not affect bacterial phagocytosis.

      Overall, this is a well-done study. The authors have already done a very thorough job addressing the key points and I congratulate the authors.

      My only minor comment is that the authors could try to make the comment better about whether or not such a 'negative regulatory' bridging molecules may exist in other species, and particularly mammals. If so, this is quite novel. The authors refer to CD47 but this is a membrane protein. The other minor comment is whether the authors ever tried express the PS binding domains as a fusion protein - this would provide a more direct evidence for the binding to PS (although the authors do competitive inhibition with Annexin V). This could be commented upon although testing this is not necessary if they have not already done so.

      Significance

      The identification of the negative regulator bridging protein NimB1 is novel and could be broadly interesting to those studying efferocytosis.

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      Reply to the reviewers

      1. General Statements [optional]

      • *

      *We thank the reviewers for finding the manuscript enjoyable and well-written, with experiments that were performed well, show solid results and provide useful data for the community. The reviewers have provided meaningful feedback to improve this study. We have addressed the comments point-by-point below. The main text will also be further modified to incorporate new analysis where it has not yet been done. *

      • *

      • *

      2. Description of the planned revisions

      Reviewer 1:

      Summary OTX2 is a pivotal transcription factor that regulates the fate choice between somatic and primordial germ cell (PGC) lineages in early mouse development. In the current study, the authors use in vitro stem cell models to demonstrate that OTX2 mediates this developmental fate decision through controlling chromatin accessibility, whereby OTX2 helps to activate putative enhancers that are associated with somatic fate. By extension, those somatic-associated regulatory regions therefore become inaccessible in cells adopting PGC identity in which Otx2 is downregulated.

      Comments I enjoyed reading this manuscript. The experiments have been carried out well and for the most part the results provide convincing evidence to support the claims and conclusions in the manuscript. I particularly liked the experiments using the inducible Otx2 transgene to examine the acute changes in chromatin accessibility following restoration of OTX2.

      I include some suggestions below to the authors for additional analyses that I feel would further strengthen their study.

      I also felt that the authors focus almost exclusively on the subset of OTX2-bound sites that lose accessibility in the absence of OTX2. But, as they show in several figure panels, these sites tend to be the minority and that most OTX2-occupied sites do not lose accessibility in Otx2-null cells (actually, more sites tend to gain accessibility). I encourage the authors to modify the text and some of the analyses to give a better balance to their study. We are pleased that this reviewer enjoyed our manuscript. As suggested by the reviewer, we included analyses on the regions that are bound by OTX2 but do not show an increase in accessibility (see section 3 reviewer 1 point 6). The text will be expanded to include the new data and to include the description of the subset of OTX2 sites that do not show accessibility changes in the absence of OTX2. We have responded to other points they raised as detailed in the sections below

      • *

      Figure 1: The authors write: "...OTX2 binds mostly to putative enhancers." Whether these distal sites are enhancers is not sufficiently evidenced in the manuscript, but it is important information to collect to support their model of OTX2 function. The authors should strengthen their analysis by examining whether OTX2 peaks are enriched at previously defined enhancer regions.

      We plan to compare OTX2 bound regions with defined lists of enhancers identified in ESCs grown in Serum/LIF (e.g. Whyte et al 2013) and, if available, in 2i/LIF and EpiLCs. We will also analyse publicly available datasets for H3K4me1 (enhancer marker) and H3K27ac (marker of active regulatory regions) at the regions bound by OTX2 in ESCs and EpiLCs.

      Figure 2: I'm still puzzled why the authors did not examine flow-sorted WT+cyto cells?

      *We agree with the reviewer that it would be interesting to examine flow-sorted WT +cyto PGCLCs. Unfortunately, the expression of CD61 and SSEA1 only becomes visible from day 4 of PGCLC differentiation. Therefore, we were not able to isolate PGCLC at day 2 from WT cells differentiated in the presence of cytokines. We then used OTX2-/- cells at day 2 to model PGCLCs. This is based on the assumption that because day 6 Otx2-/- PGCLCs are transcriptionally similar to sorted day 6 WT cells (Zhang, Zhang et al Nature 2018), the same will be true at day 2. We will modify the text in the final version of this manuscript to clarify this point that has also been raised by reviewers 2 and 3. *

      • *

      Figure 3: I would be tempted to put Figure S3A and S3B into Figure 3. It would be better to show all 1246 DARs together, either ordered by OTX2 CT&RUN signal, or presented in two pre-defined groups (OTX2-bound vs unbound). I also suggest that the author show OTX2 signals and ATAC-seq signals for the 3028 DARs that gain accessibility in Otx2-null EpiLCs (this could be added to a supplemental figure).

      Although the analysis has been carried out and the figures have been amended, the main text will be modified in a future updated version of the manuscript to incorporate these results.

      • *

      Figure 3: What is special about the 8% of OTX2-bound site that lose accessibility, versus the 92% of sites that do not?

      *The 8% of the OTX2-bound regions that lose accessibility in the absence of OTX2 appear to be more sensitive to the loss of OTX2. One possible explanation is that the accessibility of the rest of OTX2 bound regions relies on other TFs, such as OCT4, that are expressed in EpiLCs. We will modify the main text to discuss this interesting point raised by the reviewer. *

      Figure 6F: If the 4221 sites are split into those bound by OTX2 versus those that are not (related to Figure 6C) then is there a difference? i.e. are the OTX2-bound sites opening up?

      We separated the 4,221 sites in OTX2 bound and unbound. The result is reported below:

      *Although there is a slight increase in accessibility in the OTX2 bound subset, the average accessibility reaches less than ¼ of the accessibility of these regions when OTX2 is present from day 0 to day 4, while the OTX2 unbound regions do not show an increase in accessibility. Although we can not rule out that a longer treatment with tamoxifen may lead to higher accessibility in the OTX2 bound subset, the dynamics are extremely slower compared to the EpiLC regions where accessibility reaches 50% of the d0-d4 sample in just 1 hour of tamoxifen treatment. *

      • *

      Is there any evidence that OTX2 binds and compacts PGCLC enhancers in somatic cells? I appreciate this is different to the main thrust of the authors' model, but being able to show that OTX2 does not compact these sites lends further support to their preferred model of OTX2 opening sites of somatic lineages.

      *Comparing the ATAC-seq in PGCLCs with ESCs and EpiLCs, we identify a subset of regions that are open in PGCLC only (PGCLC-specific accessible regions, see below). These regions do not show binding of OTX2 in WT EpiLCs or the d0-d2 Tam sample, suggesting that OTX2 does not bind and compact PGCLC-specific enhancers. *

      • *

      PGCLC-specific regions showing high accessibility only in PGCLCs.

      • OTX2 CUT&RUN signal in WT EpiLC, OTX2-ERT2 PGCLCs in presence or absence of Tamoxifen, showing that OTX2 does not bind PGCLC-specific regions even when it is overexpressed in GK15 medium.*

      *These analyses will be incorporated in the manuscript. *

      • *

      Discussion: Have prior studies established a connection between OTX2 and chromatin remodellers that can open chromatin? Or, if not, then perhaps this could be proposed as a line of future research.

      We thank the reviewer for suggesting to amplify the discussion on the possible connection between OTX2 and chromatin remodellers. Although there is no evidence in the literature of a direct interaction between OTX2 and chromatin remodellers, this can not be excluded. The connection might also be indirect: OTX2 is known to interact with OCT4, which in turn interacts and recruits to chromatin the catalytic subunit of the SWI/SNF complex, BRG1. This point will be discussed in a modified version of the manuscript.

      • *

      • *

      Reviewer 2:

      Barbieri and Chambers explore the role of OTX2 on mouse pluripotency and differentiation. To do so, they examine how the chromatin accessibility and OTX2 binding landscape changes across pluripotency, the exit of pluripotency towards formative and primed states, and through to PGCLC/somatic differentiation. The work mostly represents a resource for the community, with possible implications for our understanding of how OTX2 might mediate the germline-soma switch of fates. While the findings of the work are modest, the results seem solid and the manuscript is clear and well-written.

      *We are pleased that this reviewer found our results solid and the manuscript clear. *

      I have some comments as indicated below:

      1. The comparison between Otx2-/- cells in the presence of PGCLC cytokines compared to WT cells in the absence of cytokines seems like it is missing controls to me. I assume the authors wanted to enable homogeneous populations to facilitate their bulk sequencing methods, but it seems to me like they are comparing apples with oranges. It would have been better to have the reciprocal situations (Otx2-/- cells in basal differentiation medium, and WT cells in PGCLC cytokines) with a sorting strategy to better unpick the differences between the presence and absence of Otx2 in the 2 protocols. Having said that, the authors are careful not to draw many comparisons between those populations so I don't think this omission affects their current claims. They should however clarify whether the flow cytometry (Supp Fig2) was used for sorting cells or if all cells were taken for bulk sequencing. *We agree with the reviewer that it would be of interest to compare the PGCLC and somatic population derived from the OTX2-/- cells in GK15 without cytokines with the same populations derived from WT cells differentiated in the presence of cytokines. Our work aims to identify what happens at the stages of PGCLC differentiation when cells are still competent for both germline and somatic differentiation. Previous work from the lab showed that this dual competence is lost after day 2, therefore we focus our attention on this time of differentiation. Unfortunately, the two surface markers characteristics of PGCs (CD61 and SSEA1) are not expressed at day2 and, therefore we are not able to sort PGCLCs derived from OTX2-/- cells in GK15 without cytokines or WT cells differentiated in the presence of cytokines. As recognised by this reviewer, we aimed to obtain two homogenous populations that can model PGCLCs and somatic cells. This is based on data obtained at day 6 when Otx2-/- PGCLCs show a similar transcriptome to sorted day 6 WT cells (Zhang, Zhang et al Nature 2018) and the assumption that the same will be true at day 2. We will clarify that the supplementary Figure 2 is not a sorting strategy. As this point has been raised by reviewers 1 and 2 as well, we will modify the text to clarify the choice and the assumption behind using OTX2-/- cells in the presence of cytokines and WT cells in the absence of cytokines to model PGCLCs and somatic cells respectively. *

      2. *

      Throughout the text, the authors subject cells (WT / Otx2-/- /Otx2ER ) to different protocols to look at accessibility and Otx2 binding, but with no mention of the cell fate differences that occur in these different conditions. For instance, it is unclear to me to which fate the WT cells without PGCLC cytokines go - I presume this is neural but perhaps this is a mixed fate, given that they are in GK15 rather than N2B27. Likewise, the OTX2ER experiments may promote a mixed population between PGCLC/somatic fates, and this is never described. Ideally transcriptomic data would be collected, but failing that, qPCR data should be obtained to examine this more closely.

      *We are planning to generate RT-qPCR data for germ layer markers (ectoderm, endoderm and mesoderm) in WT cells in GK15 without cytokines at day 2, as well as OTX2-ERT2 cells with and without Tamoxifen at day 2 (noTam, d0-d2) and day 4 (no Tam, d0-d4). *

      The authors also state that "OTX2 facilitates Fgf5 transcription' (page10) but provide no transcriptional data to substantiate this claim. Again RT-qPCR would help make this point.

      *We will analyse the level of Fgf5 by RT-qPCR in OTX2-ERT2 EpiLCs treated for 1 hour and 6 hours with Tamoxifen to show the effect of OTX2 on Fgf5 transcription. *

      • *

      It is unclear to me what the 'increase[d] accessibility' (eg abstract final sentence, Figure 3E) really means at the cellular level. Does this indicate that more cells have this site open, and does this have implications for the heterogeneity of cell fates observed? Since the authors are concerned with fate decisions, this seems like an important consideration that should at least be discussed.

      The possibility that the increased accessibility is due to higher heterogeneity in the population is interesting and it will be included in the discussion in a revised version of the manuscript.

      • *

      • *

      Reviewer 3:

      In this manuscript, the authors perform OTX2 CUT&RUN and ATAC-seq in Otx2-null and WT ESCs, EpiLCs and PGCLCs to understand whether the role of OTX2 in restricting mouse germline entry that they previously described (Zhang Nature 2018) mechanistically depends on chromatin remodeling. They identify differentially accessible regions (DARs) between Otx2-null and WT cells at different stages of differentiation and show that many of these are OTX2 bound in WT. They then show using cells expressing OTX2-ER^T2 in Otx2-null Epiblast cells that when OTX2 is moved into the nucleus, the regions that were differentially closed in Otx2-null open within an hour, suggesting chromatin accessibility is directly controlled by OTX2 (rather than indirect effects involving transcription and translation which one would expect to take longer). The scope is narrow, but this is nice work and useful data for the mouse PGC field. However, there are a few places where the data could be strengthened, and the writing is a little confusing in places, for example by stating as fact in early sections what is not proven until later.

      We thank the reviewer for finding our work nice and useful for the mouse PGC field, and for the useful comments to improve the manuscript. We have included new analysis and modified the text as suggested to improve the writing, avoiding early statements that were not fully proven until later in the manuscript. We have responded to other points they raised as detailed below and in the next section.

      • *

      1) "we compared Otx2-/- cells cultured in the presence of PGC-promoting cytokines with wild-type cells cultured in the absence of PGC-promoting cytokines. Under these conditions Otx2-/- cells produce an essentially pure (>90%) CD61+/SSEA1+ population that we refer to as PGCLCs, while wild-type cells yield a cell population from which PGCLCs are absent"

      This is not a controlled comparison since one cannot separate the day 2 effect of cytokines from that of the Otx2 knockout. The manuscript would be strengthened if the authors include WT somatic and PGCLCs from the +cytokine conditions, which could be easily sorted out as shown in Supp. Fig. 2. Ideally they would also include Otx2-null somatic cells, although Supp. Fig. 2 shows those are rare under the conditions considered.

      *This work aimed to analyse early stages of EpiLC to PGCLC differentiation when cells are still competent for both somatic and germline differentiation. This stage has been described previously to be at day 2 of differentiation in GK15 + cytokines (PGCLC differentiation medium, Zhang, Zhang et al, Nature 2018). Unfortunately, CD61 and SSEA1 are not expressed at day 2 of PGCLC differentiation, and they start to be expressed on the cell surface by day 4. Consequently, it is impossible to sort cells at day 2 using the CD61+/SSEA1+ strategy. To overcome this problem, we used WT cells grown in GK15 without cytokines to model a population of somatic cells and OTX2-/- cells grown in GK15+ cytokines to model a homogeneous population of PGCLCs. As explained in a similar point raised by reviewers 2 and 3, we assumed that, as OTX2-/- cells grown in the presence of cytokines are transcriptionally similar to sorted WT cells at day 6 (Zhang, Zhang et al, Nature 2018), OTX2-/- cells at day 2 are similar to their WT counterpart at day 2. The main text will be modified to clarify that we are using homogeneous populations to model both PGCLC and somatic cells and that Figure S2 does not show a sorting strategy. *

      • *

      3) "In ESCs, OTX2 binds We are planning to perform a statistical analysis to ascertain that the small number of DARs bound by OTX2 are or are not bound by chance by OTX2.

      • *

      4) It would be good if the discussion was broadened to include both human and other transcription factors that are involved. How much of these conclusions could one expect to carry over to human or other mammals? There is some work from the Surani lab considering OTX2 in human. One could even look at published ATAC or OTX2 chip-seq data in hPSCs and potentially learn something interesting. Furthermore, there are studies on other transcription factors modulating chromatin accessibility in the decision between germline and somatic cells, for example PRDM1, PRDM14 (refs in e.g. Tang et al Nat Rev Gen 2016) or TFAP2A (at least in human (Chen et al Cell Rep 2019)). Do these factors affect the same genes? Is a coherent picture emerging of their respective roles in germline entry?

      *As suggested by the reviewer, we will discuss the role of OTX2 in human PGCLC formation and include studies on PGC-specific transcription factors concerning changes in chromatin accessibility in germline and somatic cells. This will be included in a revised version of the manuscript. *

      • *

      • *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Reviewer 1:

      1. Figure 1: The authors report in the methods that they performed OTX2 CUT&RUN in biological duplicates. It would strengthen their results if they showed in Figure S1 some representative data from each replicate separately to show the consistency. As suggested by the reviewer to show consistency between replicates, two representative tracks of the two CUT&RUN replicates at the Tet2 (ESCs) and Fgf5 (EpiLCs) loci have been included in Figure S1A. The corresponding tracks of the average bigwig files are reported in Figure 1E. The main text (page 5) and the figure legends have been amended to incorporate the new panels.

      2. *

      Figure 2: I think it would be helpful to remind the reader here that Otx2 is normally downregulated in PGCs, and that Otx2 expression is maintained (at least initially) in somatic cells. This would help explain the logic behind the choice of samples that were profiled.

      We modified the text with the following sentence, as suggested by the reviewer, emphasising the level of OTX2 in early somatic vs early PGCLCs: “Otx2 expression is rapidly downregulated in the EpiLC to PGCLC transition while its expression is maintained longer in cells entering the somatic lineage [8]*” (page 7). *

      • *

      Figure 2D: I appreciate that the highlighted region at the Tet2 locus is a DAR, but from the genome tracks it looks as though the region still has high accessibility. Are there any other examples to exemplify a more obvious DAR? Additionally, since twice as many DARs gain accessibility in Otx2-null ESCs compared to lose accessibility, why not show examples of these as well? The same is true of EpiLCs. (Or alternatively, provide a good explanation for why not to show these other categories)

      We substituted the Tet2 DAR with a more clear example of ESC DAR located in the Hes1 locus that shows low accessibility in Otx2-/- ESCs versus WT ESCs. Examples of ESC DARs and EpiLC DARs that show higher accessibility in Otx2-/- vs WT cells have been added as new panels 2E (DAR in Pebp4 locus) and 2G (DAR in Tdh locus). We also simplified the panels showing only ATAC-seq tracks in WT and OTX2-/- cells, either ESCs (2D-E) or EpiLCs (G-H). Text and figure legends have been modified to accommodate the changes made in Figure 2.

      • *

      Figure 3: I would be tempted to put Figure S3A and S3B into Figure 3. It would be better to show all 1246 DARs together, either ordered by OTX2 CT&RUN signal, or presented in two pre-defined groups (OTX2-bound vs unbound). I also suggest that the author show OTX2 signals and ATAC-seq signals for the 3028 DARs that gain accessibility in Otx2-null EpiLCs (this could be added to a supplemental figure).

      Figures S3A and S3B have been moved to the main figure. Figure S3A is now part of Figure 3C, where all the 1,246 DARs are shown together, separated into two groups (OTX2-bound and -unbound). Figure S3B is now part of Figure 3F. A new heatmap showing the OTX2 and ATAC-seq signals for the 3028 regions that gain accessibility in Otx2-/- EpiLCs has been added as new Figure S3B. Only 28 out of the 3,028 regions overlap an OTX2 peak as shown in the new Figure S3A. These regions appear to be already open in ESCs (Figure S3C) and they do not fully close when OTX2 is absent. This can be explained by either a) the lack of expression of an OTX2 target gene that represses these regions or b) the continuous expression of a gene that is usually repressed by OTX2 in the transition to EpiLCs. In both cases, OTX2 does not directly repress these regions. Figure legends have been amended to incorporate the new panels. The main text will be modified to incorporate these results.

      • *

      Figure 6: Do the PGCLCs with OTX2 expression have chromatin accessibility profiles similar to somatic cells? Consider adding WT somatic cell data to Figure 6A, which could be an interesting comparison with the Tam d0-d2 samples.

      *The heatmap showing the ATAC-seq signal at the additional OTX2-induced regions in somatic cells has been added to Figure 6A. The data show that the regions induced by OTX2 are not open in somatic cells generated in GK15. One possible explanation is the overexpression of OTX2 induces the opening of neural-associated regions, but neural differentiation is not fully supported in GK15 medium (see reviewer 2, point 3). As suggested by reviewer 2, we will perform RT-qPCR of germ layer markers to analyse the identity of somatic cells grown in GK15 (without cytokines) and somatic cells induced by OTX2 overexpression. *

      • *

      • *

      • *

      Reviewer 2:

      The authors focus solely on the activating role of Otx2 in their data, but given the substantial proportion of DARs that decrease following Otx2 depletion, I presume it is possible that it also has a repressive effect? Either way, this should be discussed.

      *As also suggested by reviewer 1 (point 6), we analysed the accessibility level and the OTX2 signal at the 3,028 regions that gain accessibility in Otx2-/- EpiLCs (new Figure S3A-C). These regions show high accessibility in ESCs suggesting that these are ESC regions that do not close properly in the transition to EpiLCs in the absence of OTX2. OTX2 CUT&RUN show a low to absent signal at these regions, with just 28 regions overlapping EpiLCs DARs that show higher accessibility in Otx2-/- cells, suggesting that OTX2 does not have a direct suppressive effect on them. *

      • *

      The authors state that d2 PGCLCs "show an intermediate position between ESCs and EpiLCs" based on the PCA location. They should be careful to qualify that this is only in the first 2 principal components, because it may well be the case (and is likely) that in other components the PGCLC population is far removed from the pluripotent states.

      • The text has been updated as follows: d2 PGCLCs “show an intermediate position between ESCs and EpiLCs on both PC1 and PC2”.*

      • *

      Reviewer2 Minor Suggestions:

      1. Presumably the regions bound by OTX2 in Tet2, Mycn and Fgf5 (Fig1E) are called enhancers because these are known from existing literature. It would be helpful to cite the relevant references to this in the text for those unfamiliar with these. References (Whyte et al, Cell, 2018 – Tet2 and Mycn, Buecker et al, Cell Stem Cell, 2013, Thomas et al, Mol Cell 2021 – Fgf5) have been added to the text and the figure legends.

      On page 13, the authors say "To determine whether OTX2 expression is essential to maintain chromatin accessibility in somatic cells..." but this does not seem to be what they test because they are using PGCLC medium. Perhaps I misunderstood, but this could be clarified.

      *Expression of OTX2 during the first 2 days of PGCLC differentiation leads to a block of germline differentiation as previously shown in Zhang, Zhang et al, Nature 2018. After 2 days of tamoxifen treatment, cells have acquired somatic fate and cells will undergo somatic differentiation even after tamoxifen is withdrawn after day 2. Nevertheless, we agree with the reviewer that the sentence is of difficult interpretation and we modified the sentence as shown below and as reported in the updated manuscript: “To determine whether OTX2 expression is essential to maintain chromatin accessibility in cells differentiating in the presence of PGC-inducing cytokines after day 2” (page 12). *

      On page 14 the authors claim, "These results indicate that...the partner proteins that OTX2 act alongside differ...". While this may be the case, their results do not substantiate this, it is just speculation. Should be toned down.

      The text has been modified as follows: "These results suggest that...the partner proteins that OTX2 act alongside differ..."

      Page 18, PGCLC differentiation method sections needs to be described as such (ie. Add "For PGCLC differentiation..." before the second paragraph)

      *The text “For PGCLC differentiation” has been added at the beginning of the PGCLC differentiation method section. *

      It would be helpful to indicate time on the protocol schematics (eg Fig4A, 5A, 5D etc) as I had to keep checking the methods to find out how long the full differentiation time-course was.

      *Indication of time has been added to Figures 1, 2, 4, 5 and 6. *

      Since the authors compare between the Tam d0-d2 treatments assessed at d2 versus d4 (Figure5B vs 5E) it would be helpful to make the colourbars the same scale, for both ATAC and Cut&Run datasets.

      *The heatmap in Figure 5B has been modified. The colourbars of Figure 5B and 5E are now using the same scale. *

      • *

      Reviewer 3:

      1) As a minor point related to this, the second sentence is confusing since it kind of sounds like Otx2-/- and WT cells are compared under the same conditions unless one carefully reads the previous sentence.

      The text has been modified to clarify the different medium conditions for WT and OTX2-/- cells, as follows: “In the presence of PGC-inducing cytokines, Otx2-/- cells produce an essentially pure (>90%) CD61+/SSEA1+ population that we refer to as PGCLCs, while wild-type cells differentiated in GK15 medium without cytokines yield a cell population from which PGCLCs are absent” (page 7).

      • *

      2) "This suggests that OTX2 acts as a pioneer TF to regulate the accessibility of enhancers E1, E2 and E3."

      This is from the text corresponding to Fig. 2. That data actually only shows that Otx2-null cells have DARs, so somehow OTX2 affects chromatin accessibility but it could be indirect by controlling transcription of genes that modify chromatin accessibility. It is not until figure 4 that the data suggests that OTX2 directly affects accessibility, perhaps as a pioneer TF.

      The authors continue to make many statements about the direct action of OTX2 before the data supporting this is shown, on which I got hung up as a reader. I suggest the authors edit the manuscript to improve this. E.g. "OTX2 may directly control accessibility at these sites (Figure 3E)." and the fact that in 3E and other figure, it says "DARs increased by OTX2 binding" which at that point is not proven, so would better say "Otx2-null vs WT DARs" or something like that.

      The sentence "This suggests that OTX2 acts as a pioneer TF to regulate..” has been removed from the text (page 9). The sentence “OTX2 may directly control accessibility at these sites” has been modified with “*suggesting that the presence of OTX2 affects accessibility at these sites” (page 9). The sentence “ Together, these results suggest that OTX2 is required to open these chromatin regions” has been modified to “Together, these results suggest that OTX2 is required for the accessibility of these chromatin regions”. *

      The subset of DARs that increase in WT EpiLC and are bound by OTX2 that was called “DARs increased by OTX2 binding” has been renamed as “DARs higher in WT with OTX2 binding”. For consistency, the subset of DARs showing increased accessibility in WT EpiLCs that are not bound by OTX2 are now called “DARs higher in WT without OTX2 binding” (Figure 3, Figure 4, main text and figure legends). We will further revise the manuscript to avoid statements or hypotheses that are not yet supported by data throughout the text.

      • *

      Reviewer 3 – minor comments:

      1) "Comparing wild-type and Otx2-/- ESCs identified 375 differentially accessible regions (DARs) with increased accessibility in wild-type cells, and 743 regions with higher accessibility in Otx2-/- ESCs (Figures 2C). An example of ESC DARs where accessibility is increased in cells expressing OTX2 is the intragenic enhancer of Tet2. Tet2 is expressed at high levels in ESCs but at low levels in EpiLCs."

      The authors compare Otx2-null and WT ESCs then proceed to give an example comparing ESCs to EpiLCs, instead of Otx2-null vs WT ESCs, which is confusing.

      Furthermore, here and in other places the authors describe ESCs as not expressing OTX2. However, they also show CUT&RUN data for OTX2 in ESCs etc, clearly indicating that it is expressed, just lower (otherwise how could one get anything?).

      *We originally chose Tet2 enhancer as an example of the 375 ESC DAR with higher accessibility in WT vs Otx2-/- ESCs as it shows a slightly decreased level of accessibility and OTX2 binding in ESCs. Therefore, the sentence “where accessibility is increased in cells expressing OTX2” refers to WT cells (expressing OTX2) when compared to Otx2-/- cells (OTX2-null). The text has been changed to describe the new panel. The rest of the main text will be checked and modified where appropriate to avoid possible misinterpretations. *

      *We also appreciate that the change in accessibility is not clearly visible in the original Figure 2, as also pointed out by Reviewer 1 (point 6). In the updated Figure 2, we show a region in the Hes1 locus as an example of the 375 ESC DARs. Moreover, we simplified the panels showing ATAC-seq tracks of WT and OTX2-/- ESC (Fig. 2D-E) or EpiLCs (Fig. G-H). *

      2) "In contrast, in EpiLCs, OTX2 binds almost 40% (446 out of 1,246) of the DARs that are more accessible in wild-type than in Otx2-/- cells (Figure 3B-C). Notably, these regions are mainly located distal to genes (91%, Figure 3D), despite the increased fraction of promoter regions bound by OTX2 in EpiLCs (Figure S1A)."

      Are the authors rounding percentages with 2 significant digits, as suggested by the "91%"? If so, 446/1245 ~ 36%, not 40%.

      *The text has been modified from “OTX2 binds almost 40%” to “OTX2 binds 36%”. *

      3) The results in Figure 4 are nice and the real meat of the paper. One suggestion: It would be helpful is Fig. 4B were split up between the 446 and 800 genes instead of showing all 1246, and if the WT control was shown in the same figure as well.

      *Panels with the 446 and 800 regions have been added to Figure 4 instead of the panels with all 1246 regions. WT control has been inserted in Figure 4. The main text and the figure legends have been updated accordingly. *

      4) "Enforced OTX2 expression opens additional somatic regulatory regions" - it would be clearer to say "OTX2 overexpression opens additional somatic regulatory regions", since this is really about DARs between EpiLCs that already express OTX2 and those forced to express higher than WT endogenous levels by the OTX2-ER system?

      We thank the reviewer for their suggestion. The text has been modified (page 12)

      • *

      • *

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors perform OTX2 CUT&RUN and ATAC-seq in Otx2-null and WT ESCs, EpiLCs and PGCLCs to understand whether the role of OTX2 in restricting mouse germline entry that they previously described (Zhang Nature 2018) mechanistically depends on chromatin remodeling. They identify differentially accessible regions (DARs) between Otx2-null and WT cells at different stages of differentiation and show that many of these are OTX2 bound in WT. They then show using cells expressing OTX2-ER^T2 in Otx2-null Epiblast cells that when OTX2 is moved into the nucleus, the regions that were differentially closed in Otx2-null open within an hour, suggesting chromatin accessibility is directly controlled by OTX2 (rather than indirect effects involving transcription and translation which one would expect to take longer). The scope is narrow, but this is nice work and useful data for the mouse PGC field. However, there are a few places where the data could be strengthened, and the writing is a little confusing in places, for example by stating as fact in early sections what is not proven until later.

      Major Comments:

      1. "we compared Otx2-/- cells cultured in the presence of PGC-promoting cytokines with wild-type cells cultured in the absence of PGC-promoting cytokines. Under these conditions Otx2-/- cells produce an essentially pure (>90%) CD61+/SSEA1+ population that we refer to as PGCLCs, while wild-type cells yield a cell population from which PGCLCs are absent"

      This is not a controlled comparison since one cannot separate the day 2 effect of cytokines from that of the Otx2 knockout. The manuscript would be strengthened if the authors include WT somatic and PGCLCs from the +cytokine conditions, which could be easily sorted out as shown in Supp. Fig. 2. Ideally they would also include Otx2-null somatic cells, although Supp. Fig. 2 shows those are rare under the conditions considered.

      As a minor point related to this, the second sentence is confusing since it kind of sounds like Otx2-/- and WT cells are compared under the same conditions unless one carefully reads the previous sentence. 2. "This suggests that OTX2 acts as a pioneer TF to regulate the accessibility of enhancers E1, E2 and E3."

      This is from the text corresponding to Fig. 2. That data actually only shows that Otx2-null cells have DARs, so somehow OTX2 affects chromatin accessibility but it could be indirect by controlling transcription of genes that modify chromatin accessibility. It is not until figure 4 that the data suggests that OTX2 directly affects accessibility, perhaps as a pioneer TF.

      The authors continue to make many statements about the direct action of OTX2 before the data supporting this is shown, on which I got hung up as a reader. I suggest the authors edit the manuscript to improve this. E.g. "OTX2 may directly control accessibility at these sites (Figure 3E)." and the fact that in 3E and other figure, it says "DARs increased by OTX2 binding" which at that point is not proven, so would better say "Otx2-null vs WT DARs" or something like that. 3. "In ESCs, OTX2 binds <10% (30 out of 375) of DARs that are more accessible in wild-type cells than in Otx2-/- cells (Figure 3A), suggesting that accessibility of ESC DARs is directly due to OTX2 in a small subset of DARs."

      When a small number of DARs are OTX2 bound, it does not necessarily suggest that that small set is directly affected by OTX2. It could just mean no DARs are controlled directly by OTX2 and then some are bound by chance by OTX2. Some appropriate statistical null hypotheses about the occurrence of OTX2 motifs might help to see if 10% is more than chance. 4. It would be good if the discussion was broadened to include both human and other transcription factors that are involved. How much of these conclusions could one expect to carry over to human or other mammals? There is some work from the Surani lab considering OTX2 in human. One could even look at published ATAC or OTX2 chip-seq data in hPSCs and potentially learn something interesting. Furthermore, there are studies on other transcription factors modulating chromatin accessibility in the decision between germline and somatic cells, for example PRDM1, PRDM14 (refs in e.g. Tang et al Nat Rev Gen 2016) or TFAP2A (at least in human (Chen et al Cell Rep 2019)). Do these factors affect the same genes? Is a coherent picture emerging of their respective roles in germline entry?

      Minor comments:

      1. "Comparing wild-type and Otx2-/- ESCs identified 375 differentially accessible regions (DARs) with increased accessibility in wild-type cells, and 743 regions with higher accessibility in Otx2-/- ESCs (Figures 2C). An example of ESC DARs where accessibility is increased in cells expressing OTX2 is the intragenic enhancer of Tet2. Tet2 is expressed at high levels in ESCs but at low levels in EpiLCs."

      The authors compare Otx2-null and WT ESCs then proceed to give an example comparing ESCs to EpiLCs, instead of Otx2-null vs WT ESCs, which is confusing. Furthermore, here and in other places the authors describe ESCs as not expressing OTX2. However, they also show CUT&RUN data for OTX2 in ESCs etc, clearly indicating that it is expressed, just lower (otherwise how could one get anything?). 2. "In contrast, in EpiLCs, OTX2 binds almost 40% (446 out of 1,246) of the DARs that are more accessible in wild-type than in Otx2-/- cells (Figure 3B-C). Notably, these regions are mainly located distal to genes (91%, Figure 3D), despite the increased fraction of promoter regions bound by OTX2 in EpiLCs (Figure S1A)."

      Are the authors rounding percentages with 2 significant digits, as suggested by the "91%"? If so, 446/1245 ~ 36%, not 40%. 3. The results in Figure 4 are nice and the real meat of the paper. One suggestion: It would be helpful is Fig. 4B were split up between the 446 and 800 genes instead of showing all 1246, and if the WT control was shown in the same figure as well. 4. "Enforced OTX2 expression opens additional somatic regulatory regions" - it would be clearer to say "OTX2 overexpression opens additional somatic regulatory regions", since this is really about DARs between EpiLCs that already express OTX2 and those forced to express higher than WT endogenous levels by the OTX2-ER system?

      Significance

      Also see summary. Understanding what restricts cells to germline vs somatic lineages is an important question. By providing functional data showing that OTX2 directly controls chromatin accessibility, the authors add an important layer of understanding to their previous finding that OTX2 plays a key role in preventing mouse germline entry. The use of their previously established OTX2-null cells expressing OTX2-ER to rapidly induce nuclear OTX2 in a mutant background or the most part makes their experiments elegant and convincing. In focusing on the role of one gene in one event in one species, it is specialized and narrow in scope and will mostly be of interest to experts in the field, but there is nothing wrong with that.

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      Referee #2

      Evidence, reproducibility and clarity

      Barbieri and Chambers explore the role of OTX2 on mouse pluripotency and differentiation. To do so, they examine how the chromatin accessibility and OTX2 binding landscape changes across pluripotency, the exit of pluripotency towards formative and primed states, and through to PGCLC/somatic differentiation. The work mostly represents a resource for the community, with possible implications for our understanding of how OTX2 might mediate the germline-soma switch of fates. While the findings of the work are modest, the results seem solid and the manuscript is clear and well-written. I have some comments as indicated below:

      • The comparison between Otx2-/- cells in the presence of PGCLC cytokines compared to WT cells in the absence of cytokines seems like it is missing controls to me. I assume the authors wanted to enable homogeneous populations to facilitate their bulk sequencing methods, but it seems to me like they are comparing apples with oranges. It would have been better to have the reciprocal situations (Otx2-/- cells in basal differentiation medium, and WT cells in PGCLC cytokines) with a sorting strategy to better unpick the differences between the presence and absence of Otx2 in the 2 protocols. Having said that, the authors are careful not to draw many comparisons between those populations so I don't think this omission affects their current claims. They should however clarify whether the flow cytometry (Supp Fig2) was used for sorting cells or if all cells were taken for bulk sequencing.
      • The authors focus solely on the activating role of Otx2 in their data, but given the substantial proportion of DARs that decrease following Otx2 depletion, I presume it is possible that it also has a repressive effect? Either way, this should be discussed.
      • Throughout the text, the authors subject cells (WT / Otx2-/- /Otx2ER ) to different protocols to look at accessibility and Otx2 binding, but with no mention of the cell fate differences that occur in these different conditions. For instance, it is unclear to me to which fate the WT cells without PGCLC cytokines go - I presume this is neural but perhaps this is a mixed fate, given that they are in GK15 rather than N2B27. Likewise, the OTX2ER experiments may promote a mixed population between PGCLC/somatic fates, and this is never described. Ideally transcriptomic data would be collected, but failing that, qPCR data should be obtained to examine this more closely.
      • The authors also state that "OTX2 facilitates Fgf5 transcription' (page10) but provide no transcriptional data to substantiate this claim. Again RT-qPCR would help make this point.
      • The authors state that d2 PGCLCs "show an intermediate position between ESCs and EpiLCs" based on the PCA location. They should be careful to qualify that this is only in the first 2 principal components, because it may well be the case (and is likely) that in other components the PGCLC population is far removed from the pluripotent states.
      • It is unclear to me what the 'increase[d] accessibility' (eg abstract final sentence, Figure 3E) really means at the cellular level. Does this indicate that more cells have this site open, and does this have implications for the heterogeneity of cell fates observed? Since the authors are concerned with fate decisions, this seems like an important consideration that should at least be discussed.

      Minor Suggestions:

      • Presumably the regions bound by OTX2 in Tet2, Mycn and Fgf5 (Fig1E) are called enhancers because these are known from existing literature. It would be helpful to cite the relevant references to this in the text for those unfamiliar with these.
      • On page 13, the authors say "To determine whether OTX2 expression is essential to maintain chromatin accessibility in somatic cells..." but this does not seem to be what they test because they are using PGCLC medium. Perhaps I misunderstood, but this could be clarified.
      • On page 14 the authors claim, "These results indicate that...the partner proteins that OTX2 act alongside differ...". While this may be the case, their results do not substantiate this, it is just speculation. Should be toned down.
      • Page 18, PGCLC differentiation method sections needs to be described as such (ie. Add "For PGCLC differentiation..." before the second paragraph)
      • It would be helpful to indicate time on the protocol schematics (eg Fig4A, 5A, 5D etc) as I had to keep checking the methods to find out how long the full differentiation time-course was.
      • Since the authors compare between the Tam d0-d2 treatments assessed at d2 versus d4 (Figure5B vs 5E) it would be helpful to make the colourbars the same scale, for both ATAC and Cut&Run datasets.

      Significance

      The study examines the binding of OTX2 and subsequent chromatin accessibility in pluripotent, primed and differentiated (PGCLC/Somatic) cell states, including through Otx2-/- cell lines and temporally-controlled exogenous expression of Otx2. As such, it represents a valuable resource into the potential direct targets of Otx2 and their change in accessibility state across cell types. The work is likely to be of interest to those working on understanding the exit of pluripotency, gene regulatory networks, and chromatin remodelling. My expertise is in cell fate decisions, pluripotency regulation and PGC(LC) differentiation.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      OTX2 is a pivotal transcription factor that regulates the fate choice between somatic and primordial germ cell (PGC) lineages in early mouse development. In the current study, the authors use in vitro stem cell models to demonstrate that OTX2 mediates this developmental fate decision through controlling chromatin accessibility, whereby OTX2 helps to activate putative enhancers that are associated with somatic fate. By extension, those somatic-associated regulatory regions therefore become inaccessible in cells adopting PGC identity in which Otx2 is downregulated.

      Comments

      I enjoyed reading this manuscript. The experiments have been carried out well and for the most part the results provide convincing evidence to support the claims and conclusions in the manuscript. I particularly liked the experiments using the inducible Otx2 transgene to examine the acute changes in chromatin accessibility following restoration of OTX2.

      I include some suggestions below to the authors for additional analyses that I feel would further strengthen their study.

      I also felt that the authors focus almost exclusively on the subset of OTX2-bound sites that lose accessibility in the absence of OTX2. But, as they show in several figure panels, these sites tend to be the minority and that most OTX2-occupied sites do not lose accessibility in Otx2-null cells (actually, more sites tend to gain accessibility). I encourage the authors to modify the text and some of the analyses to give a better balance to their study.

      1. Figure 1: The authors report in the methods that they performed OTX2 CUT&RUN in biological duplicates. It would strengthen their results if they showed in Figure S1 some representative data from each replicate separately to show the consistency.
      2. Figure 1: The authors write: "...OTX2 binds mostly to putative enhancers." Whether these distal sites are enhancers is not sufficiently evidenced in the manuscript, but it is important information to collect to support their model of OTX2 function. The authors should strengthen their analysis by examining whether OTX2 peaks are enriched at previously defined enhancer regions.
      3. Figure 2: I think it would be helpful to remind the reader here that Otx2 is normally downregulated in PGCs, and that Otx2 expression is maintained (at least initially) in somatic cells. This would help explain the logic behind the choice of samples that were profiled. That said, I'm still puzzled why the authors did not examine flow-sorted WT+cyto cells?
      4. Figure 2D: I appreciate that the highlighted region at the Tet2 locus is a DAR, but from the genome tracks it looks as though the region still has high accessibility. Are there any other examples to exemplify a more obvious DAR? Additionally, since twice as many DARs gain accessibility in Otx2-null ESCs compared to lose accessibility, why not show examples of these as well? The same is true of EpiLCs. (Or alternatively, provide a good explanation for why not to show these other categories)
      5. Figure 2: the authors write: "This suggests that OTX2 acts as a pioneer TF...". However, at this point in the manuscript, there is no evidence to support that OTX2 might have pioneer activity. I think this claim would be better suited to later in the manuscript, or in the discussion, following the finding that reintroduction of OTX2 can induce chromatin accessibility at previously closed sites.
      6. Figure 3: I would be tempted to put Figure S3A and S3B into Figure 3. It would be better to show all 1246 DARs together, either ordered by OTX2 CT&RUN signal, or presented in two pre-defined groups (OTX2-bound vs unbound). I also suggest that the author show OTX2 signals and ATAC-seq signals for the 3028 DARs that gain accessibility in Otx2-null EpiLCs (this could be added to a supplemental figure).
      7. Figure 3: What is special about the 8% of OTX2-bound site that lose accessibility, versus the 92% of sites that do not?
      8. Figure 6: Do the PGCLCs with OTX2 expression have chromatin accessibility profiles similar to somatic cells? Consider adding WT somatic cell data to Figure 6A, which could be an interesting comparison with the Tam d0-d2 samples.
      9. Figure 6F: If the 4221 sites are split into those bound by OTX2 versus those that are not (related to Figure 6C) then is there a difference? i.e. are the OTX2-bound sites opening up?
      10. Is there any evidence that OTX2 binds and compacts PGCLC enhancers in somatic cells? I appreciate this is different to the main thrust of the authors' model, but being able to show that OTX2 does not compact these sites lends further support to their preferred model of OTX2 opening sites of somatic lineages.
      11. Discussion: Have prior studies established a connection between OTX2 and chromatin remodellers that can open chromatin? Or, if not, then perhaps this could be proposed as a line of future research.

      Significance

      Strengths

      The results presented provide a careful dissection of the role of OTX2 in controlling chromatin accessibility in different stages of pluripotent to somatic and PGC fates. The authors do a good job of revealing the stage-specific differences in OTX2 occupancy and chromatin accessibility as well as the different responses following the acute reintroduction of OTX2.

      Limitations

      I felt that the authors could present/discuss a bit more on alternative possibilities and models, as it would help the reader to better understand why they favour one model over other ones, and presenting these other possibilities could also provide more support for their preferred model.

      Whether OTX2 is binding to putative enhancers is inferred but could be evidenced more strongly, as that is important for their model.

      Advance

      This study provides key information to understand the mechanisms of OTX2 function in cell fate choice. Similar functions have been shown in other contexts for other transcription factors, but this is a nicely done study and adds to our understanding of how transcription factors function in early development to direct cell-fate decisions.

      Expertise

      My field of expertise lies in the gene regulatory control of early developmental decisions.

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      Reply to the reviewers

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

      The study investigates the relationship between replication timing (RT) and transcription. While there is evidence that transcription can influence RT, the underlying mechanisms remain unclear. To address this, the authors examined a single genomic locus that undergoes transcriptional activation during differentiation. They engineered the Pln locus by inserting promoters of varying strengths to modulate transcription levels and assessed the impact on replication timing using Repli-seq. Key Findings: • Figure 1C and 1D: The data show that higher transcription levels correlate with an advanced RT, suggesting that transcriptional activity influences replication timing. • Figure 2: To determine whether transcription alone is sufficient to alter RT, the authors inserted an hPGK reporter at different genomic locations. However, given the findings in Figure 1, which suggest that this is not the primary mechanism, • Figure 3: The authors removed the marker to examine whether the observed effects were due to the promoter-driven Pln locus, which has significantly larger then the marker. • Figure 4: The study explores the effect of increased doxycycline (Dox) treatment at the TRE (tetracycline response element), further supporting the role of transcription in RT modulation. • Figure 5: The findings demonstrate that Dox-induced RT advancement occurs rapidly, is reversible, and correlates with transcription levels, reinforcing the hypothesis that transcription plays a direct role in influencing replication timing. • Figure 6. Shows that during differentiation transcription of Pln is not required for RT advancement.

      Overall, the study presents a compelling link between transcription and replication timing, though some experimental choices warrant further clarification. I have no major comments.

      __Minor Comments: __Overall, the results are convincing, and the study appears to be well-conducted. In Figure 2, the authors use the hPGK promoter. However, it is unclear why they did not use the constructs from the previous experiments. Given that the hPGK promoter did not advance RT in Figure 1, the results in Figure 2 may not be entirely unexpected.

      We took advantage of previously published cell lines using a PiggyBac Vector designed to pepper the reporter gene at random sites throughout the genome; the point of the experiment was to acquire supporting evidence for the hypothesis that any vector with its selectable marker driven by the hPGK promoter will not advance RT no matter where it is inserted. Since there are reports concluding that transcription per se is sufficient to advance RT, it was important to confirm that there was nothing unique about the particular vector or locus into which we inserted our panel of vectors.

      ACTION DONE: We have now added the following sentence to the results describing this experiment: “____By analyzing RT in these lines, we could evaluate the effect of a different hPGK vector on RT when integrated at many different chromosomal sites. “

      Additionally, the study does not formally exclude the possibility that Pln protein expression itself influences RT. In Figure 1, readthrough transcription at the Pln locus could potentially drive protein expression. It would be useful to know whether the authors address this point in the discussion.

      NOT DONE FOR NEED OF CLARIFICATION: It is unclear why a secreted neural growth factor would have a direct effect on replication timing in embryonic stem cells and, in particular, only in cis (remember there is a control allele that is unaffected). We would be happy to address this in the Discussion if we understood the reviewers’ hypothesis. We cannot respond to this comment without understanding the hypothesis being tested as we do not know how a secreted protein could affect the RT of one allele without affecting the other.

      Regarding the mechanism, if transcription across longer genomic regions contributes to RT changes, transcription-induced could DNA supercoiling play a role. For instance, could negative supercoiling generated by active transcription influence replication timing?

      Yes, many mechanisms are possible.

      ACTION DONE: ____We have added the following sentence to the discussion, referencing a seminal paper on that topic by Nick Gilbert: “ ____For example, long transcripts could remodel a large segment of chromatin, possibly by creating domains of DNA supercoiling (Naughton et. al., 2013____).____”

      It remains puzzling why Pln transcription does not contribute to replication timing during differentiation. Is there any evidence of chromatin opening during this process? For example, are ATAC-seq profiles available that could provide insights into chromatin accessibility changes during differentiation?

      We thank the reviewer for asking this as we should have mentioned something very important here. Lack of necessity for transcription implies that independent mechanisms are functioning to elicit the RT switch. In other work (Turner et. al., bioRxiv, provisionally accepted to EMBO J.), we have shown that specific cis elements (ERCEs) can function to maintain early replication in the absence of transcription.

      ACTION DONE: We now explicitly state in the Discussion: “____This is not surprising, given that ERCEs can maintain early RT in the absence of transcription (Turner, bioRxiv).”

      ACTION TO BE DONE SOON: We will provide a new Figure 6D showing ATAC-seq changes upon differentiation of mESCs to mNPCs and their location relative to the promoter/enhance deletion. As you will see, there is an ATAC-seq site that appears during differentiation, upstream of the deletion. We will hypothesize in the revised manuscript that these are the elements that drive the RT switch and that future studies need to investigate that hypothesis. We have also added the following sentences to the discussion after the sentence above, stating: “____In fact, new sites of open chromatin, consistent with ERCEs appear outside of the deleted Ptn transcription control elements after differentiation (soon to be revised Figure 6D). The necessity and sufficiency of these sites to advance RT independent of transcription will be important to follow up.”

      We also have preliminary data that are part of a separate project in the lab so they are not ready for publication, but are directly relevant to the reviewer’s question. This data shows evidence for a region upstream of the Ptn promoter/enhancer deletion described in Figure 6 that, when deleted, DOES have an effect on the RT switch during differentiation. This deletion overlaps an ATAC-seq site we will show in the new figure 6D.

      Reviewer #1 (Significance (Required)):

      This is a compelling basic single-locus study that systematically compares replication timing (RT) and transcription dynamics while measuring several key parameters of transcription.

      My relevant expertise lies in transcriptional regulation and understanding how noncoding transcription influences local chromatin and gene expression.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In the manuscript entitled: Transcription can be sufficient, but is not necessary, to advance replication timing", the authors use as they state a "reductionist approach" to address a long-standing question in the replication field on what level the process of transcription within a replication domain can alter the underlying replication timing of this domain. The authors use an elegant hybrid mouse embryonic stem cell line to discriminate the two allelic copies and focus on a specific replication domain harboring the neuronal Ptn gene that is only expressed upon differentiation. The authors first introduce four different promoters in the locus upstream of Ptn gene that drive expression of small transgenes. Only the promoters with highest transcriptional induction could advance RT. If the promoters are placed in such a way that they drive expression of the 96kb Ptn gene, then also some the weaker promoters can drive RT advancement, suggesting that it is a combination of transcriptional strength and size of the transcribed domain important for RT changes. Using a DOX-inducible promoter, the authors show that this happens very fast (3-6h after transcription induction) and is reversible as removal of DOX leads to slower RT again. Finally, deleting the promoter of Ptn gene and driving cells into differentiation still advances RT, allowing the authors to conclude that "transcription can be sufficient but not necessary to advance replication timing."

      Major comments: Overall, this is a well designed study that includes all necessary controls to support the author's conclusions. I think it is a very interesting system that the authors developed. The weakness of the manuscript is that there is no mechanistic explanation how such RT changes are achieved on a molecular basis. But I'm confident that the system could be indeed used to further dissect the mechanistic basis for the transcription dependence of RT advancements.

      Therefore, I support publication of this manuscript if a few comments below can be addressed.

      1) Figure 4 shows a titration of different DOX concentrations and provides clear evidence that the degree of RT advancement tracks well with the level of transcription. As the doses of DOX are quite high in this experiment, have the authors checked on a global scale to what extent transcription might be deregulated in neighbouring genes or genome-wide?

      The DOX concentration that we use for all experiments other than the titration is 2 µg/ml, which is quite standard. The high concentrations (up to 16µg/ml) are only used in the titration experiments shown in Figure 4 to demonstrate that we have reached a plateau. In fact, we stated in Materials and Methods that high doses of Dox led to cell toxicity. Looking at the transcription datasets, there are no significant changes in transcription below 8µg/ml, a few dozen significant changes at 8 and more such changes at 16µg/ml of DOX. The tables of genome wide RT and transcription are provided in the manuscript for anyone wishing to investigate the effects of Dox on cellular physiology but at the concentration used in all other experiments (2µg/ml) there are no effects on transcription.

      __ACTION DONE: We have now modified the statement in the Materials and Methods to read: “ ____Mild toxicity and changes in genome-wide transcription were observed at 8µg/ml and more so at 16µg/ml”. __

      2) One general aspect is that the whole study is only focused on the one single Ptn replication domain. Could the authors extend this rather narrow view a bit and also show RT data in the neighbouring domains. This would be particularly important for the DOX titration experiment that has the potential to induce transcriptional deregulation (see comment above).

      __ACTION DONE: We have now added to revised Supplemental Figure 4 a zoom out of 10 Mb surrounding the Ptn gene showing no detectable effects on RT at any of the titration concentrations. __

      __ACTION TO BE DONE SOON: To address the generalization of the findings (length and strength matter), we have repeated the ESC to NPC differentiation and performed both Repli-seq and BrU-seq to evaluate RT changes relative to total genomic nascent transcriptional changes. The sequencing reads for this experiment are in our analyst’s hands so we expect this to be ready within a few weeks. We will provide a new Figure 7 comparing genome-wide changes in RT vs. transcription to determine the significance of length and strength of transcription induction to RT advances and the necessity of transcriptional induction for RT advances. We and other laboratories have performed many integrative analyses of RNA-microarray/RNA-seq data vs. RT changes, but not total genomic nascent transcription and not with a focus on the effect of length and strength of transcription. For example, outcomes that would be consistent with our reductionist findings at the Ptn locus would be if we find domains that are advanced for RT with no induction of transcription (transcription not necessary) and little to no regions showing significant induction of transcription without RT advances. __

      3) Figure 5 shows that the full capacity to advance RT upon DOX induction of the Ptn gene is achieved after 3h to 6h of DOX induction, so substantially less than a full cell cycle in mEScs (12h). This result suggests that origin licensing/MCM loading cannot be the critical mechanism to drive the RT change because only a small fraction of the cells has undergone M/G1-phase where origins are starting to get loaded. As a large fraction of mESCs (60-70%) are S-phase cells in an asynchronous population, the mechanism is likely taking place directly in S-phase. Could the authors try to synchronize cells in G1/S using double-thymidine block, then induce DOX for 3h before allowing cells to reenter S-phase and then check replication timing of the domain? This can be compared to an alternative experiment where transcription is only induced for 3h upon release into S-phase. This could provide more mechanistic insights as to whether transcription is sufficient to drive RT changes in G1 versus S-phase cells.

      We agree that the timing of induction is such that it is very likely that alterations in RT can occur during S phase. The reviewer proposes a reasonable experiment that could be done, but it would require a long delay of this publication to develop and validate those synchronization protocols and we do not have personnel at this time to carry out the experiment. This would be a great initiating experiment for someone to pursue the mechanisms by which transcription can advance RT.

      ACTION DONE: We have added the following sentence to the Discussion section on mechanisms: ____The rapid nature of the RT change after induction of transcription suggests that RT changes can occur after the functional loading of inactive MCM helicases onto chromatin in telophase/early G1 (Dimitrova, JCB, 1999; Okuno, EMBO J. 2001; Dimitrova, J. Cell Sci, 2002), and possibly after S phase begins.

      Minor comments: • Figure 1B and Figure 6A. Quality of the genome browser snapshots could be improved and certain cryptic labelling such as "only Basic displayed by default" could be removed

      ACTION DONE: We have modified these figures.

      • The genome browser tracks appear a bit small across the figures and could be visually improved.

      ACTION DONE: We have modified the genome browser tracks to improve their presentation

      • In figure 1E we see an advancement in RT in Ptn gene caused by nearby enhanced Hyg-TK gene expression induced by mPGK promoter. However, in figure 3D we see mPGK promoter has reduced ability to advance RT of Ptn gene. It would be nice to address this discrepancy in the results.

      The reviewer’s point is well taken. We are not sure of the answer. You can see that the transcription is very low in both cases, while the RT shift is greater in one replicate vs. the other.

      ACTION DONE: We have, rather unsatisfactorily, added the following sentence to the results section describing Figure 3. “____We do not know why the mPGK promoter was so poor at driving transcription in this context.”

      Reviewer #2 (Significance (Required)):

      In my point of view, this is an important study that unifies a large amount of literature into a conceptual framework that will be interesting to a broad audience working on the intertwined fields of gene regulation, transcription and DNA replication, as well as cell fate switching and development.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ In their manuscript, "Transcription can be sufficient, but is not necessary, to advance replication timing," Vouzas et al. take a systematic and reductionist approach to investigate a late-replicating domain on chromosome VI. Here, they examine the effect of transcribing a single gene locus, Pleiotrophin, on replication timing. When inserting or manipulating promoters or transcript lengths using CRISPR-Cas9, replication timing was altered in mESCs as judged by a combination of Repli-Seq, Bru-Seq, and RNA-Seq. Importantly, they found that transcription can be sufficient to advance replication timing depending on the length and strength of the expression of an ectopically transcribed gene. Taken together, the manuscript presents a compelling argument that transcription can advance replication timing but is not necessary for it.

      Major comments • A schematic or conceptual model summarising the major findings of transcription-dependent and independent mechanisms of RT advancement should be included in the discussion to add to the conceptual framework

      NOT DONE: We discussed this at length between the two senior authors and the first author and we do not feel ready to draw a summary model. We do not know what is advancing RT when transcription is induced or not induced, and we are not comfortable choosing one possible model of many. We hope that the added speculations on mechanism in the Discussion will sufficiently convey the future research that we feel needs to be done.

      ACTIONS DONE: In addition to the speculation on mechanism that already was in our Discussion section, we have added: On mechanisms of rapid induction of RT change, we have added to the Discussion: “____The rapid nature of the RT change after induction of transcription suggests that RT changes can occur after the functional loading of inactive MCM helicases onto chromatin in telophase/early G1 (Dimitrova, JCB, 1999; Okuno, EMBO J. 2001; Dimitrova, J. Cell Sci, 2002), and possibly after S phase begins.” And “For example, long transcripts could remodel a large segment of chromatin, possibly by creating domains of DNA supercoiling (Naughton et. al., 2013, PMID ____23416946).____ “ On mechanisms of RT advance in the absence of transcription, we have added the following to the Discussion: “____This is not surprising, given that ERCEs can maintain early RT in the absence of transcription (Turner, bioRxiv). In fact, chromatin features with the properties of ERCEs do appear outside of the deleted Ptn transcription control elements after differentiation (soon to be revised Figure 6C). The necessity and sufficiency of these new chromatin features to advance RT independent of transcription will be important to follow up.”

      • Vouzas et al. spend a substantial part of the manuscript to delve into the requirements to advance RT and even use a Doxycycline-based titration for temporal advancement of RT. Yet, all conclusions come from the use of hybrid-genome mouse embryonic stem cells (mESCs). Therefore, it remains speculative if and whether findings can be generalized to other cell types or organisms. The authors could include another organism/ cell type to strengthen the relevance of their findings to a broader audience, particular as they identified promoters that drive ectopic gene expression without affecting RT. Showcasing this in other model organisms would be of great interest.

      NOT DONE: To set this system up in another cell type or species would take a very long time. We also do not have personnel to carry that approach.

      ACTION TO BE DONE SOON: As an alternative approach that partially addresses this reviewer’s concern, we will provide a new Figure 7 with an analysis of RT changes vs. transcriptional changes when mESCs are differentiated to neural precursor cells. As described above in response to Revier #2s criticism #2, we have repeated the ESC to NPC differentiation and performed both Repli-seq and BrU-seq to evaluate RT changes relative to total genomic nascent transcriptional changes. The sequencing reads for this experiment are in our analyst’s hands so we expect this to be ready within a few weeks. We will compare genome-wide changes in RT vs. transcription to determine the significance of length and strength of transcription induction to RT advances and the necessity of transcriptional induction for RT advances. We and other laboratories have performed many integrative analyses of RNA-microarray/RNA-seq data vs. RT changes, but not total genomic nascent transcription and not with a focus on the effect of length and strength of transcription. For example, outcomes that would be consistent with our reductionist findings at the Ptn locus would be if we find domains that are advanced for RT with no induction of transcription (transcription not necessary) and little to no regions showing significant induction of transcription without RT advances.

      • OPTIONAL: as with the previous point, the authors went to great depth and length to show how ectopic manipulations affect RT changes on a single locus using genome-wide methods. In addition, the manuscript would benefit from the inclusion of other loci, particularly as transcription of the Ptn locus wasn't needed during differentiation to advance RT at all.

      NOT DONE: This rigorous reductionist approach is laborious and to set it up at one gene at a time at additional loci would be a huge effort taking quite a long time.

      ACTION TO BE DONE SOON: (same as response above) As an alternative approach that partially addresses this reviewer’s concern, we will provide a new Figure 7 with an analysis of RT changes vs. transcriptional changes when mESCs are differentiated to neural precursor cells. As described above in response to Reviewer #2s criticism #2, we have repeated the ESC to NPC differentiation and performed both Repli-seq and BrU-seq to evaluate RT changes relative to total genomic nascent transcriptional changes. The sequencing reads for this experiment are in our analyst’s hands so we expect this to be ready within a few weeks. We will compare genome-wide changes in RT vs. transcription to determine the significance of length and strength of transcription induction to RT advances and the necessity of transcriptional induction for RT advances. We and other laboratories have performed many integrative analyses of RNA-microarray/RNA-seq data vs. RT changes, but not total genomic nascent transcription and not with a focus on the effect of length and strength of transcription. For example, outcomes that would be consistent with our reductionist findings at the Ptn locus would be if we find domains that are advanced for RT with no induction of transcription (transcription not necessary) and little to no regions showing significant induction of transcription without RT advances.

      • The same point of Ptn not needing to be transcribed to advance RT of the respective domain, albeit being a very interesting observation, disturbs the flow of the manuscript, as the whole case was built around transcription and this particular locus-containing domain. Maybe one can adapt the storytelling to fit better within the overall framework.

      We would argue that demonstrating induction of Ptn, the only gene in this domain, is sufficient to induce early RT is a logical segway to asking whether, in the natural situation, induction is correlated with advance in RT. Our results show that transcription is sufficient but not necessary, which is expected if there are other mechanisms that regulate RT.

      __ACTION DONE: To make this transition more smooth, we have added the following sentence to the beginning of the results section describing Figure 6: “ ____This raises the question as to whether the natural RT advance that accompanies Ptn induction during differentiation requires Ptn transcription, or whether other mechanisms, such as ERCEs (Sima / Turner) can advance RT independent of transcription. “ __

      ACTION TO BE DONE SOON:____ To finish the work flow in a way that ties length and strength and sufficiency but not necessity in to the theme of natural cellular differentiation, we will provide a new Figure 7 with an analysis of RT changes vs. transcriptional changes when mESCs are differentiated to neural precursor cells, as described above.

      Minor comments • While citations are thorough, some references (e.g., "need to add Wang, Klein, Mol. Cell 2021") are incomplete.

      __ACTION TO BE DONE SOON: We apologize that some references seemed to not be incorporated into the reference manager Mendely. Since we are still planning to add one more figure soon and we will need to add some references for the datasets that will be shown in future Figure 6D, after that draft is ready, we will comb the manuscript for any references that were not entered and correct them. __

      • The text corresponding to Figure 1C could use more explanation for readers not familiar with the depiction of Repli-Seq data.

      ACTION DONE: “____Repli-seq labels nascent DNA with BrdU, followed by flow cytometry to purify cells in early vs. late S phase based on their DNA content, then BrdU-substituted DNA from each of these fractions is immunoprecipitated, sequenced and expressed as a log2 ration of early to late synthesized DNA (log2E/L). BrU-seq labels total nascent RNA, which is then immunoprecipitated an expressed as reads per million per kilobase (RPMK).”

      • Figure 1C needs labelling of the x-axes.

      ACTION DONE: We have now labeled the X axes.

      • Statistical analyses should be used consistently throughout the manuscript and explained in more detail, i.e. significance levels, tests, instead of "Significant differences....calculated using x".

      We used the same analysis for all the Repliseq data and the same analysis for all the Bruseq data. We agree that we did not present this consistently in the figure legends and methods.

      ACTION DONE:____ To correct the confusion we have clarified the statistical methods in the methods section and referred to methods in the figure legends as follows:

      The methods description of statistical significance for RT now reads: “____Statistical significance of RT changes for all windows in each sample, relative to WT, were calculated using RepliPrint (Ryba et al., 2011), with a p-value of 0.01 used as the cut-off for windows with statistically significant differences.”

      The methods description of statistical significance for transcription now reads: “____Differential expression analysis, including the calculation of statistically significant differences in expression, was conducted using the R package DESeq2____. In Figure 1, statistical significance was calculated relative to HTK expression in the parental cell line, which is expected to be zero, since the parental line does not have an HTK insertion. In all other Figures significance was calculated relative to Ptn expression in the parental line, which is expected to be zero, since the parental line does not express Ptn.____”

      The legend to Figure 1C now reads: The red shading indicates 50kb windows with statistically significant differences in RT between WT casteneus and modified 129 alleles, determined as described in Methods.

      The legend to Figure 1E now reads: “The asterisks indicate a significant difference in the levels of HTK expression relative to HTK expression in the parental cell line as described in Methods. ____There are no asterisks for the RT data, as statistical significance was calculated for individual 50kb windows as shown in panel (C).”

      Each time significance is measured in the subsequent legends, it is followed by the phrase “, determined as described in Methods” or “presented as in Figure 1C” or “presented as in Figure 1E” as appropriate.

      __ __ **Referees cross-commenting** __ Comment on Reviewer#1's review__, comment mentioning ATAC-Seq: Another way to look at this could be to investigate for origin usage changes (BrdU-Seq or GLOE-Seq) of chromosome 6 during differentiation.

      NOT DONE: Unfortunately we could not find any studies comparing origin mapping in mESCs and mNPCs.

      Comment on Reviewer#2's review, major comment 3: I do agree with their statement that origin loading cannot be the driver of RT change, as MCM2-7 double hexamer loading is strictly uncoupled from origin firing. Hence, any mechanism responsible for RT advance must happen at the G1/S phase transition or during S-phase, most likely due to the regulated activity of DDK/CDK or the limitation and preferred recruitment of firing factors to early origins. This could be tested through overexpression of said factors.

      NOT DONE: We agree that manipulating these factors would be a reasonable next approach to sort out mechanism. Due to limited resources and personnel, we will not be able to do this in a short period of time. We also argue that these are experiments for the next chapter of the story, likely requiring an entire PhD thesis (or multiple) to sort out.

      ACTION DONE: We have added the following sentence to the Discussion section on mechanisms: ____The rapid nature of the RT change after induction of transcription suggests that RT changes can occur after the functional loading of inactive MCM helicases onto chromatin in telophase/early G1 (Dimitrova, JCB, 1999; Okuno, EMBO J. 2001; Dimitrova, J. Cell Sci, 2002), and possibly after S phase begins.

      Reviewer #3 (Significance (Required)):

      General: This manuscript presents a compelling study investigating the relationship between transcription and replication timing (RT) using a reductionist approach. The authors systematically manipulated transcriptional activity at the Ptn locus to dissect the elements of transcription that influence RT. The study's strengths lie in its rigorous experimental design, clear results, and the reconciliation of seemingly contradictory findings in the existing literature. However, some aspects could be improved, particularly in exploring the mechanistic details of transcription-independent RT regulation at the investigated domain, the generalisability of the findings to other cells/organisms, and enhancing the presentation of certain data (explanation of e.g. Figure 1c, dense figure arrangement, lack of a summary figure illustrating key findings (e.g., correlation between transcription rate, readthrough effects, and RT advancement)).

      Advance: The manuscript directly addresses and reconciles contradictory findings in the literature regarding the effect of ectopic transcription on RT. Previous studies have reported varying effects, with some showing that transcription advances RT (Brueckner et al., 2020; Therizols et al., 2014), while others have shown no effect or only partial effects depending on the insertion site (Gilbert & Cohen, 1990; Goren et al., 2008). The current study conceptually advances the field by systematically testing different promoters and transcript lengths at a single locus (mechanistic insight), demonstrating that the length and strength of transcription, as well as promoter context, influence RT. This presents a unifying concept on how RT can be influenced. The authors also present a tunable system (technical advance) that allows rapid and reversible alterations of RT, which will certainly be useful for future studies and the field.

      Audience: The primary audience will be specialised researchers in the fields of replication timing, epigenetics, and gene regulation. This study may be of interest beyond the specific field of replication timing, such as cancer biology, developmental biology, particularly if a more broader applicability of its tools and concepts can be shown.

      Expertise: origin licensing, origin activation, MCM2-7, yeast and human cell lines

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      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript, "Transcription can be sufficient, but is not necessary, to advance replication timing," Vouzas et al. take a systematic and reductionist approach to investigate a late-replicating domain on chromosome VI. Here, they examine the effect of transcribing a single gene locus, Pleiotrophin, on replication timing. When inserting or manipulating promoters or transcript lengths using CRISPR-Cas9, replication timing was altered in mESCs as judged by a combination of Repli-Seq, Bru-Seq, and RNA-Seq. Importantly, they found that transcription can be sufficient to advance replication timing depending on the length and strength of the expression of an ectopically transcribed gene. Taken together, the manuscript presents a compelling argument that transcription can advance replication timing but is not necessary for it.

      Major comments

      • A schematic or conceptual model summarising the major findings of transcription-dependent and independent mechanisms of RT advancement should be included in the discussion to add to the conceptual framework
      • Vouzas et al. spend a substantial part of the manuscript to delve into the requirements to advance RT and even use a Doxycycline-based titration for temporal advancement of RT. Yet, all conclusions come from the use of hybrid-genome mouse embryonic stem cells (mESCs). Therefore, it remains speculative if and whether findings can be generalized to other cell types or organisms. The authors could include another organism/ cell type to strengthen the relevance of their findings to a broader audience, particular as they identified promoters that drive ectopic gene expression without affecting RT. Showcasing this in other model organisms would be of great interest.
      • OPTIONAL: as with the previous point, the authors went to great depth and length to show how ectopic manipulations affect RT changes on a single locus using genome-wide methods. In addition, the manuscript would benefit from the inclusion of other loci, particularly as transcription of the Ptn locus wasn't needed during differentiation to advance RT at all.
      • The same point of Ptn not needing to be transcribed to advance RT of the respective domain, albeit being a very interesting observation, disturbs the flow of the manuscript, as the whole case was built around transcription and this particular locus-containing domain. Maybe one can adapt the storytelling to fit better within the overall framework.

      Minor comments

      • While citations are thorough, some references (e.g., "need to add Wang, Klein, Mol. Cell 2021") are incomplete.
      • The text corresponding to Figure 1C could use more explanation for readers not familiar with the depiction of Repli-Seq data.
      • Figure 1C needs labelling of the x-axes.
      • Statistical analyses should be used consistently throughout the manuscript and explained in more detail, i.e. significance levels, tests, instead of "Significant differences....calculated using x".

      Referees cross-commenting

      Comment on Reviewer#1's review, comment mentioning ATAC-Seq: Another way to look at this could be to investigate for origin usage changes (BrdU-Seq or GLOE-Seq) of chromosome 6 during differentiation.

      Comment on Reviewer#2's review, major comment 3: I do agree with their statement that origin loading cannot be the driver of RT change, as MCM2-7 double hexamer loading is strictly uncoupled from origin firing. Hence, any mechanism responsible for RT advance must happen at the G1/S phase transition or during S-phase, most likely due to the regulated activity of DDK/CDK or the limitation and preferred recruitment of firing factors to early origins. This could be tested through overexpression of said factors.

      Significance

      General: This manuscript presents a compelling study investigating the relationship between transcription and replication timing (RT) using a reductionist approach. The authors systematically manipulated transcriptional activity at the Ptn locus to dissect the elements of transcription that influence RT. The study's strengths lie in its rigorous experimental design, clear results, and the reconciliation of seemingly contradictory findings in the existing literature. However, some aspects could be improved, particularly in exploring the mechanistic details of transcription-independent RT regulation at the investigated domain, the generalisability of the findings to other cells/organisms, and enhancing the presentation of certain data (explanation of e.g. Figure 1c, dense figure arrangement, lack of a summary figure illustrating key findings (e.g., correlation between transcription rate, readthrough effects, and RT advancement)).

      Advance: The manuscript directly addresses and reconciles contradictory findings in the literature regarding the effect of ectopic transcription on RT. Previous studies have reported varying effects, with some showing that transcription advances RT (Brueckner et al., 2020; Therizols et al., 2014), while others have shown no effect or only partial effects depending on the insertion site (Gilbert & Cohen, 1990; Goren et al., 2008). The current study conceptually advances the field by systematically testing different promoters and transcript lengths at a single locus (mechanistic insight), demonstrating that the length and strength of transcription, as well as promoter context, influence RT. This presents a unifying concept on how RT can be influenced. The authors also present a tunable system (technical advance) that allows rapid and reversible alterations of RT, which will certainly be useful for future studies and the field.

      Audience: The primary audience will be specialised researchers in the fields of replication timing, epigenetics, and gene regulation. This study may be of interest beyond the specific field of replication timing, such as cancer biology, developmental biology, particularly if a more broader applicability of its tools and concepts can be shown.

      Expertise: origin licensing, origin activation, MCM2-7, yeast and human cell lines

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      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript entitled: Transcription can be sufficient, but is not necessary, to advance replication timing", the authors use as they state a "reductionist approach" to address a long-standing question in the replication field on what level the process of transcription within a replication domain can alter the underlying replication timing of this domain. The authors use an elegant hybrid mouse embryonic stem cell line to discriminate the two allelic copies and focus on a specific replication domain harboring the neuronal Ptn gene that is only expressed upon differentiation. The authors first introduce four different promoters in the locus upstream of Ptn gene that drive expression of small transgenes. Only the promoters with highest transcriptional induction could advance RT. If the promoters are placed in such a way that they drive expression of the 96kb Ptn gene, then also some the weaker promoters can drive RT advancement, suggesting that it is a combination of transcriptional strength and size of the transcribed domain important for RT changes. Using a DOX-inducible promoter, the authors show that this happens very fast (3-6h after transcription induction) and is reversible as removal of DOX leads to slower RT again. Finally, deleting the promoter of Ptn gene and driving cells into differentiation still advances RT, allowing the authors to conclude that "transcription can be sufficient but not necessary to advance replication timing."

      Major comments:

      Overall, this is a well designed study that includes all necessary controls to support the author's conclusions. I think it is a very interesting system that the authors developed. The weakness of the manuscript is that there is no mechanistic explanation how such RT changes are achieved on a molecular basis. But I'm confident that the system could be indeed used to further dissect the mechanistic basis for the transcription dependence of RT advancements. Therefore, I support publication of this manuscript if a few comments below can be addressed.

      1. Figure 4 shows a titration of different DOX concentrations and provides clear evidence that the degree of RT advancement tracks well with the level of transcription. As the doses of DOX are quite high in this experiment, have the authors checked on a global scale to what extent transcription might be deregulated in neighbouring genes or genome-wide?
      2. One general aspect is that the whole study is only focused on the one single Ptn replication domain. Could the authors extend this rather narrow view a bit and also show RT data in the neighbouring domains. This would be particularly important for the DOX titration experiment that has the potential to induce transcriptional deregulation (see comment above).
      3. Figure 5 shows that the full capacity to advance RT upon DOX induction of the Ptn gene is achieved after 3h to 6h of DOX induction, so substantially less than a full cell cycle in mEScs (12h). This result suggests that origin licensing/MCM loading cannot be the critical mechanism to drive the RT change because only a small fraction of the cells has undergone M/G1-phase where origins are starting to get loaded. As a large fraction of mESCs (60-70%) are S-phase cells in an asynchronous population, the mechanism is likely taking place directly in S-phase. Could the authors try to synchronize cells in G1/S using double-thymidine block, then induce DOX for 3h before allowing cells to reenter S-phase and then check replication timing of the domain? This can be compared to an alternative experiment where transcription is only induced for 3h upon release into S-phase. This could provide more mechanistic insights as to whether transcription is sufficient to drive RT changes in G1 versus S-phase cells.

      Minor comments:

      • Figure 1B and Figure 6A. Quality of the genome browser snapshots could be improved and certain cryptic labelling such as "only Basic displayed by default" could be removed
      • The genome browser tracks appear a bit small across the figures and could be visually improved.
      • In figure 1E we see an advancement in RT in Ptn gene caused by nearby enhanced Hyg-TK gene expression induced by mPGK promoter. However, in figure 3D we see mPGK promoter has reduced ability to advance RT of Ptn gene. It would be nice to address this discrepancy in the results.

      Significance

      In my point of view, this is an important study that unifies a large amount of literature into a conceptual framework that will be interesting to a broad audience working on the intertwined fields of gene regulation, transcription and DNA replication, as well as cell fate switching and development.

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      Referee #1

      Evidence, reproducibility and clarity

      The study investigates the relationship between replication timing (RT) and transcription. While there is evidence that transcription can influence RT, the underlying mechanisms remain unclear. To address this, the authors examined a single genomic locus that undergoes transcriptional activation during differentiation. They engineered the Pln locus by inserting promoters of varying strengths to modulate transcription levels and assessed the impact on replication timing using Repli-seq.

      Key Findings:

      • Figure 1C and 1D: The data show that higher transcription levels correlate with an advanced RT, suggesting that transcriptional activity influences replication timing.
      • Figure 2: To determine whether transcription alone is sufficient to alter RT, the authors inserted an hPGK reporter at different genomic locations. However, given the findings in Figure 1, which suggest that this is not the primary mechanism,
      • Figure 3: The authors removed the marker to examine whether the observed effects were due to the promoter-driven Pln locus, which has significantly larger then the marker.
      • Figure 4: The study explores the effect of increased doxycycline (Dox) treatment at the TRE (tetracycline response element), further supporting the role of transcription in RT modulation.
      • Figure 5: The findings demonstrate that Dox-induced RT advancement occurs rapidly, is reversible, and correlates with transcription levels, reinforcing the hypothesis that transcription plays a direct role in influencing replication timing.
      • Figure 6. Shows that during differentiation transcription of Pln is not required for RT advancement.

      Overall, the study presents a compelling link between transcription and replication timing, though some experimental choices warrant further clarification. I have no major comments.

      Minor Comments:

      Overall, the results are convincing, and the study appears to be well-conducted. In Figure 2, the authors use the hPGK promoter. However, it is unclear why they did not use the constructs from the previous experiments. Given that the hPGK promoter did not advance RT in Figure 1, the results in Figure 2 may not be entirely unexpected.

      Additionally, the study does not formally exclude the possibility that Pln protein expression itself influences RT. In Figure 1, readthrough transcription at the Pln locus could potentially drive protein expression. It would be useful to know whether the authors address this point in the discussion.

      Regarding the mechanism, if transcription across longer genomic regions contributes to RT changes, transcription-induced could DNA supercoiling play a role. For instance, could negative supercoiling generated by active transcription influence replication timing?

      It remains puzzling why Pln transcription does not contribute to replication timing during differentiation. Is there any evidence of chromatin opening during this process? For example, are ATAC-seq profiles available that could provide insights into chromatin accessibility changes during differentiation?

      Significance

      This is a compelling basic single-locus study that systematically compares replication timing (RT) and transcription dynamics while measuring several key parameters of transcription.

      My relevant expertise lies in transcriptional regulation and understanding how noncoding transcription influences local chromatin and gene expression.

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      Reply to the reviewers

      Manuscript number____: RC-2024-02806R

      Corresponding author(s): Hamed Jafar-Nejad, Carmen Paradas

      Point-by-point description of the revisions

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

      The manuscript by Cho et al uses conditional and inducible conditional mouse models to characterize the function of protein O-glucosyltransferase 1 (POGLUT1), known to cause a type of Limb Girdle Muscular Dystrophy (LGMD-R21), in skeletal muscle satellite cells, differentiation and regeneration. The Authors find that conditional deletion of POGLUT1 in the myogenic progenitors leads to postnatal muscle defects and lethality by postnatal day 30 or so. Muscle progenitors lacking POGLUT1 undergo reduced proliferation and accelerated differentiation, possibly leading to impairment in muscle regeneration. This is supported by an inducible conditional deletion of POGLUT1 in adult satellite cells. Finally, in vitro experiments suggest that POGLUT1 is required for NOTCH pathway activation in myogenic cells and that POGLUT1 could potentially glycosylate specific residues in NOTCH3.

      • We thank the reviewer for the concise summary of the major findings of our manuscript and for her/his comments, which have helped us improve the manuscript.

        Major comments

      1. What is the control used in Figure 1B and other panels? The genotype should be clearly specified instead of saying controls, in all the figures. It will be easier to interpret the data if densitometry of the western blots is provided, normalized to GAPDH levels. Also, is there some POGLUT1 protein remaining in the satellite cells in the cKOs? Data should be shown for an adult time point also (P30 or later), in addition to P0 and P4, to see whether Poglut1 levels are reduced in adult stages in the muscle and satellite cells in the cKOs.
      • As described in the Methods section of the original submission, for all experiments involving Poglut1-cKO, siblings without Poglut1 deletion (Poglut1+/+, Poglut1flox/flox, or Pax7Cre; Poglut1+/+) were used as controls. To address the reviewer's comment, we added this information to the legend of all figures showing Poglut1-cKO
      • In the revised version, the densitometry is provided for all western blot images (Figures 1B and 7A). We added western blot with anti-POGLUT1 on whole muscle extract from P21 animals (Figure 1B), which is the latest stage at which we consistently obtain Poglut1-cKO PAX7 staining and injury studies indicate that at P21 there is a significant reduction in the number of satellite cells. Therefore, isolation of sufficient satellite cells from later stages is not feasible.

        1. In Figure 1C, can the tibia weight be normalized to total body weight instead of tibia length and analyzed? Similarly, can the grip strength measurements in Figure 1G be normalized to total body weight and represented? Grip strength measurements in neonates is tricky; the Authors should clearly explain how this was done. The NMJ defects can be characterized better, especially since the Authors discuss about Agrin in detail.
      • Normalizing tibialis anterior muscle weight to tibia bone length is commonly used and is especially useful when assessing muscle-specific growth or atrophy, as it provides a standardized measure to compare between different mice. To address the reviewer's comment, in the revised manuscript we have provided the tibialis anterior weight (not normalized), tibia length, and then the normalized TA weight (which was also shown in the first submission). Before normalization, the TA weight in cKO showed ~ 56% reduction compared to control (on average). The tibia length did not show a statistically significant difference between cKO and sibling control. After normalization to tibia length, the TA muscle weight still showed a substantial (55%) reduction in cKO mice compared to controls. These data indicate that a reduction in muscle size is the major if not the sole reason for the reduction observed in cKO body size.

      • We reanalyzed the grip strength based on the reviewer's comment and found that when normalized to body weight, the grip strength is not significantly different between cKO and control animals (please see the following figure for the reviewer). Accordingly, we have modified the Results section to avoid any direct suggestion that the muscle function itself is impaired in the cKO animals. Specifically, instead of writing "... suggesting that the muscle function is impaired in the mutant mice" we wrote " ... suggesting reduced muscle strength in the mutant mice". Moreover, at the end of this paragraph, instead of referring to "a severe muscle weakness" we just describe the phenotype as "muscle weakness". Our goal from this paragraph is to suggest that since the animals have much smaller muscles, they are weaker and cannot eat properly; that's why switching to soft food somewhat improves their survival. The observed NMJ defects suggest that there might be functional muscle defects as well. However, to draw conclusions about the muscle function we will need additional experiments, which we believe are beyond the scope of the current manuscript. If the reviewer believes the normalized grip test should be added to the manuscript, we will be happy to do so as a supplementary figure.
      • As stated in the original manuscript, grip test was done starting at P12. Following the Boston University Guideline (https://www.bu.edu/research/ethics-compliance/animal-subjects/animal-care/anesthesia/anesthesia-and-analgesia-neonatal-mice-and-rats-iacuc/; recommended by Jacksons Laboratory when working with neonatal mice), we consider P10 to be the end of the neonatal period in mice. This is also supported by the majority of the papers in literature. For example, in a paper entitled "Quantitative analysis of neonatal skeletal muscle functional improvement in the mouse" (PMID 18310108), the authors show that by P28, most anatomical and gene expression parameters of mouse skeletal muscle are close to adults, and that by P21, these parameters are closer to P28 than the early postnatal mice. Also, they show that the expression of neonatal myosin heavy chain is dramatically reduced between P7 and P14, with another 3-4-fold reduction between P14 and P21. Therefore, for muscle development, similar to many other contexts studied in mice, neonatal period ends between P7 and P14 (most likely P8-P10). Therefore, the grip tests have not been performed on neonatal mice. In the revised manuscript, we have clarified this issue in the Methods section.
      • The NMJ defects were not the main focus of the manuscript and were performed as a standard characterization of this new mouse model. We agree that additional experiments can be performed to better characterize the NMJ defects in this model, but we believe those experiments are beyond the scope of the current manuscript. Also, given the new data and text added to the revised manuscript, we removed the section related to NMJ and agrin from the Discussion to reduce the manuscript length.

        1. What are the small myofibers seen at the corners of the larger myofibers at P21 in the cKOs in Figure 2E? What is the point that the Authors want to conclude from Figure 2C and D? Clearly, if there are fewer satellite cells, Pax7 transcript levels will decrease. In Figure 2D, how are the satellite cell samples normalized between control and cKOs; did they start with equal number of satellite cells or equal amount of satellite cell RNA between control and cKOs? In Figure 2F, representative images for cKOs should be shown? The conclusion from Figure 2F-G is unclear. Since the Authors claim that the weak laminin staining is resolved in the cKOs by P21, why is α-dystroglycan hypoglycosylation seen in the P21 muscle?
      • The structures to which the reviewer is referring are blood vessels. As shown in the following figure generated for the reviewer, staining the muscle sections from control and cKO mice using the endothelial marker CD31 confirms the vascular nature of those structures.

      • The decrease in PAX7+ cells was shown based on antibody staining, but this does not necessarily mean that Pax7 mRNA is similarly reduced. Figure 2C and 2D show that loss of Poglut1 in these cells affects Pax7 expression at the mRNA level. We started with equal amounts of RNA for Figure 2D and have added additional text to describe RNA isolation and RT-PCR in the Methods section of the revised manuscript.

      • Representative images for cKOs are added to the revised Figure 2, as suggested.
      • The conclusion from 2F-G is that a statistically significant reduction is specifically observed in those PAX7+ cells which co-express M-cadherin, which is another well-established satellite cell marker. The data also indicate that some PAX7+ cells persist in the mutant muscle, highlighting the importance of examining the ability of mutant muscle to repair muscle injury, as shown in Figure 4.
      • The glycans on α-dystroglycan mediate its binding to laminin and other extracellular matrix proteins, but laminin expression level is not thought to regulate the degree of α-dystroglycan glycosylation. In other words, laminin staining intensity and the degree of α-dystroglycan are not causally related to each other. What we observed in P21 animals mirrors what we have reported for adult patient biopsies: normal level of extracellular matrix protein collagen VI associated with α-dystroglycan hypoglycosylation. It is worth mentioning that based on our previous report using primary LGMD-R21 patient myoblasts and C2C12 cells treated with a Notch inhibitor, reducing Notch signaling leads to α-dystroglycan hypoglycosylation, likely by altering the differentiation dynamics of the myoblasts (PMID: 27807076).

        1. Representative images should be shown as examples for all time points for both genotypes in Figure 3A. Figure 3H should be represented with statistically significant differences marked clearly. Have the Authors checked whether cell death contributes to the decrease in cultured satellite cells and Pax7+ cells in the cKOs in Figure 3F, G? Are the differences between the control and cKOs in fusion index and Myogenin expression (Figure 3J, K) statistically significant?
      • Representative images are added in a new supplementary figure (Figure S3A) as requested by the reviewer.

      • Thanks for pointing out the lack of statistics in Figure 3H. To address this point and a comment by reviewer 2, we removed those quantifications from the manuscript. Instead, we performed PAX7/MYOD/Ki67 co-staining on cultured cells and quantified the percentage of each cell state based on the expression of these three markers as a more accurate measure of quiescent versus cycling satellite cells, as well as progenitors and precursor cells. The revised quantification and statistical analysis is presented in the revised Figure 3H. This quantification is represented similarly to the data shown in Gattazzo et al 2020.
      • Thanks for bringing up the issue of cell death. To address this point, we performed two rounds of anti-Caspase 3 staining on a limited number of myogenic progenitors that we were able to isolate from cKO and control animals during the revisions. Unfortunately, the stainings did not work. To ensure that we do not rule out the possibility of apoptosis without experimental data, we added the following sentence to the Results section: "These data suggest reduced proliferation of myogenic cells upon loss of Poglut1, although we cannot exclude that cell death also contributes to the reduced cell number."
      • Thanks for pointing out the lack of statistics for these data. We have added additional independent samples to the data presented in 3J, K and found that the differences between the control and cKOs in fusion index and Myogenin expression are indeed statistically significant.

        1. Figure 4 has multiple problems in my opinion. First, P21 is too early a time point to be talking about regeneration, since satellite cells are just transitioning to become quiescent cells at this time (described in Lepper et al, Nature 2009). It is difficult to distinguish the regenerative and neonatal developmental roles of satellite cells in the experiment that the Authors have carried out. Another issue is that the Authors show a decrease in satellite cells in the cKOs; satellite cell function is clearly known to be required for regeneration. Therefore, there is not much novelty that the cKOs exhibit poor regenerative capability. The data shown in Figure 4B-E is more or less a repetition of what has been shown in Figures 2 and 3.
      • After carefully reviewing publications including the one suggested by the reviewer, we found that multiple studies have performed muscle injury experiments at P21 or even younger ages to study regeneration capabilities. Also, please consider that many of our mutant mice are lethal around weaning age which was another reason for selecting P21 as our injury timepoint. To better explain the logic for this choice, we have added the following sentences to the revised manuscript (new text is underlined: "To address this question, we induced muscle injury in mutant and control mice by injecting cardiotoxin (CTX) into TA muscles at P21 (Fig. 4A), an age at which we consistently obtain Poglut1-cKO animals without any dietary changes (Fig. 1E). Importantly, in WT C57BL/6 mice, 51% of PAX7+ cells are reported to be in the quiescent state at P21 (Gattazzo et al., 2020) and the TA muscles of P21 mice exhibit a robust regenerative response to cardiotoxin-induced injury (Lepper et al., 2009)."

      • While the reduction in satellite cells (SCs) in cKOs suggests impaired regeneration, it was essential to confirm this experimentally, as remaining SCs could still compensate. Therefore, it was necessary to assess whether the residual satellite cells could proliferate and contribute to muscle repair. Our results demonstrate that our mutant mice fail to repair muscle, providing evidence that the reduction in PAX7+ cells in Poglut1-cKO mice is functionally significant. The goal of these experiments was not to increase novelty; it was to increase experimental rigor and reproducibility so that we don't draw functional conclusions merely based on tissue staining.
      • While Figures 2 and 3 examine muscle sections and myoblast cultures, Figure 4B-E presents isolated fibers in an ex vivo culture system. This approach allows us to assess satellite cell activation and proliferation in a different injury model. Figure 4 lets us confirm that the proliferation defect observed in the cKO is consistent across multiple experimental conditions. Additionally, we utilized two forms of muscle injury-in vivo and ex vivo-to comprehensively evaluate the function of PAX7+ cells in WT versus mutant muscles. The ex vivo findings from EDL fibers align with our cell culture experiments in Figure 3, further supporting the observed defect in muscle repair. We believe this approach will increase the likelihood of obtaining reproducible data.

        1. In Figure 5C, D, have the Authors checked the Pax7+ cell numbers between the controls and the i-cKOs? Is the difference seen in 1X TAM due to preexisting reduction in satellite cells in the i-cKOs? What is the explanation by the Authors for the large number of Tomato+ fibers seen in the 2X TAM and 3X TAM uninjured muscle (Figure 3C, E)? Figure 5E is difficult to comprehend and should be represented in a clearer manner.
      • New PAX7 staining shows that most of the TOM+ cells (93% in i-cKO, 97% in control) co-express PAX7 (revised Figure 6D and 6E). If preexisting meant "before tamoxifen injection", a preexisting reduction in satellite cells should be highly unlikely, as the Pax7-Cre-ERT2 line has been used by multiple groups to study adult satellite cells, and we have not observed any abnormalities in the Poglut1[flox/flox] strain. Moreover, our control animals harbor both Pax7-Cre-ERT2 and the tdTomato transgenes. The observed difference in 1xTAM most likely reflects spontaneous activation and fusion of these cells following loss of Poglut1, as evidenced by the enhanced appearance of Tomato+ fibers in i-cKO muscles compared to control muscles. At 1xTAM, we can observe a modest increase in Tomato⁺ The reduction becomes more pronounced with repeated tamoxifen, consistent with progressive differentiation of the satellite cell pool and their fusion with myofibers.

      • For Figure 5E (now Figure 6C), we made some changes to make the graph clearer and added a sentence to the figure legend to make it easier to follow.

        1. Densitometry for Figure 6A should be included, since N1 ICD levels seem quite variable in the controls also. POGLUT1 expression and shRNA knockdown efficiency should be shown in Figure 6B. What is the correlation between the NOTCH3 glycosylation with reduced NOTCH pathway activation and satellite cell function? This should be clearly discussed. Why was the NOTCH3 glycosylation assay done in HEK293 cells?
      • In the revised version, we have included densitometry for Figure 6A (now Figure 7A).

      • In the revised version, we show qRT-PCR data indicating that C2C12 cells stably expressing Poglut1 shRNA show ~ 75% decrease in Poglut1 mRNA levels compared to control cells (now Figure 7B), in agreement with our original report of these cells (PMID 21490058). We also performed qRT-PCR for Pax7 and found a significant reduction in Pax7 mRNA levels in Poglut1-knockdown C2C12 cells, further supporting their usage in these signaling assays.
      • As discussed in detail in the Results and Discussion sections of the original submission, reports from our lab and others have provided strong evidence that glycosylation of NOTCH1 and NOTCH2 by POGLUT1 promotes signaling mediated by these receptors. Since it was not clear whether this phenomenon is ligand-specific, in the current manuscript we showed that both DLL1-madiated and JAG1-mediated signaling by NOTCH1 and NOTCH2 require the expression of POGLUT1 in the signal-receiving cells. Since NOTCH3 also plays a key role in muscle stem cell biology (albeit a not so well characterized one), we showed in the current manuscript that reducing POGLUT1 in the NOTCH3-expressing cell also leads to a significant reduction in signaling mediated by this receptor. Moreover, we found that NOTCH3 is broadly glycosylated by POGLUT1. In fact, during the revision, we succeeded in confirming four additional POGLUT1 target sites on the mouse NOTCH3 protein (added to the revised Figure 7D and Supplementary Figure S4). These data suggest that similar to NOTCH1 and NOTCH2, POGLUT1-medaited O-glucosylation of NOTCH3 is required for its signaling. To address the reviewer's comment, in the revised manuscript, we have added the following phrase to the Discussion to address the reviewer's comment: "..., strongly suggesting that O-glucosylation of NOTCH3 by POGLUT1 promotes its ligand-mediated activation."
      • HEK293 cells express robust levels of POGLUT1 and have been used in previous studies for the characterization of POGLUT1-mediated glycosylation of its target proteins. Analysis of overexpressed target proteins in this cell line is used to establish assay conditions and glycopeptide identification parameters and thereby sets the stage to analyze glycosylation of endogenous protein (in this case, mouse NOTCH3) in specific cell types. We note that in cases examined so far, the glycosylation data obtained from overexpressed proteins in HEK293 or other commonly used mammalian cell lines recapitulate the glycosylation of endogenously expressed POGLUT1 targets very well (both in cell culture and in vivo; for example see PMID 27268051).

        Reviewer #1 (Significance (Required)):

        Based on my expertise as a muscle and stem cell biologist, the manuscript is not clearly thought through, with not many novel inferences that one can draw from the data provided. While the manuscript could be informative to muscle biologists and stem cell investigators, several additional experiments are required to better characterize the phenotypes and provide meaningful conclusions from the study. The role of POGLUT1 in the muscle could be of great interest, especially in light of its role in LGMD-R21, as described by some of the Authors previously. Several pieces of data provided in the current manuscript are disjointed, with few connecting links, such as the NMJ characterization, the NOTCH glycosylation data and the regeneration experiments done on P21 neonates. Better quantitation of data is required as well, as detailed below. Overall, the manuscript may be revised to address the specific comments and reconsidered at a suitable journal.

      Response: We hope that our explanations, additional experiments, and changes to the text have helped address the concerns raised by the reviewer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Poglut1 mutations have been identified in an autosomal recessive form of muscular dystrophy. Poglut1 encodes an O-glucosyltransferase that modifies Notch receptors and ligands but also has other substrates. In mice, null mutations in Poglut1 are embryonic lethal, which has previously precluded the analysis of Poglut1's role in muscle development and regeneration using murine models. To address this limitation, the authors generated a conditional Poglut1 allele and introduced mutations using Pax7Cre or Pax7CreERT2. They characterize the phenotypic consequences of these mutations and further demonstrate that signaling through the Notch1, Notch2, and Notch3 receptors requires Poglut1 using the C2C12 cell culture model.

      Some aspects of the manuscript's description of muscle stem cell behavior and regeneration are not fully up to date. These points should be addressed before publication.

      Specific points

      1) Evidence Supporting Reduced Notch Signaling as the Cause of the Phenotype The comparison of muscle phenotypes observed in other Notch signaling mutations strongly supports the hypothesis that the phenotype is due to reduced Notch signaling. Relevant studies, such as those addressing development (PMID: 17194759, PMID: 17360543) and those focusing on adult muscle and regeneration (PMID: 21989910, PMID: 22069237, PMID: 22045613, PMID: 30862660) should be cited and discussed in the manuscript. Including these references will strengthen the argument and provide a broader context for the findings.

      • We thank the reviewer for this comment. To address this point and a similar comment made by reviewer 3, we have modified the Introduction and Discussion in the revised manuscript. In the Introduction, we have added the following sentences and have cited all 6 references listed by the reviewer: "Loss of function studies for key components of the Notch signaling pathway including Rbpj and Dll1 indicated that disruption of this pathway during muscle development leads to premature differentiation and depletion of muscle progenitor cells (Schuster-Gossler et al., 2007; Vasyutina et al., 2007). In addition, multiple studies have shown that Notch signaling is required in adult mice to prevent the spontaneous or premature differentiation of satellite cells and to maintain a muscle stem cell pool capable of repairing muscle damage (Bjornson et al., 2012; Fukada et al., 2011; Lahmann et al., 2019; Mourikis et al., 2012)." In the third paragraph of the Discussion, we have highlighted the similarities between Poglut1-cKO and i-cKO phenotypes and the phenotypes observed in "animals with germline or conditional loss of various Notch pathway components (Bjornson et al., 2012; Fukada et al., 2011; Lahmann et al., 2019; Mourikis et al., 2012; Schuster-Gossler et al., 2007; Vasyutina et al., 2007)".

        2) Figure 1: NMJ Deficits - Pre- or Postsynaptic? The authors describe the mutant synaptic vesicles as showing a significantly reduced evoked neurotransmitter release (quantal content) compared to controls. This phrasing raises the question: are motor neurons mutated in these animals? It should be clarified why the synaptic vesicles are referred to as "mutant." To my knowledge, Pax7Cre does not recombine in motor neurons, and this discrepancy needs to be addressed. The text should be rephrased to accurately reflect the origins of the observed deficits.

      • We are not aware of any reports on recombination in motor neurons by the Pax-Cre line used in our study and had implied this in the initial submission in the following sentence in Discussion: "Therefore, even if agrin is indeed glycosylated by POGLUT1, loss of Poglut1 with Pax7-Cre is not expected to affect the glycosylation of agrin expressed by motoneurons." [please note that given the new data and text added to the revised manuscript, we removed the paragraph related to NMJ and agrin from the Discussion to reduce the manuscript length.] We agree that referring to synaptic vesicles as "mutant" can be misleading and have revised "mutant synaptic vesicles" to "in the NMJs of Poglut1-cKO LAL muscles". To address the discrepancy, we have added the following sentence to the Results section after describing a reduction in quantal content: "Since the Pax7-Cre strain used in our study is not reported to induce recombination in motor neurons (Murdoch et al., 2012), this presynaptic NMJ defect might be secondary to defects in postsynaptic NMJ abnormalities."

        3) Quantification of Myofibers with Internal Nuclei The statement, "We first quantified the ratio of myofibers with internal nuclei, which is an indication for recent fusion of myoblasts to myofibers," is not entirely accurate. Recent studies, such as PMID: 38569550, provide a more nuanced explanation of this phenomenon. The manuscript should reference this study and update the description to ensure it accurately reflects the current understanding of myofiber internal nuclei as markers of muscle pathology or regeneration.

      • We thank the reviewer for bringing this paper to our attention. In the revised manuscript, we have changed the above sentence to make it aligned with the observations of the paper mentioned by the reviewer: "We first quantified the ratio of myofibers with internal nuclei, recently reported to specifically result from the fusion of embryonic myogenic cells during limb myogenesis and also driven by myocyte-myocyte fusion in the first phase of postnatal muscle regeneration (Collins et al., 2024)." We also added the following sentence, which we believe makes our interpretation more accurate in light of Collins et al, 2024: "These data suggest enhanced differentiation of Poglut1-deficient progenitors into myocytes followed by continued myocyte-myocyte fusion and/or a delay in the peripheral migration of internal nuclei which normally occurs in the perinatal period (Collins et al., 2024)." This way, in the revised manuscript we do not link the presence of internal nuclei with recent fusion of myoblasts anymore. Finally, we also referred to Collins et al 2024 when describing the appearance of internal nuclei in injured muscles 5 days after injury (Figure 4A).

        4) Figure 3H: Cell Identification in Culture

        The use of PAX7+ MYOD− to identify quiescent cells in culture is not accurate. Instead, PAX7+ Ki67− should be used for this purpose. Similarly, PAX7− MYOD+ does not reliably identify differentiating cells. Instead, staining for MYOG should be used to ensure accuracy. The figure and accompanying text should be adjusted accordingly to reflect these updates.

      • To address this issue and a comment by reviewer 1, we performed PAX7/MYOD/Ki67 co-staining on cultured cells and quantified the percentage of each cell state based on the expression of these three markers as a more accurate measure of quiescent versus cycling satellite cells, as well as progenitors and precursor cells. The revised quantification and statistical analysis is presented in the revised Figure 3H. This quantification is represented similarly to the data shown in Gattazzo et al 2020. Based on the reviewer's comment and the results of these quantification, we have modified the Results section as follows (new text is underlined): "These observations indicate that loss of Poglut1 impairs the ability of muscle stem cells to remain in a quiescent state and suggest that the mutant myogenic progenitors might undergo premature differentiation." We then present the fusion index and myogenin staining data to conclude enhanced differentiation.

        5) Description of Satellite Cell Quiescence

        The statement, "About 2-3 weeks after birth, some of the PAX7+ cells generated by active proliferation of embryonic myogenic progenitors enter a quiescent state to generate adult satellite cells," is not entirely correct. The description should be updated based on the findings in PMID: 32763161, which provide a more accurate account of the transition of PAX7+ cells to quiescence and their role in generating adult satellite cells.

      • We thank the reviewer for bringing this paper to our attention. To address this issue and one of the concerns raised by Reviewer 1, we have made the following changes to the corresponding paragraph in the revised version: (A) We removed the statement highlighted by the reviewer. (B) We provided better justification for using P21 mice to assess muscle regeneration by quiescent satellite cells by adding the underlined sentences to this section: "To address this question, we induced muscle injury in mutant and control mice by injecting cardiotoxin (CTX) into TA muscles at P21 (Fig. 4A), an age at which we consistently obtain Poglut1-cKO animals without any dietary changes (Fig. 1E). Importantly, 51% of PAX7+ cells are reported to be in the quiescent state at P21 (Gattazzo et al., 2020) and the TA muscles of P21 mice exhibit a robust regenerative response to cardiotoxin-induced injury (Lepper et al., 2009)." We note that Gattazzo et al 2020 is PMID: 32763161, the paper with a more accurate account of satellite cell quiescence mentioned by the reviewer.

        6) Figure 5: Loss of Quiescence in Satellite Cells

        A hallmark phenotype of mutations in Notch signaling genesis the loss of quiescence in satellite cells when the mutation is introduced in the adult,. The authors should include data on Ki67 and MyoD expression in PAX7+ cells of mice with the Poglut1 mutation introduced in the adult by an analysis of the uninjured muscle. This would provide insight into the maintenance of quiescence in the mutant satellite cells.

      • Based on the reviewer's recommendation, we aimed to assess Ki67 and MyoD expression in PAX7⁺ cells of adult inducible-cKO (i-cKO) mice in the uninjured muscle. However, due to technical limitations including antibody species incompatibility and the number of available fluorescence channels, we were unable to perform simultaneous triple staining combined with tdTomato visualization on the same tissue sections. Instead, we utilized the Tomato reporter as a marker for PAX7⁺ cells, based on new data indicating that 93-97% of TOM⁺ cells co-express PAX7 (Figure 6E). We performed Ki67 and MyoD double staining and quantified the percentage of Tomato⁺ cells that expressed one or both of these markers. Our analysis showed that TOM⁺/Ki67⁻/MyoD⁻ (quiescent) satellite cells were significantly reduced in the i-cKO mice compared to controls, while the proportions of single-positive cells (Ki67⁺ or MyoD⁺) were increased (Figure 6F-G). These findings are consistent with loss of quiescence and increased activation of satellite cells upon loss of POGLUT1 in the adult mice.

      • During the revision, we performed additional experiments (not suggested by the reviewers) to better assess the role of Poglut1 in self-renewal of satellite cells upon injury. First, in a new cohort of i-cKO and control animals, we performed two rounds of tamoxifen injections but skipped the first round of injury and only induced CTX injury after the second round of recombination. Interestingly, analysis of 14 dpi muscles from these animals showed a full repair in both control and i-cKO mice (shown in revised Figure 5B). Second, we inspected the repaired control and i-cKO muscle after one round of recombination + injury for the presence of single tdTomato+ cells next to the repaired myofibers, which would represent the satellite cells formed after the repair. While single tdTomato+ cells were readily seen in the repaired muscle in control mice, we did not see any such cells next to the repaired muscle from i-cKO animals (shown in the revised Fig 5C, n = 4 animals per condition). Together, these new data provide strong evidence that loss of Poglut1 in adult muscle stem cells impairs their ability to return to quiescence and form new satellite cells upon repair of injured muscle.

        7) Figure 6A: Assay of Cleaved NOTCH1 Intracellular Domain

        The text describes that satellite cells isolated from Poglut1-cKO muscles showed a strong reduction in the level of cleaved (active) NOTCH1 intracellular domain compared to control cells. It is unclear whether these satellite cells were freshly isolated and directly assayed or cultured before the assay. Please specify this in the result section.

      • These satellite cells were freshly isolated from whole muscle, and the proteins were extracted directly from the isolated satellite cells without culturing them. This is now included in the Results section related to the revised Figure 7A.

        8) Figure 6: Notch Receptor Overexpression in C2C12 Cells The results section should explicitly explain that the Notch 1-3 receptors were overexpressed or transfected in the C2C12 cells. This detail is essential for understanding the experimental design.

      • Thank you. The C2C12 cells were transfected with mouse NOTCH receptors 1, 2, or 3 to overexpress each receptor. This is now included in the Results section as suggested.

        Reviewer #2 (Significance (Required)):

        The manuscript by Cho et al. demonstrates that the muscular dystrophy phenotype associated with Poglut1 mutations is caused by a Notch signaling deficit in muscle stem cells. Although previous studies had suggested this connection, alternative mechanisms could not be excluded, as Poglut1 glycosylates a multitude of proteins. Overall, this is a thorough and careful analysis of Poglut1's role in muscle development and regeneration, providing valuable insights into the mechanisms underlying this rare muscle disease.

      Response: We sincerely thank the reviewer for her/his positive assessment of our manuscript and for the constructive comments.

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

      POGLUT1 is a key enzyme essential for the glycosylation and signaling activity of Notch receptors in C2C12 muscle cells. Elegant work from multiple groups has demonstrated the essential role of Notch signaling in maintaining the quiescence of muscle stem cells known as satellite cells. For instance, genetic deletion of Rbpj in tamoxifen-inducible conditional knockout mouse models causes ectopic expression of MyoD, diminished Pax7 expression, and precocious differentiation and fusion of quiescent satellite cells without entering the cell cycle. This is accompanied by the complete loss of tissue regeneration following repetitive muscle injuries. Hypomorphic mutations of POGLUT1 cause limb-girdle muscular dystrophy (type R21), accompanied by a significant reduction of Notch signaling and a decrease in the number of satellite cells in patient muscles.

      Despite the substantial body of evidence establishing the key function of the POGLUT1-Notch-Pax7/MyoD axis in satellite cells, the in vivo function of POGLUT1 in mice has not been studied. In the present study, Cho et al. carefully examined muscle development and regeneration using the POGLUT1 conditional knockout mouse model. The results, at both phenotypic and cellular/molecular levels, align perfectly with the previously established working model of the POGLUT1-Notch-Pax7/MyoD axis. Overall, the experiment is well-designed and executed, and the data are generally of high quality supporting the conclusions. No specific experimental issues was identified.

      Major suggestions include adding references in the discussion section: 1) studies of Rbpj cKO in mice (PMID: 22069237; 22045613), as genetic deletion of POGLUT1 using the same Pax7 driver showed almost identical phenotypes across all levels: phenotype, cellular fate changes, and gene expression changes; 2) studies of Notch-Rbpj targets (Fig. S1, PMID: 29795344), as this paper identified multiple genes encoding collagen V and VI as direct targets of Notch signaling in quiescent satellite cells. These findings are consistent with the authors' observations in Fig. 2e.

      • We thank the reviewer for the suggestion to add these highly relevant papers to the manuscript. 1) To address this comment and the specific point #1 raised by reviewer 2, we have highlighted the similarities between Poglut1-cKO and i-cKO phenotypes and the phenotypes observed in "animals with germline or conditional loss of various Notch pathway components (Bjornson et al., 2012; Fukada et al., 2011; Lahmann et al., 2019; Mourikis et al., 2012; Schuster-Gossler et al., 2007; Vasyutina et al., 2007)" in the Discussion. Mourikis et al 2012 and Bjornson et al 2012 are the two papers that the reviewer has referred to. 2) To address this point, we have added the following sentences to the Discussion: "Notch signaling has been shown to directly activate the transcription of several genes encoding collagen V and VI in muscle stem/progenitor cells, and collagen V expressed by satellite cells plays a key role in the niche to maintain satellite cell quiescence (Baghdadi et al., 2018). Therefore, ECM abnormalities caused by reduced Notch signaling might contribute to the loss of satellite cell quiescence in i-cKO animals as well." Baghdadi et al, 2018 is PMID: 29795344, to which the reviewer has referred.

        Minor suggestions: 1B: "P0" is an awkward term. Does it refer to newborn day 1 or a near full-term embryo?

      • By saying "P0", we are referring to the day the litters are born.

        Fig. 1C: The y-axis label includes an extra space.

      • The extra space is removed in the revised figure.

        Fig. 1D: Provide the N number for each genotype/diet group.

      • The N number has been added (now Figure 1E).

        Gene nomenclature: Use POGLUT1 exclusively for the human protein and Poglut1 for the mouse protein.

      • According to the guidelines of the International Committee for Standardized Genetic Nomenclature for Mice, protein symbols for mice should use all uppercase letters. Therefore, while the gene symbols for mouse and human are different in terms of uppercase versus lowercase usage, the protein symbols for mouse and human are usually identical. Please see the following webpage: https://www.informatics.jax.org/mgihome/nomen/gene.shtml#ps (accessed January 18, 2025).

        Reviewer #3 (Significance (Required)):

        In the present study, Cho et al. carefully examined muscle development and regeneration using the POGLUT1 conditional knockout mouse model. The results, at both phenotypic and cellular/molecular levels, align perfectly with the known working model of the POGLUT1-Notch-Pax7/MyoD axis in satellite cells and muscle regeneration. Overall, the experiment is well-designed and executed, and the data are generally of high quality.

      Response: We sincerely thank the reviewer for the positive evaluation of our manuscript as well as the helpful suggestions.

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      Referee #3

      Evidence, reproducibility and clarity

      POGLUT1 is a key enzyme essential for the glycosylation and signaling activity of Notch receptors in C2C12 muscle cells. Elegant work from multiple groups has demonstrated the essential role of Notch signaling in maintaining the quiescence of muscle stem cells known as satellite cells. For instance, genetic deletion of Rbpj in tamoxifen-inducible conditional knockout mouse models causes ectopic expression of MyoD, diminished Pax7 expression, and precocious differentiation and fusion of quiescent satellite cells without entering the cell cycle. This is accompanied by the complete loss of tissue regeneration following repetitive muscle injuries. Hypomorphic mutations of POGLUT1 cause limb-girdle muscular dystrophy (type R21), accompanied by a significant reduction of Notch signaling and a decrease in the number of satellite cells in patient muscles.

      Despite the substantial body of evidence establishing the key function of the POGLUT1-Notch-Pax7/MyoD axis in satellite cells, the in vivo function of POGLUT1 in mice has not been studied. In the present study, Cho et al. carefully examined muscle development and regeneration using the POGLUT1 conditional knockout mouse model. The results, at both phenotypic and cellular/molecular levels, align perfectly with the previously established working model of the POGLUT1-Notch-Pax7/MyoD axis. Overall, the experiment is well-designed and executed, and the data are generally of high quality supporting the conclusions. No specific experimental issues was identified.

      Major suggestions include adding references in the discussion section: 1) studies of Rbpj cKO in mice (PMID: 22069237; 22045613), as genetic deletion of POGLUT1 using the same Pax7 driver showed almost identical phenotypes across all levels: phenotype, cellular fate changes, and gene expression changes; 2) studies of Notch-Rbpj targets (Fig. S1, PMID: 29795344), as this paper identified multiple genes encoding collagen V and VI as direct targets of Notch signaling in quiescent satellite cells. These findings are consistent with the authors' observations in Fig. 2e.

      Minor suggestions:

      Fig. 1B: "P0" is an awkward term. Does it refer to newborn day 1 or a near full-term embryo?

      Fig. 1C: The y-axis label includes an extra space.

      Fig. 1D: Provide the N number for each genotype/diet group.

      Gene nomenclature: Use POGLUT1 exclusively for the human protein and Poglut1 for the mouse protein.

      Significance

      In the present study, Cho et al. carefully examined muscle development and regeneration using the POGLUT1 conditional knockout mouse model. The results, at both phenotypic and cellular/molecular levels, align perfectly with the known working model of the POGLUT1-Notch-Pax7/MyoD axis in satellite cells and muscle regeneration. Overall, the experiment is well-designed and executed, and the data are generally of high quality.

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      Referee #2

      Evidence, reproducibility and clarity

      Poglut1 mutations have been identified in an autosomal recessive form of muscular dystrophy. Poglut1 encodes an O-glucosyltransferase that modifies Notch receptors and ligands but also has other substrates. In mice, null mutations in Poglut1 are embryonic lethal, which has previously precluded the analysis of Poglut1's role in muscle development and regeneration using murine models. To address this limitation, the authors generated a conditional Poglut1 allele and introduced mutations using Pax7Cre or Pax7CreERT2. They characterize the phenotypic consequences of these mutations and further demonstrate that signaling through the Notch1, Notch2, and Notch3 receptors requires Poglut1 using the C2C12 cell culture model.

      Some aspects of the manuscript's description of muscle stem cell behavior and regeneration are not fully up to date. These points should be addressed before publication.

      Specific points

      1. Evidence Supporting Reduced Notch Signaling as the Cause of the Phenotype

      The comparison of muscle phenotypes observed in other Notch signaling mutations strongly supports the hypothesis that the phenotype is due to reduced Notch signaling. Relevant studies, such as those addressing development (PMID: 17194759, PMID: 17360543) and those focusing on adult muscle and regeneration (PMID: 21989910, PMID: 22069237, PMID: 22045613, PMID: 30862660) should be cited and discussed in the manuscript. Including these references will strengthen the argument and provide a broader context for the findings. 2. Figure 1: NMJ Deficits - Pre- or Postsynaptic?

      The authors describe the mutant synaptic vesicles as showing a significantly reduced evoked neurotransmitter release (quantal content) compared to controls. This phrasing raises the question: are motor neurons mutated in these animals? It should be clarified why the synaptic vesicles are referred to as "mutant." To my knowledge, Pax7Cre does not recombine in motor neurons, and this discrepancy needs to be addressed. The text should be rephrased to accurately reflect the origins of the observed deficits. 3. Quantification of Myofibers with Internal Nuclei

      The statement, "We first quantified the ratio of myofibers with internal nuclei, which is an indication for recent fusion of myoblasts to myofibers," is not entirely accurate. Recent studies, such as PMID: 38569550, provide a more nuanced explanation of this phenomenon. The manuscript should reference this study and update the description to ensure it accurately reflects the current understanding of myofiber internal nuclei as markers of muscle pathology or regeneration. 4. Figure 3H: Cell Identification in Culture

      The use of PAX7+ MYOD− to identify quiescent cells in culture is not accurate. Instead, PAX7+ Ki67− should be used for this purpose. Similarly, PAX7− MYOD+ does not reliably identify differentiating cells. Instead, staining for MYOG should be used to ensure accuracy. The figure and accompanying text should be adjusted accordingly to reflect these updates. 5. Description of Satellite Cell Quiescence

      The statement, "About 2-3 weeks after birth, some of the PAX7+ cells generated by active proliferation of embryonic myogenic progenitors enter a quiescent state to generate adult satellite cells," is not entirely correct. The description should be updated based on the findings in PMID: 32763161, which provide a more accurate account of the transition of PAX7+ cells to quiescence and their role in generating adult satellite cells. 6. Figure 5: Loss of Quiescence in Satellite Cells

      A hallmark phenotype of mutations in Notch signaling genesis the loss of quiescence in satellite cells when the mutation is introduced in the adult,. The authors should include data on Ki67 and MyoD expression in PAX7+ cells of mice with the Poglut1 mutation introduced in the adult by an analysis of the uninjured muscle. This would provide insight into the maintenance of quiescence in the mutant satellite cells. 7. Figure 6A: Assay of Cleaved NOTCH1 Intracellular Domain

      The text describes that satellite cells isolated from Poglut1-cKO muscles showed a strong reduction in the level of cleaved (active) NOTCH1 intracellular domain compared to control cells. It is unclear whether these satellite cells were freshly isolated and directly assayed or cultured before the assay. Please specify this in the result section. 8. Figure 6: Notch Receptor Overexpression in C2C12 Cells

      The results section should explicitly explain that the Notch 1-3 receptors were overexpressed or transfected in the C2C12 cells. This detail is essential for understanding the experimental design.

      Significance

      The manuscript by Cho et al. demonstrates that the muscular dystrophy phenotype associated with Poglut1 mutations is caused by a Notch signaling deficit in muscle stem cells. Although previous studies had suggested this connection, alternative mechanisms could not be excluded, as Poglut1 glycosylates a multitude of proteins. Overall, this is a thorough and careful analysis of Poglut1's role in muscle development and regeneration, providing valuable insights into the mechanisms underlying this rare muscle disease.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Cho et al uses conditional and inducible conditional mouse models to characterize the function of protein O-glucosyltransferase 1 (POGLUT1), known to cause a type of Limb Girdle Muscular Dystrophy (LGMD-R21), in skeletal muscle satellite cells, differentiation and regeneration. The Authors find that conditional deletion of POGLUT1 in the myogenic progenitors leads to postnatal muscle defects and lethality by postnatal day 30 or so. Muscle progenitors lacking POGLUT1 undergo reduced proliferation and accelerated differentiation, possibly leading to impairment in muscle regeneration. This is supported by an inducible conditional deletion of POGLUT1 in adult satellite cells. Finally, in vitro experiments suggest that POGLUT1 is required for NOTCH pathway activation in myogenic cells and that POGLUT1 could potentially glycosylate specific residues in NOTCH3.

      Major comments

      1. What is the control used in Figure 1B and other panels? The genotype should be clearly specified instead of saying controls, in all the figures. It will be easier to interpret the data if densitometry of the western blots is provided, normalized to GAPDH levels. Also, is there some POGLUT1 protein remaining in the satellite cells in the cKOs? Data should be shown for an adult time point also (P30 or later), in addition to P0 and P4, to see whether Poglut1 levels are reduced in adult stages in the muscle and satellite cells in the cKOs.
      2. In Figure 1C, can the tibia weight be normalized to total body weight instead of tibia length and analyzed? Similarly, can the grip strength measurements in Figure 1G be normalized to total body weight and represented? Grip strength measurements in neonates is tricky; the Authors should clearly explain how this was done. The NMJ defects can be characterized better, especially since the Authors discuss about Agrin in detail.
      3. What are the small myofibers seen at the corners of the larger myofibers at P21 in the cKOs in Figure 2E? What is the point that the Authors want to conclude from Figure 2C and D? Clearly, if there are fewer satellite cells, Pax7 transcript levels will decrease. In Figure 2D, how are the satellite cell samples normalized between control and cKOs; did they start with equal number of satellite cells or equal amount of satellite cell RNA between control and cKOs? In Figure 2F, representative images for cKOs should be shown? The conclusion from Figure 2F-G is unclear. Since the Authors claim that the weak laminin staining is resolved in the cKOs by P21, why is α-dystroglycan hypoglycosylation seen in the P21 muscle?
      4. Representative images should be shown as examples for all time points for both genotypes in Figure 3A. Figure 3H should be represented with statistically significant differences marked clearly. Have the Authors checked whether cell death contributes to the decrease in cultured satellite cells and Pax7+ cells in the cKOs in Figure 3F, G? Are the differences between the control and cKOs in fusion index and Myogenin expression (Figure 3J, K) statistically significant?
      5. Figure 4 has multiple problems in my opinion. First, P21 is too early a time point to be talking about regeneration, since satellite cells are just transitioning to become quiescent cells at this time (described in Lepper et al, Nature 2009). It is difficult to distinguish the regenerative and neonatal developmental roles of satellite cells in the experiment that the Authors have carried out. Another issue is that the Authors show a decrease in satellite cells in the cKOs; satellite cell function is clearly known to be required for regeneration. Therefore, there is not much novelty that the cKOs exhibit poor regenerative capability. The data shown in Figure 4B-E is more or less a repetition of what has been shown in Figures 2 and 3.
      6. In Figure 5C, D, have the Authors checked the Pax7+ cell numbers between the controls and the i-cKOs? Is the difference seen in 1X TAM due to preexisting reduction in satellite cells in the i-cKOs? What is the explanation by the Authors for the large number of Tomato+ fibers seen in the 2X TAM and 3X TAM uninjured muscle (Figure 3C, E)? Figure 5E is difficult to comprehend and should be represented in a clearer manner.
      7. Densitometry for Figure 6A should be included, since N1 ICD levels seem quite variable in the controls also. POGLUT1 expression and shRNA knockdown efficiency should be shown in Figure 6B. What is the correlation between the NOTCH3 glycosylation with reduced NOTCH pathway activation and satellite cell function? This should be clearly discussed. Why was the NOTCH3 glycosylation assay done in HEK293 cells?

      Significance

      Based on my expertise as a muscle and stem cell biologist, the manuscript is not clearly thought through, with not many novel inferences that one can draw from the data provided. While the manuscript could be informative to muscle biologists and stem cell investigators, several additional experiments are required to better characterize the phenotypes and provide meaningful conclusions from the study. The role of POGLUT1 in the muscle could be of great interest, especially in light of its role in LGMD-R21, as described by some of the Authors previously. Several pieces of data provided in the current manuscript are disjointed, with few connecting links, such as the NMJ characterization, the NOTCH glycosylation data and the regeneration experiments done on P21 neonates. Better quantitation of data is required as well, as detailed below. Overall, the manuscript may be revised to address the specific comments and reconsidered at a suitable journal.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity)

      *This study examines the reorganization of the microtubule (MT) cytoskeleton during early neuronal development, specifically focusing on the establishment of axonal and dendritic polarity. Utilizing advanced microscopy techniques, the authors demonstrate that stable microtubules in early neurites initially exhibit a plus-end-out orientation, attributed to their connection with centrioles. Subsequently, these microtubules are released and undergo sliding, resulting in a mixed-polarity orientation in early neurites. Furthermore, the study elegantly illustrates the spatial segregation of microtubules in dendrites based on polarity and stability. The experiments are rigorously executed, and the microscopy data are presented with exceptional clarity. The following are my primary concerns that warrant further consideration by the authors. *

      1. Potential Bias in the MotorPAINT Assay: Kinesin-1 and kinesin-3 motors exhibit distinct preferences for post-translationally modified (PTM) microtubules. Given that kinesin-1 preferentially binds to acetylated microtubules over tyrosinated microtubules in the MotorPAINT assay, the potential for bias in the results arises. Have the authors explored the use of kinesin-3, which favors tyrosinated microtubules, to corroborate the observed microtubule polarity?

      We thank the reviewer for the careful assessment of our manuscript. As the reviewer noted, it has indeed been demonstrated that kinesin-1 prefers microtubules marked by acetylation (Cai et al., PLoS Biol 2009; Reed et al., Curr Biol 2006) and kinesin-3 prefers microtubules marked by tyrosination in cells (Guedes-Dias et al., Curr Biol 2019; Tas et al., Neuron 2017); however, these preferences are limited in vitro, as demonstrated for example in Sirajuddin et al. (Nat Cell Biol 2014). When motor-PAINT was introduced, it was verified that purified kinesin-1 moves over both acetylated and tyrosinated microtubules with no apparent preference in this assay (Tas et al., Neuron 2017). This could be due to the more in vitro-like nature of the motor-PAINT assay (e.g. some MAPs may be washed away) and/or because of the addition of Taxol during the gentle fixation step, which converts all microtubules into those preferred by kinesin-1. We will clarify this in the text.

      Planned revisions:

      • We will clarify the lack of kinesin-1 selectivity in motor-PAINT assays in the text by adding the following sentence in the main text when introducing motor-PAINT: Importantly, while kinesin-1 has been shown to selectively move on stable, highly-modified microtubules in cells (Cai et al., PLoS Biol 2009; Reed et al., Curr Biol 2006), this is not the case after motor-PAINT sample preparation (Tas et al., Neuron 2017).

      Axon-Like Neurites in Stage 2b Neurons: The observation of axon-like neurites in Stage 2b neurons, characterized by an (almost) uniformly plus-end-out microtubule organization, is noteworthy. Have the authors confirmed this polarity using end-binding (EB) protein tracking (e.g., EB1, EB3) in Stage 2b neurons? Do these neurites display distinct morphological features, such as variations in width? Furthermore, do they consistently differentiate into axons when tracked over time using live-cell EB imaging, rather than the MotorPAINT assay? Could stable microtubule anchoring impede free sliding in these neurites or restrict sliding into them? Investigating microtubule sliding dynamics in these axon-like neurites would provide valuable insights.

      We thank the reviewer for highlighting this finding. Early in development, cultured neurons are known to transiently polarize and have axon-like neurites that may or may not develop into the future axon (Burute et al., Sci Adv 2022; Schelski & Bradke, Sci Adv 2022; Jacobson et al., Neuron 2006). In the absence of certain molecular or physical factors (e.g. Burute et al., Sci Adv 2022; Randlett et al., Neuron 2011), this transient polarization is seemingly random and as such, we do not expect the axon-like neurites in stage 2b neurons to necessarily become the axon. Interestingly, anchoring stable microtubules in a specific neurite using cortically-anchored StableMARK (Burute et al., Sci Adv 2022) or stabilizing microtubules in a specific neurite using Taxol (Witte et al., JCB 2008) has been shown to promote axon formation, but these stable microtubules have slower turnover (perhaps necessitating the use of laser severing as in Yau et al., J Neurosci 2016) and may not always bear EB comets given that EB comets are less commonly seen at the ends of stable microtubules (Jansen et al., JCB 2023).

      Planned revision:

      • We will add additional details to the text to clarify the likely transient nature of this polarization in agreement with previous literature and specify that they are otherwise not morphologically distinct.
      • We will perform additional EB3 tracking experiments in Stage 2b neurons to examine potential differences between neurites.

      *Taxol and Microtubule Sliding: Taxol-induced microtubule stabilization is known to induce the formation of multiple axons. Does taxol treatment diminish microtubule sliding and prevent polarity reversal in minor neurites, thereby facilitating their development into axons? *

      We thank the reviewer for this interesting suggestion. Taxol converts all microtubules into stable microtubules. Given that the initial neurites tend to be of mixed polarity, having stable microtubules pointing the "wrong" way may impede sliding and polarity sorting. Alternatively, since it is precisely the stable microtubules that we see sliding between and within neurites using StableMARK, Taxol may also increase the fraction of microtubules undergoing sliding. Because of this, it is not straightforward to predict how Taxol affects microtubule (re-)orientation and sliding. Preliminary motor-PAINT experiments do suggest that the multiple axons induced by Taxol treatment all contain predominantly plus-end-out microtubules, as expected, and that this is the case from early in development. We will further develop these findings to include them in our manuscript.

      Planned revision:

      • We have already performed some experiments in which we treat neurons with 10 nM Taxol and verify that we observe the formation of multiple axons by motor-PAINT. We will perform additional experiments in which we add this low dose of Taxol to the cells and determine its effect on microtubule sliding dynamics.

      *Sorting of Minus-End-Out Microtubules (MTs) in Developing Axons: Traces of minus-end-out MTs are observed proximal to the soma in both Stage 2b axon-like neurites and Stage 3 developing axons (Figure S4). Does this indicate a clearance mechanism for misoriented MTs during development? If so, is this sorting mechanism specific to axons? Could dynein be involved? Pharmacological inhibition of dynein (e.g., ciliobrevin-D or dynarrestin) could assess whether blocking dynein disrupts uniform MT polarity and axon formation. *

      We indeed think that a clearance mechanism is involved for removing misoriented microtubules in the axon after axon specification. Many motor proteins have been implicated in the polarity sorting of microtubules in neurons and for axons, dynein is believed to play a role (Rao et al., Cell Rep 2017; del Castillo et al., eLife 2015; Schelski & Bradke, Sci Adv 2022). A few of these studies already employed ciliobrevin, noting that it increases the fraction of minus-end-out microtubules in axons (Rao et al., Cell Rep 2017) and reduces the rate of retrograde flow of microtubules in immature neurites (Schelski & Bradke, Sci Adv 2022). These findings are in line with the suggestion of the reviewer. Interestingly, however, as we highlight in the discussion, the motility we observe for polarity reversal is extremely slow on average (~60 nm/minute) because the microtubule end undergoes bursts of motility and periods in which it appears to be tethered and rather immobile. Given that most neurites are non-axon-like, we assume these sliding events are mostly not taking place in axons or axon-like neurites. These events may thus be orchestrated by other motor proteins (e.g. kinesin-1, kinesin-2, kinesin-5, kinesin-6, and kinesin-12) that have been implicated in microtubule polarity sorting in neurons. We do observe retrograde sliding of stable microtubules in these neurites at a median speed of ~150 nm/minute, which is again much slower than typical motor speeds and occurs in almost all neurites and not specifically in one or two axon-like neurites. It is thus unclear which motors may be involved, and it is difficult to predict how any drug treatments would affect microtubule polarity.

      Dissecting the mechanisms of microtubule sliding will require many more experiments and will first require the recruitment and training of a new PhD student or postdoc. Therefore, we feel this falls outside the scope of the current work, which carefully maps the microtubule organization during neuronal development and demonstrates the active polarity reversal of stable microtubules during this process.

      Planned revision:

      • We will expand our discussion of the potential mechanisms facilitating polarity sorting in axons and axon-like neurites in the discussion.

      Impact of Kinesin-1 Rigor Mutants on MT Polarity and Dynamics: Would the expression of kinesin-1 rigor mutants alter MT dynamics and polarity? Validation with alternative methods, such as microtubule photoconversion, would be beneficial.

      It is important to note that StableMARK and its effects on microtubule stability have been extensively verified in the paper in which it was introduced (Jansen et al., JCB 2023). At low expression levels (where StableMARK has a speckled distribution along microtubules), StableMARK does not alter the stability of microtubules (e.g., they are still disassembled in response to serum starvation), alter their post-translational modification status or their distribution in the cell, or impede the transport of cargoes along them. Given that we chose to image neurons with very low expression levels of StableMARK (as inferred by the speckled distribution along microtubules), we expect its effects on the microtubule cytoskeleton to be minimal.

      Planned revision:

      • We will clarify the potential effects of StableMARK in the manuscript. We will perform experiments with photoactivatable tubulin to examine whether we still see microtubules that live for over 2 hours. We will furthermore examine whether it allows us to see microtubule sliding between neurites similar to work performed in the Gelfand lab (Lu et al., Curr Biol 2013).

      *Molecular Motors Driving MT Sliding: Which specific motors drive MT sliding in the soma and neurites? If a motor drives minus-end-out MTs into neurites, it must be plus-end-directed. The discussion should clarify the polarity of the involved motors to strengthen the conclusions. *

      We thank the reviewer for highlighting this point and will improve our discussion to clarify the polarity of the involved motors.

      Planned revision:

      • We will expand our discussion of the motors potentially involved in sliding microtubules when revising the manuscript.

      Stability of Centriole-Derived Microtubules: Microtubules emanating from centrioles are typically young and dynamic. How do they acquire acetylation and stability at an early stage? Do centrioles exhibit active EB1/EB3 comets in Stage 1/2a neurons? If these microtubules are severed from centrioles, could knockdown of MT-severing proteins (e.g., Katanin, Spastin, Fidgetin) alter microtubule polarity during neuronal development? A brief discussion would be valuable.

      We thank the reviewer for raising these interesting questions and suggestions. As suggested, we will include a brief discussion of these issues. What is known about the properties of stable microtubules is limited, so it is currently unclear how they are made. For example, we do not know if they are converted from labile microtubules or nucleated by a distinct pathway. If they are nucleated by a distinct pathway, do these microtubules grow in a similar manner as labile microtubules and do they have EB comets at their plus-ends (given that EB compacts the lattice (Zhang et al., Cell 2015, PNAS 2018) and stable microtubules have an expanded lattice in cells (de Jager et al., JCB 2025))? If they are converted, does something first cap their plus-end to limit further growth (given that EB comets are rarely observed at the ends of stable microtubules (Jansen et al., JCB 2023))?

      We also do not know how the activity of the tubulin acetyltransferase αTAT1 is regulated. Is its access to the microtubule lumen regulated or is its enzymatic activity stimulated by some means (e.g., microtubule lattice conformation or a molecular factor)?

      We find the possibility that microtubule severing enzymes release these stable microtubules from the centrioles very exciting and hope to test the effects of their absence on microtubule polarity in the future. We will discuss this in the manuscript as suggested.

      Planned revision:

      • We will expand our discussion about the centriole-associated stable microtubules in the revised manuscript. Minor Points

      • In Movies 3 and 4, please use arrowheads or pseudo-coloring to highlight microtubules detaching from specific points. In Movie 5, please mark the stable microtubule that rotates within the neurite. These annotations would enhance clarity.

      Planned revision:

      • We will add arrowheads/traces to the movies to enhance clarity.* *

      The title states: 'Stable microtubules predominantly oriented minus-end-out in the minor neurites of Stage 2b and 3 neurons.' However, given that the minus-end-out percentage increases after nocodazole treatment but only reaches a median of 0.48, 'predominantly' may be an overstatement. Please consider rewording.

      We thank the reviewer for catching this mistake and will adjust the statement to better reflect the median value.

      Planned revision:

      • We will reword this statement in the revised text.

      *Please compare the StableMARK system with the K560Rigor-SunTag approach described by Tanenbaum et al. (2014). What are the advantages of StableMARK over the SunTag method? *

      While the SunTag is certainly a powerful tool to visualize molecules at low copy number, we believe that StableMARK is more appropriate than the K560Rigor-SunTag tool for our assays due to two main reasons. Firstly, K560Rigor-SunTag is based on the E236A kinesin-1 mutation, while StableMARK is based on the G234A mutation. These are both rigor mutations of kinesin-1 but behave differently; the E236A mutant is strongly bound to the microtubule in an ATP-like state (neck linker docked), while the G234A mutant is also strongly bound, but not in an ATP-like state (Rice et al., Nature 1999). This means that they may have different effects on or preferences of the microtubule lattice. Indeed, while StableMARK (G234A) has been shown to preferentially bind microtubules with an expanded lattice (Jansen et al., JCB 2023; de Jager et al., JCB 2025), this may not be the case for the E236A mutant. In support of this, it has been shown that, while nucleotide free kinesin-1 can expand the lattice of GDP-microtubules at high concentrations (>10% lattice occupancy) in vitro (Peet et al., Nat Nanotechnol 2018; Shima et al., JCB 2018), kinesin-1 in the ATP-bound state does not maintain this expanded lattice (Shima et al., JCB 2018). Thus, we expect the kinesin-1 rigor used by Tanenbaum et al. (Cell 2014) to not be specific for stable microtubules (with an expanded lattice) in cells. In addition, given the dense packing of microtubules in neurites (not well-established in developing neurites, but with an inter-microtubule distance of ~25 nm in axons and ~65 nm in dendrites (Chen et al., Nature 1992)), the very large size of the SunTag could be problematic. The K560Rigor-SunTag tool from Tanenbaum et al. (Cell 2014) is bound by up to 24 copies of GFP (each ~3 nm in size), meaning that it may obstruct or be obstructed by the dense microtubule network in neurites.

      Planned revision:

      • Given that, unlike the K560Rigor-SunTag construct, StableMARK has been carefully validated as a live-cell marker for stable microtubules, we believe that the above discussion goes beyond the scope of the manuscript.* *

      Microscopy data (Movies 2, 3, and 4) show microtubule bundling with StableMARK labeling, which is absent in tubulin immunostaining. Could this be an artifact of ectopic StableMARK expression? If so, a brief note addressing this potential effect would be beneficial.

      As with any overexpression, there is a risk of artifacts. We feel that in the cells presented, the risk of artifacts is limited because we have chosen neurons expressing StableMARK at very low levels. Prior work has demonstrated that in cells where StableMARK has a speckled appearance on microtubules, it has limited undesired effects on stable microtubules or the cargoes moving along them (Jansen et al., JCB 2023). Perhaps some of the apparent differences in the amount of bundling can be explained in that the expansion microscopy images shown may have less apparent bundling because of the improved z-resolution and thus optical sectioning. Any z-slice imaged using expansion microscopy will contain fewer microtubules, so bundling may be less obvious. If we compare the amount of bundling seen in StableMARK expressing cells with the amount of bundling of acetylated microtubules (a marker for stable microtubules) in DMSO/nocodazole treated (non-electroporated) cells imaged by confocal microscopy in Figure S7, we feel that the difference is not so large. Nonetheless, we can briefly address this potential effect in the text.

      Planned revision:

      • We will improve the transparency of the manuscript by briefly mentioning this in the text. Reviewer #1 (Significance)

      It is an important paper challenging established ideas of microtubule organization in neurons. It is important to the wide audience of cell and neurobiologists.__ __

      Reviewer #2 (Evidence, reproducibility and clarity)

      *The manuscript uses state-of-the-art microscopy (e,g. expansion microscopy, motorPAINT) to observe microtubule organization during early events of differentiation of cultured rat hippocampal neurons. The authors confirm previous work showing that microtubules in neurites and dendrites are of mixed polarity whereas they are of uniform plus-end-out polarity in axons. They show that stable microtubules (labeled with antibody against acetylated tubulin) are located in the central region of neurite cross-section across all differentiation stages. They show that acetylated microtubules are associated with centrioles early in differentiation but less so at later stages. And they show that stable microtubules can move from one neurite to another, presumably by microtubule sliding. *

      Comments

      1. *I found the manuscript difficult to read. There are lots of "segregations" of microtubules occurring over these stages of neuronal differentiation: segregation between the center of a neurite and the outer edge with respect to neurite cross-section, segregation between the region proximal to the cell body and the region distal to the cell body, and segregation over time (stages). The authors don't do a good job of distinguishing these and reporting the major findings in a way that is clear and straightforward. *

      We thank the reviewer for their feedback and will go over the text to make it easier to read. Within neurites, we use the word 'segregated' in the manuscript to mean that the microtubules form two spatially separate populations across the width of the neurites (i.e., their cross-section if viewed in 3D). Because of variability seen in the neurites of this stage, this segregation does not always present as a peripheral vs. central enrichment of the different populations of microtubules as we sometimes observed two side-by-side populations instead. We will make sure that we properly define this in the manuscript to avoid any confusion.

      When discussing other types of segregation, we tried to use different wording such as when discussing the proximal-distal distribution of microtubules with different orientations in axon-like neurites in this excerpt:

      Sometimes these axons and axon-like neurites had a small bundle of minus-end-out microtubules proximal to the soma (Figure S4). This suggests that plus-end-out uniformity emerges distally first in these neurites, perhaps by retrograde sliding of these minus-end-out microtubules (see Discussion).

      When discussing changes related to a particular stage, we instead aimed to list which stage we were talking about, such as seen in the discussion:

      Emerging neurites of early stage 2 neurons already contain microtubules of both orientations and these are typically segregated. These emerging neurites also contain segregated networks of acetylated (stable) and tyrosinated (labile) microtubules. In later stage 2, stage 3, and stage 4 neurons, stable (nocodazole-resistant) microtubules are oriented more minus-end-out compared to the total (untreated) population of microtubules; however, in early stage 2 neurons, stable microtubules are preferentially oriented plus-end-out, likely because their minus-ends are still anchored at the centrioles at this stage. The fraction of anchored stable microtubules decreases during development, while the appearance of short stumps of microtubules attached to the centrioles suggests that these microtubules may be released by severing.

      We appreciate the reviewer's concerns and will review the text carefully for clarity.

      Planned revision:

      • We will carefully go through the text when revising the manuscript to ensure that these distinctions are clear and consider using synonyms or other descriptors where they would enhance clarity.

      *The major focus is on microtubule changes between stages 2a and 2b. This is introduced in the text and in the methods but not reflected in Figure 1A which should serve as an orientation of what is to come. It would be helpful to move the information about stages to the main text and/or Figure 1A. *

      We thank the reviewer for pointing this out and will be more explicit about the distinction between stages 2a and 2b in the main text and make the suggested change to Figure 1A.

      Planned revision:

      • We will incorporate the suggested changes in the revised manuscript.

      For Figure 1, the conclusions are generally supported by the data with the exception of the data for stage 2b in 1D and 1H. The images in D and the line scan in H suggest that for stage 2b, minus-end-out are on one edge whereas the plus-end-out are on the other edge of the neurite cross-section. But this is only true for one region along this example neurite. If the white line in D was moved proximal or distal along the neurite, the line scan for stage 2b would look like those of stages 2a and 3.

      We thank the reviewer for noting this in the figure. For these earlier stages in neuronal development, the distribution of different types of microtubules within the neurite is more variable and does not always adhere to the central-peripheral distribution described for more mature neurons (Tas et al., Neuron 2017). We did not intend to suggest that neurites of stage 2b neurons consistently have a different radial distribution of microtubules of opposite orientation, but rather that microtubules of the same orientation tend to bundle together. Sometimes this bundling produces a central or peripheral enrichment, as described for mature neurons (Tas et al., Neuron 2017) and as seen in Figure 1D-F at certain points along the length of the neurites, and sometimes the bundling simply produces two side-by-side populations. To reflect this diversity, we chose two different examples in the figure. The line scans presented in Figure 1H were taken approximately at the midpoint of the presented ROIs. In addition, as our imaging in this case is two-dimensional, we do not want to make explicit claims about the radial distribution of the different populations of microtubules.

      Planned revision:

      • We will adjust our description of this figure in the main text to be more explicit about how we interpret these results. We will ensure that it is apparent that we do not think there is a specific radial distribution of microtubules depending on the developmental stage.

      *For Figure 2, I found it difficult to relate panels A-F to panels G-J. I recommend combining 2G-J with 3A-B for a separate figure focused on the orientation of stable microtubules across different stages. *

      We thank the reviewer for this suggestion and will take it into consideration when preparing the revised manuscript, making sure that our figure organization is well justified.

      For Figure 3, it is difficult to reconcile the traces with the corresponding images - that is, there are many acetylated microtubules in the top view image that appear to contact centrioles but are not in the tracing. Perhaps the tracings would more accurately reflect the localization of the acetylated microtubules in the top view images if a stack of images was shown rather than the max projections. Or if the authors were to stain for CAMSAPs to identify non-centrosomal microtubules. I find the data unconvincing but I do believe their conclusion because it is consistent with published data in the field. The data need to be quantified.

      We thank the reviewer for noting this. Importantly, the tracing was done on a three-dimensional stack of images, whereas we present maximum projections of a few slices in Figure 3C for easy visualization. Projection artifacts indeed make it look as though some additional microtubules are attached to the centrioles, whereas in the three-dimensional stacks it is apparent that they are not. We can include the z-stacks as supplementary material so that readers can also verify this themselves. We will additionally clarify that this is the case in the text related to Figure 3C.

      Planned revision:

      • We will better explain how the tracing was done in the methods section and make a brief note of the projection artifacts in the main text.
      • We will also include the z-stacks as supplementary data.

      *I have a major concern with the conclusions of Figure 4. Here the authors use StableMARK to argue that microtubules do not depolymerize in one neurite and then repolymerize in another neurite but rather can be moved (presumably by sliding) from one neurite to another. The problem is that StableMARK-decorated microtubules do not depolymerize. So yes, StableMARK-decorated microtubules can move from one neurite to another but that does not say anything about what normally happens to microtubules during neuronal differentiation. In addition, the text says that the focus on Figure 4 is on how microtubules change between stages 2a and 2b but data is only shown for stage 2b. *

      As noted by the reviewer, StableMARK can indeed hyperstabilize microtubules when over-expressed; however, it is important to note that this strongly depends on the level of overexpression of the marker. This is discussed in detail in the paper introducing StableMARK, where it is described that at low expression levels, StableMARK does not alter the stability of microtubules (i.e., StableMARK decorated microtubules can still depolymerize/disassemble and they are disassembled in response to serum starvation), alter their post-translational modification status or their distribution in the cell, or impede the transport of cargoes along them (Jansen et al. JCB 2023). Despite this, we agree that it is important to validate these findings in our experimental system (primary rat hippocampal neurons) and so we plan to perform experiments with photoactivatable tubulin to verify the long lifetime of stable microtubules and aim to also observe microtubule sliding (similar to assays performed in the Gelfand lab (Lu et al., Curr Biol 2013)) in the absence of StableMARK.

      Planned revision:

      • We will confirm our findings using photoactivatable tubulin. We hope to demonstrate the long lifetime of the microtubules in this case and observe the sliding of microtubules by another means.
      • We will also revise the text to better explain the potential impacts of StableMARK and that we chose the lowest expressing cells we could find so early after electroporation.

      *The data are largely descriptive and it is of course important to first describe things before one can dive into mechanism. But most of the findings confirm previous work and new findings are limited to showing that e.g. microtubule segregation appears earlier than previously observed. *

      Our study is the first to use Motor-PAINT to carefully map changes in microtubule orientations during neuronal development. Furthermore, it is the first to use the recently introduced live-cell marker for stable microtubules to directly demonstrate the active polarity reversal of stable microtubules during this process.

      Optional: It would be nice if the authors could investigate some potential mechanisms. For example, does knockdown or knockout of severing enzymes prevent the loss of centriolar microtubules shown in Figure 3? Does knockdown or knockout of kinesin-2 or EB1 prevent the reorientation of microtubules (Chen et al 2014)?

      We agree with the reviewer that these are exciting experiments to perform, and we hope to unravel the mechanisms underlying microtubule reorganization in future work. However, this will require many more experiments, as well as the recruitment and training of a new PhD student or postdoc, given that the first author has left the lab. Therefore, we feel that this falls outside the scope of the current work, which carefully maps the microtubule organization during neuronal development and demonstrates the active polarity reversal of stable microtubules during this process.

      *Overall, the methods are presented in such a way that they can be reproduced. One exception is in the motor paint sample prep section: is it three washes for 1 min each or three washes over 1 min? *

      We thank the reviewer for pointing out this mistake and will adjust this step in the methods section accordingly.

      Planned revision:

      • We will revise the methods section to read 'washed three times for 1 minute each'.

      *No statistical analysis is provided. The spread of the data in the violin plots is very large and it is difficult to ascertain how strongly one should make conclusions based on different data spreads between different conditions. *

      We thank the reviewer for noting this and will add statistical tests to the graphs showing the fraction of minus-end-out microtubules in different stages/conditions.

      Planned revision:

      • We will include statistical tests in the specified graphs.

      For Figure S5, the excluded data (axons and axon-like neurites) should also be shown.

      We thank the reviewer for this suggestion and will include this data.

      Planned revision:

      • We will adjust this supplemental figure to also include the specified data.

      *For the movies, it would be helpful to have the microtubule moving from one neurite to another identified in some way as it is difficult to tell what is going on. *

      We thank the reviewer for pointing this out.

      Planned revision:

      • We will trace the microtubule in this movie to enhance clarity.* * Reviewer #2 (Significance)

      A strength of the study is the state-of-the-art microscopy (e,g. expansion microscopy, motorPAINT) and its application to a classic experimental model (rat hippocampal neurons). The information will be useful to those interested in the details of neuronal differentiation. A limitation of the study is that it appears to mostly confirm previous findings in the field (microtubule segregation, loss of centriolar anchoring, microtubule sliding). The advance to the field is that the manuscript shows that these events occur earlier in differentiation that previously known.

      • *

      Reviewer #3 (Evidence, reproducibility and clarity)

      *The study by Iwanski and colleagues explores the establishment of the specific organisation of the neuronal microtubule cytoskeleton during neuronal differentiation. They use cultures of dissociated primary hippocampal rat neurons as a model system, and apply the optimised motor-PAINT technology, expansion microscopy/immunofluorescence and live cell imaging to investigate the polarity establishment and the distribution of differentially modified microtubules during early development. *

      They show that in young neurons microtubules are of mixed polarity, but at this stage already the stable (acetylated) microtubules are preferentially oriented plus-end-out, and are connected to the centrioles. In later stages, the stable microtubules are released from the centrioles and reverse their orientation by moving around inside the cell body and the neurites.

      *Overall, the conclusions are well supported by the presented data. The experiments are conducted thoroughly, the figures are clearly presented (for minor comments, see below) and the manuscript is well and clearly written. *

      Major comments

      1. What is the proportion of neurons with different types of neurites (axon-like, non-axon-like) in stage 2b? (middle paragraph page 5 and Fig 1E). Please provide a quantification. * How was the quantification in Fig 2B-D-F done? Why do the curves all start at 0? Please provide a scheme explaining these measurements. Furthermore, the data in Fig 2B do not reflect the statement "the segregation (...) was less evident" than in later stages (top of page 6): while it is less evident than in stage 2b, it is extremely similar to stage 3. Please revise accordingly.*

      We thank the reviewer for pointing out these important details. We will make the suggested changes in the text, adding the proportion of neurons with different types of neurites and adjusting statement mentioned.

      The radial intensity distributions were quantified as described in Katrukha et al. (eLife 2021). In the methods section, we describe the process in brief:

      To analyze the radial distribution of acetylated and tyrosinated microtubules in expanded neurites, deconvolved image stacks were processed using custom scripts in ImageJ (v1.54f) and MATLAB (R2024b) as described in detail elsewhere (Katrukha et al., 2021). Briefly, on maximum intensity projections (XY plane), we drew polylines of sufficient thickness (300 px) to segment out neurite portions 44 µm (10 µm when corrected for expansion factor) in length proximal to the cell soma. Using Selection > Straighten on the corresponding z-stacks generated straightened B-spline interpolated stacks of the neurite sections. These z-stacks were then resliced perpendicularly to the neurite axis (YZ-plane) to visualize the neurite cross-section. From this, we could semi-automatically find the boundary of the neurite in each slice using first a bounding rectangle that encompasses the neurite (per slice) and then a smooth closed spline (approximately oval). To build a radial intensity distribution from neurite border to center, closed spline contours were then shrunken pixel by pixel in each YZ-slice while measuring ROI area and integrated fluorescence intensity. From this, we could ascertain the average fluorescence intensity per contour iteration, allowing us to calculate a radial intensity distribution by calculating the radius corresponding to each area (assuming the neurite cross-section is circular).

      The curves thus all start at 0 because no intensity "fits" into a circle of radius 0 and then gradually increase because very few microtubules "fit" into circles with the smallest radii.

      Planned revision:

      • We will revise the text to include the suggested changes and add a brief statement to the methods section to explain why the curves start at 0.* *

      *It should be stressed in the text, that the modification-specific antibodies only detect modified microtubules. Thus, in figure 3, in the absence of total tubulin staining, it is possible that there are more microtubules than revealed with the anti-acetylated tubulin antibody. A possible explanation should be discussed. *

      We thank the reviewer for highlighting this point and will adjust the text accordingly.

      Planned revision:

      • We will clarify this in the revised text by adding the following sentence: In addition, given that we specifically stained for acetylated tubulin (a marker for stable microtubules), it is possible that other non-acetylated and thus perhaps dynamic microtubules are also associated with the centrioles.* *

      *OPTIONAL: As discussed in the manuscript's discussion, testing some of the proposed mechanisms regulating microtubule cytoskeleton architecture in development (motors, crosslinkers, severing enzymes) would significantly increase the impact of this study. Exploring these phenomena in a more complex system (3D culture, brain explants) closer to the intricate character of the brain than the 2D dissociated neurons would be a real game-changer. *

      We agree that sorting out the mechanisms driving microtubule reorganization would be very exciting. However, this will require many more experiments, as well as the recruitment and training of a new PhD student or postdoc, given that the first author has left the lab. Therefore, we feel this falls outside the scope of the current work, which carefully maps the microtubule organization during neuronal development and demonstrates the active polarity reversal of stable microtubules during this process.

      Minor comments

      1. *It could be useful to write on each panel whether the images were obtained with expansion or motor-PAINT technique: the rendering of the figures is very similar, and despite the different colour scheme can be confusing. *

      We thank the reviewer for pointing this out.

      Planned revision:

      • We will incorporate this suggestion when revising our manuscript.

      Reviewer #3 (Significance)

      This manuscript provides insights into the establishment of the microtubule cytoskeleton architecture specific to highly polarised neurons. The imaging techniques used, improved from the ones published before (motor-PAINT: Kapitein lab in 2017, U-ExM: Hamel/Guichard lab in 2019), yield beautiful and convincing data, marking an improvement compared to previous studies.

      *However, the novelty of some of the findings is relatively limited. Indeed, a mixed microtubule orientation in very young neurites has already been shown (Yau et al, 2016, co-authored by Kapitein), as has the separate distribution of acetylated and tyrosinated / stable and labile / plus-end-out and plus-end-in microtubules in dendrites (Tas, ..., Kapitein, 2017). *

      *On the other hand, observation of the live movement of microtubules with the resolution allowing to see single (stable) microtubules is new and important. It provides an exciting setup to explore the mechanisms of polarity reversal of microtubules in neuronal development and it is regrettable that these mechanisms have not been explored further. *

      *The association of (stable) microtubules with the centrioles is also a technically challenging analysis. Despite not being able to visualise all microtubules, but only acetylated ones, these data are novel and exciting. *

      *This work will be of interest for neuronal cell biologists, developmental neurobiologists. The impact would be larger if the mechanistic questions were addressed using these sophisticated methodologies. *

      *This reviewer's expertise is the regulation of the microtubule cytoskeleton and its impact on molecular, cellular and organism levels. *

      • *


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      Referee #3

      Evidence, reproducibility and clarity

      The study by Iwanski and colleagues explores the establishment of the specific organisation of the neuronal microtubule cytoskeleton during neuronal differentiation. They use cultures of dissociated primary hippocampal rat neurons as a model system, and apply the optimised motor-PAINT technology, expansion microscopy/immunofluorescence and live cell imaging to investigate the polarity establishment and the distribution of differentially modified microtubules during early development. They show that in young neurons microtubules are of mixed polarity, but at this stage already the stable (acetylated) microtubules are preferentially oriented plus-end-out, and are connected to the centrioles. In later stages, the stable microtubules are released from the centrioles and reverse their orientation by moving around inside the cell body and the neurites.

      Major comments:

      • Overall, the conclusions are well supported by the presented data.

      • What is the proportion of neurons with different types of neurites (axon-like, non-axon-like) in stage 2b? (middle paragraph page 5 and Fig 1E). Please provide a quantification. How was the quantification in Fig 2B-D-F done? Why do the curves all start at 0? Please provide a scheme explaining these measurements. Furthermore, the data in Fig 2B do not reflect the statement "the segregation (...) was less evident" than in later stages (top of page 6): while it is less evident than in stage 2b, it is extremely similar to stage 3. Please revise accordingly.

      • It should be stressed in the text, that the modification-specific antibodies only detect modified microtubules. Thus, in figure 3, in the absence of total tubulin staining, it is possible that there are more microtubules than revealed with the anti-acetylated tubulin antibody. A possible explanation should be discussed.

      • OPTIONAL: As discussed in the manuscript's discussion, testing some of the proposed mechanisms regulating microtubule cytoskeleton architecture in development (motors, crosslinkers, severing enzymes) would significantly increase the impact of this study. Exploring these phenomena in a more complex system (3D culture, brain explants) closer to the intricate character of the brain than the 2D dissociated neurons would be a real game-changer.

      Minor comments:

      • The experiments are conducted thoroughly, the figures are clearly presented (for minor comments, see below) and the manuscript is well and clearly written.

      • It could be useful to write on each panel whether the images were obtained with expansion or motor-PAINT technique: the rendering of the figures is very similar, and despite the different colour scheme can be confusing.

      Significance

      • This manuscript provides insights into the establishment of the microtubule cytoskeleton architecture specific to highly polarised neurons. The imaging techniques used, improved from the ones published before (motor-PAINT: Kapitein lab in 2017, U-ExM: Hamel/Guichard lab in 2019), yield beautiful and convincing data, marking an improvement compared to previous studies.

      • However, the novelty of some of the findings is relatively limited. Indeed, a mixed microtubule orientation in very young neurites has already been shown (Yau et al, 2016, co-authored by Kapitein), as has the separate distribution of acetylated and tyrosinated / stable and labile / plus-end-out and plus-end-in microtubules in dendrites (Tas, ..., Kapitein, 2017).

      • On the other hand, observation of the live movement of microtubules with the resolution allowing to see single (stable) microtubules is new and important. It provides an exciting setup to explore the mechanisms of polarity reversal of microtubules in neuronal development and it is regrettable that these mechanisms have not been explored further.

      • The association of (stable) microtubules with the centrioles is also a technically challenging analysis. Despite not being able to visualise all microtubules, but only acetylated ones, these data are novel and exciting.

      • This work will be of interest for neuronal cell biologists, developmental neurobiologists. The impact would be larger if the mechanistic questions were addressed using these sophisticated methodologies.

      • This reviewer's expertise is the regulation of the microtubule cytoskeleton and it's impact on molecular, cellular and organism levels.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript uses state-of-the-art microscopy (e,g. expansion microscopy, motorPAINT) to observe microtubule organization during early events of differentiation of cultured rat hippocampal neurons. The authors confirm previous work showing that microtubules in neurites and dendrites are of mixed polarity whereas they are of uniform plus-end-out polarity in axons. They show that stable microtubules (labeled with antibody against acetylated tubulin) are located in the central region of neurite cross-section across all differentiation stages. They show that acetylated microtubules are associated with centrioles early in differentiation but less so at later stages. And they show that stable microtubules can move from one neurite to another, presumably by microtubule sliding.

      Comments:

      • I found the manuscript difficult to read. There are lots of "segregations" of microtubules occurring over these stages of neuronal differentiation: segregation between the center of a neurite and the outer edge with respect to neurite cross-section, segregation between the region proximal to the cell body and the region distal to the cell body, and segregation over time (stages). The authors don't do a good job of distinguishing these and reporting the major findings in a way that is clear and straightforward.

      • The major focus is on microtubule changes between stages 2a and 2b. This is introduced in the text and in the methods but not reflected in Figure 1A which should serve as an orientation of what is to come. It would be helpful to move the information about stages to the main text and/or Figure 1A.

      • For Figure 1, the conclusions are generally supported by the data with the exception of the data for stage 2b in 1D and 1H. The images in D and the line scan in H suggest that for stage 2b, minus-end-out are on one edge whereas the plus-end-out are on the other edge of the neurite cross-section. But this is only true for one region along this example neurite. If the white line in D was moved proximal or distal along the neurite, the line scan for stage 2b would look like those of stages 2a and 3.

      • For Figure 2, I found it difficult to relate panels A-F to panels G-J. I recommend combining 2G-J with 3A-B for a separate figure focused on the orientation of stable microtubules across different stages.

      • For Figure 3, it is difficult to reconcile the traces with the corresponding images - that is, there are many acetylated microtubules in the top view image that appear to contact centrioles but are not in the tracing. Perhaps the tracings would more accurately reflect the localization of the acetylated microtubules in the top view images if a stack of images was shown rather than the max projections. Or if the authors were to stain for CAMSAPs to identify non-centrosomal microtubules. I find the data unconvincing but I do believe their conclusion because it is consistent with published data in the field. The data need to be quantified.

      • I have a major concern with the conclusions of Figure 4. Here the authors use StableMARK to argue that microtubules do not depolymerize in one neurite and then repolymerize in another neurite but rather can be moved (presumably by sliding) from one neurite to another. The problem is that StableMARK-decorated microtubules do not depolymerize. So yes, StableMARK-decorated microtubules can move from one neurite to another but that does not say anything about what normally happens to microtubules during neuronal differentiation. In addition, the text says that the focus on Figure 4 is on how microtubules change between stages 2a and 2b but data is only shown for stage 2b.

      • The data are largely descriptive and it is of course important to first describe things before one can dive into mechanism. But most of the findings confirm previous work and new findings are limited to showing that e.g. microtubule segregation appears earlier than previously observed.

      • Optional: It would be nice if the authors could investigate some potential mechanisms. For example, does knockdown or knockout of severing enzymes prevent the loss of centriolar microtubules shown in Figure 3? Does knockdown or knockout of kinesin-2 or EB1 prevent the reorientation of microtubules (Chen et al 2014)?

      • Overall, the methods are presented in such a way that they can be reproduced. One exception is in the motor paint sample prep section: is it three washes for 1 min each or three washes over 1 min?

      • No statistical analysis is provided. The spread of the data in the violin plots is very large and it is difficult to ascertain how strongly one should make conclusions based on different data spreads between different conditions.

      • For Figure S5, the excluded data (axons and axon-like neurites) should also be shown.

      • For the movies, it would be helpful to have the microtubule moving from one neurite to another identified in some way as it is difficult to tell what is going on.

      Significance

      A strength of the study is the state-of-the-art microscopy (e,g. expansion microscopy, motorPAINT) and its application to a classic experimental model (rat hippocampal neurons). The information will be useful to those interested in the details of neuronal differentiation. A limitation of the study is that it appears to mostly confirm previous findings in the field (microtubule segregation, loss of centriolar anchoring, microtubule sliding). The advance to the field is that the manuscript shows that these events occur earlier in differentiation that previously known.

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      Referee #1

      Evidence, reproducibility and clarity

      This study examines the reorganization of the microtubule (MT) cytoskeleton during early neuronal development, specifically focusing on the establishment of axonal and dendritic polarity. Utilizing advanced microscopy techniques, the authors demonstrate that stable microtubules in early neurites initially exhibit a plus-end-out orientation, attributed to their connection with centrioles. Subsequently, these microtubules are released and undergo sliding, resulting in a mixed-polarity orientation in early neurites. Furthermore, the study elegantly illustrates the spatial segregation of microtubules in dendrites based on polarity and stability. The experiments are rigorously executed, and the microscopy data are presented with exceptional clarity. The following are my primary concerns that warrant further consideration by the authors.

      1. Potential Bias in the MotorPAINT Assay: Kinesin-1 and kinesin-3 motors exhibit distinct preferences for post-translationally modified (PTM) microtubules. Given that kinesin-1 preferentially binds to acetylated microtubules over tyrosinated microtubules in the MotorPAINT assay, the potential for bias in the results arises. Have the authors explored the use of kinesin-3, which favors tyrosinated microtubules, to corroborate the observed microtubule polarity?

      2. Axon-Like Neurites in Stage 2b Neurons: The observation of axon-like neurites in Stage 2b neurons, characterized by an (almost) uniformly plus-end-out microtubule organization, is noteworthy. Have the authors confirmed this polarity using end-binding (EB) protein tracking (e.g., EB1, EB3) in Stage 2b neurons? Do these neurites display distinct morphological features, such as variations in width? Furthermore, do they consistently differentiate into axons when tracked over time using live-cell EB imaging, rather than the MotorPAINT assay? Could stable microtubule anchoring impede free sliding in these neurites or restrict sliding into them? Investigating microtubule sliding dynamics in these axon-like neurites would provide valuable insights.

      3. Taxol and Microtubule Sliding: Taxol-induced microtubule stabilization is known to induce the formation of multiple axons. Does taxol treatment diminish microtubule sliding and prevent polarity reversal in minor neurites, thereby facilitating their development into axons?

      4. Sorting of Minus-End-Out Microtubules (MTs) in Developing Axons: Traces of minus-end-out MTs are observed proximal to the soma in both Stage 2b axon-like neurites and Stage 3 developing axons (Figure S4). Does this indicate a clearance mechanism for misoriented MTs during development? If so, is this sorting mechanism specific to axons? Could dynein be involved? Pharmacological inhibition of dynein (e.g., ciliobrevin-D or dynarrestin) could assess whether blocking dynein disrupts uniform MT polarity and axon formation.

      5. Impact of Kinesin-1 Rigor Mutants on MT Polarity and Dynamics: Would the expression of kinesin-1 rigor mutants alter MT dynamics and polarity? Validation with alternative methods, such as microtubule photoconversion, would be beneficial.

      6. Molecular Motors Driving MT Sliding: Which specific motors drive MT sliding in the soma and neurites? If a motor drives minus-end-out MTs into neurites, it must be plus-end-directed. The discussion should clarify the polarity of the involved motors to strengthen the conclusions.

      7. Stability of Centriole-Derived Microtubules: Microtubules emanating from centrioles are typically young and dynamic. How do they acquire acetylation and stability at an early stage? Do centrioles exhibit active EB1/EB3 comets in Stage 1/2a neurons? If these microtubules are severed from centrioles, could knockdown of MT-severing proteins (e.g., Katanin, Spastin, Fidgetin) alter microtubule polarity during neuronal development? A brief discussion would be valuable.

      Minor Points:

      1. In Movies 3 and 4, please use arrowheads or pseudo-coloring to highlight microtubules detaching from specific points. In Movie 5, please mark the stable microtubule that rotates within the neurite

      2. In Movies 3 and 4, please use arrowheads or pseudo-coloring to highlight microtubules detaching from specific points. In Movie 5, mark the stable microtubule that rotates within the same neurite and the microtubule that exits and enters another neurite in the opposite orientation. These annotations would enhance clarity."

      3. The title states: 'Stable microtubules predominantly oriented minus-end-out in the minor neurites of Stage 2b and 3 neurons.' However, given that the minus-end-out percentage increases after nocodazole treatment but only reaches a median of 0.48, 'predominantly' may be an overstatement. Please consider rewording.

      4. Please compare the StableMARK system with the K560Rigor-SunTag approach described by Tanenbaum et al. (2014). What are the advantages of StableMARK over the SunTag method?

      5. Microscopy data (Movies 2, 3, and 4) show microtubule bundling with StableMARK labeling, which is absent in tubulin immunostaining. Could this be an artifact of ectopic StableMARK expression? If so, a brief note addressing this potential effect would be beneficial.

      Significance

      It is an important paper challenging established ideas of microtubule organization in neurons. It is important to the wide audience of cell and neurobiologists.

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      Reply to the reviewers

      We would like to warmly thank all the reviewers for their helpful and fair comments which will increase the quality of our manuscript.

      We would like to inform the reviewers that changes have been made concerning the Figures numbers as follows :

      Figure number in old version

      Figure number in revised manuscript

      1B

      S1C

      S1C

      S1D

      1C

      S2A

      S1D

      S2B

      S1E

      S2C

      1D

      1B

      S2

      S3

      S3

      S4

      S4

      S5

      1. Description of the planned revision

      Reviewer #1

      Major comments 3) Upon food supplementation with 20E the authors could not measure a significant effect on systemic growth or midgut maturation (Fig. S3), whereas the dose of 20E they fed (20µg/ml) was already much higher than endogenous 20E level they measured in the midgut (Fig. 2B).

      We thank the reviewer #1 for this comment.

      Fig. S3 is now Fig. S4

      First, the concentration of 20µg/mL is the final concentration in the fly food and is different from the levels of 20HE we measured in the organs and in the haemolymph, due to the different cell absorption and degradation of the product.

      This concentration of 20µg/mL corresponds to a molar concentration of approximately 0.04mM which is less than the common concentration of 20HE used in the literature in the food (1mM).

      Tiffany V. Roach, Kari F. Lenhart; Mating-induced Ecdysone in the testis disrupts soma-germline contacts and stem cell cytokinesis. Development 1 June 2024; 151 (11): dev202542. doi: https://doi.org/10.1242/dev.202542

      Ahmed, S.M.H., Maldera, J.A., Krunic, D. et al. Fitness trade-offs incurred by ovary-to-gut steroid signalling in Drosophila. Nature 584, 415-419 (2020). https://doi.org/10.1038/s41586-020-2462-y

      The authors should consider to feed larvae with RH5849 (Dr. Ehrenstorfer), which is an insecticide functioning as an ecdysone agonist and was designed for high stability (Wing et al, 1988). RH5849 was already successfully fed to adult Drosophila to investigate the impact of Ecdysone signalling on the adult midgut (Neophytou et al, 2023; Zipper et al, 2025; Zipper et al, 2020) and elicits 20E response. Furthermore, uptake of RH5849 is not limited by the levels of EcI.

      We thank the reviewer #1 for this comment. We ordered that compound and the experiment should be performed in July since the sending date is expected in late June.

      8) The authors should include a discussion of how Ecdysone signalling in postmitotic EC is regulating midgut size, which may include recent data from Edgar and Reiff labs (Ahmed et al, 2020; Zipper et al., 2025; Zipper et al., 2020).

      We thank the reviewer #1 for this comment. We would like to target a format of report for the journal, thus there are some constraints about the number of words. Of course, if the editor allows us to bypass that limit, we would be delighted to cite and discuss these papers.

      9) There are several recent publications showing a role for gut microbiota in regulating oestrogen metabolism in humans, and implications in oestrogen-related diseases such as endometriosis (Baker et al, 2017; Xholli et al, 2023). More precisely bacteria including Lactobacilli strains produce gut microbial β-glucuronidase enzymes, which reactivate oestrogens (Ervin et al, 2019; Hu et al, 2023). As Drosophila ecdysone is the functional equivalent of mammalian oestrogens (Aranda & Pascual, 2001; Martinez et al, 1991; Oberdörster et al, 2001) these publications should be discussed by the authors.

      We thank the reviewer #1 for this comment. We would like to target a format of report for the journal, thus there are some constraints about the number of words. Also, the topics of these papers seem a little bit out of the scope of our manuscript which is focused on the microbiota impact on midgut growth.

      Reviewer #2 Minor Comments

      Figure S2: columns A and B are box plots, while columns C and D are columns with error bars. Presentation of quantitative data should be uniform and ideally as box plots throughout.

      The authors thank the reviewer #2 for this advice and the figure will be further revised.

      Fig. S2 is now Fig. S3


      __Reviewer #3 __

      Major comments:

      The study relies on loss-of-function experiments to manipulate ecdysone signaling; gain-of-function experiments would provide an informative complement. Does feeding ecdysone phenocopy Lp association in GF larvae? Would ecdysone feeding have an additive effect with Lp association? Given the pleiotropic effects of ecdysone on larval phenotypes, a more targeted approach could be used to overexpress transgenes to augment ecdysone signaling.

      We thank the reviewer #3 for this comment. This thought is shared with reviewer #1 and this experiment will be repeated with RH5849. The results are expected in July.

      Minor comments:

      1. For gut and carcass length analysis, the EcR-RNAi and shd-RNAi conditions look slightly smaller in both GF and Lp conditions. Is there a genetic background effect on larval size? It would be helpful to calculate the interaction score between genotype and microbiome status via a 2-way ANOVA with post hoc tests.

      The authors thank the reviewer #3 for this comment. We will further analyse statistically that differences.


      6) In Fig. 3 the authors added the values for numbers of biological replica within the graphs. In Fig. 4 M-P they added the values for number of technical replicas. They should apply adding these two types of values to all graphs and I would suggest to make the difference between biological replica 'n' and technical replica 'N' obvious in the figure.

      The authors thank the reviewer #3 for this comment. We will modify these numbers in the Figures and/or we will clarify these numbers in the legends to not overwrite the Figures.


      The scope of the bibliography seems limited in scope. As one example, Shin et al., 2011 seems quite relevant for this study.

      We thank the reviewer #1 for this comment. We would like to target a format of report for the journal, thus there are some constraints about the number of words. Of course, if the editor allows us to bypass that limit we would be delighted to cite and discuss this paper.

      • *

      2. Description of the revisions that have already been incorporated in the transferred manuscript

      All changes are visible in red in the text of the revised manuscript.

      __Reviewer #1 __

      __Major remarks __

      1) In Fig.2 E - G there is a remarkable difference between controls in D compared to F and E compared to G. The difference between the controls in E and G is stronger than the shown significant difference of EcRRNAi to the control in E. How do the authors explain such a difference of the two (basically equal) controls and the high variance in control values shown in G?

      We thank the reviewer #1 for this comment. As mentioned in the material and methods, the controls are different due to the different RNAi construct. Thus, this can generate variability in such type of developmental experiment.

      Line 253: "UAS-EcRRNAi (BDSC 9327), UAS-dsmCherryRNAI (BDSC 35785), UAS-shadeRNAi (VDRC 108911), and respective RNAi control lines (KK60101)."

      Are the comparisons of control and EcRRNAi shown in D significantly different?

      As mentioned in the figure panel, the EcRRNAi GF and control GF are significantly different and this is discussed in the text as follows in Line 154: "This phenomenon could be explained by genetic background and/or by additional deleterious effect of germ-freeness, as well as a putative contribution of EcR to intestinal functions that are important for systemic growth independently of the contributions of microbiota to adaptive growth."


      4) Lines 167-169: the authors state that 'Size-matched Lp associated larvae, controlRNAi or EcRRNAi, show longer midguts than their relative GF condition (Fig. 3A, B)', but there are no significant statistics shown for this comparison in Fig. 3A, B.

      We thank the reviewer #1 for this comment and we agree that the sentence can be misleading. Thus, we reformulated it as : "Size-matched Lp-associated EcRRNAi larvae show longer midguts than their relative GF controls (Fig. 3A, B)."

      10) Fig. S4 is not mentioned at all in the manuscript.

      We thank the reviewer #1 for this comment and we added the reference to the supplementary Figure 4, now Figure S5 on Line 202 : "In the anterior part, the cells and nuclei are bigger in Lp-associated than GF animals (Fig. 4M-N, Fig.S5). For the posterior part, the cell area was significantly increased in Lp- monoassociated animals compared to GF cell while no change was shown for the nucleus area (Fig. 4O-P, Fig.S5)."

      Minor comments: • The authors are inconsistent in indicating their experimental groups. One example is Fig. S3: In A and B they write the GF groups non-italic, whereas the L.p. groups are written italic. In C - E they only partially write the L.p. groups italic. Furthermore, in A, C - E they write 'L.p.', whereas its written 'Lp' and missing the 'WJL' in B.

      We thank the reviewer #1 for this comment and we corrected that mistake in Fig. S3.

      Fig. S3 is now Fig. S4

      • Line 52: The last 'i' in 'Lactobacilli' is not italic.

      We thank the reviewer #1 for this comment and we corrected that mistake. • Line 122: Spelling error in 'Surpringsinly'

      We thank the reviewer #1 for this comment and we corrected that mistake. • Line 151: Spelling error in 'progenies'. Needs to read 'progeny'.

      We thank the reviewer #1 for this comment and we corrected that mistake. • Lines 231-235: Last part of the sentence is repetitive

      We thank the reviewer #1 for this comment and we corrected that mistake as "Our work paves the way to deciphering the signals delivered by the bacteria that are sensed at the host cellular level and to understand how this microbe-mediated Ecd-dependent midgut growth contributes to the Drosophila larval growth upon malnutrition."

      Reviewer #2 Minor Comments 1. Figure 1 is interesting but challenging to follow. The fonts are very small and challenging to read. Pink on blue background is particularly hard to read and doesn't seem necessary. As the entire manuscript follows from data in Figure 1, I would encourage the authors to revise it with a vie3w to making the results more accessible.

      The authors thank the reviewer #2 for this advice and the Figure 1 has been revised.

      Figure 4 is impressive and important for the overall manuscript. The authors should provide representative images to show how they measured cell area and nucleus area.

      The authors thank the reviewer #2.

      How cell area and nucleus area were measured is described in Figure S4. The reference to this supplementary Figure was missing in the initial manuscript and we deeply apologize for that.

      Reviewer #1 also pointed out that the reference of Figure S4 covering that point was missing in the text and we corrected that point.

      I struggled to follow this sentence (line 215): "Also, it will be interesting to test, beyond their shared growth phenotype, whether they respond differently at the mechanistical level to the presence of bacteria in the anterior compartment." I would encourage the authors to consider alternative formulations.

      The authors thank the reviewer #2 and revised that sentence as follows :

      "Also, it will be interesting to investigate whether the midgut comprises sub-populations of enterocytes that differ in their physiological functions. Indeed, these sub-populations could be differently distributed along the midgut and be localized on anterior and/or posterior parts. Thus, they could present varied responses to the presence of the bacteria."

      __Reviewer #3 __

      Major comments

      Figure 4 title is misleading. No manipulations of ecdysone signaling are performed to demonstrate whether scaling relationships across tissues differ depending on ecdysone. The same experiment should be performed using mex>EcR-RNAi larvae and/or mex>shd-RNAi larvae.

      We thank the reviewer #3 for this comment.

      We agree with the reviewer and the title has been changed as follows and mentioned in red in the manuscript : Midgut-specific adaptive growth promoted by Lp in Drosophila larvae.


      Minor comments:

      It is notable that mex>EcR-RNAi in germ-free larvae exacerbates developmental delay. A possible interpretation is that ecdysone signaling in the germ-free context promotes increased growth rate. Could the authors comment?

      We thank reviewer #3 for this comment.

      Since we described a local effect at the intestine level for Ecd it is unlikely but not totally excluded that intestinal Ecd promotes systemic growth.

      Our comments are here in the text :

      "This phenomenon could be explained by genetic background and/or by additional deleterious effect of germ-freeness, as well as a putative contribution of EcR to intestinal functions that are important for systemic growth independently of the contributions of microbiota to adaptive growth."

      Experimental variation is substantial between the control conditions of the EcR and Shd knockdown experiments; median control + Lp D50 in the EcR experiment is ~6 days whereas in the shade experiment it is ~9 days. Can the authors comment on this between-experiment variation, which seems substantial (similar to the effect size between control + Lp and control GF)?

      We thank reviewer #3 for this comment which was also highlighted by the reviewer #1 and we answered as follows :

      As mentioned in the material in methods, the controls are different due to the different RNAi construct. Thus, this can generate variability in such type of developmental experiment.

      Line 253: "UAS-EcRRNAi (BDSC 9327), UAS-dsmCherryRNAI (BDSC 35785), UAS-shadeRNAi (VDRC 108911), and respective RNAi control lines (KK60101)."

      As mentioned in the figure panel, the EcRRNAi GF and control GF are significantly different and this is discussed in the text as follows in Line 154: "This phenomenon could be explained by genetic background and/or by additional deleterious effect of germ-freeness, as well as a putative contribution of EcR to intestinal functions that are important for systemic growth independently of the contributions of microbiota to adaptive growth."

      The methods detail an ecdysone feeding protocol that I could not find used in the experiments. Please clarify.

      We thank reviewer #3 for this comment.

      We would like to highlight that this protocol is related to an experiment described in Fig. S3 (now Fig.S4) and that supplementary Figure was cited here in the text of the manuscript Line 179 as follows "While the systemic growth of animals is not affected by addition of 20E, a slight trend to faster midgut maturation of GF larvae is observed through the measurements of longer guts (Fig. S4)."

      Also, in supplementary data :

      Fig. S3 : Feeding larvae with 20E does not impact the gut growth.

      (A-B) Addition of 20E has no impact on larval developmental timing (DT) and their D50. From size-matched animals (C), Lp promotes intestinal growth compare to GF (D) but no significant difference is shown in the gut/carcass ratio (E). Animals receiving 20E are represented with color filled circles +Lp (blue), GF (black) and controls without 20E supplementation with empty circles.

      The manuscript would benefit from additional proofreading. The text contains spelling errors throughout. The in-text reference formatting is inconsistent. Figure legends could be improved to better describe the data.

      We thank reviewer #3 for this comment and following the different reviewers comments we improved the manuscript in that way.

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

      Reviewer #1

      __Major remarks __ 2) The authors should consider investigating an EcIRNAi in addition to EcRRNAi. EcR functions as activator, but also as suppressor in the absence of Ecdysone and a EcRRNAi suppresses both functions of EcR. By knocking down EcI the authors would prevent uptake of Ecdysone and thus interfere only with the ligand-induced activating function of EcR.

      We thank reviewer #1 for this comment.

      This experiment has been performed using EcI RNAi but not shown here because in our hands the genetic tool was not efficient (RNA interference does not work effectively) and thus the experiment was not conclusive.

      No phenotype was observed in our study (see Figure attached). Also, the others Oatp family members were tested for their expression in midgut and were found close to null expression.

      5) Why are the authors comparing the carcass length of GF shade RNAi with L.p. control in Fig. 3 D?

      We thank reviewer #1 for this comment. For transparency of the results, these statistics were added. Because in these conditions GF larvae were difficult to rise at the same size than their relative Lp monoassociated. Hence, the carcass length was used to normalize the data.

      7) In Fig. S3C the authors compared L.p. WJL 20E with the GF EtOH control, where is the comparison to the corresponding L.p. WJL EtOH control? The L.p. WJL EtOH control is compared to GF 20E instead.

      We thank reviewer #1 for this comment that will help to clarify our experiment.

      Fig. S3 is now Fig. S4

      For the Fig. S4C, it is a larval size that allows to compare sizes in all conditions independently. That explains that statistics are shown between all conditions. To not overload the Figure the p values not different are not mentioned.

      Reviewer #2 Minor Comments 3. Figure S3 confuses me. It seems that addition of 20E to GF larvae leads to a significant reduction of larval size, and that mono-association with Lp also significantly shortens larval size. Data in Figure 4G suggest that Lp should not affect larval body length relative to GF larvae. Can the authors explain the apparent discrepancy?

      The authors thank the reviewer #2 for this question. Fig. S3 is now Fig. S4.

      This difference could be explained as follows :

      • The developmental experiment in Fig. S3B shows no difference between the two GF conditions. Thus, at the end of the is larval development, systemic growth is similar in both conditions.

      Because performed earlier during development, the larval size experiment shows higher variability in measurements of larval size. Moreover, less larvae are present in the GF 20E condition that could explained that difference.

      • We have previously shown that Lp mono-associated larvae grow faster than GF. Thus, to collect size-matched larvae on the same day, GF or Lp animals come from a different initial day of experiment. Due to biological variability, some differences in timing could be observed between GF and Lp animals.

      Reviewer #3

      Major comments

      1. The authors conclude that intestinal ecdysone signals are not required for Lp-promoted systemic growth. However, their data shows that circulating 20E titer increases in an Lp-dependent manner, and this circulating 20E presumably affects multiple tissues throughout the organism. Since EcR is broadly expressed, can the authors examine how EcR knockdown in other tissues influences systemic growth in Lp-associated larvae? Fat body-specific EcR knockdown seems particularly of interest here given the established relationship between fat body ecdysone signaling and growth (Delanoue et al., 2010). This additional analysis would help clarify whether ecdysone signaling in non-intestinal tissues mediates the Lp-associated growth phenotype.

      We thank reviewer #3 for this comment that will help to clarify our manuscript.

      We would like to emphasize that we never mention in this manuscript that intestinal ecdysone signals are not required for systemic growth. Nevertheless, we highlighted that it is required for Lp-related midgut growth and not rate limiting for Lp-promoted systemic growth:

      Line 179 : "While the systemic growth of animals is not affected by addition of 20E, a slight trend to faster midgut maturation of GF larvae is observed through the measurements of longer guts (Fig. S3). Thus, the intestinal Ecd signaling is required for the midgut growth effect mediated by Lp in a context of malnutrition."

      Line 227: "Specifically, intestinal Ecd signaling is not rate-limiting for Lp-mediated adaptive growth."

      While it will be very interesting to study the effects of Ecd modulation from Fat Body, we feel this is out of the scope of our manuscript that focused on the Lp-based intestinal growth.

      The experimental design compares larvae associated with live Lp versus germ-free larvae provided sterile PBS. Since Lp cells constitute a potential nutrient source for developing larvae, it's unclear whether gene expression differences arise from larvae digesting Lp cells as a nutrient source or from active, microbe-host signaling interactions. To conclusively address this ambiguity, the authors should perform RNA-seq on larvae inoculated with live versus heat-killed Lp. Alternatively, qPCR could be used to provide evidence for the extent to which changes in ecdysone-related gene expression specifically require live Lp.

      We thank reviewer #3 for this comment.

      We (the lab) previously showed that the systemic growth phenotype is supported by bacteria during development and that bacteria have to be alive to support optimal effects (Storelli et al 2018, PMID: 29290388; Consuegra et al 2020a, PMID: 32196485; Consuegra et al 2020b, PMID: 32563155). This topic of bacteria viability has also been directly addressed independently by colleagues and reported recently (da Silva Soares NF, PMID: 37488173). Hence, we did not design our RNAseq with inactivated bacteria. However, if the editor believes this is essential to provide qPCR results on Ecd-related gene expression in live vs inactivated bacteria associations, we shall provide them but at this stage we believe this notion is not core to our message.

      Shade is expressed in the larval midgut, however the larval fat body is thought to be a major site of 20E to 20HE conversion. Can the authors test how Shd knockdown in the fat body affects systemic growth in the Lp-associated condition?

      We thank reviewer #3 for this comment. Nevertheless, we think this is out of the scope of our manuscript that focused on the Lp-based intestinal growth.

      In the knockdown experiments, body size is not measured for larvae/pupae. Given that ecdysone signaling impacts pupal volume (Delanoue et al., 2010) and controls metamorphosis timing, D50 plots by pupal volume would be informative to give a rough estimate of growth rate. For example, do germ-free EcR-RNAi larvae, which develop slower, have an equivalent body size to germ-free control larvae?

      We thank the reviewer #3 for this comment.

      All experiments were done with size-matched larvae because the aim of this manuscript is to detail what is the impact of Lp on the relative midgut vs systemic growth. Hence, we are using animals of similar systemic size to study their midgut size and identify allometry changes (midgut/larval size ratios) at a similar developmental point, which is same larval systemic growth (here L3). Thus, we feel that focusing on growth rates and systemic sizes in different genetic conditions, while interesting in general, is out of the scope of the study since we focus our study on midgut/larval size allometry.


      __Minor comments __

      The number of pupae in the EcR-RNAi and shd-RNAi experiments (Fig 2D, F) differ. Were larval densities controlled during development?

      I could not find this mentioned in the methods, and it is an important control parameter as larval density impacts developmental growth. Presenting this data as % viability of a known number of larvae deposited in food would be preferable.

      We thank the reviewer #3 for this comment.

      As mentioned in the material and methods, 40 eggs from axenic animals were deposited on each tube. It is true that the final number of pupae is different and could come from differential viability of the genetic backgrounds used. It would be difficult to follow from the same tube the larval development because of the manipulation of gnotobiotics animals. Nevertheless, in all experiments more than 25% of initial eggs deposited in tubes emerged as adults.

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      Referee #3

      Evidence, reproducibility and clarity

      Bai and colleagues present a detailed characterization of the larval midgut transcriptome, comparing size-matched germ-free and Lactobacillus plantarum (Lp)-associated L3 larvae. The authors identify Lp-mediated changes in ecdysone signaling genes, show increased ecdysone titer in Lp-associated larval hemolymph, and explore the functional role of intestinal ecdysone signaling and Lp in gut-specific vs systemic growth. This work offers insights into microbiome-intestinal EcR signaling that will attract broad interest in the Drosophila community.

      Major comments:

      1. The authors conclude that intestinal ecdysone signals are not required for Lp-promoted systemic growth. However, their data shows that circulating 20E titer increases in an Lp-dependent manner, and this circulating 20E presumably affects multiple tissues throughout the organism. Since EcR is broadly expressed, can the authors examine how EcR knockdown in other tissues influences systemic growth in Lp-associated larvae? Fat body-specific EcR knockdown seems particularly of interest here given the established relationship between fat body ecdysone signaling and growth (Delanoue et al., 2010). This additional analysis would help clarify whether ecdysone signaling in non-intestinal tissues mediates the Lp-associated growth phenotype.
      2. The experimental design compares larvae associated with live Lp versus germ-free larvae provided sterile PBS. Since Lp cells constitute a potential nutrient source for developing larvae, it's unclear whether gene expression differences arise from larvae digesting Lp cells as a nutrient source or from active, microbe-host signaling interactions. To conclusively address this ambiguity, the authors should perform RNA-seq on larvae inoculated with live versus heat-killed Lp. Alternatively, qPCR could be used to provide evidence for the extent to which changes in ecdysone-related gene expression specifically require live Lp.
      3. Shade is expressed in the larval midgut, however the larval fat body is thought to be a major site of 20E to 20HE conversion. Can the authors test how Shd knockdown in the fat body affects systemic growth in the Lp-associated condition?
      4. In the knockdown experiments, body size is not measured for larvae/pupae. Given that ecdysone signaling impacts pupal volume (Delanoue et al., 2010) and controls metamorphosis timing, D50 plots by pupal volume would be informative to give a rough estimate of growth rate. For example, do germ-free EcR-RNAi larvae, which develop slower, have an equivalent body size to germ-free control larvae?
      5. Figure 4 title is misleading. No manipulations of ecdysone signaling are performed to demonstrate whether scaling relationships across tissues differ depending on ecdysone. The same experiment should be performed using mex>EcR-RNAi larvae and/or mex>shd-RNAi larvae.
      6. The study relies on loss-of-function experiments to manipulate ecdysone signaling; gain-of-function experiments would provide an informative complement. Does feeding ecdysone phenocopy Lp association in GF larvae? Would ecdysone feeding have an additive effect with Lp association? Given the pleiotropic effects of ecdysone on larval phenotypes, a more targeted approach could be used to overexpress transgenes to augment ecdysone signaling.

      Minor comments:

      1. For gut and carcass length analysis, the EcR-RNAi and shd-RNAi conditions look slightly smaller in both GF and Lp conditions. Is there a genetic background effect on larval size? It would be helpful to calculate the interaction score between genotype and microbiome status via a 2-way ANOVA with post hoc tests.
      2. It is notable that mex>EcR-RNAi in germ-free larvae exacerbates developmental delay. A possible interpretation is that ecdysone signaling in the germ-free context promotes increased growth rate. Could the authors comment?
      3. The number of pupae in the EcR-RNAi and shd-RNAi experiments (Fig 2D, F) differ. Were larval densities controlled during development? I could not find this mentioned in the methods, and it is an important control parameter as larval density impacts developmental growth. Presenting this data as % viability of a known number of larvae deposited in food would be preferable.
      4. Experimental variation is substantial between the control conditions of the EcR and Shd knockdown experiments; median control + Lp D50 in the EcR experiment is ~6 days whereas in the shade experiment it is ~9 days. Can the authors comment on this between-experiment variation, which seems substantial (similar to the effect size between control + Lp and control GF)?
      5. The methods detail an ecdysone feeding protocol that I could not find used in the experiments. Please clarify.
      6. Figure S4 is not called out in the text.
      7. The scope of the bibliography seems limited in scope. As one example, Shin et al., 2011 seems quite relevant for this study.
      8. The manuscript would benefit from additional proofreading. The text contains spelling errors throughout. The in-text reference formatting is inconsistent. Figure legends could be improved to better describe the data.

      Significance

      The authors identify Lp-mediated changes in ecdysone signaling genes, show increased ecdysone titer in Lp-associated larval hemolymph, and explore the functional role of intestinal ecdysone signaling and Lp in gut-specific vs systemic growth. This work offers insights into microbiome-intestinal EcR signaling that will attract broad interest in the Drosophila community.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Gut-resident bacteria promote growth and development in the host intestine. With remarkable genetic accessibility and broad physiological relevance, flies are an excellent experimental system to ask how intestinal bacteria modify growth. Previous studies established a role for Lactobacillus planetarium in larval fly growth, particularly during periods of nutrient deprivation. Growth is principally supported by signals from insulin-like peptides and the melting hormone ecdysone. In this manuscript, the authors present transcriptomic data that Lp association has large effects on transcription in larval hosts raised on low-protein diets, including signatures of ecdysone signalling activation. Through a series of follow up studies the authors present evidence that ecdysone signalling is particularly important for midgut growth in Lp-associated larvae raised on low protein food.

      Major Comments

      I have no major comments. The manuscript is well written, easy to follow, and data are interpreted appropriately. Most of my remarks are minor in nature and can be addressed by simple changes to the text or figures.

      Minor Comments

      1. Figure 1 is interesting but challenging to follow. The fonts are very small and challenging to read. Pink on blue background is particularly hard to read and doesn't seem necessary. As the entire manuscript follows from data in Figure 1, I would encourage the authors to revise it with a vie3w to making the results more accessible.
      2. Figure S2: columns A and B are box plots, while columns C and D are columns with error bars. Presentation of quantitative data should be uniform and ideally as box plots throughout.
      3. Figure S3 confuses me. It seems that addition of 20E to GF larvae leads to a significant reduction of larval size, and that mono-association with Lp also significantly shortens larval size. Data in Figure 4G suggest that Lp should not affect larval body length relative to GF larvae. Can the authors explain the apparent discrepancy?
      4. Figure 4 is impressive and important for the overall manuscript. The authors should provide representative images to show how they measured cell area and nucleus area.
      5. I struggled to follow this sentence (line 215): "Also, it will be interesting to test, beyond their shared growth phenotype, whether they respond differently at the mechanistical level to the presence of bacteria in the anterior compartment." I would encourage the authors to consider alternative formulations.

      Significance

      Strengths and limitations

      This is a well written manuscript that adds to our understanding of the effects of intestinal bacteria on host development. Except for labeling in Figure 1, all data are accessibly presented, and accurately interpreted.

      Advance

      The study accurately links gut microbes to endocrinological control of organ growth in the fly. It also advances our understanding of Drosophila intestinal development by showing how ecdysone is important for gut development.

      Audience

      The work is focussed in scope and will likely have greater appeal to groups that work primarily with the Drosophila model. However, the work will likely have a broader appeal to those that study microbial control of development in other animal models.

      My expertise

      This study aligns with my area of expertise.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript entitled 'Drosophila larval gut transcriptome reveals a microbe-mediated intestinal tissue growth via Ecdysone during adaptive growth' identifies endocrine ecdysone signalling to regulate Lactobacilli-mediated adaptive growth during Drosophila larval development in context of malnutrition. The authors performed transcriptomic analysis to pin down genes that are deregulated in Lactiplantibacillus plantarum (L.p.) associated animals compared to germ-free raised animals. Besides the newly highlighted ecdysteroid kinase-like genes the other isolated deregulated candidates will be of interest for the audience specialized on gut microbiomes. Furthermore, by knockdown of the ecdysone receptor and the converting enzyme shade in midgut enterocytes of L.p. associated and GF raised larvae Bai et al. validated a requirement for ecdysone signalling in midgut growth, but not systemic growth of malnourished animals.

      Although the manuscript is mostly well-written and concise, I have major remarks (and suggestions) that the authors need to consider during a major revision process to sustain their claims. In addition, in several occasions the shown graphs do not support the text statements statistically, which I point out in the following major remarks. In addition, an entire Figure is not referred to in the manuscript. Overall, that leaves a bit of a 'premature' impression of the manuscript.

      Major remarks:

      1. In Fig.2 E - G there is a remarkable difference between controls in D compared to F and E compared to G. The difference between the controls in E and G is stronger than the shown significant difference of EcRRNAi to the control in E. How do the authors explain such a difference of the two (basically equal) controls and the high variance in control values shown in G? Are the comparisons of control and EcRRNAi shown in D significantly different?
      2. The authors should consider investigating a EcIRNAi in addition to EcRRNAi. EcR functions as activator, but also as suppressor in the absence of Ecdysone and a EcRRNAi suppresses both functions of EcR. By knocking down EcI the authors would prevent uptake of Ecdysone and thus interfere only with the ligand-induced activating function of EcR.
      3. Upon food supplementation with 20E the authors could not measure a significant effect on systemic growth or midgut maturation (Fig. S3), whereas the dose of 20E they fed (20µg/ml) was already much higher than endogenous 20E level they measured in the midgut (Fig. 2B). The authors should consider to feed larvae with RH5849 (Dr. Ehrenstorfer), which is an insecticide functioning as an ecdysone agonist and was designed for high stability (Wing et al, 1988). RH5849 was already successfully fed to adult Drosophila to investigate the impact of Ecdysone signalling on the adult midgut (Neophytou et al, 2023; Zipper et al, 2025; Zipper et al, 2020) and elicits 20E response. Furthermore, uptake of RH5849 is not limited by the levels of EcI.
      4. Lines 167-169: the authors state that 'Size-matched Lp associated larvae, controlRNAi or EcRRNAi, show longer midguts than their relative GF condition (Fig. 3A, B)', but there are no significant statistics shown for this comparison in Fig. 3A, B.
      5. Why are the authors comparing the carcass length of GF shade RNAi with L.p. control in Fig. 3 D?
      6. In Fig. 3 the authors added the values for numbers of biological replica within the graphs. In Fig. 4 M-P they added the values for number of technical replicas. They should apply adding these two types of values to all graphs and I would suggest to make the difference between biological replica 'n' and technical replica 'N' obvious in the figure.
      7. In Fig. S3C the authors compared L.p. WJL 20E with the GF EtOH control, where is the comparison to the corresponding L.p. WJL EtOH control? The L.p. WJL EtOH control is compared to GF 20E instead.
      8. The authors should include a discussion of how Ecdysone signalling in postmitotic EC is regulating midgut size, which may include recent data from Edgar and Reiff labs (Ahmed et al, 2020; Zipper et al., 2025; Zipper et al., 2020).
      9. There are several recent publications showing a role for gut microbiota in regulating oestrogen metabolism in humans, and implications in oestrogen-related diseases such as endometriosis (Baker et al, 2017; Xholli et al, 2023). More precisely bacteria including Lactobacilli strains produce gut microbial β-glucuronidase enzymes, which reactivate oestrogens (Ervin et al, 2019; Hu et al, 2023). As Drosophila ecdysone is the functional equivalent of mammalian oestrogens (Aranda & Pascual, 2001; Martinez et al, 1991; Oberdörster et al, 2001) these publications should be discussed by the authors.
      10. Fig. S4 is not mentioned at all in the manuscript.

      Minor comments:

      • The authors are inconsistent in indicating their experimental groups. One example is Fig. S3: In A and B they write the GF groups non-italic, whereas the L.p. groups are written italic. In C - E they only partially write the L.p. groups italic. Furthermore, in A, C - E they write 'L.p.', whereas its written 'Lp' and missing the 'WJL' in B.
      • Line 52: The last 'i' in 'Lactobacilli' is not italic.
      • Line 122: Spelling error in 'Surpringsinly'
      • Line 151: Spelling error in 'progenies'. Needs to read 'progeny'.
      • Lines 231-235: Last part of the sentence is repetitive

      References

      Ahmed SMH, Maldera JA, Krunic D, Paiva-Silva GO, Penalva C, Teleman AA, Edgar BA (2020) Fitness trade-offs incurred by ovary-to-gut steroid signalling in Drosophila. Nature 584: 415-419

      Aranda A, Pascual A (2001) Nuclear hormone receptors and gene expression. Physiol Rev 81: 1269-1304

      Baker JM, Al-Nakkash L, Herbst-Kralovetz MM (2017) Estrogen-gut microbiome axis: Physiological and clinical implications. Maturitas 103: 45-53

      Ervin SM, Li H, Lim L, Roberts LR, Liang X, Mani S, Redinbo MR (2019) Gut microbial β-glucuronidases reactivate estrogens as components of the estrobolome that reactivate estrogens. J Biol Chem 294: 18586-18599

      Hu S, Ding Q, Zhang W, Kang M, Ma J, Zhao L (2023) Gut microbial beta-glucuronidase: a vital regulator in female estrogen metabolism. Gut Microbes 15: 2236749

      Martinez E, Givel F, Wahli W (1991) A common ancestor DNA motif for invertebrate and vertebrate hormone response elements. The EMBO journal 10: 263-268

      Neophytou C, Soteriou E, Pitsouli C (2023) The Sterol Transporter Npc2c Controls Intestinal Stem Cell Mitosis and Host-Microbiome Interactions in Drosophila. Metabolites 13

      Oberdörster E, Clay MA, Cottam DM, Wilmot FA, McLachlan JA, Milner MJ (2001) Common phytochemicals are ecdysteroid agonists and antagonists: a possible evolutionary link between vertebrate and invertebrate steroid hormones. J Steroid Biochem Mol Biol 77: 229-238

      Wing KD, Slawecki RA, Carlson GR (1988) RH 5849, a Nonsteroidal Ecdysone Agonist: Effects on Larval Lepidoptera. Science 241: 470-472

      Xholli A, Cremonini F, Perugi I, Londero AP, Cagnacci A (2023) Gut Microbiota and Endometriosis: Exploring the Relationship and Therapeutic Implications. Pharmaceuticals (Basel) 16 Zipper L, Corominas-Murtra B, Reiff T (2025) Steroid hormone-induced wingless ligands tune female intestinal size in Drosophila. Nature Communications 16: 436

      Zipper L, Jassmann D, Burgmer S, Görlich B, Reiff T (2020) Ecdysone steroid hormone remote controls intestinal stem cell fate decisions via the PPARγ-homolog Eip75B in Drosophila. eLife 9

      Significance

      Brief general assessment before a revision: The study provides important new insights into organ versus systemic growth and show that this is regulated by a central steroid hormonal pathway making this study interesting for a broad audience.

  3. Apr 2025
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      Reply to the reviewers

      We thank the reviewers for their thoughtful comments and suggestions. Our plans for revisions are first summarized. Below you can find the original reviews and our responses and detailed plans (indicated by "Response").

      Revision plan summary:

      1. Many of the concerns can be addressed by changes in the text and better explanations of how the experiments were done. These changes are detailed in the point-by-point responses.
      2. The reviewers suggested experiments such as ChIP-seq and immunoprecipitation which require collection of a large number of mutants. Since our mutants are sterile, the line needs to be maintained as heterozygotes, from which we can pick out individual mutant worms. Therefore, with the current reagents it is impossible to collect mutants in sufficient quantities for ChIP-seq or IP. We understand that it limits the conclusions that can be drawn.
      3. For some figures, additional quantification of fluorescence signal will be done to show differences between mutant and wild type.
      4. A few experiments will be repeated:
      5. We will repeat the ATPase assays shown on Fig 1 with additional independently prepared and purified protein samples.
      6. Additional replicates will be performed for the few immunofluorescence experiments that were only performed once. Point-by-point responses:

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

      Dosage compensation (DC) in C. elegans involves halving the gene expression from the two hermaphrodite X chromosomes to match the output of the single X in male worms. The key regulator of this repression is a specialized condensin complex, which is defined by a dedicated SMC-4 paralog, termed DPY-27. SMC-4 in other animals is an ATPase that functions as a motor of loop extrusion in cohesion complexes. In their current manuscript, Chawla et al. assessed whether DPY-27 has ATPase function and whether this activity is required for dosage compensation. It had previously been shown that an ATPase-deficient 'EQ' mutant DPY-27 protein interacts with other DC complex members, yet fails to localize to the X. This observation was made with an extra copy of DPY-GFP expressed in addition to the endogenous wildtype protein [Ref 77]. No dominant negative effect was observed. The authors have now engineered the 'EQ' mutation into the endogenous gene locus and genetically generated hetero- and homozygous ATPase mutant worms. Their data suggest that the ATPase activity is required or X-chromosome localization, complex assembly, chromosome compaction as well as enrichment of H4K20me1 on the dampened X chromosome.

      Major comments: 1. ATPase assays, Figure 1.Preparations of individual recombinant proteins may vary significantly and may occasionally show much reduced enzymatic activity. A conclusion about the failure of an ATPase activity should not be concluded from a single preparation, but several protein preps need to be tested, which then serve as 'biological replicates' for the in vitro reaction. Apparently, the ATPase assays shown only involved technical replicates, which is not sufficient.

      Response: We will express and purify additional protein samples and will repeat the assay.

      CRISPR-mediated engineering may lead to unwanted reactions, exemplified by the 'indel' mutation that was recovered in one clone. As a good practice and important control, the sequences of the mutated alleles in the worms should be determined by sequencing of PCR products. Restrictions enzyme cleavage or gel electrophoresis of the PCR products is not sufficient to document the nature of the mutation.

      Response: The sequence of the edit was confirmed by Sanger sequencing. We will make it clear in the text.

      All IF data need to be collected from at least 2 biological replicates, i.e. the experiment must have been carried out independently on two different days. The replicates should deliver consistent results. The number of independent replicates should be mentioned in each figure legend.

      Response: Most of our experiments were performed multiple times. We will indicate the number of replicates in the figure legends. The one or two experiments that were only performed once, will be repeated an additional time.

      The expression levels of wildtype and mutant proteins are concluded from IFM. This is very qualitative; quantitative measurements would strengthen the paper.

      Response: We will quantify fluorescence intensity on our existing images to show differences between mutant and wild type.

      Figure 4B: What are the criteria for classification of the three classes of mutant nuclei? To the uninitiated eye they look very similar. I am a bit worried about the human bias, if such diffuse staining are to be categorized. The two categories of localization need be documented better.

      Response: We will provide more images to show the range of phenotypes and provide a better explanation of how they were classified. We will also try a few ways to quantify “diffuseness” to provide a numerical readout.

      Figure 5: volume of the X chromosome. Related to (5): Apparently, the mask that contains the X chromosome was drawn by hand on each individual nucleus? I find it very difficult to see how the X chromosomal territory would be assessed in the examples shown. I would be good to see a panel of nuclei, in which the masks are visible. I think the analysis should be blinded, in which a researcher not involved in the analysis draws masks on coded nuclei and their classes are only revealed later. The same concern holds for the FISH/IP overlaps or DPY-27/SDC-2 overlaps.

      Response: The masks used were not drawn by hand but were based on fluorescence intensity thresholds. We will make a supplementary figure that shows the masks used for quantification to help clarify how the experiment and quantification were performed.

      For figure 5, age-matched hermaphrodites were analyzed. How was the age determined and what would be the consequence of age-variations? What is the effect of the mutations on development?

      Response: For our staining experiments, we routinely use young adult which we define as 24 hr past larval L4 stage. At this stage, young adults have started laying eggs. We have unpublished data that shows that dosage compensation and chromosome compaction deteriorates with age. To avoid using old worms in our assays, we pick L4 larvae, and then use them for experiments the following day.

      Minor comments: 8. The labeling of p-values as a-f in the figures with the values listed in a supplemental table is not comfortable. The p-values corresponding to the letters should be listed in the corresponding legends.

      Response: p values can be added to the figure or the figure legend (they are currently in supplementary tables).

      How were the concentrations of the ATPase preparations determined? It would help to see a proteins gel in the supplement to assess their purity.

      Response: Concentrations were determined using a spectrometer. We can show protein gels of the preparations as a supplementary figure.

      In figure 1, heterodimers are assumed, but not shown. Do they dimerize under these conditions?

      Response: We can cite papers from others that show heterodimerization in these conditions (for example, Hassler et al, 2019).

      Reviewer #1 (Significance (Required)):

      Significance: The involvement of the ATPase function of DPY-27 was somewhat expected, in light of the earlier findings published in reference 77 using a transgene. The current study confirms and extends these earlier findings. In principle, the genetic experiment presented here is stronger, if documented better.

      Strengths: The study investigates endogenous proteins and measures different phenomena known to be correlated from previous work. The data are internally consistent.

      Limitations: The lack of biological replicates, and unclear procedures of how to draw the IF masks that underlie the conclusions about X chromosome (co)localization and nuclear volume determination render the argument less convincing. For this reviewer, who is not in the C. elegans field, the analysis of mutant phenotypes is difficult to follow. The conclusions are based on only one type of experiment. In reference 77, the X chromosome binding was done by ChIP-seq, clearly a superior, complementary method.

      Response: As explained above, since the strain has to be maintained as a heterozygote, we are unable to collect enough mutants for a ChIP-seq experiment. We can perform and better document the experimental replicates and we can better explain the quantification methods used.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The authors analyzed the ATPase function of an SMC-4 variant required for dosage compensation in C. elegans. They made a single amino acid mutation that significantly reduced ATPase activity of the protein as shown by in vitro ATP hydrolysis. They showed that the mutation results in the phenotypic consequences of those shown for other DC mutants, including viability assay, immunofluorescence and DNA FISH. These results demonstrate the important role of ATPase activity in transcription repression.

      Major comments: - Are the key conclusions convincing? The key conclusion that DPY-27 has ATPase activity and using a classic mutation that reduces it largely eliminates its function is convincing. The interpretation of the IF experiments to build the model in the final figure requires stronger evidence, as commented below in additional experiment section.

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Yes, as explained below.

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      The main issue with the current model is that the authors assume that the EQ proteins that they are analyzing is in complex with the rest of the condensin IDC subunits. However, there is no evidence in the paper suggesting that this occurs. The results are consistent with the possibility that a large portion of the DPY27-EQ is not in a complex.

      IP-western experiments comparing the proportion of other subunits pulled down by the wild type versus the EQ mutant (perhaps extract from ~50% EQ containing population could be reached) is needed to understand the incorporation of the EQ mutant in the complex. This is particularly important for the interpretation of the data in Figure 4A, where 70% of the nuclei show diffuse CAPG-1 and DPY-27 EQ. Is this signal due to disassembled subunits diffusing freely, or as depicted in the model figure, bound less stably everywhere? The immunofluorescence results are consistent with both EQ mutation 1) forming a full complex and unstably binding or 2) destabilizing the complex but incompletely assembled complexes sustaining a pool of free EQ detected by the immunofluorescence experiments.

      Response: We agree that to conclusively show interactions, an IP would be necessary. However, as explained above for ChIP, it is not possible to collect enough mutants to make enough protein extract for an IP. An IP in heterozygous worms is also not ideal, as it would be nearly impossible to distinguish wild protein from the mutant. The antibody we used recognizes the N terminus, which is identical in the two proteins. The only way to distinguish them would be mass spec. However, during the fragmentation process for mass spec, Q can deaminate to E, which would complicate interpretation of our data. To do this experiment properly, we would need to introduce a different tag into the mutant protein. With the current reagents, an IP is not possible.

      Instead, we have to rely on indirect evidence. The fact that DPY-27 and CAPG-1 colocalize (figure 4) does provide some support for the hypothesis. From previous studies,including our recent publication Trombley et al PLoS Genetics 2025, we know that the condensin IDC complex is not stable unless all subunits are present. It is therefore highly unlikely, although not impossible, that what we detect is diffuse individual subunits.

      We can make changes in the text to soften this claim and better discuss the caveats of the experiment and the conclusions.

      Along the same point, authors show that EQ protein that binds to the X is incapable of bringing H4K20me1, which is consistent with the possibility that a large portion of the EQ protein is not in a complex. : "To our surprise, we observed that there was no discernable enrichment of H4K20me1, even though there is discernable enrichment of DPY-27 EQ on the X chromosomes in the dpy-27 EQ mutants (Figure 8A).

      Response: There is an important difference. CAPG-1 and DPY-27 are both members of condensin IDC. The five subunits of this complex depend on each other for stability. DPY-21, the protein that introduces the H4K20me1 mark, also localizes to the X chromosomes, but is not part of condensin IDC. Condensin IDC is able to localize to the X chromosomes in the absence of DPY-21, and is not dependent on DPY-21 for stability. However, DPY-21 is dependent on condensin IDC for X localization (Yonker et al 2003). It is then possible that the mutant condensin IDC is X-bound, but it is unable to recruit DPY-21. We can clarify this in the text.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. It is unclear how long it would take to collect enough het/mutant worms can be collected for IP-western. Without additional evidence, interpretation of the data would be affected.

      Response: As explained above, collecting enough mutant worms is essentially impossible. Collecting enough heterozygotes is possible, but distinguishing the mutant protein from the wild type in hets is not.

      • Are the data and the methods presented in such a way that they can be reproduced? Yes
      • Are the experiments adequately replicated and statistical analysis adequate? Yes, except the presentation of the test (see minor comment below)

      Minor comments: - Specific experimental issues that are easily addressable. The use of letters for statistical test result is confusing and the figure legend is not clear about what actual p values were produced "Letters represent multiple comparison p values, with different letters indicating statistically significant differences, and any repeated letter demonstrating no significance. " Providing the values at a reasonably concise manner in the legend will help the reader a lot.

      Response: P values can be added to the figures, or the legend

      • Are prior studies referenced appropriately? The authors state that "Surprisingly, this mutant did not phenocopy the transgenic EQ mutant in [77], .." however in the previous paragraph, the authors state that the transgenic was expressed in the presence of wild type copy. Therefore, the endogenous mutant showing phenotypes rather than the transgenic is rather expected.

      Response: What we referred to were ways in which the protein behaved (for example in ability to bind to the X at all), and not mutant phenotypes of worms. We can clarify this in the text.

      The authors state that "One possible explanation could be that mitotic condensation has multiple drivers of equal consequence including changes in histone modifications [129], whereas condensation of dosage compensated X chromosomes is predominantly dependent on the DCC. " In a dpy-21 mutant, X chromosome decondenses but DPY-27 stays on the chromosome. Therefore, the effect of the EQ mutation may be due to lack of H4K20me1 enrichment in addition to the lack of loop extrusion.

      Response: We can add the role of H4K20me1 to the discussion.

      • Are the text and figures clear and accurate? Yes
      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions? The Pearson correlation coefficient for assessing colocalization between SDC-2 and DPY-27 was helpful for quantification, because there is a lot of background signal that makes the support for or lack of colocalization with the X in the other IF/FISH figures difficult to assess. Additionally, please provide information on how chromatic aberration was assessed when analyzing colocalization experiments.

      Response: Chromatic aberration was not considered for these experiments.

      Reviewer #2 (Significance (Required)):

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. Although long assumed to be a functional SMC, the demonstration of DPY-27 function depending on ATPase activity is important. This demonstrates that an X-specific condensin retained its SMC activity.

      • Place the work in the context of the existing literature (provide references, where appropriate). The authors do an adequate job in doing this in their discussion.

      • State what audience might be interested in and influenced by the reported findings. The field of 3D genome organization and function would be influenced by the reported findings.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Genomic analyses of 3D genome organization and gene expression.

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      Referee #3

      This reviewer did not leave any comments

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors analyzed the ATPase function of an SMC-4 variant required for dosage compensation in C. elegans. They made a single amino acid mutation that significantly reduced ATPase activity of the protein as shown by in vitro ATP hydrolysis. They showed that the mutation results in the phenotypic consequences of those shown for other DC mutants, including viability assay, immunofluorescence and DNA FISH. These results demonstrate the important role of ATPase activity in transcription repression.

      Major comments:

      • Are the key conclusions convincing? The key conclusion that DPY-27 has ATPase activity and using a classic mutation that reduces it largely eliminates its function is convincing. The interpretation of the IF experiments to build the model in the final figure requires stronger evidence, as commented below in additional experiment section.

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Yes, as explained below.

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      The main issue with the current model is that the authors assume that the EQ proteins that they are analyzing is in complex with the rest of the condensin IDC subunits. However, there is no evidence in the paper suggesting that this occurs. The results are consistent with the possibility that a large portion of the DPY27-EQ is not in a complex.

      IP-western experiments comparing the proportion of other subunits pulled down by the wild type versus the EQ mutant (perhaps extract from ~50% EQ containing population could be reached) is needed to understand the incorporation of the EQ mutant in the complex. This is particularly important for the interpretation of the data in Figure 4A, where 70% of the nuclei show diffuse CAPG-1 and DPY-27 EQ. Is this signal due to disassembled subunits diffusing freely, or as depicted in the model figure, bound less stably everywhere? The immunofluorescence results are consistent with both EQ mutation 1) forming a full complex and unstably binding or 2) destabilizing the complex but incompletely assembled complexes sustaining a pool of free EQ detected by the immunofluorescence experiments.

      Along the same point, authors show that EQ protein that binds to the X is incapable of bringing H4K20me1, which is consistent with the possibility that a large portion of the EQ protein is not in a complex. : "To our surprise, we observed that there was no discernable enrichment of H4K20me1, even though there is discernable enrichment of DPY-27 EQ on the X chromosomes in the dpy-27 EQ mutants (Figure 8A).

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. It is unclear how long it would take to collect enough het/mutant worms can be collected for IP-western. Without additional evidence, interpretation of the data would be affected.

      • Are the data and the methods presented in such a way that they can be reproduced? Yes

      • Are the experiments adequately replicated and statistical analysis adequate? Yes, except the presentation of the test (see minor comment below)

      Minor comments:

      • Specific experimental issues that are easily addressable. The use of letters for statistical test result is confusing and the figure legend is not clear about what actual p values were produced "Letters represent multiple comparison p values, with different letters indicating statistically significant differences, and any repeated letter demonstrating no significance. " Providing the values at a reasonably concise manner in the legend will help the reader a lot.

      • Are prior studies referenced appropriately? The authors state that "Surprisingly, this mutant did not phenocopy the transgenic EQ mutant in [77], .." however in the previous paragraph, the authors state that the transgenic was expressed in the presence of wild type copy. Therefore, the endogenous mutant showing phenotypes rather than the transgenic is rather expected.

      The authors state that "One possible explanation could be that mitotic condensation has multiple drivers of equal consequence including changes in histone modifications [129], whereas condensation of dosage compensated X chromosomes is predominantly dependent on the DCC. " In a dpy-21 mutant, X chromosome decondenses but DPY-27 stays on the chromosome. Therefore, the effect of the EQ mutation may be due to lack of H4K20me1 enrichment in addition to the lack of loop extrusion.

      • Are the text and figures clear and accurate? Yes
      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions? The Pearson correlation coefficient for assessing colocalization between SDC-2 and DPY-27 was helpful for quantification, because there is a lot of background signal that makes the support for or lack of colocalization with the X in the other IF/FISH figures difficult to assess. Additionally, please provide information on how chromatic aberration was assessed when analyzing colocalization experiments.

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. Although long assumed to be a functional SMC, the demonstration of DPY-27 function depending on ATPase activity is important. This demonstrates that an X-specific condensin retained its SMC activity.

      • Place the work in the context of the existing literature (provide references, where appropriate). The authors do an adequate job in doing this in their discussion.

      • State what audience might be interested in and influenced by the reported findings. The field of 3D genome organization and function would be influenced by the reported findings.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Genomic analyses of 3D genome organization and gene expression.

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      Referee #1

      Evidence, reproducibility and clarity

      Dosage compensation (DC) in C. elegans involves halving the gene expression from the two hermaphrodite X chromosomes to match the output of the single X in male worms. The key regulator of this repression is a specialized condensin complex, which is defined by a dedicated SMC-4 paralog, termed DPY-27. SMC-4 in other animals is an ATPase that functions as a motor of loop extrusion in cohesion complexes. In their current manuscript, Chawla et al. assessed whether DPY-27 has ATPase function and whether this activity is required for dosage compensation. It had previously been shown that an ATPase-deficient 'EQ' mutant DPY-27 protein interacts with other DC complex members, yet fails to localize to the X. This observation was made with an extra copy of DPY-GFP expressed in addition to the endogenous wildtype protein [Ref 77]. No dominant negative effect was observed. The authors have now engineered the 'EQ' mutation into the endogenous gene locus and genetically generated hetero- and homozygous ATPase mutant worms. Their data suggest that the ATPase activity is required or X-chromosome localization, complex assembly, chromosome compaction as well as enrichment of H4K20me1 on the dampened X chromosome.

      Major comments:

      1. ATPase assays, Figure 1.Preparations of individual recombinant proteins may vary significantly and may occasionally show much reduced enzymatic activity. A conclusion about the failure of an ATPase activity should not be concluded from a single preparation, but several protein preps need to be tested, which then serve as 'biological replicates' for the in vitro reaction. Apparently, the ATPase assays shown only involved technical replicates, which is not sufficient.

      2. CRISPR-mediated engineering may lead to unwanted reactions, exemplified by the 'indel' mutation that was recovered in one clone. As a good practice and important control, the sequences of the mutated alleles in the worms should be determined by sequencing of PCR products. Restrictions enzyme cleavage or gel electrophoresis of the PCR products is not sufficient to document the nature of the mutation.

      3. All IF data need to be collected from at least 2 biological replicates, i.e. the experiment must have been carried out independently on two different days. The replicates should deliver consistent results. The number of independent replicates should be mentioned in each figure legend.

      4. The expression levels of wildtype and mutant proteins are concluded from IFM. This is very qualitative; quantitative measurements would strengthen the paper.

      5. Figure 4B: What are the criteria for classification of the three classes of mutant nuclei? To the uninitiated eye they look very similar. I am a bit worried about the human bias, if such diffuse staining are to be categorized. The two categories of localization need be documented better.

      6. Figure 5: volume of the X chromosome. Related to (5): Apparently, the mask that contains the X chromosome was drawn by hand on each individual nucleus? I find it very difficult to see how the X chromosomal territory would be assessed in the examples shown. I would be good to see a panel of nuclei, in which the masks are visible. I think the analysis should be blinded, in which a researcher not involved in the analysis draws masks on coded nuclei and their classes are only revealed later. The same concern holds for the FISH/IP overlaps or DPY-27/SDC-2 overlaps.

      7. For figure 5, age-matched hermaphrodites were analyzed. How was the age determined and what would be the consequence of age-variations? What is the effect of the mutations on development?

      Minor comments:

      1. The labeling of p-values as a-f in the figures with the values listed in a supplemental table is not comfortable. The p-values corresponding to the letters should be listed in the corresponding legends.

      2. How were the concentrations of the ATPase preparations determined? It would help to see a proteins gel in the supplement to assess their purity.

      3. In figure 1, heterodimers are assumed, but not shown. Do they dimerize under these conditions?

      Significance

      Significance: The involvement of the ATPase function of DPY-27 was somewhat expected, in light of the earlier findings published in reference 77 using a transgene. The current study confirms and extends these earlier findings. In principle, the genetic experiment presented here is stronger, if documented better.

      Strengths: The study investigates endogenous proteins and measures different phenomena known to be correlated from previous work. The data are internally consistent.

      Limitations: The lack of biological replicates, and unclear procedures of how to draw the IF masks that underlie the conclusions about X chromosome (co)localization and nuclear volume determination render the argument less convincing. For this reviewer, who is not in the C. elegans field, the analysis of mutant phenotypes is difficult to follow. The conclusions are based on only one type of experiment. In reference 77, the X chromosome binding was done by ChIP-seq, clearly a superior, complementary method.

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      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their thoughtful and detailed feedback, which we found highly constructive and encouraging. The comments have been invaluable in guiding improvements to the clarity, rigor, and impact of our manuscript. Below, we provide our responses and outline the specific revisions we plan to make in response to each point raised. It was extremely encouraging that all the comments were highly relevant to the study demonstrating careful work by experts in the field and they truly help to improve the clarity and message of the manuscript.

      2. Description of the planned revisions


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

      The manuscript by Gizaw et al characterizes the cholesterol biosynthetic pathway and the effect of its knockdown or inhibition on rhabdomyosarcoma tumor properties. The Authors find that the PROX1 transcription factor mediated cholesterol biosynthesis regulates rhabdomyosarcoma cell growth and proliferation. Blocking the cholesterol biosynthetic pathway leads to reduced proliferation, cell cycle arrest and ER-stress mediated enhanced apoptosis. Detailed transcriptomic analysis indicate gene expression patterns that support these findings. Reviewer #1 (Significance (Required)):

      Based on my expertise on rhabdomyosarcoma tumors, the manuscript is clear, concise and provides a significant advance to the field. Detailed mechanistic characterization is lacking, which takes away some of the significance of the findings, but the work done stands alone as description of the effect of the cholesterol biosynthetic pathway in rhabdomyosarcoma. Another aspect to be considered by the Authors is the potential specificity of targeting a ubiquitous pathway such as cholesterol biosynthesis, which is important to most cells and not only cancer cells. Overall, the manuscript may be revised to address the specific comments below.

      Responses to Reviewer #1 comments

      We thank the reviewer for the thoughtful and encouraging comments on our manuscript. We appreciate the recognition of the significance of our findings and the detailed suggestions provided. We are committed to addressing each of the reviewer's points to strengthen the manuscript and ensure clarity and rigor. Below, we outline how we plan to address each comment.

      Major Comments:

      1. __ Details of the healthy human myoblasts that are used in Figure 1A are not provided and should be updated. Evidence of PROX1 knockdown should be presented. What kind of pathways and gene ontology predictions were associated with the 225 genes that are commonly downregulated between all three cell lines in Figure 1A?__

      Response: In the revised manuscript, we will include complete information regarding the origin and characterization of the healthy human myoblasts used in the Figure 1A. We will also provide additional data confirming PROX1 knockdown. Furthermore, we will present more details on the gene ontology (GO) and pathway enrichment analyses, and include the full results as supplemental data to highlight key biological processes affected by PROX1 silencing.

      __ In Figure 2, while the effect of the shRNAs targeting DHCR7 or the DHCR7 inhibitor AY9944 are striking, it is not clear whether these effects are specific to rhabdomyosarcoma cells or cancer cells. A control, human myoblast cell line or another non-cancerous cell line should be used to repeat these experiments quantifying Caspase3/7 activity, cell growth etc. to assess the cancer cell specificity of such treatments. Evidence of DHCR7 knockdown at the protein level would add to the study.__


      Response: We fully agree with the reviewer's suggestion and will conduct additional experiments using non-cancerous human myoblasts to assess the specificity of DHCR7 inhibition. These will include assays for Caspase 3/7 activation, cell viability, and proliferation under similar conditions. We have already performed western blot validation of DHCR7 knockdown at the protein level in RMS cell lines and will include this data in the manuscript. We will also highlight in the discussion that RMS cells in our experiments were highly vulnerable when cultured with full media (incl. FBS), whereas previous studies with breast cancer cells have shown that their growth is affected by cholesterol biosynthesis inhibition only if they are cultured without serum (containing cholesterol). We also show that cholesterol supplementation does not rescue RMS cells demonstrating the essential role of de novo cholesterol synthesis.

      __ Western blots for Caspase3 quantification and a cell proliferation marker such as Cyclin D in shSCR and shDHCR7 tumor lysates would validate the data shown in the Figure 3. Are the shRNA constructs used inducible ones? If not, how do the Authors distinguish the effect of shDHCR7 on tumor engraftment versus tumor proliferation and growth? Many of the graphs need proper labeling of the axes and what the bars represent.__


      Response: We will include western blot analysis for cleaved Caspase 3 and Cyclin D1 in tumor lysates to support the observed effects on apoptosis and proliferation. We will clarify in the revised manuscript that the shRNA constructs used were constitutive. To distinguish between effects on tumor engraftment versus tumor growth, we will provide additional detail on how we controlled for initial cell viability and engraftment potential prior to injection. We will also revise figure panels to ensure all axes and error bars are clearly labeled.

      __ Gene ontology and pathway analysis will add to Figure 4.__


      Response: We will expand Figure 4 to include GO and pathway enrichment analyses of the RNA-seq data following DHCR7 knockdown. This will help illustrate the functional significance of the transcriptional changes and further support our conclusions regarding ER stress, apoptosis, and cell cycle regulation.

      __ In Figure 5A, how do the Authors explain the upregulation of cholesterol biosynthetic pathway genes upon shDHCR7 treatment? Are these effects seen at the protein level and if alternate pathways maintain cholesterol biosynthesis, how do the Authors think this strategy will be viable to treat such tumors? In Figure 5G-H, was a loading control used? If so, blots for that should be included.__


      Response: We will expand the discussion to address the compensatory transcriptional upregulation of cholesterol biosynthesis genes following DHCR7 knockdown, likely driven by SREBP-mediated feedback regulation. To support this, we will include western blot data for key enzymes in the pathway. We will also clarify that despite this transcriptional compensation, functional cholesterol synthesis is impaired due to DHCR7 silencing, which cannot be rescued by increased upstream pathway activity. Regarding Figure 5G-H, we will include the missing loading control images in the revised version. Protein normalization was performed using Stain-Free technology, which enables the quantification of total protein in each lane, and was analyzed using ImageLab 6.0.1 software (Bio-Rad). We will include the Stain-Free gel images to demonstrate equal protein loading and will also indicate the molecular weights of the presented proteins in the updated figure legend.

      __ Lines 286-287 refer to Figure S1G, H; it should be corrected to Figure S1I, J.__

      Response: We thank the reviewer for pointing this out. We will correct the figure citation in the revised manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript entitled "Targeting de novo cholesterol synthesis in rhabdomyosarcoma induces cell cycle arrest and triggers apoptosis through ER stress-mediated pathways" Gizaw et al investigate the crucial effect of targeting cholesterol biosynthesis in RMS. While this manuscript gives novel insights into putative therapeutic approach, there are some comments that should be address by the authors.

      Reviewer #2 (Significance (Required)):

      A nice and coherent study. Please see text above.


      Response to Reviewer #2

      We are grateful to the reviewer for the thoughtful and constructive comments on our manuscript. We appreciate your recognition of the novelty and therapeutic potential of our findings, and we thank you for highlighting specific areas that will help further improve the clarity, rigor, and reproducibility of our work. Below, we respond point-by-point to your comments and outline how we plan to address each issue in the revised version of the manuscript.

      Major Comments:

      1. __ The authors demonstrated a correlation between PROX1 levels and the cholesterol synthesis pathway. Which genes from the pathway are mostly affected? The manuscript could benefit from a graphical representation of the pathway showing up- and downregulated genes from the RNA-seq analysis. This will help in understanding why the authors decided to study HMGCR silencing as shown in Supplementary Figure 1A.__

      Response: We fully agree and will include a new graphical figure showing the cholesterol biosynthesis pathway, with up- and downregulated genes from our RNA-seq data visually mapped. This is, indeed, interesting as the whole pathway is consistently downregulated. We chose to study specifically these two rate-limiting genes in the pathway, as DHCR7 is the last enzyme in the mevalonate pathway and its inhibition does not affect other arms deviating from this pathway. It was also recently found to be highly upregulated in pancreatic cancer, suggesting its role in cancer development/growth. HMGCR was chosen as it is the target for statins, which are widely used in treating high cholesterol and shown to be rather safe in clinical use. We will add this rationale to the manuscript to clarify our focus on HMGCR and DHCR7.

      __ Based on the previous comment, are the genes from the cholesterol synthesis identified in the RNA-seq similar to those detected in the publicly available data set presented in Figure 1E? In addition, validation of changes of these genes should be performed in the RMS cell lines as well as in myoblasts.__


      Response: Yes, there is a significant overlap between the cholesterol biosynthesis genes identified in our RNA-seq dataset and those from the public dataset in Figure 1E. In the revised version, we will include this comparative analysis with the inclusion of the schematic figure (see our response #1). We also plan to perform qPCR validation of several key cholesterol biosynthesis genes in additional RMS cell lines and healthy myoblasts to reinforce the disease-specific regulation of this pathway.

      __ In Figure 3, the authors study the impact of DHCR7-silencing in tumor growth in vivo. Please, provide stainings also for DHCR7 to show that cells indeed have silenced DHCR7.__


      Response: Thank you for this important suggestion. We will include immunofluorescence staining for DHCR7 in xenograft tumor sections to confirm DHCR7 knockdown in vivo and visually validate the efficiency of our silencing strategy. We will also add qPCR results from the cells at the time when they were implanted confirming the deletion.

      __ In Figure 4, the RNA-seq data revealed downregulation in E2F genes as well as genes involved in cell cycle progression. It would be important that the authors provide examples of these genes and validate this data by performing qPCR.__


      Response: We will select representative cell cycle-related genes, including members of the E2F family and other G1/S and G2/M regulators, for qPCR validation in RMS cells following DHCR7 knockdown. Comparison to healthy myoblasts will be also performed. This will further substantiate the transcriptomic findings.

      __ In Figure 4J-M, cell cycle distribution using flow cytometry should be assessed in an additional cell line.__


      Response: We will repeat the flow cytometry-based cell cycle analysis in an additional RMS cell line to ensure reproducibility and confirm the generalizability of the observed G2/M arrest phenotype.

      __ In line 271, the authors described that PROX1 is associated with an increase in DHCR7. However, in the next paragraph they evaluated the effect of silencing HMGCR. Is this enzyme also increased? Please clarify.__


      Response: We appreciate the need for clarity. HMGCR expression is also elevated in RMS cells and regulated by PROX1. We will clarify this in the revised manuscript and update the text to explain the rationale behind examining both enzymes: HMGCR as the rate-limiting enzyme at the top of the cholesterol biosynthesis pathway, and DHCR7 as the final step enzyme. See also our response to question #1.

      __ The authors show that cholesterol biosynthesis is crucial in RMS. Would overexpression of DHCR7 in shDHCR7 cells rescue the anti-tumor effects? A rescue experiment would give information on whether this enzyme has a direct role in driving RMS cell behavior.__


      Response: This is an excellent suggestion. We are currently generating a DHCR7 rescue construct and plan to perform these experiments. While these data may not be available in time for the current revision, we will clearly outline this approach as a key next step in our Discussion section and incorporate results if available.

      Minor Comments:

      1. __ In line 287 "Supplementary Fig.1G and 1H" are mentioned, while it should be "Supplementary Fig.1I and 1J" since it regards the treatment with lovastatin.__

      Response: Thank you for catching this. We will correct the figure references accordingly.

      __ In line 340, authors mentioned the data "Supplementary Figure 4A and 4E", but there is not any corresponding data available in the Supplementary Information.__


      Response: We apologize for this oversight. These references will be corrected, and any missing supplementary data will be properly included and labeled.

      __ In the Legend of Figure 2L, authors mention "PRXO-1 silencing", this should be corrected to "shDHCR7". Also, please change "l" to capital "L".__


      Response: This will be corrected in the revised figure legend.

      __ In Figure 5G-H, please provide the data regarding loading control in the Western blot, as well as the molecular weights of the proteins presented.__


      Response: We thank the reviewer for this important point. For the Western blot analysis in Figure 5G-H, normalization was performed by quantifying the total protein in each lane using Bio-Rad's Stain-Free technology and analyzed with ImageLab 6.0.1 software. This approach allows for accurate lane-to-lane comparison without relying on a single housekeeping protein. We will add the Stain-Free total protein images as a supplemental figure (Supplementary Figure) and include the molecular weights for each of the proteins in the figure legend to improve clarity and reproducibility.

      __ Please, include the information of what black, red etc refer to in each figure. This information is missing in several figures including Figure 2D, 2K, 3C, 3J, 3K, 3L which makes it difficult to follow.__


      Response: We agree and will update all relevant figure legends to clearly explain color coding, symbols, and what each bar or line represents to improve figure clarity.

      __ The authors should indicate the numbers of biological replicates in individual experiments throughout whole figure legends.__


      Response: Thank you for the suggestion. We will include the number of biological replicates for each experiment in the figure legends to enhance transparency and reproducibility.


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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript entitled "Targeting de novo cholesterol synthesis in rhabdomyosarcoma induces cell cycle arrest and triggers apoptosis through ER stress-mediated pathways" Gizaw et al investigate the crucial effect of targeting cholesterol biosynthesis in RMS. While this manuscript gives novel insights into putative therapeutic approach, there are some comments that should be address by the authors.

      Major comments

      1. The author demonstrated a correlation between PROX1 levels and the cholesterol synthesis pathway. Which genes from the pathway are mostly affected? The manuscript could benefit from a graphical representation of the pathway showing up- and downregulated genes from the RNAseq analysis. This will help in understanding why the authors decided to study HMGCR silencing as shown in Supplementary Figure 1A.
      2. Based on the previous comment, are the genes from the cholesterol synthesis identified in the RNA-seq similar to those detected in the publicly available data set presented in Figure 1E? In addition, validation of changes of these genes should be performed in the RMS cell lines as well as in myoblasts.
      3. In Figure 3, the authors study the impact of DHCR7-silencing in tumor growth in vivo. Please, provide stainings also for DHCR7 to show that cells indeed have silenced DHCR7.
      4. In Figure 4, the RNAseq data revealed downregulation in E2F genes as well as genes involved in cell cycle progression. It would be important that the authors provide examples of these genes and validate this data by performing qPCR.
      5. In Figure 4J-M, cell cycle distribution using flow cytometry should be assessed in an additional cell line.
      6. In line 271, the authors described that PROX1 is associated with an increase in DHCHR7. However, in the next paragraph they evaluated the effect of silencing HMGCR. Is this enzyme also increased? Please clarify.
      7. The authors show that cholesterol biosynthesis is crucial in RMS. Would overexpression of the DHCR7 in shDHCR7 cells rescue the anti-tumor effects? A rescue experiment would give information on whether this enzyme has a direct role in driving RMS cell behavior.

      Minor comments:

      1. In line 287 "Supplementary Fig.1G and 1H" are mentioned, while it should be "Supplementary Fig.1I and 1J" since it regards the treatment with lovastan.
      2. In line 340, authors mentioned the data "Supplementary Figure 4A and 4E", but there is not any corresponding data available in the Supplementary Information.
      3. In the Legend of Figure 2L, authors mention "PRXO-1 silencing", this should be corrected to "shDHCR7". Also, please change "l" to capital "L".
      4. In Figure 5G-H, please provide the data regarding loading control in the Western blot, as well as the molecular weights of the proteins presented.
      5. Please, include the information of what black, red etc refer to in each Figure. This information is missing in several figures including Figure 2D, 2K, 3C, 3J, 3K, 3L which makes it difficult to follow.
      6. The authors should indicate the numbers of biological replicates in individual experiments through whole figure legends.

      Significance

      A nice and coherent study. Please see text above.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Gizaw et al characterizes the cholesterol biosynthetic pathway and the effect of its knockdown or inhibition on rhabdomyosarcoma tumor properties. The Authors find that the PROX1 transcription factor mediated cholesterol biosynthesis regulates rhabdomyosarcoma cell growth and proliferation. Blocking the cholesterol biosynthetic pathway leads to reduced proliferation, cell cycle arrest and ER-stress mediated enhanced apoptosis. Detailed transcriptomic analysis indicate gene expression patterns that support these findings.

      Major comments

      1. Details of the healthy human myoblasts that are used in Figure 1A are not provided and should be updated. Evidence of PROX1 knockdown should be presented. What kind of pathways and gene ontology predictions were associated with the 225 genes that are commonly downregulated between all three cell lines in Figure 1A?
      2. In Figure 2, while the effect of the shRNAs targeting DHRC7 or the DHRC7 inhibitor AY9944 are striking, it is not clear whether these effects are specific to rhabdomyosarcoma cells or cancer cells. A control, human myoblast cell line or another non-cancerous cell line should be used to repeat these experiments quantifying Caspase3/7 activity, cell growth etc. to assess the cancer cell specificity of such treatments. Evidence of DHRC7 knockdown at the protein level would add to the study.
      3. Western blots for Caspase3 quantification and a cell proliferation marker such as Cyclin D in shSCR and shDHRC7 tumor lysates would validate the data shown in the Figure 3. Are the shRNA constructs used inducible ones? If not, how do the Authors distinguish the effect of shDHRC7 on tumor engraftment versus tumor proliferation and growth? Many of the graphs need proper labeling of the axes and what the bars represent.
      4. Gene ontology and pathway analysis will add to Figure 4.
      5. In Figure 5A, how do the Authors explain the upregulation of cholesterol biosynthetic pathway genes upon shDHRC7 treatment? Are these effects seen at the protein level and if alternate pathways maintain cholesterol biosynthesis, how do the Authors think this strategy will be viable to treat such tumors? In Figure 5G-H, was a loading control used? If so, blots for that should be included.
      6. Lines 286-287 refers to Figure S1G, H; it should be corrected to Figure S1I, J.

      Significance

      Based on my expertise on rhabdomyosarcoma tumors, the manuscript is clear, concise and provides a significant advance to the field. Detailed mechanistic characterization is lacking, which takes away some of the significance of the findings, but the work done stands alone as description of the effect of the cholesterol biosynthetic pathway in rhabdomyosarcoma. Another aspect to be considered by the Authors is the potential specificity of targeting a ubiquitous pathway such as cholesterol biosynthesis, which is important to most cells and not only cancer cells. Overall, the manuscript may be revised to address the specific comments below.

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      Reply to the reviewers

      We prefer not to post our response to reviewers on BioRXiv as it is optional

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      Referee #3

      Evidence, reproducibility and clarity

      This study applies cellular and molecular assays, together with transcriptome analysis, to dissect how certain heterochromatin-based epimutations can confer resistance to caffeine and other drugs in fission yeast cells. The findings indicate that compromising the function of two mitochondrial proteins, Cup1 and Ppr4, leads to increased oxidants and the activation of the mito-nuclear retrograde response, which in turn causes the activation of the Pap1-mediated oxidative stress response, including the induction of transmembrane transporters to increase the efflux of drugs. This provides mechanistic insights into how the chromatin-mediated silencing of mitochondrial factors can result in fungal drug resistance. The authors also show that these phenotypes are variable within a cell population, allowing phenotypic plasticity to changing environments. This is a straightforward and clearly presented study, and the conclusions are generally justified based on the experiments presented.

      Minor comments:

      1. Fig. 3A: The legend needs more information to understand what is shown here. Does this show the normalized read counts (cpm?) for each gene scaled per average counts in all samples? Another possibility would be to show relative data for the two mutants compared to wild-type. Also, the labels for the bottom two clusters seem the wrong way round, i.e. the last cluster should be cup1-tt only. How many genes are shown here which made the cutoff?
      2. To strengthen some of the conclusions, it would be meaningful to calculate the significance of overlaps between key gene lists, given the size of the lists involved and the background gene list (Fig. 3B; Fig. 4).
      3. The font size indicating the significance of differences is too tiny in some bar plots (Fig. 5C-E; Fig. 6D; Fig. 7C).

      Referees cross-commenting

      In response to issues raised by Reviewer 1:

      In my opinion, the growth, TPF and ROS assays applied are robust and diagnostic to show a mitochondrial dysfunction. Additional assays, like Seahorse, would provide more specific insights about particular aspects of mitochondrial dysfunction, but this is not really relevant to this study. The key point is that the epimutations compromise mitochondrial function by downregulating mitochondrial proteins, which, in turn, are exploited by the cell to trigger a stress response that protects against antifungal compounds. The exact nature of the mitochondrial dysfunction, any changes in morphology, or details of differentially expressed genes are not critical for this mechanism, as it relies on downstream processes like the retrograde response that is activated by diverse mitochondrial problems.

      The question of whether heterochromatin-mediated resistance phenotypes are prevalent in human fungal pathogens is interesting and an important avenue for future study. But it is not evident to me how this could be addressed bioinformatically.

      Significance

      This manuscript builds on a previous study by the same group, which showed that different heterochromatin-based epimutations can provide cellular resistance to caffeine (Torres-Garcia et al. Nature, 2000). Here they use the UR1 and UR2 epimutations to highlight an example of how such mutations can generate antifungal resistance and phenotypic plasticity by exploiting side effects of mitochondrial dysfunction. Epimutations are an interesting case of cellular adaptation that lasts longer than gene-expression responses but are more readily reversible and flexible than genetic mutations, allowing bet-hedging by generating variable phenotypes in a clonal cell population. This study provides fresh insights into the downstream effects of epimutations causing altered cellular traits, thus complementing previous studies focusing on the patterns and mechanisms of establishing heterochromatin-based genomic islands. The current study is of interest to researchers working on genome regulation, mitochondrial function, cellular adaption/evolution, and has possible applications to combat antifungal resistance.

      Field of expertise: genome regulation, gene function, fission yeast, stress response

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      Referee #2

      Evidence, reproducibility and clarity

      This very interesting manuscript describes the impact of heterochromatin in triggering down-regulation of mitochondrial respiratory activity in S. pombe, and thereby causing increased efflux and consequently increased resistance to several compounds, including the azole class of antifungal drugs. The authors performed detailed mechanistic studies, focusing on two mitochondrial genes, cup1 and ppr4, which are under heterochromatin-dependent repression. Based on their findings, they conclude that reduced mitochondrial respiration causes increased levels of reactive oxygen species (ROS), which activates the transcription factor Pap1. Pap1 then upregulates several genes, including efflux pumps. The authors performed an excellent set of experiment to address heterogenous cell populations in the epimutants, which are described in the second part of the Results section and provide strong evidence of plastic drug resistance phenotypes.

      The manuscript is beautifully written and the data is presented well. Overall, the conclusions are supported by the data.

      I have a reservation with one particular conclusion that I discuss below under point 1. This can be addressed by modifications to the text. Under point 2, I suggest an easy to do experiment, which would strengthen the conclusion that ROS produced due to mitochondrial dysfunction are driving the drug resistance phenotype. This is an interesting mechanism, and the data in the manuscript supports it, but the authors' have not demonstrated it directly. They could do so by using antioxidants, as I suggest below.

      1. Most of the mechanistic analysis is centred around the transcription factor Pap1. The authors performed experiments to connect the production of ROS in mitochondrial mutants, with higher nuclear localisation of Pap1 and its activation of several genes, including the membrane transporter Cas5 and to a lesser extent Bfr1, which might be responsible for increased efflux. There is no question that efflux is elevated in mitochondrial mutants (a phenotype consistent with previous work in other yeast models). The authors also present data to show that inhibition of efflux reverses drug resistance. The data for Pap1's involvement is good in the cup1 mutant (one of the mitochondrial mutants that was studied) but not so much in the ppr4 mutant (the other mitochondrial mutant that was studied). There was little enrichment of Pap1 on the Cas5 promoter in the ppr4 mutant, and no effects of Pap1 on the expression of Cas5 in the ppr4 mutant (Fig 5C and 5D). While the pap1 mutation reduced resistance of the ppr4 mutant to drugs, the authors acknowledge that this could be due to increased sensitivity of the pap1 mutant to drugs. The enrichment of Pap1 on the bfr1 promoter was also modest in the mitochondrial mutants.

      I would therefore suggest that another transcription factor might be responsible for the upregulation of these efflux pumps and/or other efflux pumps are involved in Pap1' s contribution to drug resistance. The authors should consider modifying their conclusions on Pap1-dependent targets that are responsible for drug resistance in the mitochondrial mutants. 2. The authors' conclusion that increased ROS levels upon dysfunctional respiration might be driving the drug resistance phenotype in S. pombe (via Pap1 but perhaps other mechanisms too), presents a novel mechanistic link between mitochondria and drug resistance. I would suggest solidifying this conclusion by asking if antioxidants can reduce ROS levels and thereby decrease drug resistance in S. pombe. N-acetyl-L-cysteine could be used for this purpose.

      Significance

      That mitochondrial dysfunction causes drug resistance has been known for over 20 years. This manuscript describes a new mechanism, which relies on the formation of semi-stable epimutants, whereby the expression of genes encoding key mitochondrial proteins is down-regulated. As the authors propose, the beauty of epimutations is that they cause a heterogenous phenotype and are reversible, which would create an opportunity for the organism to use a bet-hedging strategy in drug. The ability to reverse the phenotype would be particularly important with using mitochondrial dysfunction as a strategy to increase drug resistance, because mitochondrial dysfunction lowers metabolic flexibility and growth rates for the organism. Therefore, it is only beneficial in the presence of drugs. This is to my knowledge one of the first logical mechanistic explanations for how fungal cells (but likely applicable more broadly) might use mitochondrial dysfunction to their advantage when needed, and then this can be reversed back to respiratory competence to maintain metabolic flexibility when drug selection is no longer present.

      This study will be of high interest to researchers studying drug resistance and how phenotypic plasticity and bet-hedging mechanisms are used by cells to survive toxic compounds. This is applicable across fields. This study will further be very interesting to the fields of antifungal drug resistance and fungal pathogenesis, and will provide the foundation for studying similar mechanisms in relevant fungal pathogens of animals and plants.

      My expertise is in metabolism and mitochondrial roles in fungal pathogens. I really enjoyed reading the manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript under review appears to present findings on heterochromatin-mediated antifungal resistance, specifically focusing on the role of mitochondrial dysfunction in the model organism Schizosaccharomyces pombe. However, there are several significant concerns regarding the novelty and robustness of the conclusions drawn by the authors.

      The key conclusions of the paper lack sufficient convincing evidence. While the authors attribute resistance phenotypes to heterochromatin-mediated repression, the evidence presented does not strongly support these claims. Significant claims should be qualified as preliminary or speculative, particularly those that extend beyond the experimental results provided. For example, asserting a definitive link between heterochromatin status and antifungal resistance mechanisms requires more comprehensive empirical data.

      Additional experiments are crucial to bolster the claims made in the manuscript. The authors rely heavily on growth assays on nonfermentable carbon sources to supposedly elucidate respiratory function. However, this approach is outdated, and advancements in the field should be employed for a more robust assessment of mitochondrial integrity and function. Techniques such as the Seahorse assay could provide critical insights into the respiration capacity of the mutants. Furthermore, the use of electron transport chain (ETC) inhibitors like antimycin A would offer stronger evidence regarding mitochondrial dysfunction. The current use of generalized DCF staining to assess reactive oxygen species (ROS) lacks specificity. MitoSox and MitoTracker should be utilized to measure mitochondrial ROS levels and examine mitochondrial morphology effectively.

      The authors claim that mitochondrial dysfunction correlates with significant changes in the transcriptome related to aerobic respiration, yet this crucial aspect lacks adequate elaboration in their analysis. Given that mitochondrial function is a primary theme of the manuscript, in-depth discussion and interpretation of the differentially expressed aerobic respiratory genes in the transcriptome data are necessary to validate their conclusions.

      Additionally, as a pathogenic fungal microbiologist, I express interest in investigating whether heterochromatin-mediated resistance phenotypes are prevalent in human fungal pathogens, including Candida albicans and Cryptococcus neoformans. A bioinformatic analysis could help address this inquiry and potentially broaden the relevance of the findings.

      Lastly, in the section "Cup1 and Ppr4 deficiencies...retrograde gene repression," the conclusions are made primarily based on transcriptome analysis and lack empirical confirmation through molecular biology techniques. This section should be revised to include comprehensive molecular evidence supporting the claims.

      Significance

      General Assessment: Strengths and Limitations

      Strengths:

      The study introduces a potentially novel mechanism of antifungal resistance in Schizosaccharomyces pombe through heterochromatin-mediated epimutations. This is particularly relevant in the context of rising antifungal resistance globally.

      The focus on mitochondrial dysfunction as a contributor to drug resistance provides a deeper understanding of how fungi adapt to environmental stressors.

      The manuscript raises important questions about the epigenetic factors influencing fungal resistance, which could inspire subsequent investigations in the field.

      Limitations:

      The research relies heavily on traditional methods for assessing respiratory function, which may not fully characterize the complexities of mitochondrial integrity and function. This may weaken the overall conclusions regarding mitochondrial dysfunction.

      The evidence supporting key claims is not robust enough to confidently assert a direct link between heterochromatin changes and antifungal resistance. The lack of confirmatory experiments using more advanced techniques limits the study's impact.

      The analysis of transcriptome data is insufficiently detailed, leaving significant gaps in understanding the specific mechanisms at play.

      Advance: Comparison to Existing Published Knowledge

      This study contributes to the existing literature by exploring the role of epimutations in antifungal resistance, aligning with emerging interests in epigenetic mechanisms in microbial adaptation. While previous studies have focused on genetic mutations and efflux mechanisms, this research attempts to link heterochromatin dynamics to resistance pathways, thereby filling a conceptual gap in understanding how eukaryotic microorganisms may adapt to antifungal treatments.

      However, the advances made by the study appear to be incremental rather than groundbreaking. ​While it does shed light on the potential role of heterochromatin in drug resistance, further empirical evidence and a stronger methodological approach are required to substantiate these findings convincingly.

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      Reply to the reviewers

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      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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      Referee #2

      Evidence, reproducibility and clarity

      Kemp et al. aimed to explore the transcriptional cell cycle regulation of replication-dependent (RD) histone genes at histone locus body (HLB) in Drosophila. They evaluate the accumulation of RNA pol II and RD histone transcripts at HLB during the cell cycle using live and fixed imaging of Drosophila tissues at different stages of development. They find that RNA pol II is enriched at HLB, not only during S phase when RD histone genes are transcribed but throughout the cell cycle. Outside of S phase, they detect short but not full-length RD histone transcripts suggesting a mechanism of RNA pol II pausing. Full length RD transcripts are only produced upon cyclin E/Cdk2 activation when cells enter S phase, arguing that Cyclin E/Cdk2 can activate transcription elongation. They propose that the elongation release triggered by Cyclin E/Cdk2 is the critical step linking RD histone gene expression and cell cycle progression rather than the recruitment of RNA pol II to HLB. The data are interesting and robust, however, additional experiments could reinforce the findings and the proposed model.

      Specific comments/concerns are listed below.

      1. In Figure 3, quantifications of the fluorescence at HLBs for mCherry-RBP1 and MXC-mScarlet should be provided.
      2. In Figure 5C, both 5' and 3' transcripts are observed in G214 cells. However, their accumulation in the cytoplasm is not visible. How do the authors explain this result? What happens in S14 cells?
      3. In Figure 6, the authors observed RD histone 3' transcripts only in replicating cells (EdU positive) while they detected 5' transcripts in both replicating and non-replicating cells. They argue that the appearance of 3' transcripts is due to the release from transcriptional pausing. To further support particular states in the transcriptional arrest, data by immunofluorescence using specific antibodies recognizing either RNA pol II ser5P or ser2P would determine whether the presence of 3' transcripts is associated with the accumulation at HLB of RNA pol II ser2P (elongating polymerase). Moreover, is there a correlation between P-MXC and RNA pol II ser2P?
      4. In Figure 7 panels C and D, the 5' transcripts should be shown. Although RD histone 3' transcripts accumulate in CyE+ embryonic cells, unfortunately, their presence at HLBs (pointed by arrows) is not visible in the image of panel E. To firm up conclusions quantifications of the 3' and 5' transcripts should be provided for CycE+ and CycEnull cells. In Hur et al., 2020, the authors looked at RD histone transcripts in WT embryo and CycE+/-/Cdk2+/- mutant. They found that the amount of H3 transcripts using a probe corresponding to the coding sequence is not changed in the mutant as compared to the WT. In contrast, they found that there is an increase of transcripts that are not correctly processed using probes downstream the stem-loop region. This seems inconsistent with the results presented here where a decrease of 3' transcripts is observed. This needs an explanation/discussion. Are such incorrectly processed transcripts observed in CycEnull mutant?
      5. The authors suggest that active Cyclin E/Cdk2 triggers the release of RNA pol II promoter-proximal pausing and therefore induces transcriptional elongation at RD histone genes when cells enter S phase. To further support this hypothesis, determining whether there is an enrichment of the elongation factor p-TEFb at HLB when Cyclin E/Cdk2 is active would help.
      6. Instead of using cycling E mutants, to determine whether it is the phosphorylation of MXC which directly impacts the elongation of RD histone genes, it would be interesting to generate phospho-null or phospho-mimetic mutant of MXC.
      7. In Suzuki et al., 2022, the authors described 3' RNA pol II pausing at RD histone genes. Although this study used human cells, it would be interesting to discuss that in addition to a promoter-proximal pausing that regulates transcription elongation, a 3' pausing could also regulate the transcription termination and 3' processing.
      8. In the discussion, the authors should point out some limitations of their studies linked to the method and could propose for the future that a more precise and molecular view of the pausing mechanism could be carried out using sequencing methods such as ChIP-seq of various isoforms of the RNA pol II (total, ser2P, ser5P) and elongation regulators (p-TEFb.....) and PRO-seq.

      Minor points:

      1. In Figure 1, for panels B and D as well as for panels C and E, to falicitate comparison of the localization of the different proteins, it would help to show the same developmental stages and the same image scales.
      2. In Figures 3 and 7 (C-F), the developmental stages should be indicated on the images, as it is done in the other figures.
      3. In the legend of Figure 7, it is indicated (D) and (E) instead of (C) and (D) in the sentence: "Endocycling midgut cells in (D) contain cytoplasmic histone mRNA which is absent in (E) (boxed regions)."

      Significance

      Kemp et al. aimed to explore the transcriptional cell cycle regulation of replication-dependent (RD) histone genes at histone locus body (HLB) in Drosophila. They evaluate the accumulation of RNA pol II and RD histone transcripts at HLB during the cell cycle using live and fixed imaging of Drosophila tissues at different stages of development. They find that RNA pol II is enriched at HLB, not only during S phase when RD histone genes are transcribed but throughout the cell cycle. Outside of S phase, they detect short but not full-length RD histone transcripts suggesting a mechanism of RNA pol II pausing. Full length RD transcripts are only produced upon cyclin E/Cdk2 activation when cells enter S phase, arguing that Cyclin E/Cdk2 can activate transcription elongation. They propose that the elongation release triggered by Cyclin E/Cdk2 is the critical step linking RD histone gene expression and cell cycle progression rather than the recruitment of RNA pol II to HLB.

      The data are interesting and robust, however, additional experiments could reinforce the findings and the proposed model.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Kemp et al. seek to define the molecular interactions that limit replication-dependent histone gene transcription to S-phase of the cell cycle. They use the Drosophila model system and leverage live-imaging tools, such as tagged proteins and Jabba trap, and RNA FISH in several tissues to determine that RNA Pol II is enriched at the locus throughout the cell cycle and is paused outside of S-phase. Therefore, they conclude that it is not Pol II recruitment to the locus that couples histone transcription to S-phase, but release of Pol II pausing.

      Major comments:

      The data presented are clean and well-presented. The claims are supported by the data without exaggeration. It would be helpful to provide -omics support for this entirely image-based analysis (e.g. PRO- or GRO-seq data from synchronized, sorted Drosophila cells may already exist- OPTIONAL).

      A major requirement is that the authors make clear in Introduction and Discussion that the observation of Pol Ii pausing at RD histone genes is not novel. This requires, at minimum, a discussion of Liu (2024) and Suzuki (2022). This allows readers to focus on the advance novel to this work, which is specifically the cell cycle coupling of Pol II pausing.

      As the authors are claiming different dynamics between Spt6 and RPB1 in Figure 1, they should provide similarly-staged embryos for comparison. For example, the authors should show RPB1 in early/mid S of NC 14, as this is when they see Spt6 variability. In theory, this should be relatively easy as these are stills from the live videos.

      Minor comments:

      The use of Spt6 live imaging early on was slightly confusing. The authors should consider moving this data later in the results or providing more written justification for why they investigated Spt6 (further than "to further explore the regulation of RNA pol II dynamics... p6). Similarly, Spt6 is included in the model figure, which might be a stretch given the only Spt6 data involves the timing of Spt6 colocalization with Mxc during the cell cycle.

      Misleading language/missed citations:

      p3: "600 kB array" is misleading. The whole locus is ~ 600 kB.

      p3: Mxc may remain at the locus throughout the cell cycle, so the whole HLB does not disassemble (Terzo, 2015).

      p4: H1-specific factors include cramped (Gibert and Karch, 2011; Bodner et al. 2024 bioRxiv)

      p4: Hodkinson, 2023 is not the correct reference. The correct reference is Hodkinson, 2024, Genetics.

      p5: The Drosophila HLB is detectable at NC 10 (White, 2011; Terzo, 2015) not White, 2007

      p5: A typo: "imagining"

      p7: The section title "RNA pol II is necessary for HLB assembly" is incorrect, as Figure 3 shows that pol II is NOT necessary for Mxc recruitment, but for HLB growth. Mxc, however, is necessary for pol II recruitment.

      p9: The authors should clarify what "HisC" means as this is the first usage.

      Figures/experiments:

      Fig 2: The authors should show the gating in Figure I that led to the three categories in Figure J. The legend/colors in Figure J are not necessary.

      An "easy" experiment would be to use the FUCCI cell lines and 5'/3' RNA FISH in combination (assuming fluorophores allow) - OPTIONAL

      Discussion:

      p13: The reference to the work of Gugliemlmi, 2013 should first come up in the Introduction, as it provides rationale.

      p13: "without engaging in transcription" is misleading, as pol II is transcribing, but paused.

      p15: It makes sense for pol II to pause at histone genes in G1, as they are preparing for the rapid burst of histone transcription needed in S phase. But what might be the functional rationale for pol II pausing in G2, if the HLB disassembles in M?

      Methods:

      It should be made clear how embryos were staged for live imaging, as it is likely by timing after cell cycle events. What is this timing? It would be best if this detail is not just mentioned in the methods, but also in the main text. This is especially important for readers not familiar with Drosophila embryogenesis. Please cite/acknowledge DGRC for Fly-FUCCI line (if appropriate)

      Significance

      This study provides convincing evidence that pol II is enriched at the histone locus and paused outside of S-phase. What limits the significance is that several prior studies concluded that Pol II is paused at the histone locus:

      Lu et al. bioRxiv 2024, "Integrator-mediated clustering of poised RNA polymerase II synchronizes histone transcription"

      Suzuki et al. Nat Comm 2022, "The 3' Pol II pausing at replication-dependent histone genes is regulated by Mediator through Cajal bodies' association with histone locus bodies"

      Neither of these studies is discussed or even cited in the manuscript, which is disappointing. Therefore, the advance is limited to the cell-cycle coupling of pausing. This is still important, as a major knowledge gap as outlined by the authors is that it is not clear how histone transcription is coupled to S-phase and they rule out Pol II pausing as a possible mechanism, and point toward Pol II pausing release.

      Moreover, there is also evidence (from these authors) that Mxc phosphorylation is not always coupled to histone transcription in Drosophila ovaries. This work is also not discussed or cited:

      Potter-Birriel et al. J Cell Sci 2021, "A region of SLBP outside the mRNA-processing domain is essential for deposition of histone mRNA into the Drosophila egg"

      The current research may be of interest to the broad cell cycle field, but it may also be useful as a model for those conducting basic, foundational research who seek to describe how Pol II is released from pausing. The histone locus may be of interest as a novel, facile model for pausing.

      Reviewer expertise: Drosophila, chromatin, gene expression

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      Reply to the reviewers

      1. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      The following revisions are in progress:

      - From Reviewer-1: The authors observe defects in CNCCs through genomic experiments. It would be really nice to perform simple wound healing/scratch assays and/or transwell assays to test if the CNCC migration phenotype is reduced in the CHD3 KO as well which would support the transcriptomic data.

      As recommended by the Reviewer, we are performing a transwell assays to investigate whether CHD3 loss leads to defects in cell migration. These experiments should be completed in the next two weeks.

      __- From Reviewer-2: __Since CHD3 shows a progressive upregulation in expression during CNCC differentiation (Fig. 2E), one hypothesis can be that it is not necessary involved in the activation of the CNCC programs but instead it is involved in maintaining these programs active - by keeping regulatory elements accessible. Thus, authors should check expression of CNCC markers, and EMT genes at the same time point than Fig. 2E in both WT and KO cells.

      As recommended by the reviewer we are differentiating the cells to perform RT-qPCR timecourse for CNCC and EMT markers. These experiments will be completed in the next two weeks.

      __- From Reviewer-2: __It has been shown that CNCC regulatory elements controlling differentiation genes are primed/accessible prior migration (PMID: 31792380; PMID: 33542111). Since the authors claim "CHD3 may have the role of priming the developing CNCCs to respond to BMP by opening the chromatin at the BMP responsive enhancers", it will be good to perform ATAC-seq are several time point during the differentiation process to assess the dynamic of chromatin reorganization to see when the switch to mesoderm fate occurs and how accessibility of BMP responsive element changes in WT and KO cells during CNCC differentiation to be able to demonstrate the KO fail to make BMP responsive element accessible or whether it is a defect in the maintenance of this accessibility.

      As recommended by the Reviewer, we are differentiating the cells to perform ATAC-seq timecourse. These experiments will be completed in the next two/three weeks.

      2. Description of the revisions that have already been incorporated in the transferred manuscript

      The following revisions have already been carried out:

      Reviewer1

      1. Figure 1 presents nice confirmation of the CHD3 KO cell lines being used. However, given that these cell lines were previously published, I suggest moving these data to the supplement. As suggested by the Reviewer, we moved most of Figure 1 to the supplement, merging the remaining Figure 1 with Figure 2.

      In the results section for Figure 1, the authors discuss the CHD3 heterozygotes, but I only see the KO cell line data presented. It would be especially nice to see the protein levels of Chd3 in the het.

      As suggested, we have now performed western blot and qPCR for CHD3 in the heterozygous line and added it to Supplementary Figure S1.

      The authors discuss which genes are up and downregulated in the Chd3 KO D18 RNA-seq, and show a clear heatmap in Figure 2A for WT cells. The same heatmap for candidate genes discussed in the results would be appreciated for Chd3 KO.

      As recommended by the Reviewer, we have added CHD3-KO RNA-seq to the heatmap in Fig. 2A.

      In general 2-3 replicates are presented. While the authors are showing heatmaps for selected locations for individual clones, which is appreciated (ex: Figure 4B and Fig 6), the QC for data quality is missing. For example, show spearmean correlation across the genome for datasets as a supplement.

      We performed spearman correlation of ATAC-seq and RNA-seq data, which confirmed the replicates are very highly correlated, and created new dedicated supplemental figures (Supplemental Figures S3, S4, S5, S6, S7).

      In the section discussing the results presented in Figure 4, the authors discuss the ATAC-seq peak number changes and overlap with gene expression changes. However, the overlap with gene expression changes is not shown. Making a simple Venn diagram would help readers.

      As suggested, we added a Venn diagram with ATAC-seq/RNA-seq overlap in Figure 3D.

      In addition, showing a heatmap for unchanged ATAC-seq peaks can help to demonstrate the increase/decrease.

      As recommended, we have added an heatmap for unchanged ATAC-seq regions as Supplementary Figure S7.

      In Figure 6, the authors present ChIPseq data for CHD3 in D14 and D18 samples, focusing on locations losing or gaining accessibility. What is enrichment at unchanged sites? Is CHD3 specifically enriched at changed locations? Then what about over genes with altered gene expression vs not changed? Is CHD3 only bound to distal elements? Performing an analysis of the peak distribution, perhaps with ChromHMM or other methods to look at promoter vs enhancer vs other locations. These types of analyses could really enrich the interpretation of direct CHD3 function.

      Unfortunately, there is no ChromHMM data for neural crest cells, nor for closely related cell types. Therefore, to address the Reviewer's suggestion, we have taken two approaches: 1) We have further broken down the distribution of the peaks, dividing them between intergenic, intronic, exonic and TSS. Moreover, we have leveraged publicly available H3K27ac ChIP-seq data generated (by our group) in iPSC-derived CNCCs to identify CHD3 peaks that are decorated by this histone modification which typically marks active enhancers. This analysis revealed that 91% of the peaks are either intergenic (50%) or intronic (41%) and that ~a third of the peaks are decorated with H3K27ac in human iPSC-derived CNCCs, suggesting that they are bona-fide active enhancers in this cell type.

      Related to the above, I am not sure if there is a phenotypic test for enhanced mesoderm. I suspect only IF/expression and morphology are possible, which the authors did. However, sorting the cells (with some defined markers) to ask how many are mesoderm-like vs CNCC in WT vs CHD3 KO would give some information outside of the bulk expression data.

      The manuscript already included IF experiments for mesodermal markers, which clearly show that nearly all the cells acquired the mesodermal fate. See for example Brachyury IF in Figure 2E.

      Minor points Reviewer-1: 12. 1A seems to fit better with Figure 2. Done 13. The authors say that the KO cell lines are not defective in pluripotency, but Figures 1G suggests a slight decrease in SSEA-1. Is this reproducibly observed? It is not statistically significant and not reproducibly observed. 14. Would be nice to show number of up and downregulated genes in volcano plots for fast viewing of readers (ex: Fig 2B). We have modified the volcano plot as suggested. 15. Is it fair to use violin plots when data points are only 2-3 replicates (as in Figures 2C, 3D). To address this, we have layered the actual datapoints on top of the violin plots.

      The labels in Fig 4A and 5E are very hard to read.We have changed color to improve readability. 17. For browser tracks, the authors show very zoomed in examples (Fig 4C, and especially Fig 6C). showing a bit more of the area around these peaks would give readers a more clear appreciation of the data. Related to browser tracks, including more information just as including the gene expression changes (such as in Fig 6C) to enhance the interpretation of the impact of Chd3 binding, accessibility change and then, I presume, reduced Sox9 expression. Similar suggestion for Figure 4C, where I anticipate coordinate transcription changes of the associated genes. We have zoomed out the tracks, as suggested, and added expression data next to them. 19. Do the authors observe any clone variability between the two CHD3 KO clones? There is variability I see in some of the heatmaps, but don't know if that it is because of clones or technical variation. We do not observe any significant variability between the clones.

      Reviewer-2 1. What is the expression level of CHD3 in the heterozygote line? Does the remaining allele compensate for the loss which will explain the absence of phenotype?

      Ass suggested also by Reviewer-1, we have performed western blot for CHD3 in the heterozygous line and added it to Supplementary Figure S1. The bot shows that the remaining allele does not compensate. However it is likely that even a reduced amount of wild-type CHD3 is sufficient for proper CNCC specification.

      The authors should use the term "regulatory elements" instead of "enhancers" as they can act either as activator or repressors.

      As suggested, we have changed nomenclature from enhancers to cis-regulatory elements.

      On the same line, while the authors indicate "Motif analysis of the enhancers aberrantly active in CHD3-KO cells ", they haven't shown these are active. They should say they perform the analysis on regulatory elements aberrantly accessible in CHD3 KO. Done.

      See point 3 above.

      The rationale that led the authors to focus on genes typically expressed in the primitive streak and in the early pre-migratory mesoderm, and BMP responsive transcription factors could be better explained. Are they part of the most deregulated genes in the RNA-seq analysis?

      Not only mesodermal genes are among the most upregulated genes in the RNA-seq, but the motifs for the transcription factors encoded by these genes (e.g. TBR2, Brachyury, GATA, TBX3, TBX6) are among the most frequently represented in the aberrantly accessible cis-regulatory elements. The same applies to BMP responsive factor, but the other way around (they are downregulated and enriched in the aberrantly closed ATAC-seq regions).

      In the absence of CHD3, BMP response is not effective. While the authors nicely showed this is linked with changes in chromatin accessibility, it is necessary to check the expression levels of BMP receptors in CHD3 KO cells.

      We have checked the expression of these genes, and they were not differentially expressed. This is consistent with the downstream response being affected rather than ligand binding to the receptors.

      Aberrant early mesoderm signature of the CHD3-KO cells needs to be better shown. It is not obvious from the GO analysis in Fig. 2 and the authors then showed expression of some markers but it is unclear how they picked them up.

      See point 5: not only mesodermal genes are among the most upregulated genes in the RNA-seq, but the motifs for the transcription factors encoded by these genes (e.g. TBR2, Brachyury, GATA, TBX3, TBX6) are among the most frequently represented in the aberrantly accessible cis-regulatory elements. See for example expression levels of typical mesodermal genes below:

      EOMES - upregulated log2FC: 5.5

      TBXT - upregulated log2FC: 4.6

      MESP1 - upregulated log2FC: 4.7

      MIXL1 - upregulated log2FC: 5.4

      TBX6 - upregulated log2FC: 3.2

      MSGN1 - upregulated log2FC: 4.6

      HAND1 - upregulated log2FC: 5.5

      The authors claim CHD3 directly binds at BMP responsive enhancers, but in the figure, they show the data for all the region gaining or losing activity. It will be nice to add the information for the BMP responsive elements only.

      As recommended, we have added an heatmap for BMP responsive regions only, clearly showing that CHD3 binds them (Supplementary Figure S7).

      The authors need to support better that CHD3-KO express more Wnt signaling/activity.

      We have checked expression of many genes that are typically Wnt responsive during mesoderm specification (see also point 7). These include:

      EOMES - upregulated log2FC: 5.5

      TBXT - upregulated log2FC: 4.6

      MESP1 - upregulated log2FC: 4.7

      MIXL1 - upregulated log2FC: 5.4

      TBX6 - upregulated log2FC: 3.2

      MSGN1 - upregulated log2FC: 4.6

      HAND1 - upregulated log2FC: 5.5

      These data clearly support that the Wnt-mediated mesodermal program is markedly upregulated.

      Minor points Reviewer-2: 13. In the discussion, the authors could indicate whether CHD3 mutants somehow phenocopies some of the craniofacial defects observed in DLX5 mutant patients. Done. 14. It is not indicated were to find the data regarding expression epithelial and mesenchymal genes in the CHD3-KO cells. They are in the heatmap in Fig. 1C. 15. Authors could add in the discussion what is known about how CHD3 function changes from opening or closing chromatin is very intriguing a could be discussed. To our knowledge, nothing is known on this. CHD3 is significantly understudied.

      OPTIONAL: While this is not necessary for the current study, it is very intriguing that other CHD family member do not compensate. How this tissue or DNA sequence activity is achieved could be discussed. What are CHD4 or CHD5 expressed during CNCC differentiation? Could they be used to rescue the CHD3 KO phenotype? While this may be difficult to test, it could perhaps be discussed.

      We have added a paragraph on this in the discussion.

      3. Description of analyses that authors prefer not to carry out* *

      From Reviewer 1: Given the changes in the CHD3-KO accessibility are mostly gene distal, are there existing Hi-C/microC/promoter CaptureC or other that can be used to ask if these are interacting with the predicted genes?

      We are not aware of this type of essays being performed genome-wide in human CNCCs. The only studies performed in human CNCCs are SOX9-centred. Looking at 3D chromatin conformation would also be out of the scope of the paper.

      From Reviewer-2:

      OPTIONAL: Does increasing BMP concentration early during CHD3 KO differentiation has a better effect at rescuing CNCC differentiation?

      Indicated by Reviewer as OPTIONAL. We do not think that adding BMP earlier on would make a significant difference in rescuing CNCC differentiation.

      From Reviewer-1: Are the results observed NuRD-based or CHD3 NuRD independent functions? Looking at other NuRD subunit binding or effects in differentiation would help to dig into this a bit more. I realize this is a bit of a big ask, so I am not asking for everything. Are there existing binding data in CNCCs for a NuRD subunit that could be examined for overlap in where these changes occur, for example? I want to be clear I am not asking the authors to do all the experiments for an alternative NuRD subunit.

      There are no existing data on NuRD binding in CNCCs. However, while the Reviewer is definitely not recommending generating new data in this regard, we still decided to make an attempt at performing ChIP-seq for the core NuRD subunit MBD3 in our CNCC. We will only make one attempt (multiple replicates), and if it does not work we will not pursue this any further as the Reviewer clearly stated that this is not necessary nor required and we do not want to delay the resubmission.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this manuscript, Mitchell et al. study the function of CHD3 - a member of the NuRD chromatin remodeling complex - during human cranial neural crest cells (CNCC) differentiation in vitro. To this end they use iPSC CHD3-KO lines. They first observed this deletion has no impact on pluripotency levels of mutant iPSC neither on their capacity to differentiate into the three germ layers derivatives. Yet, they showed these mutant cells are unable to form CNCC as they fail to induce EMT genes and undergo CNCC differentiation. Using ATACseq, the authors then showed CHD3 KO present a reorganization of the chromatin accessible landscape, biasing these cells from a CNCC to a mesoderm fate. They further determine that upon differentiation of CHD3 KO cells, BMP responsive regulatory elements are aberrantly closed, making the cells insensitive to the signaling, explaining how they then fail to generate CNCC. Using ChIP-seq, they confirmed a direct action of CHD3 in making these elements accessible as it normally binds to these chromatin regions to allow proper differentiation. In addition, they demonstrate these BMP responsive genes are bound by DLX5, a transcription factor essential for neural crest development. Finally, the authors showed that during CNCC differentiation, CHD3 KO cells experience an imbalance between BMP and WNT signaling, leading to these cells adopting a mesoderm instead of a CNCC identity. They finally, showed this can be partially rescued by reducing the amount of Wnt signaling - that decreases the mesoderm gene expression - however, it not sufficient to induce a neural crest identity.

      Major comments

      1. What is the expression level of CHD3 in the heterozygote line? Does the remaining allele compensate for the loss which will explain the absence of phenotype?
      2. Since CHD3 shows a progressive upregulation in expression during CNCC differentiation (Fig. 2E), one hypothesis can be that it is not necessary involved in the activation of the CNCC programs but instead it is involved in maintaining these programs active - by keeping regulatory elements accessible. Thus, authors should check expression of CNCC markers, and EMT genes at the same time point than Fig. 2E in both WT and KO cells.
      3. The authors should use the term "regulatory elements" instead of "enhancers" as they can act either as activator or repressors.
      4. On the same line, while the authors indicate "Motif analysis of the enhancers aberrantly active in CHD3-KO cells ", they haven't shown these are active. They should say they perform the analysis on regulatory elements aberrantly accessible in CHD3 KO.
      5. The rationale that led the authors to focus on genes typically expressed in the primitive streak and in the early pre-migratory mesoderm, and BMP responsive transcription factors could be better explained. Are they part of the most deregulated genes in the RNAseq analysis?
      6. In the absence of CHD3, BMP response is not effective. While the authors nicely showed this is linked with changes in chromatin accessibility, it is necessary to check the expression levels of BMP receptors in CHD3 KO cells.
      7. Aberrant early mesoderm signature of the CHD3-KO cells needs to be better shown. It is not obvious from the GO analysis in Fig. 2 and the authors then showed expression of some markers but it is unclear how they picked them up.
      8. It has been shown that CNCC regulatory elements controlling differentiation genes are primed/accessible prior migration (PMID: 31792380; PMID: 33542111). Since the authors claim "CHD3 may have the role of priming the developing CNCCs to respond to BMP by opening the chromatin at the BMP responsive enhancers", it will be good to perform ATACseq are several time point during the differentiation process to assess the dynamic of chromatin reorganization to see when the switch to mesoderm fate occurs and how accessibility of BMP responsive element changes in WT and KO cells during CNCC differentiation to be able to demonstrate the KO fail to make BMP responsive element accessible or whether it is a defect in the maintenance of this accessibility.
      9. The authors claim CHD3 directly binds at BMP responsive enhancers, but in the figure, they show the data for all the region gaining or losing activity. It will be nice to add the information for the BMP responsive elements only.
      10. Motifs enrichment analysis of regions gaining accessibility in CHD3 KO do not seems to be labeled as Wnt responsive elements. The authors need to support better that CHD3 KO express more Wnt signaling/activity.
      11. OPTIONAL: Does increasing BMP concentration early during CHD3 KO differentiation has a better effect at rescuing CNCC differentiation?
      12. OPTIONAL: While this is not necessary for the current study, it is very intriguing that other CHD family member do not compensate. How this tissue or DNA sequence activity is achieved could be discussed. What are CHD4 or CHD5 expressed during CNCC differentiation? Could they be used to rescue the CHD3 KO phenotype? While this may be difficult to test, it could perhaps be discussed.

      Minor comments

      1. In the discussion, the authors could indicate whether CHD3 mutants somehow phenocopies some of the craniofacial defects observed in DLX5 mutant patients.
      2. It is not indicated were to find the data regarding expression epithelial and mesenchymal genes in the CHD3-KO cells.
      3. Authors could add in the discussion what is known about how CHD3 function changes from opening or closing chromatin is very intriguing a could be discussed.

      Significance

      General assessment:

      The link between chromatin remodelers and craniofacial defects has been shown in several studies in the past, but it still remains unclear how mutation of a given factor leads to such tissue specific defects. This manuscript represents an interesting and detailed mechanistic study on the role of chromatin remodeler in cell fate decision, showing that reorganization of chromatin accessibility is essential to proper response to signaling pathway and cell differentiation.

      Advance:

      The authors manage to link how mutant-induced changes in chromatin accessibility biased the cells towards a mesoderm fate as they directly impact the capacity of the cells to respond to BMP signaling - these regions closing upon CHD3 loss. However, the question remains to know whether CHD3 acts as an initiating factor or instead in involved in maintaining these programs active. As noted by the authors, a clinical link (with patient-derived iPCS) would be of great interest but as it stands the story already provide a good mechanistic understanding on how CHD3 control CNCC differentiation.

      Audience:

      This manuscript will be of great interest for specialized audience, yet a broader public may find it interesting too.

      Reviewer field of expertise:

      Neural crest and craniofacial development, epigenetics, transcriptomics

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Mitchell et al examine the impact of CHD3 KO (or het) on iPSC differentiation to CNCCs to model how pathogenic CHD3 mutations promote a neurodevelopmental disorder. The authors perform genomic characterization of the KO and het mutants during this differentiation model, and identify loss of CHD3 results in BMP target gene repression and increased mesoderm fate. Finally, the authors attempt to rescue the observed defects by titrating Wnt levels and observe partial rescue. I think the paper is easy to follow, contains interesting data, and establishes a novel role for CHD3 in CNCC differentiation, which may have implications in the disorder highlighted. I have the following suggestions:

      1. Figure 1 presents nice confirmation of the CHD3 KO cell lines being used. However, given that these cell lines were previously published, I suggest moving these data to the supplement.
      2. In the results section for Figure 1, the authors discuss the CHD3 heterozygotes, but I only see the KO cell line data presented. It would be especially nice to see the protein levels of Chd3 in the het.
      3. The authors discuss which genes are up and downregulated in the Chd3 KO D18 RNAseq, and show a clear heatmap in Figure 2A for WT cells. The same heatmap for candidate genes discussed in the results would be appreciated for Chd3 KO. Only a subset of markers are shown in Fig 2C.
      4. In general 2-3 replicates are presented. While the authors are showing heatmaps for selected locations for individual clones, which is appreciated (ex: Figure 4B and Fig 6), the QC for data quality is missing. For example, show spearmean correlation across the genome for datasets as a supplement.
      5. In the section discussing the results presented in Figure 4, the authors discuss the ATAC-seq peak number changes and overlap with gene expression changes. However, the overlap with gene expression changes is not shown. Making a simple venn diagram would help readers. a. In addition, showing a heatmap for unchanged ATACseq peaks can help to demonstrate the increase/decrease.
      6. In Figure 6, the authors present ChIPseq data for CHD3 in D14 and D18 samples, focusing on locations losing or gaining accessibility. What is enrichment at unchanged sites? Is CHD3 specifically enriched at changed locations? Then what about over genes with altered gene expression vs not changed? Is CHD3 only bound to distal elements? Performing an analysis of the peak distribution, perhaps with ChromHMM or other methods to look at promoter vs enhancer vs other locations. These types of analyses could really enrich the interpretation of direct CHD3 function.
      7. Given the changes in the CHD3 KO accessibility are mostly gene distal, are there existing Hi-C/microC/promoter CaptureC or other that can be used to ask if these are interacting with the predicted genes?
      8. Are the results observed NuRD-based or CHD3 NuRD independent functions? Looking at other NuRD subunit binding or effects in differentiation would help to dig into this a bit more. I realize this is a bit of a big ask, so I am not asking for everything. Are there existing binding data in CNCCs for a NuRD subunit that could be examined for overlap in where these changes occur, for example? I want to be clear I am not asking the authors to do all the experiments for an alternative NuRD subunit.
      9. The authors observe defects in CNCCs through genomic experiments. It would be really nice to perform simple wound healing/scratch assays and/or transwell assays to test if the CNCC migration phenotype is reduced in the CHD3 KO as well which would support the transcriptomic data.
      10. Related to the above, I am not sure if there is a phenotypic test for enhanced mesoderm. I suspect only IF/expression and morphology are possible, which the authors did. However, sorting the cells (with some defined markers) to ask how many are mesoderm-like vs CNCC in WT vs CHD3 KO would give some information outside of the bulk expression data.
      11. I did not see a reviewer token for the GEO data, so I could not check the deposited datasets.

      Minor points

      1. 1A seems to fit better with Figure 2.
      2. The authors say that the KO cell lines are not defective in pluripotency, but Figures 1G suggests a slight decrease in SSEA-1. Is this reproducibly observed?
      3. Would be nice to show number of up and downregulated genes in volcano plots for fast viewing of readers (ex: Fig 2B).
      4. Is it fair to use violin plots when data points are only 2-3 replicates (as in Figures 2C, 3D)
      5. The labels in Fig 4A and 5E are very hard to read.
      6. For browser tracks, the authors show very zoomed in examples (Fig 4C, and especially Fig 6C). showing a bit more of the area around these peaks would give readers a more clear appreciation of the data.
      7. Related to browser tracks, including more information just as including the gene expression changes (such as in Fig 6C) to enhance the interpretation of the impact of Chd3 binding, accessibility change and then, I presume, reduced Sox9 expression. Similar suggestion for Figure 4C, where I anticipate coordinate transcription changes of the associated genes.
      8. Do the authors observe any clone variability between the two CHD3 KO clones? There is variability I see in some of the heatmaps, but don't know if that it is because of clones or technical variation.

      Referees cross-commenting

      I think that the other reviewer and I are inline with each other in terms of our reviews and thoughts on the manuscript, so I do not have anything to add.

      Significance

      The paper presented by Mitchell et al represents a new role for CHD3 in regulating CNCC differentiation and perhaps explains why CHD3 mutations exist in neurodevelopmental disorders such as Snijders Blok-Campeau Syndrome. Limitations are the reliance on genomic datasets and modeled differentiation, although this permits for more mechanistic studies.

      I believe the fields of neural development, stem cell, chromatin biology, and others will be interested in this manuscript.

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      Reply to the reviewers

      We sincerely thank all three reviewers for their professional and constructive feedback. We appreciate the thorough evaluation of our manuscript and are committed to revising both the manuscript and supplemental materials based on the suggestions. We have carefully considered each comment and have addressed most of them in the initial revised version, which has been transferred. Additionally, we are currently conducting new experiments to provide the requested data to address a few comments. We are confident that these revision experiments will be completed in a couple of months or so, which will significantly enhance the quality of our study.

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

      In the manuscript by Agarwal and Ghosh, the authors examine yeast Scm3 function in the DNA damage response. They show that Scm3 loss results in DNA damage sensitivity and more Rad52 foci. Importantly, Scm3 is recruited to DSB sites using an HO-cut site and its loss results in an attenuated DNA damage checkpoint as measured by Rad53 phosphorylation. The authors demonstrate convincingly that Scm3, like its human counterpart HJURP, plays a role in the DNA damage response through altered Rad53 activation. However, what its specific role in DNA repair is remains ambiguous.

      Major comments:

      1. It is unusual to see multiple DNA repair foci as those observed in Figure 2B. What is the distribution of cells with 1, 2, 3, 4, or more foci? Are more observed in SCM3-AID cells perhaps suggesting that the DSB ends are not being clustered as would be expected in WT cells exposed to DNA damage?

      Response: As per the reviewer’s comment, we have included a graph (Figure R2) showing the distribution of cells with 1, 2, 3, 4, or more Rad52-GFP foci when they are treated with MMS. There are more cells with 4 or more foci when Scm3 is depleted (SCM3-AID + Auxin) compared to the wild type (SCM3-AID). The average number of Rad52-GFP foci per cell presented in Figure 2B (2.8 in the mutant vs. 1.9 in the wild type) is well in accord with the previous report (Conde and San-Segundo, 2008), where the same was reported as ~2.5 in the cells lacking a methyl transferase Dot1, vs ~ 1.5 in the wild type. More Rad52-GFP foci in MMS-treated cells lacking Scm3 may arise due to the creation of too many damaged sites to be accommodated in 1-2 foci and/or due to the inability of the cells to cluster the DSB ends.

      This result has been incorporated as a new supplementary Figure S4C and new text has been added in the revised manuscript as: “We further quantified the distribution of cells with 1, 2, 3, or >4 Rad52-GFP foci in wild type (SCM3-AID) or Scm3 depleted (SCM3-AID + auxin) cells treated with MMS. Scm3 depleted cells showed a significantly higher number of cells with more than >4 Rad52-GFP foci, suggesting the possibility of the creation of too many damaged sites to be accommodated in 1-2 foci or the inability of such cells to cluster the DSB ends.” in page 7, lines: 237-241.

      2. The peaks with increased Scm3 recruitment by ChIP-seq upon MMS is confusing as MMS does not induce specific damage at genomic locations. Is Scm3 being recruited at other genomic sites that might be more susceptible to DNA damage? Is Scm3 recruited to Pol2 sites for example? Or fragile sites?

      __Response: __We believe that the MMS induced increase in association of Scm3 with the non-centromeric chromatin loci depends on MMS sensitive vulnerable chromosomal sites. We agree with the reviewer that MMS might cause DNA damage at these sites, leading to Scm3 occupancy at those sites. Therefore, we compared the sites of Scm3 occupancy with possible such sites available from the literature that include fragile sites, RNA Pol II binding sites, double strand break hotspots, and coldspots. Based on our analysis, we have included the following lines in the ‘discussion’ section in page 16-17, lines 566-594 as follows:

      “Moreover, an overall increase in the chromatin association of Scm3 in response to MMS also suggests that Scm3 might be recruited to several repair centers or sites that are susceptible to DNA damage, for example, the fragile sites (Figure 3B, C, E, S6). These sites in yeast are DNA regions prone to breakage under replication stress, often corresponding to replication-slow zones (RSZs) (Lemoine et al., 2005). These regions include replication termination (TER) sequences, tRNA genes, long-terminal repeats (LTRs), highly transcribed genes, inverted repeats/palindromes, centromeres, autonomously replicating sequences (ARS), telomeres, and rDNA (Song et al., 2014). Since the helicase Rrm3 is often associated with these fragile regions (Song et al., 2014), we compared Scm3 binding sites with the top 25 Rrm3 binding sites from the literature (Azvolinsky et al., 2009). In untreated cells, Scm3 sites overlapped with three Rrm3 sites on chromosomes X, XII, and XIV. Whereas in MMS treated cells, overlapping was found with four Rrm3 sites, with two (on chromosomes XII and XIV) shared with untreated cells and two new sites were observed on chromosomes II and XII (Table R1). Mapping of the Scm3 sites with the tRNA genes and LTRs revealed that these sites from the untreated cells did not overlap with the LTRs (Raveendranathan et al., 2006). However, the same from the treated cells showed overlap with two LTRs on chromosome XVI. No overlap with tRNA genes was observed in the treated cells (Table R1). We next examined Scm3 occupancy at 71 TERs documented in the literature (Fachinetti et al., 2010). Scm3 was found to bind to 6 TERs in both untreated and MMS-treated cells. Notably, MMS treatment resulted in three new peaks, while three peaks were shared with untreated samples (Table R1). Lastly, we compared Scm3 sites with top 25 RNA Pol II sites obtained from the literature (Azvolinsky et al., 2009). In untreated cells, Scm3 was found at only one of these Pol II sites, whereas after MMS treatment, Scm3 sites overlapped with four such sites (Table R1). We further checked the occupancy of Scm3 at a few DSB hotspots (BUD23, ECM3, and CCT6) and DSB coldspot (YCR093W) as mentioned in the literature (Dash et al., 2024; Nandanan et al., 2021). However, we did not find Scm3 binding to these sites. Overall, in-silico analysis of the binding sites indicates that the non-centromeric enrichment of Scm3 occurs at sites that are amenable to DNA damage.”

      Table R1: The table summarising the occupancy of Scm3 in untreated or MMS treated conditions at the indicated regions

      Region

      Chromosome

      Scm3 occupancy

      Untreated

      MMS treated

      Rrm3 binding sites

      Chr II

      YES

      Chr X

      YES

      Chr XII

      YES

      Chr XII

      YES

      YES

      Chr XIV

      YES

      YES

      LTRs

      Chr XVI

      YES

      Chr XVI

      YES

      tRNA

      Chr XV

      YES

      TERs

      Chr IV

      YES

      Chr V

      YES

      Chr VI

      YES

      YES

      Chr VII

      YES

      Chr X

      YES

      Chr X

      YES

      Chr XIV

      YES

      YES

      Chr XV

      YES

      YES

      Chr XVI

      YES

      Pol II binding sites

      Chr II

      YES

      Chr X

      YES

      Chr XII

      YES

      Chr XII

      YES

      Chr XV

      YES

      The Table R1 has been incorporated as Table S1 in the revised manuscript.

      3. The phosphorylation aspect of Scm3 is intriguing and the authors show that Mec1 is not responsible for mediating its phosphorylation. Tel1 is another kinase that should be examined.

      __Response: __We thank the reviewer for the suggestion. We are in the process of examining the role of Tel1 kinase on Scm3 phosphorylation. The results from the experiment will be incorporated in the manuscript.

      Minor comment: 1. It is hard to see what MMS resistance the authors state is observed in Mif2-depleted cells in Figure S3. Perhaps this could be better explained or the claim removed.

      __Response: __We agree with the comment and have removed the claim from the manuscript.

      2. Protein levels of Scm3 or any of the other factors depleted with AID were never assessed.

      __Response: __We have assessed the protein level of Scm3 and a control protein, tubulin using western blotting as per the reviewer’s suggestion (Figure R3). We did not observe any significant change in the protein levels in SCM3-HA or SCM3-HA-AID cells, suggesting that the AID tagging of Scm3 per se did not make the cells non-functional and the protein was degraded as expected upon addition of auxin. Moreover, the SCM3-AID cells were used previously to examine the effect of Scm3 on kinetochore assembly (Lang et al., 2018).

      This result has been incorporated as Figure S2C, and new text has been added in the revised manuscript as: “The depletion of Scm3 was verified by observing a higher percentage of G2/M arrested cells and by western blot analysis verifying degradation of Scm3-AID after auxin treatment (Figure S2B, C).” in page 5, lines: 150-152.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript describes a study implicating the Scm3 protein from budding yeast in the DNA damage response (DDR). Scm3 is a chaperone protein, whose main role is considered to be the loading of CENP-A(Cse4) at centromeres to facilitate chromosome segregation. However, the human ortholog of Scm3, HJURP, is known to have a role in DDR and in this study the authors provide evidence that Scm3 is also involved in the DDR in yeast. For the most part, the results presented support the conclusions made.

      Main Points

      1. Figure 1 Could depletion of Scm3 arrest cells in late G2/M and it is this delay that causes increased sensitivity to DNA damaging agents? A control with nocodazole or other means - that also arrests cells at this point - might provide a nice control for this. Perhaps the other kinetochore mutants, used therein, achieve this control - but cell cycle phase would need to be assessed.

      __Response: __ We thank the reviewer for pointing out to a probable effect of the cell cycle stage on the observed MMS sensitivity. In fact, we were also concerned that the observed DNA damage sensitivity in Scm3 depleted cells might be due to G2/M arrest. To rule out this possibility, we monitored Rad52-GFP foci as a marker for DNA damage in the wild type and Scm3 depleted cells both arrested at G2/M using nocodazole (Figure S4). While Scm3 depleted condition exhibited >20% Rad52-GFP positive cells, less than 10% wild type cells showed the same in the absence of any DNA damaging agents (Figure S4E, no MMS, 60 mins). Upon challenging these cells with MMS in the presence of nocodazole, Scm3 depleted condition exhibited over 40% Rad52-GFP positive cells, whereas less than 20% wild-type cells harboured Rad52-GFP. This significant increase in Rad52-GFP positive cells when Scm3 is depleted clearly indicates that the observed MMS sensitivity in these cells is due to the absence of Scm3 rather than due to an effect of a cell cycle stage. Furthermore, we have also used Cdc20 depleted G2/M arrested cells as a wild type control to test the activation of the DNA damage checkpoint by Rad53 phosphorylation. These cells showed robust Rad53 activation in response to MMS, in contrast to poor activation in Scm3 depleted cells (Figure 6), suggesting that G2/M arrest is not the reason for the DNA damage sensitivity observed in the latter cells.

      However, as per the reviewer's suggestion, we examined the MMS sensitivity of the wild type cells arrested at G2/M by nocodazole. As expected, these cells did not show increased sensitivity which further confirms that the DNA damage sensitivity observed in the scm3 mutant is not due to G2/M arrest (Figure R4B). This result has been incorporated within Figure S3, replacing the earlier Figure S3.

      To include this result, we have included new text, and revised the result section in page 5-6, lines 160-181 as follows:

      “The increased sensitivity of scm3-depleted cells to DNA-damaging agents could be due to the weakening of the kinetochores as Scm3-mediated deposition of Cse4 promotes kinetochore assembly or due to the delay in cell cycle, as Scm3 depleted cells arrest in late G2/M phase (Camahort et al., 2007; Cho and Harrison, 2011). If either of these holds true, perturbation of the kinetochore by degradation of other kinetochore proteins or wild type cells arrested at metaphase must show a similar sensitivity to MMS. In budding yeast, Ndc10 is recruited to the centromeres upstream of Scm3 (Lang et al., 2018), whereas the centromeric localization of Mif2, another essential inner kinetochore protein, depends on Scm3 and Cse4 (Xiao et al., 2017). We constructed NDC10-AID and MIF2-AID strains and used them for our assay to represent the proteins independent or dependent on Scm3 for centromeric localization, respectively. We also included one non-essential kinetochore protein, Ctf19, a protein of the COMA complex, to remove any possible mis-judgement in distinguishing cell-growth-arrest phenotype occurring due to drug-sensitivity vs. auxin-mediated degradation of essential proteins. The COMA complex is directly recruited to the centromeres through interaction with the N terminal tail of Cse4, hence dependent on Scm3 (Chen et al., 2000; Fischböck-Halwachs et al., 2019). Mid-log phase cells were harvested and spotted on the indicated plates, however, we did not observe any increased sensitivity of such cells to MMS (Figure S3). Further, wild type cells, when challenged in the presence of nocodazole and MMS, also did not show any increased sensitivity to MMS. Therefore, the increased sensitivity to MMS in scm3 mutant but not in other kinetochore mutant or metaphase arrested cells indicates that Scm3 possesses an additional function in genome stability besides its role in kinetochore assembly.”

      Further we have also revised the discussion section to include the observed results in page 15, lines 502-510 as follows:

      “However, since the primary function of Scm3 is to promote kinetochore formation by depositing Cse4 at the centromeres, it is important to address if the observed sensitivity is due to perturbation in kinetochores or due to cell cycle delay imposed in the absence of Scm3. Therefore, we similarly partially depleted two essential kinetochore proteins, Ndc10 and Mif2, and deleted one non-essential kinetochore protein, Ctf19, in separate cells and also challenged wild type cells to metaphase block but failed to detect any increased sensitivity to DNA damage stress (Figure S3). These results indicate that the drug sensitivity phenotype of Scm3 depleted cells is not due to weakly formed kinetochores or cell cycle delay.”

      2. Mutants of the HR pathway in yeast (e.g. rad52∆ with mre11∆ for example) are typically epistatic. The observation that Scm3 depletion is not epistatic with rad52∆ (Figure 1C) suggests the Scm3 acts via another pathway than the classic Rad52 HR pathway. This should be pointed out and discussed.

      __Response: __We have now included the discussion “In yeast, although HR is the preferred repair pathway, in the case of perturbed HR, an alternate pathway named non-homologous end joining (NHEJ) can occur. The absence of epistatic interaction between SCM3 and RAD52 (Figure 1C) suggests that Scm3 may function in ways other than the Rad52-mediated classical HR pathway. In this context, it would be interesting to test how Scm3 might interact with the key proteins of the NHEJ pathway, such as Ku70/Ku80 and Lig4 (Gao et al., 2016). It is possible that Scm3 may promote a certain chromatin architecture facilitating the DSB ends to stay together to be accessible for NHEJ-mediated end joining.” in page 16, lines 541-548.

      3. Figure 2 should include auxin treatment of RAD52-GFP cells (without the Scm3 degron) to show that the auxin treatment alone does not increase Rad52 foci.

      __Response: __ We performed the suggested experiment and did not observe any significant increase in Rad52-GFP positive cells when treated the cells with auxin+DMSO as compared to only DMSO (Figure R5).

      This result has been incorporated as a new supplementary Figure S4A,B and new text has been added in the revised manuscript as “To rule out the possibility that auxin treatment alone can cause increased Rad52-GFP foci formation, we challenged the wild type (RAD52-GFP) cells with auxin or DMSO and counted the number of cells with Rad52-GFP foci. We did not observe any increase in Rad52-GFP positive cells when treated with auxin+DMSO as compared to only DMSO (Figure S4A, B).” in page 7, lines: 233-236.

      4. Line 246-247 For the data presented, it seems to me possible that Scm3 depleted cells may indicate a defective DDR pathway (as stated) or may indicate defects in DNA replication or an increase in some other form of DNA damage?

      __Response: __We agree with the reviewer’s comment that the depletion of Scm3 can cause replication error or other form of DNA damage in addition to the defect in DDR pathway. To include this, we have modified the sentence as “Taken together, Scm3 depleted cells exhibit more Rad52 foci, indicating a compromised DDR pathway in these cells. Although, defects in DNA replication or creation of other DNA lesions producing more foci also cannot be ruled out.” in page 8, lines 255-257.

      5. In Figure 1 and throughout, please describe in the figure legends how error bars and p values are derived, and the number of experiments involved.

      __Response: __We have now verified all the figure legends and described how error bars and p values are derived and have mentioned the number of experiments involved.

      Minor points Line 35 replace 'cell survival' with 'cell division' - non-dividing cells can survive fine without chromosome segregation. See also line 62.

      __Response: __We have now changed ‘cell survival’ with ‘cell division’ in lines 35 and 62.

      Line 52 and throughout, I suggest replacing CenH3 with CENP-A or Cse4. The term CenH3 is confusing since regional centromeres contain both CENP-A nucleosomes and H3 nucleosomes - the latter of which can also be called CenH3 nucleosomes.

      __Response: __We have replaced CenH3 with CENP-A or Cse4 at the appropriate locations.

      Lines 69-79 specific references are needed for the sentences starting "HJURP was so named...", "In addition,...", "As a corollary,..." and "Finally,..." The final sentence of this paragraph, starting "Perhaps due to..." is unclear.

      __Response: __We have included the reference as mentioned by the reviewer. Also, we have changed the last line as “Notably, HJURP has been visualized to be diffusely present throughout the nucleus (Dunleavy et al., 2009; Kato et al., 2007), which may be due to its global chromatin binding and involvement in DDR.” in page 3, lines 77-79.

      Line 96 "gross chromatin" is unclear; also line 476.

      __Response: __We have changed gross chromatin to “bulk of the chromatin.” and incorporated it into the main text.

      Line 103 "dimerize"

      __Response: __We have replaced ‘dimerizes’ with ‘dimerize’

      Line 109 "most" and "highly" don't work together - perhaps better to say "the functions appear conserved from humans to yeast".

      __Response: __We have changed the wording as the reviewer suggested.

      Line 175 "grown" to "phase", see also line 223.

      __Response: __We have changed the wording as the reviewer suggested.

      Line 293 delete "besides"

      __Response: __We have deleted the word ‘besides’.

      Figure 5 - panels C & D, please make x axis labels clearer - they are directly underneath the 2kb ChIP. They should include a horizontal bar to indicate that all 5 ChIP experiments are included in each time point.

      __Response: __We have now included a horizontal bar in both Figure 5 and the corresponding supplementary Figure S8, to better represent the ChIP experiments. We thank the reviewer for pointing this out.

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

      Summary This manuscript studies the role of the Cse4 histone chaperone Scm3 in the S. cerevisiae DNA damage response. The authors show that decreased Scm3 levels exhibit genetic interactions with mutations in the Rad52 gene and sensitivity to MMS. They go on to show that some Scm3 co-localizes with sites of DNA damage using spreads and ChIP-seq techniques and that decreased levels of Scm3 have a reduced DNA damage checkpoint response. The Scm3 protein is also phosphorylated in response to DNA damage. Taken together, the authors propose a model whereby DNA damage recruits the Scm3 protein and Scm3 then helps mediate the checkpoint response. Overall, the data make a case that Scm3 has a relationship to the DNA damage checkpoint but the authors should be careful not to over-conclude that it has a precise role in checkpoint activation based on the data.

      Major Comments 1. The western blots in the paper are not always entirely convincing. In addition, they are not described in enough detail to understand if a membrane was cut or if multiple gels were run. For example, the tubulin loading in Figure 6D is interrupted toward the end of the blot and the bands in Figure 7D go in different directions for the two blots for the MMS treated cells. In figure 6B, there are no detectable phospho-forms of Rad53 detected on the upper blot for the WT and scm3 lanes even though quantification is given on the right. It would be good to present better examples of the westerns or at least better describe what the reader is visualizing so the quantification and conclusions can be understood. How were the blots quantified? How were the westerns run and processed?

      Response: We have now included a separate paragraph in materials and methods regarding the gel run, processing and quantification of the western blots in the revised manuscript for better understanding of the readers:

      “To detect Scm3-6HA, Rad53, and g-H2A, the total proteins isolated from the appropriate cells were run on 12%, 8%, and 15% SDS gels, respectively. The proteins were transferred to the membranes, which were cut to detect the above proteins and the control protein tubulin separately. For the quantification of the bands on the western blots, a region of interest (ROI) was made around the band of interest, and the intensity of the band was calculated using ImageJ. A same ROI from a no-band area of the blot was used to calculate the background intensity. The background intensity was subtracted from the band intensity. The same process was done for the tubulin bands. The intensity of the target bands (Scm3-6HA, Rad53, and g-H2A) was divided by control tubulin band intensity to get the normalized values for the target bands, which were plotted using GraphPad Prism 9.0 (Version 9.4.1) software.” This has been added in page 25, lines: 881-890.

      Furthermore, we will again perform the experiments for a better representation of the western blots in figures 6B, D, and 7D.

      2. The argument that scm3 depletion leads to a defect in DNA damage checkpoint activation is not strongly supported. Monitoring exit from the cell cycle by multibudding is not the most rigorous assay, especially since the image shows one cell with 5 nuclei. The authors should release cells from G1 into auxin and MMS and monitor cell cycle progression at least one other cell cycle marker, such as anaphase onset, DNA replication and/or Pds1 levels.

      Response: As per the reviewer’s suggestion, in order to support our argument that the absence of Scm3 causes a defect in DNA damage checkpoint activation, we will examine if these cells abrogate G2/M arrest and show an early anaphase onset. For this, we will monitor the levels of Pds1, as a marker of anaphase onset, along the cell cycle in wild type and Scm3-depleted cells both deleted for Mad2 to remove any inadvertent effect of spindle assembly checkpoint. The schematics of the experimental workflow is given in Figure R1. Typically, the cells will be released from alpha factor arrest in the absence or presence of auxin (for the depletion of Scm3) and in the absence or presence of MMS. The samples will be harvested at the indicated time points and will be analyzed for:

      1. Western blot: Pds1-Myc (to detect anaphase onset)
      2. Western blot: Rad53 and p-Rad53 (to detect DNA damage activation)
      3. Immunofluorescence: Tubulin (to detect cell cycle stages) The results of the above experiment will be incorporated in the revised manuscript.

      3. The quantification in Figure 3B is not clear. Is it done on a per/nuclei basis? What pools of Scm3 and Ndc10 are being normalized?

      Response: The intensity was calculated as done before (Mittal et al., 2020, Shah et al., 2023). Typically, the intensity was first measured from the total signal of Scm3/Ndc10 from each chromatin mass or spread (DAPI) by making a polygon (ROI) around the Scm3/Ndc10+DAPI signal. The same ROI was dragged to the background area, from where two separate intensities were calculated. The average of the background intensities was then subtracted from the Scm3/Ndc10 intensity obtained from the same spread to get the normalized intensity depicting each dot in the box plot of Figure 3B. At least 30 spreads were quantified in a similar manner.

      We have mentioned this in the materials and methods section under “Microscopic image analysis.” section in page 22, lines 770-777 as follows: “For intensity calculation, a Region of Interest (ROI) was drawn around the Scm3/Ndc10/g-H2A+DAPI signal, and the intensity of Scm3/Ndc10/g-H2A was measured from each chromatin mass or spread (DAPI). An ROI of the same size was put elsewhere in the background area, from where two separate intensities were calculated. The average of the background intensities was then subtracted from the Scm3/Ndc10/g-H2A intensity obtained from the same spread to get the normalized intensity depicting each dot in the box plot of the respective figures as mentioned previously (Mittal et al., 2020; Shah et al., 2023).”

      Minor Comments 1. The model is elegant but there are chromatin pools (beyond the kinetochore pool) of Scm3 that do not contain Rad52 and/or gamma-H2X and vice versa. It would be helpful if the authors could speculate on how to reconcile these different pools. It might be premature to suggest such a detailed model at this point since the function of Scm3 in the checkpoint is still very unclear so I would encourage the authors to make a less detailed model.

      Response: By showing the green hallow, we have depicted the nuclear pool of Scm3, and we have not shown that the pool contains DDR proteins viz., Rad52 or g-H2A. Rather, we have shown the recruitment of these proteins at the DNA damage sites. Since the focus of this manuscript is on the non-centromeric functions of Scm3, we have not shown the kinetochore pool of Scm3. Although the model is a detailed one, the contribution from this work has been mentioned legitimately at every stage so that the readers can judge the merit of this work. We believe that a detailed model would provide a better perspective to the readers to correlate the revealed as well as yet-to-reveal functions of Scm3 in a spatiotemporal manner with the other players of the DDR pathway. Therefore, we prefer to keep the model in a detailed form.

      2. The chip-seq data is not publicly accessible. There is no reference to the data being available to review.

      __Response: __The data will be uploaded to the public domain.

      3. Line 103: not clear what "both the proteins dimerize" means...probably should be "both proteins dimerize"

      __Response: __We have changed the wording to “both proteins dimerize”.

      4. The argument that Ndc10 does not have a growth defect on MMS is a weak conclusion given that almost no control cells grow on auxin in the absence of MMS.

      __Response: __We have now repeated the spotting assay with a lesser concentration of auxin and replaced Figure S3 with a new Figure S3 (Figure R4) to better represent and conclude that the loss of Ndc10 does not cause MMS sensitivity.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript studies the role of the Cse4 histone chaperone Scm3 in the S. cerevisiae DNA damage response. The authors show that decreased Scm3 levels exhibit genetic interactions with mutations in the Rad52 gene and sensitivity to MMS. They go on to show that some Scm3 co-localizes with sites of DNA damage using spreads and ChIP-seq techniques and that decreased levels of Scm3 have a reduced DNA damage checkpoint response. The Scm3 protein is also phosphorylated in response to DNA damage. Taken together, the authors propose a model whereby DNA damage recruits the Scm3 protein and Scm3 then helps mediate the checkpoint response. Overall, the data make a case that Scm3 has a relationship to the DNA damage checkpoint but the authors should be careful not to over-conclude that it has a precise role in checkpoint activation based on the data.

      Major Comments

      1. The western blots in the paper are not always entirely convincing. In addition, they are not described in enough detail to understand if a membrane was cut or if multiple gels were run. For example, the tubulin loading in Figure 6D is interrupted toward the end of the blot and the bands in Figure 7D go in different directions for the two blots for the MMS treated cells. In figure 6B, there are no detectable phospho-forms of Rad53 detected on the upper blot for the WT and scm3 lanes even though quantification is given on the right. It would be good to present better examples of the westerns or at least better describe what the reader is visualizing so the quantification and conclusions can be understood. How were the blots quantified? How were the westerns run and processed?

      2. The argument that scm3 depletion leads to a defect in DNA damage checkpoint activation is not strongly supported. Monitoring exit from the cell cycle by multibudding is not the most rigorous assay, especially since the image shows one cell with 5 nuclei. The authors should release cells from G1 into auxin and MMS and monitor cell cycle progression at least one other cell cycle marker, such as anaphase onset, DNA replication and/or Pds1 levels.

      3. The quantification in Figure 3B is not clear. Is it done on a per/nuclei basis? What pools of Scm3 and Ndc10 are being normalized?

      Minor Comments

      1. The model is elegant but there are chromatin pools (beyond the kinetochore pool) of Scm3 that do not contain Rad52 and/or gamma-H2X and vice versa. It would be helpful if the authors could speculate on how to reconcile these different pools. It might be premature to suggest such a detailed model at this point since the function of Scm3 in the checkpoint is still very unclear so I would encourage the authors to make a less detailed model.

      2. The chip-seq data is not publicly accessible. There is no reference to the data being available to review.

      3. Line 103: not clear what "both the proteins dimerize" means...probably should be "both proteins dimerize"

      4. The argument that Ndc10 does not have a growth defect on MMS is a weak conclusion given that almost no control cells grow on auxin in the absence of MMS.

      Significance

      Significance

      This is the first examination of the role of Scm3 in the DNA damage response in S. cerevisiae. My expertise is in the chromatin and segregation fields, but I believe this work will be of interest to the DNA damage field as well. While the homologs of Scm3 are known to have a role in DNA damage, it was unclear if this was conserved in budding yeast. The data in this manuscript are consistent with findings in other organisms but the precise role of the chaperone in the DNA damage response is still unclear.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript describes a study implicating the Scm3 protein from budding yeast in the DNA damage response (DDR). Scm3 is a chaperone protein, whose main role is considered to be the loading of CENP-A(Cse4) at centromeres to facilitate chromosome segregation. However, the human ortholog of Scm3, HJURP, is known to have a role in DDR and in this study the authors provide evidence that Scm3 is also involved in the DDR in yeast. For the most part, the results presented support the conclusions made.

      Main Points:

      1. Figure 1 Could depletion of Scm3 arrest cells in late G2/M and it is this delay that causes increased sensitivity to DNA damaging agents? A control with nocodazole or other means - that also arrests cells at this point - might provide a nice control for this. Perhaps the other kinetochore mutants, used therein, achieve this control - but cell cycle phase would need to be assessed.

      2. Mutants of the HR pathway in yeast (e.g. rad52∆ with mre11∆ for example) are typically epistatic. The observation that Scm3 depletion is not epistatic with rad52∆ (Figure 1C) suggests the Scm3 acts via another pathway than the classic Rad52 HR pathway. This should be pointed out and discussed.

      3. Figure 2 should include auxin treatment of RAD52-GFP cells (without the Scm3 degron) to show that the auxin treatment alone does not increase Rad52 foci.

      4. Line 246-247 For the data presented, it seems to me possible that Scm3 depleted cells may indicate a defective DDR pathway (as stated) or may indicate defects in DNA replication or an increase in some other form of DNA damage?

      5. In Figure 1 and throughout, please describe in the figure legends how error bars and p values are derived, and the number of experiments involved.

      Minor points:

      1. Line 35 replace 'cell survival' with 'cell division' - non-dividing cells can survive fine without chromosome segregation. See also line 62.

      2. Line 52 and throughout, I suggest replacing CenH3 with CENP-A or Cse4. The term CenH3 is confusing since regional centromeres contain both CENP-A nucleosomes and H3 nucleosomes - the latter of which can also be called CenH3 nucleosomes.

      3. Lines 69-79 specific references are needed for the sentences starting "HJURP was so named...", "In addition,...", "As a corollary,..." and "Finally,..." The final sentence of this paragraph, starting "Perhaps due to..." is unclear.

      4. Line 96 "gross chromatin" is unclear; also line 476.

      5. Line 103 "dimerize"

      6. Line 109 "most" and "highly" don't work together - perhaps better to say "the functions appear conserved from humans to yeast".

      7. Line 175 "grown" to "phase", see also line 223.

      8. Line 293 delete "besides"

      9. Figure 5 - panels C & D, please make x axis labels clearer - they are directly underneath the 2kb ChIP. They should include a horizontal bar to indicate that all 5 ChIP experiments are included in each time point.

      Significance

      This is a nice complement to the human work on HJURP and provides convincing evidence that Scm3 can be used to model the function of HJURP. Since yeast is such a tractable model, this work provides a route to study the role of this chaperone in DNA damage repair, which may also be true for human HJURP. The work itself is perhaps not too surprising, but is a solid advance in our understanding of the role of Scm3.

      My own expertise is in yeast DNA repair and chromosome segregation.

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript by Agarwal and Ghosh, the authors examine yeast Scm3 function in the DNA damage response. They show that Scm3 loss results in DNA damage sensitivity and more Rad52 foci. Importantly, Scm3 is recruited to DSB sites using an HO-cut site and its loss results in an attenuated DNA damage checkpoint as measured by Rad53 phosphorylation. The authors demonstrate convincingly that Scm3, like its human counterpart HJURP, plays a role in the DNA damage response through altered Rad53 activation. However, what its specific role in DNA repair is remains ambiguous.

      Major comment:

      1. It is unusual to see multiple DNA repair foci as those observed in Figure 2B. What is the distribution of cells with 1, 2, 3, 4, or more foci? Are more observed in SCM3-AID cells perhaps suggesting that the DSB ends are not being clustered as would be expected in WT cells exposed to DNA damage?

      2. The peaks with increased Scm3 recruitment by ChIP-seq upon MMS is confusing as MMS does not induce specific damage at genomic locations. Is Scm3 being recruited at other genomic sites that might be more susceptible to DNA damage? Is Scm3 recruited to Pol2 sites for example? Or fragile sites?

      3. The phosphorylation aspect of Scm3 is intriguing and the authors show that Mec1 is not responsible for mediating its phosphorylation. Tel1 is another kinase that should be examined.

      Minor comment:

      1. It is hard to see what MMS resistance the authors state is observed in Mif2-depleted cells in Figure S3. Perhaps this could be better explained or the claim removed.

      2. Protein levels of Scm3 or any of the other factors depleted with AID were never assessed.

      Significance

      As mentioned above, a clear link for Scm3 in DNA damage repair has now been established in this work but its function in this process is descriptive.

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      Reply to the reviewers

      Manuscript number: RC-2024-02825

      Corresponding author(s): Padinjat, Raghu

      Key to revision plan document:

      Black: reviewer comments

      Red: response to reviewer comment-authors

      Blue: specific changes that will be done in a revision-authors

      1. General Statements [optional]

      We thank the reviewers for their detailed comments on our manuscript and appreciating the novelty, quality and thoroughness of the work. Detailed responses to individual queries and revision plans are indicated below.

      2. Description of the planned revisions

      Reviewer 1:

      Summary The study by Sharma et al uses iPSC and neural differentiation in 2D and 3D to investigate how mutation in the OCRL gene affects neural differentiation and neurons. Mutation in the OCRL gene the cause of Lowe Syndrome (LS), a neurodevelopmental disorder. Neural cultures derived from LS patient iPSCs exhibited reduced excitability and increased glial markers expression. Additional data show increased levels of DLK1, cleaved Notch protein, and HES5 indicate upregulated Notch signaling in OCRL mutated neural cells. Treatment of brain organoids with a PIP5K inhibitor restored calcium signalling in neurons. These findings describe new dysregulated phenotypes in neural cultures of OCRL mutated cell shedding light on the underlaying caus of Lowe Syndrome.

      Major comments

      1. In general, I think the use of iNeurons usually means direct reprogramming from a somatic cell to neurons without the iPSC stage. Could be confusing to use this term for iPSC derived neurons. Thank you for pointing this out. We agree and will remove this term and replace it with a more suitable one in the revised manuscript.

      Please add at least one more replicate of WP cell line to the single nuclei RNAseq.

      There is no cell line called WP1 in the manuscript. We believe the reviewer was likely referring to WT1 (wild-type 1).

      10xgenomics guidelines highlight that the statistical power of a multiome experiment relies on several factors including sequencing depth, total number of cells per sample, sample size and number of cells per cell type of interest (10xgenomics). In this study, we performed a multiome experiment and obtained high-quality reads from 20,000 nuclei for each sample for both the modalities: snRNA seq and snATAC seq. The multiome kit recommends a lower limit is 10,000 nuclei per sample. Thus the number of cells sampled per cell line is double the suggested minimum. Therefore, and consistent with other single-cell seq studies already published, our study followed the approach where biological replicates were not included ( for e.g see PMID: 39487141, GSE238206; PMID: 31651061; PMID: 32109367, GSE144477; PMID: 40056913, GSE279894; PMID: 38280846 GSE250386; PMID: 36430334, GSE213798; PMID: 33333020, GSE123722; PMID: 32989314, GSE145122; PMID: 38711218, GSE243015, PMID: 38652563, GSE236197). Furthermore, single-cell RNA-seq inherently treats each individual cell as as a replicate (Satija lab guidelines, PMID: 29567991; Wellcome Sanger Institute), reducing the necessity for additional biological replicates. Overall this appears to be the current standard in the field which we have followed.

      Importantly, we took additional steps to validate the predictions our single-nuclei RNA-seq findings experimentally. For this we used a 3D brain organoid system. We confirmed key observations noted initially in 2D neural stem cells using a brain organoid model. This approach allowed us to confirm key predictions from the single cell sequencing data set. For example, in Lowe Syndrome patient derived organoids and OCRL-KO organoids, we noted increased DLK1 levels (Fig5.C-D, H-I) as well as increased GFAP+ cells and gene expression in brain organoids (Fig.S4E,F). These complementary approaches strengthen our confidence in the biological relevance of our findings from the single nuclei sequencing experiments.

      The WT1 and the patient lines are rarely analysed together with the WT2 and KO lines, thus it is tricky to understand if the KO line is mimicking the patient lines? Please, add more merged analyses. Co-analysing all lines:

      (i)would show if the KO line is more similar to the patient lines or to the WT1 or somewhere in between.

      1. ii) Could answer questions about the variation in phenotypes between the genetic backgrounds. iii) Elucidate how much variability there is between the two WT lines in your assays. If the two WT lines vary much then conclusions about phenotypes in the patients and KO lines might need to be rethought? The reviewer is right is noting that throughout the manuscript we have analysed the patient lines with WT1 and the KO line with WT2. This was a conscious decision which we believe is the correct one for the following reasons:

      It is well recognized and discussed in the literature that genetic background can be a key factor contributing to phenotypes observed in cells differentiated from iPSC (Anderson et al., 2021, PMID: 33861989; Brunner et al., 2023, PMID: 36385170; Hockemeyer and Jaenisch, 2016, PMID: 27152442; Soldner and Jaenisch, 2012, PMID: 30340033; Volpato and Webber, 2020, PMID: 31953356). Therefore, as a matter of abundant precaution, in this study we have tried to use the closest possible genetically matched control lines for analysis.

      The patient lines used in this study for Lowe syndrome were all derived from a family in India of Indian ethnic origin. Therefore, in order to reduce the potential impact of genetic background contributing to potential phenotypes, we have used a control line derived from an individual of Indian ethnic background; this line has previously been developed and published by our group (PMID: 29778976 DOI: 10.1016/j.scr.2018.05.001). By contrast, the OCRLKO line was generated using the control line NCRM5 (WT2); this line is derived from a Caucasian male (RRID: CVCL_1E75). Therefore, whenever we have analyzed OCRLKO, we have used NCRM5 as the control; throughout the manuscript, NCRM5 is referred to as WT2.

      However, in deference to the reviewer’s concerns we have performed a few analyses to compare the extent of variability between the two control lines.

      Figure Legend: Replotted [Ca2+]i transients data from LS patient lines, OCRLKO and two control cell lines WT1 and WT2. (A) There is no statistical difference in the frequency of [Ca2+]i transients between WT 1 and WT2. Test used-Mann Whitney test. (B) Plot with WT1 and WT2 data combined versus all three LS lines and OCRLKO combined. Test used-Mann Whitney test. (C) WT1 and WT2 combined plotted against three individual patient lines and OCRLKO. Statistical test used One-way ANOVA. (total neurons analysed: WT1:808; WT2:267; LSP2:150; LSP3:462; LSP4:463; OCRLKO:411)

      (i) We compared the frequency of calcium transients between neurons of age 30 DIV between WT1 and WT2 (Panel A above). We found no significant difference between these.

      Additionally, as suggest we combined the data from both control lines into a single set and that from all the LSP patient lines and OCRLKO into another one (Panel B above). At the end of the analysis the difference between control and OCRL depleted cells remains. Please note the large number of cells studied in each genotype.

      We also combined both control lines into a single control data set and compared it to each patient line and OCRLKO. We find that each patient line and OCRLKO is still significantly different from the control set (panel C above).

      We did not find that OCRLKO to be significantly different from LSP2 or LSP4, indicating that the OCRLKO line closely aligns with the patient-derived lines, supporting the idea that the observed phenotype is primarily disease-driven rather than background-dependent. However, we did observe a significant difference between LSP3 and OCRLKO, highlighting some degree of inter-patient variability. Therefore, the key point is that the disease phenotype remains stable across different backgrounds, reinforcing the idea that the observed differences are driven by OCRL loss rather than background variability. This will be discussed in the revision.

      (ii) In our RTPCR assay for HES5, when WT1 and WT2 are plotted together, there is no significant difference observed (panel A below). Similarly, western blotting data for cNotch (panel C) and DLK1 (panel B) of pooled WT1 and WT2 together on one plot shows no significant difference (Unpaired t-test, Welch’s correction). Overall, based on the above data, WT1 and WT2 are not statistically different.

      Figure legend: Comparison of control lines WT1 and WT2. (A) comparison of HES5 transcripts. (B) Western blot for DLK1 levels. (C) Western blot for cleaved notch protein levels. Statistical test: Unpaired t-test, Welch’s correction.

      Please include more discussion and rational around the link between the expression pattern of OCRL and the various phenotypes shown. From the RNAseq data performed at the NSC state where the expression of OCRL is lower than in neurons there are considerable differences in cell type distribution between lines. How can this skew cell type distribution affect downstream differentiation and neuronal function?

      We would like to highlight that we did not perform bulk RNAseq in NSC and neurons; rather, we performed snRNA seq in NSCs (Fig3). The data in Fig.1E is mined from a publicly available resource dataset (Sidhaye et.al., 2023, PMID: 36989136) as mentioned in line 155, which is an integrated proteomics and transcriptomics generated from iPSC-derived human brain organoids at different stages of development in-vitro.

      Fig 1D and 1E do indeed show lower levels of OCRL expression in NSC compared to neurons. However, it is important to bear in mind that even though OCRL may be expressed at relatively low levels during the NSC stage, its enzymatic activity could still have a substantial impact. Therefore, even at low expression levels, OCRL could be modulating the PI(4,5)P2 pool in ways that significantly influence cellular functions, especially during early stages of neurodevelopment that alter cell-fate decisions thereby affecting neuronal excitability.

      Our working model posits that loss of OCRL leads to increased levels of PI(4,5)P2 which upregulates Notch pathway thereby leading to an increase in its downstream effector HES5. HES5 is a known transcription factor influencing gliogenesis and thus leading to a precocious glial shift in OCRL deficient NSCs as seen in our multiome dataset. This temporal perturbation in differentiation affects maturation of LS/OCRL-KO neurons and/or astrocytes leading to a defective neuronal excitability.

      Also, OCRL is expressed also at the iPSC state as shown in Figure 1I, do you see any phenotypes in iPSC? If not, explain how that could be.

      Yes, OCRL is indeed expressed in iPSCs as shown in Figure 1I. In an earlier paper from our lab that described the generation of these patient derived iPSC from Lowe syndrome patients (Akhtar et.al 2022 PMID: 35023542), we have reported that PIP2 levels are elevated at the iPSC stage as well as NSC stage in OCRL patient lines. We have not performed a detailed analysis of the iPSC stage for these lines as the focus of our investigation was primarily on the later stages of differentiation, particularly in neural progenitors and differentiated neurons. However, in response to the reviewer’s questions on why there are no obvious phenotypes at the iPSC we would suggest that this is due to compensation from the activity of other genes of the 5-phosphatse family. In support of this, we would cite our previous study (Akhtar et.al 2022 PMID: 35023542), in which we show that in LS patient derived lines, at the iPSC Stage, at least six other 5-phosphatases are upregulated.

      There is not enough data in the manuscript to show mechanistic links between OCRL, DLK1 and Notch so be aware not to overstate the conclusions.

      We appreciate the reviewer’s constructive comment regarding the mechanistic links between OCRL, DLK1, and Notch. Treatment of organoids and neurons with UNC-3230 PIP5K1C inhibitor rescues the observed phenotypes suggesting a role for a PIP2 dependent process, this process itself remains to be identified. We will adjust the wording in the manuscript during the revision to ensure that this comes through and the conclusions do not appear overstated.

      Line 173, please describe what mutation in the OCRL these patients have, is it a biallelic deletion? Is the protein totally absents? Please show western blot analyses of the protein in the patient lines.

      The patients from whom these LS lines were generated, the nature of the OCRL allele in them and the status of OCRL protein have all been previously been described in detail in a paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the lines are described (Line 174, references 26 and 27). In addition, in the present manuscript, the protein status of OCRL in all the three patient lines is shown with a Western blot in Figure 3C.

      Would be good with a bit of clinical explanation of these patients? Do they have the same level of severity? Are there any differences between their clinical symptoms? This could be interesting to link to differences in cellular phenotypes.

      The clinical details of each patient are described in a preprint from our lab (Pallikonda et.al., 2021 bioRxiv 2021.06.22.449382).The potential reasons for the difference in severity, a very interesting scientific question, is also addressed in this preprint. Currently experimental analysis to support the proposed likely reasons is ongoing in our lab. We feel those analysis are beyond the scope of this manuscript and will be published later this year as a separate study.

      As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. We mimicked this by CRISPR based genome editing to introduce a stop codon and protein truncation in exon 8 to generate of WT2 to OCRLKO. This is also described in supplementary Fig 1 of the present manuscript and the technical details of line generation are fully described in the materials and methods.

      Like the patient lines OCRLKO is a protein null allele-this is shown by Western blot in Fig 2D. Also in OCRLKO, the PIP2 levels are elevated (Fig 2E) recapitulating what has been described by us in (Akhtar et.al 2022 PMID: 35023542). We will explicitly state this detail around line 185.

      Figure 1I, could the protein levels at the different stages be quantified?

      Yes, we can and will do it in the revision

      Figure 3A, there seem to be much more cells in LSP2, making it tricky to compare with the other cell lines. Density during differentiation can affect the cell fate. Please, provide images from the different lines that are comparable with similar density.

      We controlled for cell density by seeding equal number of cells 50,000 cells/cm2 for all the genotypes, as mentioned in the material and methods. However, heterogeneity between lines during terminal differentiation is well-established, leading to crowding in some genotypes while not in others. Additionally, different growth rates during terminal differentiation also leads to crowded neural cultures as a function of genotype. Therefore, to complement our immunostaining data, we have provided western blot analyses showing increased GFAP protein levels in LS patient lines compared to controls. We will provide images from different lines that are comparable in density during the revision.

      Please provide quantification to the statement that there is fewer number of S100B cells in the LSP lines.

      As we haven’t quantified the number of S100B cells, we will remove that statement.

      Figure 3B, the images show cells very different, and it is tricky to compare similarities and differences, please provide images that look more similar to each other. Avoid images with clusters of cells or make sure to select representative images with clusters from each cell line. If the clustering is a phenotype explain and quantify that. Make sure the density is similar in all pictures.

      We will provide images of matched density during the revision. Also see response to comment above.

      Line 2018, the statement "In the same cultures, there was no change in the staining pattern of the neuronal markers MAP2 and CTIP2 (Fig 3B)" is not strengthened by the figure. Please provide new pictures or data to prove the statement.

      As CTIP2 staining is inherently observed in either clumps or sparsely distributed regions across WT1 and LSP genotypes, we will replace the CTIP2 marker with TBR1, which is also a deep layer cortical marker (layer VI-V), as shown below. Using this additional marker for neurons, we continue to see no change in staining pattern of neuronal markers MAP2 and TBR1. Corresponding images for each genotype are optically zoomed-in images of individual neurons positive for MAP2 and TBR1. Scale bar=50µm, 20µm.

      Figure 3E, please describe all markers in the picture, thus also MAP2, S100B, CTIP2 and draw conclusions. Try to show comparable pictures.

      This will be attended in the revision

      Fig 3D and G, what are the replicates? please explain.

      Each point represents a single neural induction done on iPSCs to generate NSCs and then terminally differentiated 30DIV cultures. Experiments were done across 3-6 independent neural inductions. This detail will be included in the revised figure legend.

      Figure 4 A, C, there is a large difference in the ratio of different cell types between the different cell lines, also between the LSP2 and LSP3. This would indicate either that the genetic background affects the phenotype to a large extent or that there is large variability between rounds of differentiation. To understand how much variability that comes from the differentiation and culturing: another replicate of WP cell from another donor (WT2) should be included (single nuclei RNAseq). Confirm that three independed rounds of differentiation of the WT1, WT2, LSP2, LSP3, LSP4, and OCRL-KO result in similar outcome when it comes to cell type distribution. Could be done with qPCR marker.

      For scientific reasons explained in response to the reviewer’s comment #2 we feel it is not necessary to perform replicates of the single nucleus multiome seq. However to allay the reviewer’s concern of variability between differentiations leading to a conclusion of altered cell state we present the following three suggestions for a revised manuscript:

      • We will perform multiple differentiations from iPSC to NSC and test the altered cell state using Q-PCR for transcripts of glial lineage markers.
      • Shown below are western blot analyses for WT1, LSP2, LSP3 and LSP4 NSCs (left). Analyses were done from 4 independent rounds of neural inductions and exhibit a significant increase in the levels of a astrocytic fate-determinant marker NF1A in LSP NSCs wrt to WT1 (Mann Whitney test used to measure statistical significance). Each point represents sample from an independent neural differentiation.

      • We would also like to highlight that we have already demonstrated increased GFAP levels in LS patient derived differentiated cultures and OCRLKO. These data, quantified in Fig 3D are done using samples derived from multiple differentiations of iPSC to NSC and then terminally differentiated. Thus the phenotype of enhanced glial cells in LS derived cultures, is most likely a consequence of the increased number of glial precursor cells is seen across multiple differentiations.

      Line 309, "astrocytic transcripts NF1A and GFAP was elevated" It is unclear from this sentence in which cell lines NF1A and GFAP is elevated? Please explain.

      We acknowledge the incompleteness in the statement. We will add the complete statement explaining the graphs. The levels of astrocytic transcripts NF1A and GFAP were elevated in LSP3 and LSP4 compared to WT1.

      Figure 5C, E, G, there is a large variation of Notch and Hes5 expression between the different

      This comment is incomplete.

      Figure 5H, unclear which of the bands that is DLK1 and how the bands relate to the quantification. The band at 50 kDa seems to be stronger in the WT2 than in the OCRL-KO but in the quantification in Figure 5I, it shows 2x more in the KO. Thus, the other way around.

      The datasheet of DLK1 antibody used (Abcam ab21682; RRID_AB731965) describes bands seen at 50,48, 45 and 15kDa. We have quantified the bands at 50kDa and 48-45kDa for all the genotypes. This will be explicitly stated in the revised figure legend.

      Figure 6, please show that the inhibitor is inhibiting PIP5KC.

      Have you titered the added concentration of the inhibitor?

      Figure legend: Fields of view from WT1 derived NSC expressing the plasma membrane PIP2 reporter. Plasma membrane distribution of the probe indicating PIP2 levels is shown in (A) untreated cells (B) treatment with 10mM and (C) 50mM UNC-3230 PIP5K1C inhibitor. Scale bar=50µm (D) Quantification of plasma membrane PIP2 levels using this reporter. Y-axis shows probe levels at PM; X-axis shows treatment conditions.

      Yes, we used a previously generated plasma membrane PH-PLC::mCherry reporter WT1-NSCs (Akhtar et.al., 2021) and carried out a dose-response experiment using 10mM and 50mM of the UNC-3230 PIP5K1C inhibitor as shown above. We quantified intensity of PI(4,5)P2::mCherry at the plasma membrane and plotted the mean intensity. We observed a significant decrease in plasma PI(4,5)P2 levels at 50mM (Statistical used: Mann Whitney test) but not 10mM and therefore we selected that concentration for our experiments.

      Figure 6B, why do the calcium data for the WT2+1Ci look so different to the other, the dots are much more spread and seem to fewer replicates that for the other sample, please explain.

      We had only analysed a few replicates for WT2+1Ci genotype. We analysed the remaining replicates and have updated the data as shown below. The revised data set resolves the reviewer’s concern. The revised data set will be included in the revision.

      Figure 6F, there is no significant differences between the bars but the statement in the text (sentence starts on line 332) indicate it is, please update the figure or remove the statement.

      We added more replicates (now total is 7-10 biological replicates each with 15-20 organoids) and updated the figure (panel B) is shown below. The differences between treated and untreated of OCRLKO are significant whereas there is no significant difference between wild type, treated and untreated (statistical test: Mann Whitney test).

      Revised figure will be included in the revision

      Figure 6G, the HES5 expression seem to behave very similar in both WT2 and OCRL-KO cells when the inhibitor is used. What does this mean? Seems to not be linked to OCRL. Explain.

      Thank you for your comment. In our initial experiment (shown in original version of manuscript), we observed a reduction in HES5 expression upon inhibitor treatment in both WT2 and OCRL-KO cells. However, to ensure robustness of our findings, we repeated the experiment across multiple, additional independent organoid differentiation batches. In this redone experiment, we no longer observe the previous trend. Instead, we see no significant changes in WT2 on inhibitor treatment, while OCRLKO cells show a reduction in HES5 expression upon inhibitor treatment (Panel A). Similarly, the protein levels of cNotch and DLK1 are not different between WT2 and WT2+1Ci (panel B and C). This strongly suggests loss of OCRL leading to elevated levels of PIP2 perturbs Notch pathway, resulting in higher cNotch and thereby increased effector expression of HES5. New data set will be included in the revision.

      Minor comments

      The panels in Figure 6 are not completely referred to correctly in the text, please check. Double check that all figure panels are referred to properly in the text

      Yes, we will correct it in the revised manuscript.

      Reviewer #1 (Significance (Required)): The manuscript is an interesting addition to the in vitro iPSC derived cellular modelling of neurodevelopmental disorder. Strengths: The use of both patient iPSC lines and CRISPR edited lines The use of both monolayer and 3D cultures We thanks the reviewer for their detailed critique. Addressing these has helped improve the manuscript. We thank the reviewer for appreciating the strengths of the manuscript. Weaknesses: the significance decrease a bit due too few replicates (only 1 WT line in each experiment) and the variability between the patients' cell lines. We thank the reviewer for this comment. As explained above we have added substantially more data and revised the analysis which should remove this concern.

      Reviewer 2:

      This paper describes the effects of loss of OCRL (the Lowe syndrome protein) upon the function and differentiation of neurones, using an in vitro iPSC model system. Cells derived from three related Lowe syndrome patients and an OCRL knockout, generated using CRISPR, were used for these experiments. The results show that upon loss of OCRL, differentiation of stem cells into neurones is reduced, with an increased number of cells adopting glial and astrocytic fates. The neurones that are generated have reduced calcium transients and electrical activity. Gene expression data combined with biochemical analysis indicate altered Notch activity, which may account for the altered cell fate data seen in the in vitro differentiation model. Finally, rescue of cell fate and neuronal activity is seen upon knockdown of a PIP5K, which indicates that these phenotypes are due to the elevated PIP2 levels seen on the OCRL-deficient cells.

      The results provide new insights into the pathogenesis of Lowe syndrome. I found the paper to be well done, and the data supports the conclusions of the authors. I have a few comments below that may improve the manuscript:

      We thank the reviewer for summarizing the comprehensive nature of our study and appreciating the value of our study in providing new insights into the pathogenesis of Lowe syndrome with respect to the brain. Thank you for appreciating that our study is well done, and that the data supports the conclusions of the authors.

      Major points

      1. The UMAP and ATAC-Seq data indicate different maps for the two different Lowe syndrome patient-derived cells (Fig 4 and Fig S3). This suggests that the cells are quite different, and therefore that changes seen in one Lowe syndrome patient may not be applicable to the others. I think this heterogeneity has important implications for the paper i.e. how general are findings obtained? Several different glioblast types are described (numbered 1-5)- how different or similar are these? We are unclear what the reviewer means by “ the UMAP and ATAC seq data indicate different maps…….”.

      UMAP is a technique for visually representing data generated by single cell analysis methods be it RNAseq or ATAC seq. Perhaps what the reviewer means is that the UMAP generated from RNA seq and ATAC seq data looks different from each other.

      We would like to reiterate that the UMAP generated from single cell RNA seq data is based on the complement of transcripts in each cell of the analysis compared to an existing single cell RNAseq data set, whereas the UMAP generated from ATACseq is generated from regions of open chromatin detected in and around genes and therefore presumably also reflecting ongoing gene expression. In principle the two analyses for any set of cells should indicate overall clustering into similar groups on UMAPs generated using both data sets, if the ATACseq based read out of transcription largely maps the RNAseq based read out of differences in transcription. However, it may not be reasonable to expect them to be identical. This is indeed what we see for our data set, and this has been represented in Fig 4E. The cell clusters detected based on GEX (gene expression i.e single cell RNA seq) analysis are plotted against the cells clusters detected from ATACseq data using a confusion matrix. As can be seen from this panel (Fig 4E), a very large fraction of cells falls on the diagonal indicated a large degree of similarity between clusters detected by both methods (GEX and ATACseq) of analysis. This can be reiterated more strongly during the revision by strengthening this statement.

      The PIP5K inhibitor seems to have a very strong effect on both WT and KO cells in terms of Notch activity (Fig 5G). This strongly suggests the effects of this inhibitor are not through OCRL and that changes in PIP2 induced by the inhibitor override those of OCRL. Thus, the experiments shown in Fig 5 seem not to be due to a rescue of OCRL activity as such.

      We think reviewer means Fig 6G and our response is as follows:

      In our initial experiment (shown in the current version of manuscript), we observed a reduction in HES5 expression upon inhibitor treatment in both WT2 and OCRLKO cells. However, to ensure robustness of our findings, we repeated the experiment across multiple, additional independent organoid differentiation batches. In this redone experiment, we no longer observe the previous trend. Instead, we see no significant changes in WT2 on inhibitor treatment, while OCRLKO cells show a reduction in HES5 expression upon inhibitor treatment (Panel A). Similarly, the protein levels of cNotch and DLK1 are not different between WT2 and WT2+1Ci (panel B and C). This strongly suggests loss of OCRL leading to elevated levels of PIP2 perturbs Notch pathway, resulting in higher cNotch and thereby increased effector expression of HES5. The figures updated with the new data will be included in the revision.

      Minor points

      1. The main text needs to say what synapsin is and why it was analysed. In Fig 1I, synapsin abundance declines at 90 days. This appears quite strange. The authors should comment on it in the text. We will add a line about use of synapsin in the western. Synapsin is only used qualitatively to highlight mature neuronal culture age, as was done in Sidhaye et.al PMID: 36989136.

      In the revised main text, we will add the following explanation: "We also analyzed the expression of synapsin-1, a synaptic vesicle protein that serves as a marker for mature synapses and functional neuronal networks. The presence of synapsin-1 indicates the development of synaptic connections in our cultures, providing evidence of neuronal maturation."

      .

      The decline and thereby variability in synapsin-1 protein levels has been reported before. Regarding the decline in synapsin-1 at 90 days, we can add the following discussion:

      "We observed a decline in synapsin-1 levels at 90 days in vitro (DIV) compared to earlier time points. This pattern has been previously reported in iPSC-derived neuronal models (Togo et.al PMID: 34629097 and Nazir et.al PMID: 30342961). Such variability in synapsin-1 expression over extended culture periods may reflect the dynamic nature of synaptic remodeling and maturation processes in vitro. It's important to note that synapsin-1 levels can fluctuate due to various factors, including culture conditions and the heterogeneity of neuronal populations present at different time points."

      In Fig 2A and 3B there are clumps of green cells (CTIP2 positive). I am concerned that the lack of uniformity in the cell distribution could impact other analysis performed, where certain fields of view have been analysed e.g. by imaging or electrophysiology e.g. calcium measurements.

      To address the reviewers concern about uniformity, in the revised manuscript, we will provide/replace the representative images of deep layer markers along with MAP2 from all genotypes showing the areas selected for analysis to demonstrate that data collection was performed in comparable regions across all experimental conditions. As answered in the response to reviewer 1, comment 11.

      The clumps of neurons (as seen in Fig2A) poses challenges for obtaining high-quality seals during patch-clamp recordings. To address this, we primarily selected areas with sparsely distributed neurons for electrophysiology experiments. This approach ensured robust recordings. To address this, we can provide a clarification in the Methods section to explicitly state that neurons used for all patch-clamp recordings were chosen from regions where cells were sparsely distributed.

      In case of calcium imaging experiments, we focused on both crowded and sparse fields of views across genotypes to avoid potential biases introduced by clumped cells. However, it is to be noted that during the stages of terminal differentiation there are NSCs undergoing proliferation, which makes the neuronal culture denser. We can provide video files as a supplementary material to demonstrate the types of areas used for calcium imaging experiments. Additionally, we will include a statement in the Methods section specifying that regions with uniform neuronal distribution were selected for calcium imaging to ensure consistency in our analysis.

      In Fig 2J and 2K are the differences between sampels significant? The error bars are huge.

      From line 204-209, we have not used the word “significantly different”. We acknowledge that the error bars in Figures 2J and 2K are indeed large, which is not uncommon in electrophysiological recordings from iPSC-derived neurons due to their inherent variability. We have intentionally refrained from claiming statistical significance for these specific comparisons. Instead, we describe the data as showing a pattern or trend of reduced currents in OCRLKO neurons compared to WT2. To improve clarity, we propose to add a statement in the results section acknowledging the variability in these measurements and explaining our interpretation of the data as a trend rather than a statistically significant difference.

      In Fig S4- it would be good to show gene expression analysis and GFAP staining

      We are not completely sure what this comment means. However the present figure shows double staining with GFAP and S100beta. These will be split and shown separately to enhance clarity.

      Fig 5A needs more annotation- fold change comparing what to what?

      We will add the annotation “fold change wrt to WT1”.

      There should be more information provided in the main text relating to DLK1. For example, it is shown to be secreted, but no information is provided on whether this is expected. Secreted? The DLK1 blot in Fig 5F is not convincing.

      We will add more information relating to DLK1 and secretion status.

      DLK1 is a non-canonical notch ligand that is indeed known to be secreted by neighboring cells to either activate/inhibit notch pathway. While we acknowledge the blot could have been better, however, variability in the blot could arise due to differences in secretion efficiency, or protein stability in the cell culture media that could have led to inconsistencies across LSP genotypes. However, as shown in the blot, the OCRLKO shows a clear enrichment of secreted-DLK1 compared to WT2.

      We have performed the western blot analyses across two independent differentiations of organoids from WT1, LSP2, LSP3, LSP4, WT2, OCRL-KO iPSCs in phenol-free neurobasal-A medium, and quantified secreted protein. We then loaded 40mg of protein per genotype. Shown below is the quantification. The quantification of mean intensity of DLK1 band shows a moderate increase in LSP2, and substantial increase in LSP3 and LSP4 organoids as compared to WT1. While OCRL-KO a substantial increase compared to its control, WT2. A revised figure will be used in the revision.

      Rationale for choosing PIP5K1C

      PIP5K1C is one of the major regulators maintaining appropriate levels of the synaptic pool of PI(4,5)P2, synaptic transmission and synaptic vesicle trafficking (Hara et al., 2013 PMID: 23802628; Morleo et al., 2023 PMID: 37451268; Wenk et al., 2001 PMID: 11604140). Therefore, we were interested in rescuing the physiological phenotype, we chose PIP5K1C. Additionally, in initial experiments we found that inhibiting PIP5K1B using ISA-2011B killed the organoids or lead to detachment of 2D neuronal cultures.

      Fig 6D is confusing. I suspect the figure labelling is not correct- it does not correlate with the graphs.

      We apologise for the error and will correct this.

      Reviewer #2 (Significance (Required)):

      This paper is significant because it provides important new information on the neurological features of Lowe syndrome. The approach is novel in terms of studying this condition. The findings are likely to be of interest to clinicians, cell biologists, neurobiologists and those studying human development. My expertise is in membrane traffic and OCRL/Lowe syndrome. I am not a neurobiologist.

      We thank the reviewer for appreciating the importance of our study, novelty of findings and newof our approach we have used. We would light to highlight that while extensive work has been done with respect to the renal phenotype of Lowe syndrome, the brain phenotypes have remained largely a black box. This is in part because mouse knockouts of OCRL have failed to recapitulate the brain related clinical phenotypes displayed by Lowe syndrome patients (for e.g. PMID: 30590522; PMCID: PMC6548226; DOI: 10.1093/hmg/ddy449). Our study of brain development defects in Lowe syndrome depleted cells provides the first insight into the cellular and developmental changes in this disorder.

      Reviewer 3:

      This paper by Sharma et al describes findings in an iPSC model of Lowe Syndrome. This is an important line of research because no mouse models phenocopy the neurodevelopmental aspects of the condition. They identified a potential role of Notch signaling in pathogenesis, a potentially druggable target. However, several issues need to be addressed.

      We thank the reviewer for appreciating the importance of our study in covering the basis of the neurodevelopmental phenotype of Lowe syndrome. Due to a lack of a mouse model, there was previously no understanding of how the clinical features related to the brain arise.

      Major issues

      1. The sample size is very small, which is understandable to some extent given the expense and difficulty doing research using iPSCs. However, there are a couple of opportunities to improve the sample size. For example, in the analysis of DLK1 and other proteins shown in Figure 5, the analysis amounts to a single control vs the 3 patient lines, and a single control vs the KO line. The separation is justified because a complete KO of the gene might result in differences compared to hypomorphic mutation that apparently affects the 3 cases. However, there is no reason why WT1 and WT2 shouldn't be combined. They are both random controls. This might not affect the results of the other proteins analyzed, NOTCH and HES5, but the significance of DLK1 could change. Nature of the allele in LS patient lines

      There is a misconception in the reviewer comment that the OCRL allele in the three Lowe syndrome lines is a hypomorph. This is not correct. In the patients from whom these LS lines were generated, the nature of the OCRL allele and the status of OCRL protein in cells have been previously described in detail in a peer-reviewed, published paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the LS patient lines are described (Line 174, references 26 and 27). As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. This results in a protein null allele of OCRL in all three patient lines. This has been shown in Fig 1B of Akhtar et.al 2022 by immunofluorescence using an OCRL specific antibody (PMID: 35023542). It has also been demonstrated by Western blot using an OCRL specific antibody for all three LS patient lines in Fig 3C and 5C of the present manuscript. The nature of the allele will be highlighted more clearly in the revision.

      *Combining WT1 and WT2 *

      We are not in favour of combining WT1 and WT2. The reason for this is as follows.

      It is well recognized and discussed that genetic background can be a key factor contributing to phenotypes observed in cells differentiated from iPSC (Anderson et al., 2021, PMID: 33861989; Brunner et al., 2023, PMID: 36385170; Hockemeyer and Jaenisch, 2016, PMID: 27152442; Soldner and Jaenisch, 2012, PMID: 30340033; Volpato and Webber, 2020, PMID: 31953356). As a result, it is recommended that a line closely matched for genetic background be used when assessing the validity of observed phenotypes. The patient lines used in this study for Lowe syndrome were all derived from a family in India of Indian ethnic origin. Therefore, in order to reduce the impact of genetic background contributing to potential phenotypes, we have used a control line (referred to in this manuscript as WT1) derived from an individual of Indian ethnic background; this line has previously been developed and published by our group (PMID: 29778976 DOI: 10.1016/j.scr.2018.05.001).”

      In the case of OCRLKO we have genome edited NCRM5 (a white Caucasian male control line) to introduce a stop codon in exon 8 to mimic the truncation seen in our LS patient lines. This allele is also protein null as shown by Western blot using an OCRL specific antibody. The data is shown in Fig 2D of the present manuscript. Therefore, we reiterate that all the LS patient lines in this study and OCRLKO are protein null alleles.

      Status of DLK1 levels

      We have performed a combined analysis of DLK1 levels in the two control lines and all the patient lines as well as OCRLKO. As shown below the result remains unchanged, namely that DLK1 levels are elevated in OCRL depleted cells in this model system.

      Figure legend: Quantification of DLK1 protein levels in control, LS patient and OCRLKO iPSC lines. Western blot intensities for each patient line and OCRLKO were normalized to GAPDH and then to the respective internal WT control (WT1 or WT2) resulting in fold-change values. For statistical analysis across genotypes, normalized fold-change values from different gels were pooled post hoc. All statistical testing was performed on fold-change values. Statistical test used: Mann Whitney test. (A) Values for WT1 and WT2 have been combined and plotted against individual values for three patient lines and OCRLKO (B) Values for WT1 and WT2 have been combined and plotted against combined values for all three LSP lines and OCRLKO.

      Reviewer comment: DLK1 expression brings up another point. This, along with MEG3 and MEG8 are imprinted genes, two of the top differentially expressed genes in this study. However, these findings can be questioned by the well-known phenomenon that the expression of some imprinted genes may not be properly maintained during iPSC reprogramming. Thus, the differential expression of these imprinted genes might be due to a reprogramming artifact rather than the effects of OCRL per se. Analyzing both controls together could mitigate this objection. However, even if it does, the potential dysregulation of imprinted genes in the development of iPSCs should be acknowledged and addressed.

      We are aware that the DLK1 locus is imprinted. However, we feel that reprogramming artifacts are very unlikely to explain the observed changes in DLK1 levels.

      It is important to note that the patient lines and WT1 were not directly re-programmed from White blood cells to iPSC and then used for differentiation and analysis. As detailed in our previous peer-reviewed publications WT1 (PMID: 29778976) and the patient LSP lines (PMID: 35023542) were first converted to lymphoblastoid cell lines and subsequently reprogrammed into iPSC.

      We think that re-programming induced imprinting changes are unlikely to be responsible for the elevated levels of DLK1 seen in LS patient lines. The reason is as follows:

      We compared DLK1 levels in WT2 and OCRLKO which is a CRISPR edited line that introduces a stop codon in exon 8. NCRM-5/WT2 was derived from CD34+ cord blood cells. What we found is that levels of DLK1 are elevated in OCRLKO compared to WT2. Since OCRLKO was generated by genome editing WT2, it must be the case that the level of imprinting of the DLK-DIO3 locus is comparable if not identical between the two lines. Therefore, the difference in DLK1 levels between WT2 and OCRLKO cannot be a consequence of different imprinting status of the DLK1 locus between these two lines. Rather, it strongly suggests a causal link to OCRL deficiency. Following on from this, the DLK1 levels are elevated in patient lines compared to the OCRLKO. We will highlight and discuss and explain this in the revised version.

      Similarly, in the calcium signaling experiment shown in fig.2, the KO and patient lines are justifiably separated. However, again, why not combine both controls in the comparison with the patient samples?

      The data has been reanalyzed and presented as requested by the reviewer. There is no change in the conclusion.

      For the reasons described above, it remains our preference to present each set of lines with the appropriate control; i.e WT1 and the three LS patient lines and WT2 with OCRLKO. However, as the reviewer has asked for it, we also present below analysis in which WT1 and WT2 and combined and LS patient lines and OCRLKO are combined. The replotted data is shown below. The essential conclusion shown in the main manuscript remains, namely that [Ca2+]i transients in LS depleted developing neurons is lower than in wild type.

      Figure Legend: Replotted [Ca2+]i transients from LS patient lines, OCRLKO and two control cell lines WT1 and WT2 (A) There is no statistical difference in the frequency of [Ca2+]i transients between WT 1 and WT2. Test used-Mann Whitney test. (B) Plot with WT1 and WT2 data combined v all three LS lines and OCRLKO combined. Test used-Mann Whitney test. (C) WT1 and WT2 combined plotted against three individual patient lines and OCRLKO. Statistical test used One-way ANOVA. (total neurons analyzed: WT1:808; WT2:267; LSP2:150; LSP3:462; LSP4:463; OCRLKO:411)

      Regarding the hypomorphic nature of the patient-specific iPSC, I do not see the OCRL variant that was found in the family. Please correct me if I missed that, and if it was omitted, it should be included. I suspect that the variant generates a hypomorphic OCRL protein because the authors show expression in Figure 1D. Hypomorphic OCRL mutations compared with complete KO could show differences in molecular phenotypes, as found in Barnes et al. (PMID: 30147856) in an analysis of F-actin and WAVE-1 expression.

      Nature of the allele in LS patient lines

      There is a misconception in the reviewer’s comment that the OCRL allele in the three Lowe syndrome lines is a hypomorph. This is incorrect. In the patients from whom these LS lines were generated, the nature of the OCRL allele in them and the status of OCRL protein have all previously been described in detail in a peer-reviewed, published paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the LS patient lines are described (Line 174, references 26 and 27). As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. This results in a protein null allele of OCRL in all three patient lines. This has been shown in Fig 1B of Akhtar et.al 2022 by immunofluorescence using an OCRL specific antibody (PMID: 35023542). It has also been demonstrated by Western blot using an OCRL specific antibody for all three LS patient lines in Fig 3C and 5C of the present manuscript.

      The data presented in Fig.1D, E is a publicly available resource data PMID: 36989136 as mentioned in line 155, which is an integrated proteomics and transcriptomics generated from control iPSC-derived human brain organoids at different stages of development in-vitro.

      Minor issue

      The authors use the term mental retardation on line 102 to describe the cognitive phenotype in Lowe Syndrome. Medical, legal, and advocacy groups have abandoned this term because it is viewed as offensive. It is being replaced by intellectual disability, although this term also is problematic. In any event, many conferences on autism and intellectual disabilities are attended by families, and high-functioning cases sometimes address an audience of scientists. They would object to the use of this term if presented in a talk by one of the co-authors.

      Thank you. We will rephrase this line.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Not applicable at this stage. The above is a revision plan.

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

      We prefer to not carry out replicates of the single cell multiome analysis. As explained above the state of the art in the single cell analysis field is to not do so. The scientific reasons as to why such replicates are not required have been explained in the response to the reviewer comment.

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

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      Referee #3

      Evidence, reproducibility and clarity

      This paper by Sharma et al describes findings in an iPSC model of Lowe Syndrome. This is an important line of research because no mouse models phenocopy the neurodevelopmental aspects of the condition. They identified a potential role of Notch signaling in pathogenesis, a potentially druggable target. However, several issues need to be addressed.

      Major issues

      1. The sample size is very small, which is understandable to some extent given the expense and difficulty doing research using iPSCs. However, there are a couple of opportunities to improve the sample size. For example, in the analysis of DLK1 and other proteins shown in Figure 5, the analysis amounts to a single control vs the 3 patient lines, and a single control vs the KO line. The separation is justified because a complete KO of the gene might result in differences compared to hypomorphic mutation that apparently affects the 3 cases. However, there is no reason why WT1 and WT2 shouldn't be combined. They are both random controls. This might not affect the results of the other proteins analyzed, NOTCH and HES5, but the significance of DLK1 could change. DLK1 expression brings up another point. This, along with MEG3 and MEG8 are imprinted genes, two of the top differentially expressed genes in this study. However, these findings can be questioned by the well-known phenomenon that the expression of some imprinted genes may not be properly maintained during iPSC reprogramming. Thus, the differential expression of these imprinted genes might be due to a reprogramming artifact rather than the effects of OCRL per se. Analyzing both controls together could mitigate this objection. However, even if it does, the potential dysregulation of imprinted genes in the development of iPSCs should be acknowledged and addressed.
      2. Similarly, in the calcium signaling experiment shown in fig.2, the KO and patient lines are justifiably separated. However, again, why not combine both controls in the comparison with the patient samples?
      3. Regarding the hypomorphic nature of the patient-specific iPSC, I do not see the OCRL variant that was found in the family. Please correct me if I missed that, and if it was omitted, it should be included. I suspect that the variant generates a hypomorphic OCRL protein because the authors show expression in Figure 1D. Hypomorphic OCRL mutations compared with complete KO could show differences in molecular phenotypes, as found in Barnes et al. (PMID: 30147856) in an analysis of F-actin and WAVE-1 expression.

      Minor issue

      The authors use the term mental retardation on line 102 to describe the cognitive phenotype in Lowe Syndrome. Medical, legal, and advocacy groups have abandoned this term because it is viewed as offensive. It is being replaced by intellectual disability, although this term also is problematic. In any event, many conferences on autism and intellectual disabilities are attended by families, and high-functioning cases sometimes address an audience of scientists. They would object to the use of this term if presented in a talk by one of the co-authors.

      Significance

      as noted above, this study is significant because there are no mammalian models available to address the neurodevelopmental aspects of Lowe Syndrome

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      Referee #2

      Evidence, reproducibility and clarity

      This paper describes the effects of loss of OCRL (the Lowe syndrome protein) upon the function and differentiation of neurones, using an in vitro iPSC model system. Cells derived from three related Lowe syndrome patients and an OCRL knockout, generated using CRISPR, were used for these experiments. The results show that upon loss of OCRL, differentiation of stem cells into neurones is reduced, with an increased number of cells adopting glial and astrocytic fates. The neurones that are generated have reduced calcium transients and electrical activity. Gene expression data combined with biochemical analysis indicate altered Notch activity, which may account for the altered cell fate data seen in the in vitro differentiation model. Finally, rescue of cell fate and neuronal activity is seen upon knockdown of a PIP5K, which indicates that these phenotypes are due to the elevated PIP2 levels seen on the OCRL-deficient cells.

      The results provide new insights into the pathogenesis of Lowe syndrome. I found the paper to be well done, and the data supports the conclusions of the authors. I have a few comments below that may improve the manuscript:

      Major points

      1. The UMAP and ATAC-Seq data indicate different maps for the two different Lowe syndrome patient-derived cells (Fig 4 and Fig S3). This suggests that the cells are quite different, and therefore that changes seen in one Lowe syndrome patient may not be applicable to the others. I think this heterogeneity has important implications for the paper i.e. how general are findings obtained? Several different glioblast types are described (numbered 1-5 )- how different or similar are these?
      2. The PIP5K inhibitor seems to have a very strong effect on both WT and KO cells in terms of Notch activity (Fig 5G). This strongly suggests the effects of this inhibitor are not through OCRL and that changes in PIP2 induced by the inhibitor override those of OCRL. Thus, the experiments shown in Fig 5 seem not to be due to a rescue of OCRL activity as such.

      Minor points

      1. The main text needs to say what synapsin is and why it was analysed. In Fig 1I, synapsin abundance declines at 90 days. This appears quite strange. The authors should comment on it in the text.
      2. In Fig 2A and 3B there are clumps of green cells (CTIP2 positive). I am concerned that the lack of uniformity in the cell distribution could impact other analysis performed, where certain fields of view have been analysed e.g. by imaging or electrophysiology e.g. calcium measurements.
      3. In Fig 2J and 2K are the differences between sampels significant? The error bars are huge.
      4. In Fig S4- it would be good to show gene expression analysis and GFAP staining for organoids made using the OCRL KO cells
      5. Fig 5A needs more annotation- fold change comparing what to what?
      6. There should be more information provided in the main text relating to DLK1. For example, it is shown to be secreted, but no information is provided on whether this is expected. Secreted? The DLK1 blot in Fig 5F is not convincing.
      7. Of the 3 PIP5Ks, only PIP5Kc was targeted. The rationale for picking only this one needs to be provided.
      8. Fig 6D is confusing. I suspect the figure labelling is not correct- it does not correlate with the graphs.

      Significance

      This paper is significant because it provides important new information on the neurological features of Lowe syndrome. The approach is novel in terms of studying this condition. The findings are likely to be of interest to clinicians, cell biologists, neurobiologists and those studying human development. My expertise is in membrane traffic and OCRL/Lowe syndrome. I am not a neurobiologist.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The study by Sharma et al uses iPSC and neural differentiation in 2D and 3D to investigate how mutation in the OCRL gene affects neural differentiation and neurons. Mutation in the OCRL gene the cause of Lowe Syndrome (LS), a neurodevelopmental disorder. Neural cultures derived from LS patient iPSCs exhibited reduced excitability and increased glial markers expression. Additional data show increased levels of DLK1, cleaved Notch protein, and HES5 indicate upregulated Notch signaling in OCRL mutated neural cells. Treatment of brain organoids with a PIP5K inhibitor restored calcium signalling in neurons. These findings describe new dysregulated phenotypes in neural cultures of OCRL mutated cell shedding light on the underlaying caus of Lowe Syndrome.

      Major comments

      In general, I think the use of iNeurons usually means direct reprogramming from a somatic cell to neurons without the iPSC stage. Could be confusing to use this term for iPSC derived neurons.

      Please add at least one more replicate of WP cell line to the single nuclei RNAseq.

      The WT1 and the patient lines are rarely analysed together with the WT2 and KO lines, thus it is tricky to understand if the KO line is mimicking the patient lines? Please, add more merged analyses. Co-analysing all lines:

      i) would show if the KO line is more similar to the patient lines or to the WT1 or somewhere inbetween.

      ii) Could answer questions about the variation in phenotypes between the genetic backgrounds.

      iii) Elucidate how much variability there is between the the two WT lines in your assays. If the two WT lines vary much then conclusions about phenotypes in the patients and KO lines might need to be rethought?

      Please include more discussion and rational around the link between the expression patten of OCRL and the various phenotypes shown. From the RNAseq data performed at the NSC state where the expression of OCRL is lower than in neurons there are considerable differences in cell type distribution between lines. How can this skew cell type distribution affect downstream differentiation and neuronal function? Also, OCRL is expressed also at the iPSC state as shown in Figure 1I, do you see any phenotypes in iPSC? If not, explain how that could be.

      There is not enough data in the manuscript to show mechanistic links between OCRL, DLK1 and Notch so be aware not to overstate the conclusions.

      Line 173, please describe what mutation in the OCRL these patients have, is it a biallelic deletion? Is the protein totally absents? Please show western blot analyses of the protein in the patient lines. Would be good with a bit of clinical explanation of these patients? Do they have the same level of severity? Are there any differences between their clinical symptoms? This could be interesting to link to differences in cellular phenotypes. Line 185, please be clear on how the CRISPR KO line genetically mimics the patients' lines.

      Figure 1I, could the protein levels at the different stages be quantified?

      Figure 3A, there seem to be much more cells in LSP2, making it tricky to compare with the other cell lines. Density during differentiation can affect the cell fate. Please, provide images from the different lines that are comparable with similar density. Please provide quantification to the statement that there is fewer number of S100B cells in the LSP lines. Figure 3B, the images show cells very different, and it is tricky to compare similarities and differences, please provide images that look more similar to each other. Avoid images with clusters of cells or make sure to select representative images with clusters from each cell line. If the clustering is a phenotype explain and quantify that. Make sure the density is similar in all pictures.

      Line 2018, the statement "In the same cultures, there was no change in the staining pattern of the neuronal markers MAP2 and CTIP2 (Fig 3B)" is not strengthened by the figure. Please provide new pictures or data to prove the statement.

      Figure 3E, please describe all markers in the picture, thus also MAP2, S100B, CTIP2 and draw conclusions. Try to show comparable pictures.

      Fig 3D and G, what are the replicates? please explain.

      Figure 4 A, C, there is a large difference in the ratio of different cell types between the different cell lines, also between the LSP2 and LSP3. This would indicate either that the genetic background affects the phenotype to a large extent or that there is large variability between rounds of differentiation. To understand how much variability that comes from the differentiation and culturing:

      i) another replicate of WP cell from another donor (WT2) should be included (single nuclei RNAseq)

      ii) Confirm that three independed rounds of differentiation of the WT1, WT2, LSP2, LSP3, LSP4, and OCRL-KO result in similar outcome when it comes to cell type distribution. Could be done with qPCR marker.

      Line 309, "astrocytic transcripts NF1A and GFAP was elevated" It is unclear from this sentece in which cell lines NF1A and GFAP is elevenated? Please explain.

      Figure 5C, E, G, there is a large variation of Cnotch and Hes5 experession between the different

      Figure 5H, unclear which of the bands that is DLK1 and how the bands relate to the quantification. The band at 50 kDa seems to be stronger in the WT2 than in the OCRL-KO but in the quantification in Figure 5I, it shows 2x more in the KO. Thus, the other way around.

      Figure 6, please show that the inhibitor is inhibiting PIP5KC. Have you titered the added concentration of the inhibitor?

      Figure 6B, why do the calcium data for the WT2+1Ci look so different to the other, the dots are much more spread and seem to fewer replicates that for the other sample, please explain.

      Figure 6F, there is no significant differences between the bars but the statement in the text (sentence starts on line 332) indicate it is, please update the figure or remove the statement.

      Figure 6G, the HES5 expression seem to behave very similar in both WT2 and OCRL-KO cells when the inhibitor is used. What does this mean? Seems to not be linked to OCRL. Explain.

      Minor comments

      The panels in Figure 6 are not completely referred to correctly in the text, please check. Double check that all figure panels are referred to properly in the text

      Significance

      The manuscript is an interesting addition to the in vitro iPSC derived cellular modelling of neurodevelopmental disorder.

      Strengths:

      The use of both patient iPSC lines and CRISPR edited lines The use of both monolayer and 3D cultures

      Weaknesses: the significance decrease a bit due too few replicates (only 1 WT line in each experiment) and the variability between the patients' cell lines.

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      Referee #3

      Evidence, reproducibility and clarity

      Maxian et al. developed a mathematical model to explain the essential elements and interactions necessary and sufficient for the polarisation of the C. elegans zygote. The initiation of zygote polarisation has been extensively studied in recent years, highlighting the role of the centrosomal kinase Aurora-A (AIR-1) in controlling the cortical distribution of RhoGEF (ECT-2) and actomyosin contractility during polarisation. Although genetic experiments have demonstrated their function in this process, it remains to be tested whether these factors and their interactions are sufficient to induce polarisation.

      This work has provided a theoretical framework to predict the activity of AIR-1 in the cytoplasm and at the cell cortex, and the cortical distribution of ECT-2 and myosin-II (NMY-2). This framework can recapitulate the dynamic rearrangement of ECT-2 and myosin-II during polarisation, with centrosomes positioned at the posterior pole of the zygote. This model can explain, at least in part, the asymmetric distribution of ECT-2 and myosin-II in the zygote undergoing cytokinesis, suggesting that the mechanism of AIR-1-mediated control of ECT-2 and myosin-II would regulate patterning during polarisation and cytokinesis. This theoretical framework is developed with reasonable assumptions based on previous genetic experiments (except for the myosin-dependent regulation of ECT-2; see comments below).

      Issue #1

      The authors insist that this model correctly predicts the spatio-temporal dynamics of ECT-2 and myosin-II during polarisation and cytokinesis. However, the predicted results do not reproduce the in vivo pattern of ECT-2 in both phases. ECT-2 is cleared from the posterior cortex and establishes a graded pattern across the antero-posterior axis during polarisation (see their previous publication in eLife 2022, 11, e83992, Fig1A -480s) and cytokinesis (see eLife 2022, 11, e83992, Fig1C 60s and 120s). During both stages, ECT-2 does not show local enrichment at the boundary between the anterior and posterior cortical domains in vivo. In fact, when comparing the predicted results with the in vivo pattern of ECT-2 and cortical flow, the authors used non-quantitative descriptions such as 'in good agreement', 'a realistic magnitude', 'resemble'. These vague descriptions should be revised and a quantitative assessment of ECT-2 distribution between in silico and in vivo should be included in a revised manuscript.

      Issue #2

      I assume that the strange local enrichment of ECT-2 at the anteroposterior boundary is due to their assumption that the binding rate of ECT-2 is increased by a linear increase via cortical myosin-II (page 6). This assumption is not directly supported by experimental evidence. A previous study by the same group (eLife 2022, 11, e83992) showed that a progressive increase in ECT-2 concentration at the anterior cortex is partially accompanied by an increase in cortical flow and transport of myosin-II from the posterior pole to the anterior cortex. This observation supports the idea that ECT-2 may associate with cortical components transported by myosin-II based cortical flow. This unrealistic assumption makes the predicted distribution pattern of ECT-2 almost identical to that of cortical myosin-II, resulting in an increase in the concentration of ECT-2 at the anteroposterior boundary where myosin-II forms pheudo-cleavages and cleavage furrows. The authors should clarify why their mathematical model used this assumption and provide a comprehensive analysis and evaluation of the parameter value for an ECT-2-myosin-II interaction.

      Significance

      This work includes a valuable tool that can be used to explain other actomyosin-mediated polarisation processes. Although the paper provides useful insights in principle, the weakness of this work is that the model is designed with parameter sets that only recapitulate previously published phenotypes. Therefore, this paper confirms previous findings and provides less/no new mechanistic insights into cell polarisation. As such, this work would be of interest to specialised cell biologists and biophysicists working on the cytoskeleton and cell division, but will not be of general interest to biologists and biochemists.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Maxian, Longhini and Glotzer presents purely modeling work performed by the first author in conjunction with the already published experimental work by Longhini and Glotzer (eLife, 2022). The aim of the manuscript is to provide a mathematical model that connects the actomyosin contractility of the cell cortex in C. elegans zygote with the activity of the centrosomal kinase AurA (AIR-1 in C. elegans). The major claim of the authors is that their model, fitted to the experimental data pertaining to the zygote polarization, also describes dynamics during the zygote cytokinesis. In the model, the authors provide a heuristic approach to the biochemical dynamics, reducing their treatment to two variables: myosin and Ect2 Rho GEF. The biochemical model is integrated with a simple 1D active gel-type model for the cortical flow. The model uses static diffusive field of activity of AurA kinase in the cytoplasm as an input to their chemo-mechanical model. Major concerns: 1. The biochemical model is highly heuristic and several major assumptions are poorly justified. Thus, the authors explicitly introduce recruitment of Ect2 by myosin, something apparently based on the experimental observations by Longhini and Glotzer in 2022, which had not been biochemically confirmed since with a clear molecular mechanism. 2. The contribution of AurA is introduced highly schematically as a term based on enzyme inhibition biochemistry that increases the off rate of Ect2. The major assumption of the model is that AurA phosphorylates Ect2 strictly on the membrane (cortex) of the cell. Why? No molecular justification is given. If the authors cannot provide clear justification, this major assumption has to be clearly declared as such. The phosphorylation/dephosphorylation dynamics of Ect2 is not considered at all. 3. In the equation for myosin, the authors introduce disassembly/ inactivation term proportional to the fourth order of concentration of myosin. Why? This is a major assumption, which appears to be derived from the work by Michaux et al. 2018. There the authors (Michaux et al.) postulated that the rate of inactivation of RhoA GTPase was somehow proportional to the fourth power of RhoA concentration. It appears that Maxian et al. further assume that the myosin concentration is fast variable enslaved by Rho, so that M ~ [RhoA]. They then presumably assume that if the rate of degradation/ inactivation of Rho is proportional to the forth power of Rho concentration, so is true for myosin (M). This is a logical error and is not justified. An important question, why do the current authors need this unusual assumption with such a high power of M disassembly/inactivation? Perhaps, this is because without this rather dubious term the cortex flow produces a blow-up of myosin concentration? This would be expected in their mechanical model - the continuous flow of actomyosin not compensated by cortex disassembly generally causes blow-up of biochemical concentrations transported by the flow, this is a known problem of the "simple" active gel model used by the authors. Maxian et al. have to provide clear derivation of the term -kfb*M^4 and also demonstrate why they need this exotic assumption. 4. The equation for myosin M has a membrane-binding term, which is second order in concentration of Ect2 ~E^2, without which the model will not show the instability that the authors need. The only justification given is that "some nonlinearity is required". A proper derivation should be given here. 5. The diffusion coefficients for Ect2 and myosin are chosen to be the same. Why? Clearly these molecules so different in size - myosin being a gigantic cluster monster of ~300 nm believed to be bound to actin, should have a much smaller diffusion coefficient? 6. There are confusing statements regarding the role of actomyosin flows. In the beginning of the manuscript, the authors seem to state that since Ect2 has a high off rate, the effect of the flow on Ect2 localization is negligible in comparison with direct binding to myosin. Later, the authors state that flows are absolutely essential for the patterning. The authors need to clearly explain where and how the flows are important or not. Minor points: 1. page 9. Why is the rate of dephosphorylation of AurA is named Koff? 2. page 10. "Note that the model is calibrated to predict... which matches experimental observations" - this sentence needs changing. You want to say that you fit the model to experiments in the Longhini and Glotzer paper. There is no prediction here. 3. page 14. "A plot of Ect-2 accumulation as a function of distance from the nearest cortex..." - clearly the word "centrosome" is meant here instead of "cortex". 4. page 16. "Inactive, non-phosphorylatable version of Ect-2..." - non-phosphorylatable is clear, but why inactive?

      Significance

      This reviewer sees limited significance of this manuscript to the field in general. The modeling approach is hardly novel as it is based on a variety of published models, all cited by the authors, to be precise. The model, being very simplistic and heuristic, is not predictive. The main novelty of the current manuscript is the introduction of the effect of Aurora A on the activity of the actomyosin cortex. Since this is taken to be very schematic, simply via the effective increase in the off rate of Ect2, the model is showing that it is consistent with the earlier published experimental results by Longhini and Glotzer. This is to be expected. The main claim of the authors, that the model fitted to the polarization data also qualitatively describes the cytokinesis (there are no quantitative data to compare to) is probably valid, but the result is not surprising either. At best, the model can be labeled as fitted to the data and confirming the experimental results. Since it contains several postulated heuristic terms not properly justified on the mechanistic level, this is also not surprising.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this article, Maxian et al. propose a model combining 1-d simulations of ECT-2 and Myosin concentration at the cortex through binding/unbinding and advection at the cortex, with an input for AIR-1 cortical concentration based on the spatial localisation of the centrosomes in the cytoplasm. The objective of the authors is to recapitulate the role of (1) AIR-1, (2) its effector ECT-2 and (3) the downstream effector, driver of cortical flows, the molecular motors Myosin, in two key physiological processes, polarization and cell division. This is important as work over the last 10 years have emphasized the role of AIR-1 in embryo polarization. Previous biochemical-mechanical models have focused on RhoA/Myosin interactions (Nishikawa et al, 2017), the importance of a negative feedback and excitable RhoA dynamics (Michaux et al, 2018), or anterior PARs/posterior PARs/Myosin (Gross et al, 2019). The authors thus attempt to provide a new descriptive model in which RhoA is implicit, instead focusing on the role of centrosome localization on AIR-1 localization, and providing a framework to explore polarity establishment and cell division based on these 3 simple players. The first part of the model is very reminiscent of previously published models, while the second instead provides a link between the initial polarizing cue AIR-1 and polarization. Based on this description, the model is precisely tuned to achieve polarization while matching experimental observations of flow speed and ECT-2 A/P enrichment shape. The results are therefore certainly new and interesting.

      Major comments:

      1. The authors use the position of the centrosomes as a static entry, resulting in a static AIR-1 input. Is this true, or are the positions of the centrosomes dynamically modulated over the course of the different processes simulated here (for example as a consequence of cortical flows?), and if so, is the assumption of immobile position?
      2. While in its principle the model is quite simple and elegant, the detailed form of the equations describing the interactions between the players is more complex. Are all these required? If they are crucially important for the behavior of the model, these should be described more thoroughly, and if possible rooted more directly in experimental results, in particular:
        • k(ME)MEc (Linear enhancement term): why would myosin impact E concentration? The authors state, p.7, "There is a modest increase in the recruitment rate of ECT-2 due to cortical myosin (directly or indirectly), in a myosin concentration-dependent manner (Longhini and Glotzer, 2022)." I could not find the data supporting this assumption - Longhini and Glotzer apparently rather point to a modulation of cortical flows. ("During anaphase, asymmetric ECT-2 accumulation is also myosin-dependent, presumably due to its role in generating cortical flows."). Embedding this effect in the recruitment rate instead of expecting it from the model thus appears awkward. Could the authors specify how they came to this conclusion, which the authors might have derived from observations made in their previous work, but maybe did not fully document there?
        • k(EM)E^2Mc (ECT-2 non-linear impact on Myosin): does the specific of the value to convey the enhancement (square form) have an impact on the results?
        • k(fb)*M^4 "The form of this term is a coarse-grained version of previously-published work (Michaux et al., 2018)." Myosin feedback on myosin localization proportionally to M^4 does not seem to directly derive from Michaux et al... Please detail this points more extensively and detail the derivation, in the supplements if not in the main text. P23. Parameter values: "This is 1.5 times longer than the estimate for single molecules (Nishikawa et al., 2017; Gross et al., 2019) to reflect the more long-lived nature of myosin foci during establishment phase (Munro et al., 2004)." Not sure what the authors mean by more long-lived duration of foci during establishment phase. Seems rather arbitrary.
      3. It would be very helpful (and indeed more convincing) to include a direct comparison between modeling results and experimental counterpart whenever possible. This might not be possible for some data (e.g. Fig. 3d from Cowan et al), but should be possible for other, in particular Fig. 3c and Fig. 5b, for the flow speed and ECT-2 profiles. In Fig. 5b in particular, previously published experimental data could be produced to give the reader to compare model with experiments (possibly provided as an inset, at least for the wild type conditions).

      Minor comments

      Fig. 5b: ECT-2 C 6A(dhc-1) do not seem to be referenced or discussed in the main text. Also, why present the results for the flow for 2 conditions and the ECT-2 localisation for 4? Or does the variation of ECT-2 not impact the flow profile?

      p.6: Eqn 1a: ^ missing on 3rd E?

      p.6: Given that the non-normalized data is used in the main text, and the normalized only appears in the supplemental, maybe star the dimensionless and remove all hats from the main for greater legibility?

      p.14: replace "embryo treatment" with "experimental conditions"?

      p.21, S4a: add A=Â/Atot

      p.22: "L = 134.6 μm" - please write 134µm to retain the precision of original measurements

      p.22: Please provide formula for all dimensionless values as a table at the end of the supplemental for the eager but less-mathematically proficient reader.

      The authors' attention to providing specific citations including figure number corresponding to the specific point they reference in the papers they cite is appreciated.

      Significance

      General assessment:

      This modeling paper interestingly leverages existing experimental data to develop a new mathematical model of embryo polarization and cell division focusing on the role of AIR-1/Aurora Kinase. It combines classical 1-d advection/diffusion-reaction scheme with an upstream cue, AIR-1/Aurora Kinase, the profile of which is defined by the localization of the two centrosomes, and use the model as a framework to explore cortical flows and ECT-2 and Myosin cortical localization. Calibrated using information from polarization phase, the model recapitulates without any further tuning, in a variety of mutants, key localisation hallmarks of Ect-2 during cell division, simply based on the localization of the centrosomes. Finally, it provides strong, experimentally testable predictions of the validity of the proposed model.

      Advance:

      In particular, this study provide compelling evidence showing that their model, based on dynamics during polarization, is sufficient to explain the ultra-sensitivity of cortical ECT-2 accumulation to centrosome distance during cell division. Their model further predicts that short ECT-2 cortical residence time is required to prevent advection-mediated counter-flows of ECT-2 that would otherwise prevent polarization, a prediction testable experimentally by engineering modifications of ECT-2 cortical residence time.

      Audience:

      This is primarily a modeling paper. Although the bulk of the article is written to capture the interest of cellular biologists with a sound backgrounds in mathematics and an interest in minimal models of cell division and polarization, the overall conclusions and prediction are further-reaching and would be of interest to a larger audience with an interest in cell division, polarization, and the role of Aurora Kinase in these processes.

      Expertise:

      Developmental biology / Cell biology / Biological physics

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      Reply to the reviewers

      Referee #1 Major concerns:

      1) One major concern that I have about the sexual dimorphism in tolerance to nutrient deprivation is that the papers cited by the authors, and paradigms that are used broadly in the field, all use adult flies. The authors must show that in larvae, a completely different life stage from their citations, there is a sexual dimorphism in tolerance to nutrient deprivation.

      In our descriptions of previous literature that describes tolerance to nutrient deprivation, we have added language that specifies that the results from nutrient deprivation mentioned therein were performed in adults (lines 82, 91, 96, highlighted in the preliminary revision).

      In response to the concern from this reviewer that our data do not assay for nutrient deprivation in larvae, we would like to clarify that our “stress tolerance assay” more specifically demonstrates that developmental nutrient deprivation compromises male survival through pupariation to adulthood. While the effects of acute nutrient deprivation on developmental delay can be assayed in foraging or earlier larval stages, we have not tested whether ATF4 signaling is present and dimorphic in these stages and believe it to be beyond the scope of this study. In the revision, we will edit the text to be more precise in our conclusions with these data.

      Interestingly, Diaz et al 2023 (Genetics) show that male larvae have greater fat stores than female larvae. Considering fat is the main determinant of tolerance to nutrient deprivation it's not clear that females will actually survive nutrient deprivation longer as larvae. This is an essential test of whether female larvae do have increased tolerance to nutrient deprivation, which is the basic foundation of the authors' model.

      We thank the reviewer for making this clarifying point about the relationship between fat stores and nutrient deprivation. ____In response to the concern our data do not assay for nutrient deprivation in larvae (major point #1), we would like to clarify that our “stress tolerance assay” more specifically demonstrates that developmental nutrient deprivation compromises male survival through pupariation to adulthood. While the effects of acute nutrient deprivation on developmental delay can be assayed in foraging or earlier larval stages, we have not tested whether ATF4 signaling is present and dimorphic in these stages and believe it to be beyond the scope of this study. In the revision, we will edit the text to be more precise in our conclusions with these data.

      2) Another concern is the way that the authors "genetically induce nutrient deprivation using methioninase overexpression". As they acknowledge in the discussion (Line 381-390), methioninase overexpression will have many cellular effects. While there is no doubt that methionine levels would be lower in their model, it is less certain whether this is the main driver of the male-specific lethality.

      There are two potential solutions to this problem. First, the authors could change the text throughout the paper to more accurately describe their paradigm as "methioninase-induced lethality" rather than "nutrient deprivation". This would limit the scope of their scientific question and the conclusions they draw, but would eliminate the need for more experiments.

      The second solution would be to complete experiments to establish the following points: i) methioninase overexpression causes all the classical features of nutrient deprivation (e.g. changes to canonical signaling pathways such as TOR); ii) using other genetic means of nutrient deprivation such as slimfast-RNAi to see if those manipulations phenocopies the male-specific lethality they see with methioninase overexpression; iii) testing a role for ATF4 in mediating sex differences (if any) in other contexts such as slimfast-RNAi. This will take 2-3 months but is essential to draw any conclusions about whether their paradigm is truly a model for nutrient deprivation.

      We agree that methionine depletion is not the only cellular change effected by methioninase over-expression. For example, a molecular byproduct of methioninase metabolism via methioninase is the production of ammonia, which has recently been shown to indue ISR signaling in the context of____ alcohol-associated liver disease (Song et al. 2024, PMID 37995805). We believe our experimental controls and genetic rescues account for this and other possible effects in the interpretation of our data. ____To further establish the utility of methioninase overexpression as a genetic means of methionine deprivation (first described in Parkhitko et al. 2021, PMID 34588310), we will perform ____slimfastRNAi_ in the fat (another genetic means of reducing intracellular amino acid levels) per the reviewer’s suggestion. In these animals we will evaluate 1) ATF4 activity in L3 adipocytes using 4E-BPintron-GFP (1.5 months) , and 2) male vs. female lethality (as determined by counting eclosed adults) (2 months. If male lethality is observed with _UAS-slimfastRNAi _as with _methioninase ____expression, we will test the requirement for dimorphic ATF4 signaling in the fat for such male susceptibility to lethality/female resistance to lethality. (3 months)

      3) Another important point is that the authors state that sexually dimorphic ATF4 activity in the fat body is instructed by sexual identity in a cell-autonomous manner. Despite a clear decrease in ATF4 reporter levels in tra mutants, the fat body-specific tra-RNAi effect on the ATF4 reporter was less convincing. Together with the fact that changes to tra in the fat body affect insulin secretion from the insulin-producing cells, it is possible that the effect on ATF4 is not cell-autonomous. To conclusively test if sexual identity regulates ATF4 in a cell-autonomous manner the authors should use the flp-out system to make Tra-expressing or tra-RNAi-expressing clones in the fat body. This would take approximately 1.5 months to make the strain and test this.

      We thank the reviewer for making the astute observation that the effect of fat body-specific ____tra_ knockdown on female ATF4 reporter activity was more modest than whole-animal _tra_ mutants. We ascribe this to RNAi knockdown efficiency rather than non-autonomous effects of sexual identity on ATF4 expression in the fat. This is underscored by our data showing fat body knockdown of _spenito_ (_nito_), a sex determinant upstream of _tra____ that is shown to instruct female sexual identity in the larval fat (Diaz et al. 2023, PMID 36824729), does indeed reduce ATF4 levels in female fat to that of control male fat (Fig. 2K).

      4) As the authors show for the UAS-methioninase, other UAS lines used in the paper such as UAS-traF, UAS-tra-RNAi, UAS-dsx-RNAi may have leaky effects on gene/reporter expression. The authors must include a UAS only control to establish that the tra-RNAi, UAS-traF, UAS-dsx-RNAi do not affect gene/reporter expression.

      We thank the reviewer for suggesting that we evaluate the “leakiness” of all UAS lines used in this study (major point #4). To do this, we will quantify ATF4 reporter activity in the fat (4E-BPintron-GFP) in the presence of UAS lines but in the absence of GAL4 for ____UAS-traRNAi_, _UAS-traF_, and _UAS-dsxRNAi____ (1.5 months)

      5) I have concerns about the statistics used. In the methods and legends only t-tests are mentioned; however, when three groups are compared a one-way ANOVA with post-hoc tests must be used to correct for multiple comparisons. To compare differential responses to genetic/environmental manipulations between the sexes, a two-way ANOVA must be used. For example, to conclude that males and females have different responses in the two-way ANOVA, there must be a significant genotype:sex interaction. The p-values for comparisons between genotypes in either the one-way or two-way ANOVA must be derived from post-hoc tests within the ANOVA analysis.

      __We thank the reviewer for carefully assessing our usage of statistical analyses to interpret the data in the study. To the best of our understanding, such ANOVA analyses are helpful in evaluating significance when comparing multiple sample groups simultaneously. However, in all our analyses we are only ever comparing two samples at a time, making a two-tailed Student’s t-test with Welch’s correction (assuming unequal variance) to be the best statistical method. __

      Referee #1 Minor points

      1) Please ensure to make the reader aware of which life stage was tested in the literature cited supporting sexually dimorphic tolerance to nutrient deprivation.

      We thank the reviewer for pointing out this ambiguity in our description of previous and current work on nutrient deprivation tolerance. We address this minor point in tandem with major point #1 above ____by adding language that specifies that the results from nutrient deprivation mentioned therein were performed in adults (lines 82, 91, 96, highlighted in the preliminary revision).

      2) Published data about sex-specific mechanisms of metabolic regulation mean that the introduction should be more fully cited than it is. Even in the introduction "the molecular basis of these differences and how they impact tolerance to nutrient deprivation is still under investigation" is inaccurate, as there are published studies identifying some mechanisms (work on gut hormones and sex-specific effects on starvation resistance and body fat, role of ecdysone on body fat and feeding, sex-specific roles for brummer and Akh in regulating body fat, intestinal transit and gut size and feeding). Please adjust the paper to acknowledge this growing body of knowledge.

      We thank the reviewer for appropriately highlighting that there are other relevant studies in the context of sex-specific mechanisms of metabolic regulation in addition to those referenced in the original manuscript. Specifically, we will include additional citations and appropriate descriptions of previous work, such as those that report on sex-specific effects of starvation (i.e. Millington et al. 2022, PMID 35195254) and sex-specific roles for metabolic regulators such as Brummer/ATGL (Wat et al. 2020, PMID 31961851) and Adipokinetic hormone (Wat et al. 2021, PMID 34672260) in _Drosophila fat storage._ __

      3) Please list the ingredients per L so that individuals can replicate the diet easily.

      __We thank the reviewer for requesting additional details on the diet fed to animals in this study, which will improve the reproducibility of our findings. In the Methods section, we have now included additional details on the specific diet fed to animals used in this study (lines 465-468 in the preliminary revision).____ __

      4) Please cite grant numbers for all the community resources (e.g. Bloomington, DSHB), and please acknowledge FlyBase and its grants as well. For example, here are the instructions for citing BDSC https://bdsc.indiana.edu/about/acknowledge.html and similar instructions are available for the other resources.

      We thank the reviewer for underscoring the importance of citing grant numbers for all community resources used. We have added to the Acknowledgements section statements and grant numbers regarding use of community resources such as FlyBase, Bloomington Drosophila Stock Center, and DSHB (lines 533-538 in the preliminary revision).

      Referee #2:

      1. Figure 4 is an important part of this study, where the authors show a male-specific vulnerability to methioninase expression. They show that ATF4 RNAi confers vulnerability to methioninase expression even in females. An obvious question is whether ATF4 overexpression is sufficient to enhance resistance to methionine deprivation in males.

      We thank the reviewer for pointing out that the ability of increased ATF4 in male fat to enhance resistance to methionine deprivation was not interrogated. To examine this, ____we will quantify survival rates of males and females following dual over-expression of methioninase and ATF4 (3 months). We would like to state here that experimental over-expression of ATF4 at the levels induced by GAL4 activity is sometimes lethal, so this experiment may be difficult to execute/interpret due to technical limitations.

      Methioninase expression results (Figure 4) are interesting. Are the levels of methioninase expression similar between males and females?

      We thank the reviewer for asking for clarification on whether methioninase induction is similar between males and females. Whether methioninase induction is sexually dimorphic is likely a function of whether there is sexual dimorphism in the strength of the GAL4 driver used. While the drivers employed in this study are widely used for fat body expression, to our knowledge relative expression of ____Dcg-GAL4_ in males versus females has never been reported. Thus, we will perform qPCR to compare GAL4 and methioninase transcript levels in _Dcg-GAL4; UAS-methioninase____ male and female fat bodies (1 month).

      1. This manuscript focuses on ATF4, but there could be additional possible reasons for the sexually dimorphic ISR activity. For example, the degree of physiological stress that activates ISR could be different between males and females. I suggest comparing the levels of Phospho-eIF2alpha (or any other markers upstream of ATF4) in both sexes.

      We thank the reviewer for suggesting additional checks for sexual dimorphism in ISR activity in the fat, such as degree of eIF2α phosphorylation, which is directly upstream of ATF4 induction. Per their suggestion, we will compare p-eIF2α staining in male and female larval adipocytes (1.5 months).

      In Figures 1 to 3, the authors examine the intensity of ATF4 signaling after perturbing the sexual determination pathway. The methioninase experiments in Figure 4 are interesting, but there is nothing in this Figure linking male-specific vulnerability to sex determination genes. Examining the vulnerability to methioninase expression after perturbing the sexual determination genes would make Figure 4 integrate better with the rest of the manuscript.

      We thank the reviewer for highlighting that the role of male sexual identity in vulnerability of males to methioninase expression was not interrogated. Similar to our genetic interaction study proposed in point #1 from this reviewer, we will test whether feminizing male fat bodies (using UAS-traF over-expression) will change survival rate of males in our methioninase-expression paradigm (3 months).

      1. The authors write that they generated 4EBP intron-GFP because the 4EBP intron-DsRed signal was frequently observed in the cytoplasm (line 122). They seem to suggest that the DsRed reporter is less reliable than the GFP reporter. However, they continue to mix results using 4EBP intron-GFP (Fig. 4A) and 4EBP intron-DsRed (Fig. 4F). The two figures examine slightly different conditions (Fig 4A shows tra1 KO females, while Fig. 4F shows traF males). If the DsRed reporter is less reliable due to the signal from the cytoplasm, the authors should show results with the GFP reporter in traF males.

      We thank the reviewer for raising the legitimate concern that the ____4EBPintron-DsRed_ reporter used for some of the included quantifications in Fig. 3 might be less reliable then _4EBPintron-GFP_ that was generated for this study. We have updated the manuscript text (in the Results section) to more accurately describe the justification for building the _4EBPintron-GFP____ line (lines 122-127 in the preliminary revision).

      1. In Figure S1, the authors label 4EBP intron-GFP as Thor2p-GFP, which is confusing. There are other parts in the methods section referring to Thor2p. I suggest using consistent terminology throughout the manuscript.

      We thank the reviewer for pointing out this typo. We have modified the text accordingly in Figure S1.

      Referee #3 Major concerns:

      1) Sexually dimorphic ATF4 activity (Figure 1 and associated supplemental figure) as evidenced by reporter expression is the basis of this study, yet a detailed description of the immunofluorescence quantification is lacking. The methods sections needs to include information on how a) images were acquired (Were the same acquisition settings used across all images?), b) the intensity measurements were taken (What software was used? Does each data point in the distribution represent a single nucleus (the assumption is yes)? Is nuclear size adjusted for? Panels A' and B' have obvious differences in nuclear size which would in turn affect total intensity measurements), c) the sample size (How many fat images taken per animal per sample/genotype? How many trials were performed?)

      We thank the reviewer for requesting additional information describing the immunofluorescence quantification methods. ____We have now added an additional paragraph to the Methods section detailing image acquisition for quantifying reporter activity (lines 483-494 in the preliminary revision).

      2) While the authors nicely address the lack in specificity for two of the Gal4 driver lines used in the study limitation section, the fact that the one driver that is fat body-specific, 3.1Lsp2-Gal4, shows a modest, not statistically significant decrease in Figure 4C still raises some concern. There is another Lsp2-Gal4 line described in Lazareva et al., 2007 (PLoS Genetics) that drives expression in larval fat, perhaps to combat the issue of 3.1Lsp2-Gal4 have low activity, as mentioned by the authors. Alternatively, this phenotype could be assessed using Gal4 lines that only drive expression in the other tissues (if available). Otherwise, the conclusion that ISR/ATF4 signaling specifically in the fat mediates the starvation response needs to be toned down.

      We thank the reviewer for carefully analyzing our data showing survival during methioninase over-expression using different GAL4 drivers. ____The reviewer raises a valid concern that the GAL4 driver with highest specificity for the fat body (that is, with the least off-target tissue expression), ____3.1 Lsp2-GAL4_, induces the most modest methioninase-induced lethality (major point #2). We attribute this to the fact that _3.1 Lsp2-GAL4_ is reportedly (and in our hands) a weaker driver than _Dcg-GAL4_ in the larval fat body. We will demonstrate this experimentally by performing UAS-nucGFP expression using both _Dcg-GAL4_ and _3.1 Lsp2-GAL4____ side by side and quantifying nuclear GFP intensity in the larval fat (2 months).

      The reviewer also mentions that the other drivers with more statistically significant effects on male lethality (____Dcg-GAL4_ and _r4-GAL4_, Fig. 4) are not restricted to the fat body. Importantly, both these drivers are also expressed in the blood lineage (hemocytes). To examine whether ISR activation in hemocytes contributes to the female stress tolerance (and/or male lethality) observed upon methioninase induction, we will quantify male and female survival rate following methioninase induction in the blood lineage using a blood-specific driver, _HHLT-GAL4____ (Mondal et al 2014, PMID 25201876). (2.5 months)

      3) Several analyses rely on RNAi, and this is understandably important for tissue-specific knockdown of gene expression. At least one of the two following issues needs to be addressed: a) the efficiency of knockdown for each gene are not provided or reported on and b) only single RNAi lines were used for each gene targeted for knockdown.

      We thank the reviewer for pointing out that the original manuscript does not report on knockdown efficiencies of the RNAi lines used in the study. The RNAi lines from the Harvard Transgenic RNAi Project (TRiP) collection (traRNAi, dsxRNAi, nitoRNAi) have been verified in Yan & Perrimon 2015 (PMID 26324914). The ATF4RNAi line was verified in Grmai et al. 2024 (PMID 38457339). We have included all citations for these validation studies in Table S1 in the preliminary revision.

      Referee #3 Moderate concerns:

      1) Lines 137-141: It would be nice to see a gel that confirms that these newly designed primers detect the expected isoforms (supplemental perhaps).

      We thank the reviewer for requesting confirmation of isoform specificity of the primers used to detect ATF4 transcript in the fat body in Fig. 2B-C. Because these are qPCR primers, they were all designed to produce amplicons of nearly equal size. There is currently no reliable method to specifically deplete one ____ATF4_ isoform at a time, which would be the only way to experimentally demonstrate isoform specificity of each primer set. However, we have designed each primer pair to specifically detect isoform-specific regions of _ATF4_ mRNA and have verified specificity (and lack of off-target products in the _D. melanogaster_ genome) _in silico____ using Primer-BLAST (NCBI).

      2) Lines 278-282 and Figure 4D: Shouldn't the second and fourth bars be compared? Based on the hypothesis and conclusion, second bar females can resist nutrient stress because they have ATF4, but fourth bar females can't because they don't have ATF4 - is this difference statistically significant?

      We thank the reviewer for pointing out this missing statistical report that compares the second and fourth bars in Figure 4D ____(females expressing methioninase, with and without ATF4 knockdown). We have now performed this analysis and reported the p-value in text (lines 282-285 in the preliminary revision).

      3) For all scatter plot graphs, figure legends should indicate what the horizontal line represents (is this the average?). Also, error bars and what they represent (SD or SEM) are not included or described.

      We thank the reviewer for asking for additional details on our graph annotations. We have added language to explain that 1) horizontal lines on ATF4 reporter quantification graphs denote mean intensity (Fig. 1 legend, lines 567-568 in the preliminary revision) and 2) error bars on qPCR graphs represent SEM (Fig. 2 legend, line 583 in the preliminary revision).

      Referee #3 Minor concerns:

      1) Line 27: "counter parts" should be one word 2) Line 33: should the word "nutrient" be included before "stress" 3) Line 42: It would be nice to see a couple of examples of the "well documented across species" statement 4) Line 44-45: Add in the word "human" before population and use "women" instead of "females" 5) Line 53: There seems to be an issue with comma placement or word usage in the section of the sentence that reads "coincident with, or a comorbidity, for" 6) Lines 82-83: Mention of a couple examples would be nice 7) Line 104: Perhaps add the word "cellular" before "sexual" 8) Line 204: Delete the word "and" after "expression" 9) Line 234: Delete "a" before "significantly" 10) Line 276: Should "adult" be "adulthood" 11) For the discussion, a model schematic would nicely depict the findings as a whole 12) Line 330: May consider incorporating the following studies - Stobdan et al., 2019 and De Groef et al., 2021 13) Related to the point above: It would be great to see discussion/speculation of potential ATF4 targets that might be mediating this effect 14) Line 374: The placement of "yet unidentified" makes it seem like other ATF4 target genes aren't known, but really what is meant is that their sexually dimorphic expression is not known 15) Line 535: (beta-gal) "protein" instead of "gene"? 16) Figure S2: Please indicate what the two horizontal dotted lines are supposed to point out

      We thank the reviewer for carefully pointing out these minor yet critical text concerns. ____We have addressed all minor concerns raised by the reviewer in text edits to the preliminary revision, which are each highlighted in yellow in lines 27, 33, 44, 53, 105, 204, 236, 279, 375,554, 624 in the preliminary revision. The exceptions are points 3, 6, 11, 13, which we will address in the subsequent revision as described in the previous section.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: Using a combination of genetic and molecular tools, Grmai and colleagues present data showing the sexually dimorphic expression of ATF4, a transcription factor that mediates the integrated stress response, in larval fat tissue. Moreover, they find that higher basal ATF4 activity in female larvae supports the stronger resistance to nutrient deprivation that females exhibit compared to male larvae. The data are clearly described and nicely laid out in well-organized figures. Some major, moderate, and minor concerns, delineated below, regarding the approach and conclusions should be addressed prior to acceptance for publication.

      Major concerns:

      1. Sexually dimorphic ATF4 activity (Figure 1 and associated supplemental figure) as evidenced by reporter expression is the basis of this study, yet a detailed description of the immunofluorescence quantification is lacking. The methods sections needs to include information on how a) images were acquired (Were the same acquisition settings used across all images?), b) the intensity measurements were taken (What software was used? Does each data point in the distribution represent a single nucleus (the assumption is yes)? Is nuclear size adjusted for? Panels A' and B' have obvious differences in nuclear size which would in turn affect total intensity measurements), c) the sample size (How many fat images taken per animal per sample/genotype? How many trials were performed?)
      2. While the authors nicely address the lack in specificity for two of the Gal4 driver lines used in the study limitation section, the fact that the one driver that is fat body-specific, 3.1Lsp2-Gal4, shows a modest, not statistically significant decrease in Figure 4C still raises some concern. There is another Lsp2-Gal4 line described in Lazareva et al., 2007 (PLoS Genetics) that drives expression in larval fat, perhaps to combat the issue of 3.1Lsp2-Gal4 have low activity, as mentioned by the authors. Alternatively, this phenotype could be assessed using Gal4 lines that only drive expression in the other tissues (if available). Otherwise, the conclusion that ISR/ATF4 signaling specifically in the fat mediates the starvation response needs to be toned down.
      3. Several analyses rely on RNAi, and this is understandably important for tissue-specific knockdown of gene expression. At least one of the two following issues needs to be addressed: a) the efficiency of knockdown for each gene are not provided or reported on and b) only single RNAi lines were used for each gene targeted for knockdown.

      Moderate concerns:

      1. Lines 137-141: It would be nice to see a gel that confirms that these newly designed primers detect the expected isoforms (supplemental perhaps).
      2. Lines 278-282 and Figure 4D: Shouldn't the second and fourth bars be compared? Based on the hypothesis and conclusion, second bar females can resist nutrient stress because they have ATF4, but fourth bar females can't because they don't have ATF4 - is this difference statistically significant?
      3. For all scatter plot graphs, figure legends should indicate what the horizontal line represents (is this the average?). Also, error bars and what they represent (SD or SEM) are not included or described.

      Minor concerns:

      1. Line 27: "counter parts" should be one word
      2. Line 33: should the word "nutrient" be included before "stress"
      3. Line 42: It would be nice to see a couple of examples of the "well documented across species" statement
      4. Line 44-45: Add in the word "human" before population and use "women" instead of "females"
      5. Line 53: There seems to be an issue with comma placement or word usage in the section of the sentence that reads "coincident with, or a comorbidity, for"
      6. Lines 82-83: Mention of a couple examples would be nice
      7. Line 104: Perhaps add the word "cellular" before "sexual"
      8. Line 204: Delete the word "and" after "expression"
      9. Line 234: Delete "a" before "significantly"
      10. Line 276: Should "adult" be "adulthood"
      11. For the discussion, a model schematic would nicely depict the findings as a whole
      12. Line 330: May consider incorporating the following studies - Stobdan et al., 2019 and De Groef et al., 2021
      13. Related to the point above: It would be great to see discussion/speculation of potential ATF4 targets that might be mediating this effect
      14. Line 374: The placement of "yet unidentified" makes it seem like other ATF4 target genes aren't known, but really what is meant is that their sexually dimorphic expression is not known
      15. Line 535: (beta-gal) "protein" instead of "gene"?
      16. Figure S2: Please indicate what the two horizontal dotted lines are supposed to point out

      Significance

      Study novelty: This work begins to shed light on the underlying molecular mechanisms that mediate differential responses to nutrient deprivation in male and female larvae. The knowledge gained from Drosophila studies will very likely have implications for human adipose physiology given the known sex differences in adipose associated physiology and pathophysiology in men and women.

      General assessment: This study makes excellent use of the Drosophila melanogaster genetic toolkit to better understand the involvement of the ISR in mediating sexually dimorphic responses to nutrient deprivation. In addition, carefully thought-out figure layouts make the data easy to visualize. Limitations of the study include lack of specificity of fat body-specific driver lines and thus a potentially overstated conclusion.

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      Referee #2

      Evidence, reproducibility and clarity

      It is now well-established that the Integrated Stress Response (ISR) mediated by ATF4 plays important roles in metabolism and proteostasis. This manuscript by Grmai and colleagues reports that the sex determination genes tra and dsx allow higher levels of ATF4 expression in Drosophila. They further show that female flies depend on ATF4 to survive under conditions of metabolic stress.

      The presented data are technically sound, and the manuscript is generally very well written. It is a concise study with four Figures. The authors could have chosen to expand the scope: For example, they have shown the requirement, but not the sufficiency, of ATF4 in the sexually dimorphic nature of vulnerability to nutrient deprivation. They also demonstrate that ATF4 affects male-specific survival upon metabolic stress, which could be improved with additional experiments. These and other technical points are outlined below:

      1. Figure 4 is an important part of this study, where the authors show a male-specific vulnerability to methioninase expression. They show that ATF4 RNAi confers vulnerability to methioninase expression even in females. An obvious question is whether ATF4 overexpression is sufficient to enhance resistance to methionine deprivation in males.
      2. Methioninase expression results (Figure 4) are interesting. Are the levels of methioninase expression similar between males and females?
      3. This manuscript focuses on ATF4, but there could be additional possible reasons for the sexually dimorphic ISR activity. For example, the degree of physiological stress that activates ISR could be different between males and females. I suggest comparing the levels of Phospho-eIF2alpha (or any other markers upstream of ATF4) in both sexes.
      4. In Figures 1 to 3, the authors examine the intensity of ATF4 signaling after perturbing the sexual determination pathway. The methioninase experiments in Figure 4 are interesting, but there is nothing in this Figure linking male-specific vulnerability to sex determination genes. Examining the vulnerability to methioninase expression after perturbing the sexual determination genes would make Figure 4 integrate better with the rest of the manuscript.
      5. The authors write that they generated 4EBP intron-GFP because the 4EBP intron-DsRed signal was frequently observed in the cytoplasm (line 122). They seem to suggest that the DsRed reporter is less reliable than the GFP reporter. However, they continue to mix results using 4EBP intron-GFP (Fig. 4A) and 4EBP intron-DsRed (Fig. 4F). The two figures examine slightly different conditions (Fig 4A shows tra1 KO females, while Fig. 4F shows traF males). If the DsRed reporter is less reliable due to the signal from the cytoplasm, the authors should show results with the GFP reporter in traF males.
      6. In Figure S1, the authors label 4EBP intron-GFP as Thor2p-GFP, which is confusing. There are other parts in the methods section referring to Thor2p. I suggest using consistent terminology throughout the manuscript.

      Significance

      Overall, the authors report a novel and interesting observation because the sex determination pathway was not previously associated with ISR signaling. As many metabolic diseases show sex-specific outcomes, the main findings of this study will draw broad interest.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study aims to explore sexual dimorphism in tolerance to nutrient deprivation using Drosophila larvae as a model. In particular the authors focus on sex differences in the larval fat body. They show that ATF4, an ISR transcription factor, has higher mRNA levels in female fat bodies. ATF4 transcriptional activity is also higher based on a reporter of ATF4 function, where this female bias in expression is influenced by sex determination factors. When the authors

      Overall, this study is interesting, as it identifies previously unrecognized sex-specific regulation of ATF4, an important transcription factor that mediates cellular stress responses. The study also shows that sex determination genes regulate ATF4. However, I have concerns about the paradigms of nutrient deprivation used in the study, and about data interpretation and statistical analysis that should be addressed prior to publication to support the authors' conclusions.

      Major concerns

      1. One major concern that I have about the sexual dimorphism in tolerance to nutrient deprivation is that the papers cited by the authors, and paradigms that are used broadly in the field, all use adult flies. The authors must show that in larvae, a completely different life stage from their citations, there is a sexual dimorphism in tolerance to nutrient deprivation.

      Interestingly, Diaz et al 2023 (Genetics) show that male larvae have greater fat stores than female larvae. Considering fat is the main determinant of tolerance to nutrient deprivation it's not clear that females will actually survive nutrient deprivation longer as larvae. This is an essential test of whether female larvae do have increased tolerance to nutrient deprivation, which is the basic foundation of the authors' model. 2. Another concern is the way that the authors "genetically induce nutrient deprivation using methioninase overexpression". As they acknowledge in the discussion (Line 381-390), methioninase overexpression will have many cellular effects. While there is no doubt that methionine levels would be lower in their model, it is less certain whether this is the main driver of the male-specific lethality.

      There are two potential solutions to this problem. First, the authors could change the text throughout the paper to more accurately describe their paradigm as "methioninase-induced lethality" rather than "nutrient deprivation". This would limit the scope of their scientific question and the conclusions they draw, but would eliminate the need for more experiments.

      The second solution would be to complete experiments to establish the following points: i) methioninase overexpression causes all the classical features of nutrient deprivation (e.g. changes to canonical signaling pathways such as TOR); ii) using other genetic means of nutrient deprivation such as slimfast-RNAi to see if those manipulations phenocopies the male-specific lethality they see with methioninase overexpression; iii) testing a role for ATF4 in mediating sex differences (if any) in other contexts such as slimfast-RNAi. This will take 2-3 months but is essential to draw any conclusions about whether their paradigm is truly a model for nutrient deprivation. 3. Another important point is that the authors state that sexually dimorphic ATF4 activity in the fat body is instructed by sexual identity in a cell-autonomous manner. Despite a clear decrease in ATF4 reporter levels in tra mutants, the fat body-specific tra-RNAi effect on the ATF4 reporter was less convincing. Together with the fact that changes to tra in the fat body affect insulin secretion from the insulin-producing cells, it is possible that the effect on ATF4 is not cell-autonomous. To conclusively test if sexual identity regulates ATF4 in a cell-autonomous manner the authors should use the flp-out system to make Tra-expressing or tra-RNAi-expressing clones in the fat body. This would take approximately 1.5 months to make the strain and test this. 4. As the authors show for the UAS-methioninase, other UAS lines used in the paper such as UAS-traF, UAS-tra-RNAi, UAS-dsx-RNAi may have leaky effects on gene/reporter expression. The authors must include a UAS only control to establish that the tra-RNAi, UAS-traF, UAS-dsx-RNAi do not affect gene/reporter expression. 5. I have concerns about the statistics used. In the methods and legends only t-tests are mentioned; however, when three groups are compared a one-way ANOVA with post-hoc tests must be used to correct for multiple comparisons. To compare differential responses to genetic/environmental manipulations between the sexes, a two-way ANOVA must be used. For example, to conclude that males and females have different responses in the two-way ANOVA, there must be a significant genotype:sex interaction. The p-values for comparisons between genotypes in either the one-way or two-way ANOVA must be derived from post-hoc tests within the ANOVA analysis.

      Minor points

      1. Please ensure to make the reader aware of which life stage was tested in the literature cited supporting sexually dimorphic tolerance to nutrient deprivation.
      2. Published data about sex-specific mechanisms of metabolic regulation mean that the introduction should be more fully cited than it is. Even in the introduction "the molecular basis of these differences and how they impact tolerance to nutrient deprivation is still under investigation" is inaccurate, as there are published studies identifying some mechanisms (work on gut hormones and sex-specific effects on starvation resistance and body fat, role of ecdysone on body fat and feeding, sex-specific roles for brummer and Akh in regulating body fat, intestinal transit and gut size and feeding). Please adjust the paper to acknowledge this growing body of knowledge.
      3. Please list the diet ingredients per L so that individuals can replicate the diet easily.
      4. Please cite grant numbers for all the community resources (e.g. Bloomington, DSHB), and please acknowledge FlyBase and its grants as well. For example, here are the instructions for citing BDSC https://bdsc.indiana.edu/about/acknowledge.html and similar instructions are available for the other resources.

      Significance

      This study identifies for the first time the sex-specific regulation of ATF4, and reveals the sex determination genes that mediate this effect. A strength of the study is the characterization of sex-specific ATF4 regulation. Limitations of the study include the paradigm for nutrient deprivation, need for additional controls, and statistical analysis. If the concerns above are addressed, this study will be of interest to researchers studying organismal and cellular stress responses, stress signaling, and builds upon a growing body of knowledge of sex differences in stress responses (e.g. autophagy, infection responses).

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      Reply to the reviewers

      Reviewer 1:

      Evidence, reproducibility and clarity

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

      Major Comments

      Comment____:

      The major issue is related to the overall model the authors seem to build based on their data - or at least the overall model the reader may get from the paper. This model suggests that the loss / decrease in WWOX levels in RGs leads to Myc overexpression, that in turn affects the cell cycle and prevents neuronal differentiation. This model is highly attractive, but is probably incomplete, in the sense that it does not fully recapitulate the complicated picture. Indeed, all three types of mutated WWOX COs (WWOX k/o, WOREE patient-derived organoids, and SCAR12 patient-derived organoids) demonstrate strong - but equal levels of Myc upregulation. Yet the under-differentiation in each of these three types is different, as described above, and the disease manifestations among WOREE vs. SCAR12 patients are also different. Thus, another player (in addition to Myc) must be at place, that is differentially affected by the partial null mutations in WOREE and missense mutations in SCAR12. This point - ideally to be addressed experimentally - should be at least faced directly by the authors in the Discussion. Perhaps they can already point to such additional players based on their transcriptomics analysis.

      Response____:

      We thank the reviewer for this important point. We agree with the reviewer that the model of WWOX loss / decrease levels in RGs leading to MYC overexpression is incomplete, and that it is a limitation of our model. It seems plausible that other players have a high impact on the genotype and are potentially differently affected, resulting in this complexed phenotype. Following the reviewer advice, we plan to address this in the discussion as a limitation of the model, and we will compare how the expression levels of MYC change based on the genotype in comparison to the WT, using the single cell RNA-sequencing data. We would also like to clarify that MYC upregulation we observed in the patient lines in SOX2+/MYC+ populations, does not quantify expression levels of MYC, but rather positive/negative nuclear staining, in contrast to the high-resolution of scRNA-seq data.

      Minor Comments

      Comment____: 1. It would be useful if a table (perhaps supplementary) describing the details of the WWOX__ mutations__ in all the COs models studied in this paper were presented.

      Response____:

      We thank the reviewer for this suggestion, and we plan to prepare a table summarizing all the mutations in the COs models presented in the paper.

      Comment____: 2. For the new WOREE individual with complex genetics in WWOX: it is not clear why any WWOX protein is still present in this patient in Fig. S1D (please give an explanation or speculation); it is not clear which tissue was used for the Western blot in Fig. S1D; the data in Fig. S1D need to be quantified.

      Response____:

      We thank the reviewer for their observation and would like to clarify that the ‘upper’ band seen in WWOX bands in a nonspecific one that appears in the parent lines and the mutant offspring. We will quantify the WB levels and clearly state that they are the IPSCs in the figure legend.


      Comment____: 3. Western blot, quantified, should be performed on all COs under study, to compare the WWOX expression levels. Please also change the immunofluorescence shown in Fig. 1B (e.g. show WWOX in a different color), as the figure provided shows WWOX poorly in wild-type CO, and it is not clear how much it is removed in the mutant organoids. Why should there be no signal in the SCAR12 COs?

      Response____:

      We thank the reviewer for their observation, we will provide protein levels of WWOX in patient and KO cerebral organoids which will better clarify the decreased WWOX levels, specifically in SCAR12 (see WB figure below). We will also perform any necessary changes to the figure to enhance visualization of WWOX.


      Significance

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation.

      Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

      Response____:

      We sincerely thank the reviewer for their thoughtful and constructive comments, which have greatly helped us improve the clarity and rigor of our manuscript. We appreciate the recognition of our work’s significance and the careful evaluation of both our major findings and methodological details. We have addressed all the raised points to the best of our ability and believe the manuscript will be substantially strengthened as a result. We are grateful for the reviewer’s time and valuable insights.

      Reviewer 2:

      Evidence, reproducibility and clarity

      Summary

      In this study, Steinberg et al aim to elucidate the role of WWOX in human neurogenesis and model WOREE and SCAR12 syndromes which are rare neurodevelopmental disorders. They chose to investigate its function in human brain organoids after generating WWOX KO and patient-derived iPSC lines. Their major finding is that radial glial cells, the main neural progenitor population during corticogenesis, are affected. Via single-cell-RNA-sequencing, they try to decipher the perturbed molecular mechanisms identifying MYC, a proto-oncogene, as a major player. At the end of their study, they proceed to gene therapy restoration and suggest that this could become a potential therapeutic intervention for WOREE and SCAR12 syndromes. The study aims to elucidate major cellular and molecular mechanisms that modulate neurodevelopment and neurodevelopmental disorders. Although sc-RNA-seq could potentially be of great interest and unravel major mechanisms, the authors do not follow this part, but only discuss potential future avenues. Here are some suggestions that could be useful to the authros.

      Major comments

      Comment____:

      A big part of the paper focuses on generating the iPSCs and characterizing the generated brain organoids and gene restoration of the phenotype via restoration of the WWOX gene expression (Fig.1, Fig.6, Fig.S1, Fig.S8, Fig.S10 and potentially Fig.S9 - this figure is not included) however, this has already been done by the same authors (first and last authors) in a previous publication. What are the differences in the line that have been generated in previous publication (Steinberg et al 2021, EMBO Mol. Med.)? If there are differences, the authors should make a thought comparison and explain why they generated different lines. If there is no difference, the authors should reduce to minimum this part and place it to supplementary.

      Response____:

      We thank the reviewer for pointing this out. We would like to clarify that some iPSC lines used in this publication were not introduced in the previous one (Steinberg et al 2021, EMBO Mol. Med.), including the wildtype JH-iPS11, and the new compound heterozygous WOREE line LM-iPS. In this paper, we aimed to widen our understanding of the effects of WWOX mutations through advanced techniques not applied before, and by adding these lines we were able to better generalize our findings as we did not depend on a single patient for both WOREE and SCAR12. Additionally, WWOX rescue in this current paper relies on AAV9-hSynI targeting that is more clinically relevant to gene therapy, as opposed to lenti-WWOX and AAVS1 WWOX in the previous publication. We will include the differences in a summary table.

      Comment____:

      • Fig.1E: in the pictures shown, the majority of the Satb2+ cells are colocalized with SOX2. Although a small portion of neurons have been shown from many studies that in brain organoids are co-localized to SOX2, in the pictures depicted this percentage is big. Also in ctrl condition the VZ-CP like areas are not easily recognized. The authors should check if this co-localization is a more general phenotype and if not choose more representative pictures.

      Response____:

      We thank the reviewer for their observation. We will check for the presence of a trend of colocalization between SATB2 and SOX2 and address the concern experimentally if needed. We will also choose pictures that better display VZ-CO areas in the control line.

      Comment____: Information about the number of organoids per batch used in each figure is not included. This needs to be added for each experiment. Data (at least the majority of them) should be collected from brain organoids from at least two batches.

      Response____:

      We thank the reviewer for their point, and we plan to better clarify the technical parts of the experiments, and if needed will include data from more batches.

      Comment____: The expression of WWOX in cortical development has been shown in the previous publication. Although sc-RNA data are validating the previous data and are adding more information, these data should be put as supplementary. Besides, in Fig.3G where authors aim to compare WWOX expression to MYC that fits nicely with their results depicting MYC as the most affected gene in KO and mutant line, when one looks at the WWOX expression only it seems that its expression is higher in CP that VZ. This is contrary to the conclusion that WWOX is mainly characterizes RGs. Why is that? Authors should at least discuss this.

      Response____: We agree with the reviewer that Fig.3G can be misleading, and we acknowledge that it can lead to the opposite conclusion of WWOX being mainly characterized in RGs. In Fig.3G the x axis displays positive values on the left, and negative scores on the right. Following the reviewer’s suggestion, we modified the graph to show positive values on the right, demonstrating how WWOX expression is higher in the VZ compared to the CP.

      Comment____: In this study, authors show that progenitors are reduced in WWOX-KO organoids, however in the previous publication SOX2 population is not majorly affected. Why are there such differences? Given that RGs are the main population affected as authors propose in this study, these differences must be at least discussed. Similar comments regarding neurons: in previous publication there is a minimal reduction of neurons in WWOX-KO brain organoids, while here authors describe major differences.

      Response____:

      We thank the reviewer for their remark and agree that it should be mentioned in our discussion. We believe that this is unfortunately due to the inherent issue of heterogeneity between organoids and could partly be attributed to the difference in the age of organoids at that timepoint (week 10 organoids in previous paper, week 7 organoids in this one), and the difference in control lines (WiBR3 hESCs vs JH-iPSCs). Additionally, while percentages of SOX2+ populations in WT organoids vary between the previous publication and this one, WWOX-KO organoids display similar levels of SOX2 upregulation in precious and current papers: 69% and 74%, respectively. We would also like to point out that calculations in scRNA-seq data convey the phenotype at a much higher resolution, as it identifies radial glia populations that are not necessarily SOX2+, further strengthening the validation of the SOX2+ RG quantifications that are present in this study.

      Comment____: Data from sc-RNA-seq analysis highlighting MYC as major differentially regulated gene are very interesting and seem to be key to the molecular pathway affected as authors suggest. Authors also validate this with immunostainings in brain orgnaoids. However, in Fig.3J MYC expression in ctrl is not depicted, even though in the respective graph it seems that 20% of SOX2+ cells co-express MYC. Please choose a more representative picture.

      Response____:

      We agree with the reviewer’s comments that the current image size and resolution limits the ability to appreciate the MYC staining in the control, and plan to use a more representative figure of the phenotype.

      Comment____: One of the main findings in this study is the cell cycle changes observed in WWOX-KO and mutant organoids. Given that the major novelty of the publication is the cellular and molecular mechanism implicated in WOREE and SCAR12 syndromes, authors should perform additional experiments towards this direction. One suggestion would be to perform stainings in brain organoids using markers of the different cell cycle phases (eg. KI67, cyclin a, BrdU/EdU, ph3). Also, treatment of organoids with different BrdU/EdU chase experiments would be important so as to measure exactly the length of each cell cycle phase.

      Response____:

      We appreciate the reviewer’s suggestions and plan to validate the findings through staining and quantifying percentages of proliferative RGs in WT vs mutant WWOX lines.

      Comment____: Regarding the molecular cascade, is WWOX directly affecting MYC of Wnt genes? Do they have information on upstream and downstream factors in the affected molecular pathway?

      Response____:

      We thank the reviewer for highlighting this important point. To address this question, encouraged by our results, we will compile genes belonging to the regulon of MYC and study the upstream and downstream factors in our transcriptional data. Additionally, we will look at protein expression levels of WNT genes in our organoid samples.

      Comment____: Restoration of phenotype via reinsertion of WWOX gene has already been done in the previous publications by the same authors. But what about MYC? Is MYC manipulation able to rescue the phenotype?

      Response____:

      We thank the reviewer for this insightful suggestion. We fully agree that understanding the role of MYC in the observed phenotype is of great interest. However, due to the essential and widespread role of MYC in both radial glia and neurons, we refrained from direct perturbation of MYC levels—either through knockdown or overexpression—as such manipulations may have broad, uncontrolled effects that could confound the interpretation of our findings. The potential deleterious consequences of MYC modulation in radial glia have been originally discussed in the Discussion section of the manuscript. In our revisions, we will further explore the role of MYC regulons in our scRNA-seq dataset to better understand their contribution to the WWOX-related phenotype.


      Comment____: Finally, MYC association to ribosome biogenesis as mentioned by the authors in discussion is very interesting. The authors should consider investigating this direction, as it will be a great addition to the mechanisms that regulate WOREE and SCAR12 syndromes which is the main focus of this study.

      Response____:

      We thank the reviewer for highlighting this point, and we agree that MYC's association with ribosome biogenesis is a fascinating topic to discuss. This could be connected to the alterations of the proliferative potential and to the anabolic state of the cell, and we plan to expand the discussion of this observation and its implication in the context of RGs and neurons*. *


      Minor comments

      Comment: - Line 115: authors say that the data they discuss are found in Fig.S2A, maybe they mean Fig.S1A?

      Response:

      We thank the reviewer for their observation, we will correct Fig.S2A to Fig.S1A and B.

      Comment: - Fig.S9 is missing, in the current version this Fig is the same with Fig.S10. Please change it.

      Response____:

      We thank the reviewer for pointing this out and apologize for this oversight. We acknowledge the error and will correct the duplication by replacing Fig. S9 with the intended figure in the revised version of the manuscript.


      Significance

      This study is the continuation of a previous publication the authors have published. The topic is very interesting and novel especially in modelling neurodevelopmental disorders in a human context, however, given that the main phenotype has already been published, the authors should include more effort in the molecular cascade. Clinical interventions if the molecular cascade is described would be of great importance to the field.

      Response____:

      We sincerely thank the reviewer for their thoughtful, constructive, and detailed review. We appreciate the time and effort taken to carefully read our manuscript and provide insightful suggestions, taking into consideration also our previous published work. The suggestion raised, especially regarding MYC-WNT axis and its potential link to ribosome biogenesis, will help us clarify, strengthen, and expand the scope of our study. We have carefully addressed each of the points raised and have incorporated the necessary experimental validations, clarifications, and revisions accordingly. We believe these changes have substantially improved the manuscript.

      Reviewer 3:

      Summary:

      The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

      Major ____C____omments

      The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below.

      Comment____:

      A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary.

      Response____:

      We thank the reviewer for pointing this out, and we acknowledge that the clarity of our description of the batch used in each experiment can be improved. Therefore, we will provide all these details, adding information on additional batches adopted for the different validations that were not included in the manuscript.

      Comment____:

      Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches.

      Response:

      We thank the reviewer for this important point. We would like to clarify that in Figure 1B, TUBB3 staining is primarily used for visualization purposes to provide anatomical context and delineate the overall architecture of the organoids, rather than for quantitative assessment of neuronal output. As such, the focus of our quantification in Figure 1F was on SOX2+ radial glial cells. That said, we agree that clearly stating the number of organoids and batches used in the quantification is important, and we will include this information in the figure legend for clarity.

      Comment____:

      Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible.

      Response____:

      We appreciate the reviewer’s thoughtful feedback and for drawing our attention to the review by Sandoval, Soraya O., Anderson, Stewart, et al. We also thank the reviewer for their suggestions and intend to explore the proposed modifications through immunostaining, particularly to address questions related to cell cycle changes, and Wnt pathway. However, regarding DNA damage, senescence, and cell cycle arrest, we do not believe additional validation is necessary, as our current manuscript does not present findings related to these aspects.


      Comment____:

      Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months).

      Response____:

      We thank the reviewer for this insightful comment and fully agree that elucidating the functional contribution of MYC alterations in the context of WWOX deficiency would represent a major conceptual advance. We acknowledge that our current findings are correlative, based on scRNA-seq and immunostaining, and that direct manipulation of MYC could help establish causality.

      However, due to MYC’s essential and pleiotropic role in both progenitor and neuronal populations—including its regulation of cell cycle, metabolism, and apoptosis—we refrained from genetic overexpression or silencing approaches in this study. Such perturbations often lead to widespread, non-specific effects that can obscure the interpretation of lineage-specific phenotypes, particularly in a complex model like brain organoids.

      That said, we agree that further insight into the functional role of MYC is crucial. To this end, we plan to leverage our scRNA-seq dataset to analyze the activation state of MYC regulons across genotypes and cell types, and to assess how these regulons intersect with cell cycle dysregulation observed in WWOX-deficient radial glia. We also aim to integrate available transcriptomic data from primary cortical tissue to support the relevance of MYC pathway alterations in human development. While these analyses cannot replace experimental perturbation, we believe they can provide strong, hypothesis-generating evidence for MYC’s mechanistic involvement and help prioritize targeted experiments in future studies.

      Comment____:

      Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions.

      Response____:

      We thank the reviewer for this thoughtful and constructive comment. We fully agree that regional identity variability in unguided cerebral organoids is a well-recognized challenge, and that systematic assessment of dorso-ventral patterning is important to confidently interpret genotype-driven phenotypes.

      We would like to clarify that the cerebral organoid protocol used here has consistently been shown to favor a dorsal forebrain identity (PMID: 23995685, 28562594, 32483384, 33328611), and in our previous work (PMID: 34268881), we demonstrated that WWOX mutations did not substantially alter dorsal identity in this model. Nevertheless, to directly address the reviewer’s concern, we plan to perform additional immunostaining for regional patterning markers on our existing week 7 organoid tissues and explore our scRNA-seq data to evaluate potential shifts in regional identity and rule out patterning-related confounders.

      Regarding the 2D NSC cultures, while the differentiation protocol included the ventralizing factor SAG, it did not include IWP2. We acknowledge the importance of validating patterning outcomes in this model as well and will do so using immunostaining.

      Comment____:

      There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity.


      Response____:

      We thank the reviewer for this suggestion, and we agree that including a schematic summarizing the genomic alterations in all patient-derived lines and their severity will improve the genotype-phenotype comparison. We will include this clarification and provide additional information on how the mutations affect the protein level, and the genotype-phenotype correlations in WWOX mutants based on clinical and genetic evidence.


      Comment____:

      Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult.


      Response____:

      We thank the reviewer for highlighting this and pointing us to the work of Brunner, Lammertse, van Berkel et al. While we agree that isogenic controls for each mutant line would be the ideal wild-type reference, generating these through genomic editing is particularly challenging, specifically for the compound heterozygous mutants. Instead and as suggested, we plan to include additional wild-type lines derived from healthy individuals, collected from different batches. We will use these to validate our key findings, including analyses of RG and SATB2+ cell populations, as well as MYC expression through immunofluorescence.


      Comment____:

      Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes.

      Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed.

      To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies.


      Response____:

      We thank the reviewer and agree that further investigation into the proposed link between WWOX deficiency and MYC-related alterations in radial glia would provide deeper insight into the downstream effects on neuronal populations. To this end, we will first illustrate how our model of radial glia alterations accounts for changes in neuronal production without affecting overall RG abundance. Second, we will expand our comparisons of RGs and MYC expression using patient-derived and control single-cell RNA-sequencing datasets. Third, we will address the discrepancies between neuronal hyperexcitability and SATB2/CTIP2 counts more comprehensively. Notably, while SATB2 is an early marker for several cortical neuron subtypes, it is not expressed in all neurons. In contrast, SOX2 is considered a pan-radial glia marker, which may help explain the differing expression trends observed.

      Comment:

      Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above).

      First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim.


      Response____:

      We thank the reviewer for the suggestion, and based on this, we will quantify the WWOX+ NeuN+ / WWOX+ total cells, incorporating data from additional batches to strengthen the analysis.


      Comment____:

      Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript.

      Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text.


      Response____:

      *We thank the reviewer for highlighting this point. We will discuss and re-phrase the rationale for restoring WWOX in the neurons and the WWOX deficiency-associated delayed maturation. *

      Comment____:

      Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified.


      Response____:

      We thank the reviewer for mentioning this problem. We will include the details needed for reproducibility, including the number of batches and organoids.

      Comment____:

      As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.


      Response____:

      *We thank the reviewer for highlighting this point. We acknowledge that our clarity in our methods could be improved, therefore, we will expand the bioinformatics pipeline description, the reagents used, and the details for the Western Blot. *

      Minor comments:

      Comment:

      • One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations. Response____:

      We thank the reviewer for suggesting the work of Iacomino et al. which we are very aware of and shall cite appropriately in our revised version.

      Comment____:

      • For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing. Response____:

      We thank the reviewer for bringing up these important points. We will provide the exact mutations identified in exon 1 of WKO-1C and WKO-2C as determined by Sanger sequencing and include this information in the revised manuscript. Additionally, we will present data additional data regarding WWOX protein loss in all the cell lines used in the study.

      Comment:

      • Line 116 and 394, reference Steinberg et al is not formatted. Response:

      We apologize for this oversight and the formatting will be fixed.

      Comment:

      • Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization. Response____:

      We thank the reviewer for their comment. WWOX localization was previously confirmed using anti-WWOX HPA050992 (Sigma), as reported in our prior publication (PMID: 34268881). While the images were not included due to a lack of novelty, we acknowledge the importance of confirming the observed patterns. The difference in localization between organoids (cytoplasmic/membrane-bound) and iPSCs (cytoplasmic/nuclear) may be attributed to differences in cell morphology, with RGs in 3D organoid sections exhibiting distinct characteristics compared to iPSCs cultured in 2D (Supplementary Figure S1A). In fact, in 2D cultures of NSCs (Supplementary Figure S4A), WWOX also shows a nuclear localization, similar to iPSCs. We will clarify this point in the manuscript.


      Comment____:

      In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.

      Response____:

      We thank the reviewer highlighting this. We will better define the rationale for the usage of SATB2 as a marker expressed in many types of young neurons (not specifically preplate neurons), and add DCX as a marker for neurogenesis.

      Comment____:

      Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.

      Response____:

      We thank the reviewer for the keen eyes and apologize for this mistake. We will correct the legend and specify that n is indeed referring to organoids and not single neurons.


      Comment____:

      Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.

      Response____:

      • *We thank the reviewer for their comment. Upon repeated staining, we consistently observe this MYC localization across organoids and more. Similarly, the differences in Sox2 and MYC staining in WOREE organoids are reproducible. While these results may seem divergent, they accurately represent the findings. We will, however, review the staining protocols and ensure that representative images are carefully selected to best reflect the data.

      Comment____:

      Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.

      Response____:

      *We thank the reviewer for pointing this out. We will add in the legend that “w” refers to post-conceptional weeks. *

      Comment____:

      Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.

      Response____:

      We thank the reviewer for addressing this. The blurred parts of the UMAP are intentional, we will add a description of why and what it represents.

      Comment____:

      Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.

      Response____:

      *We thank the reviewer for pointing this out, we will better clarify that figures S8 and S9 are an expansion of the panels shown in Figure 6, showing ROIs per cell line and rather than per genotype. *

      Comment____:

      Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?


      Response____:____

      • *We thank the reviewer for raising this point. We will quantify the bands in the WOREE patient samples and compare them to wild-type controls. We would like to clarify that the "upper" band is a nonspecific band, and its overrepresentation in KO2 samples is not indicative of WWOX expression. Additionally, we will address the question of WWOX haploinsufficiency in healthy parents and provide a comparison of WWOX levels to unrelated wild-type controls.

      Comment____:

      Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?

      Response____:

      We would like to thank the reviewer, and we will add a clarification for the differences between top and bottom UMAPs and the rationale behind NP labeling.

      Comment____:

      Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.

      Response____:

      We thank the reviewer and will alter the figure and its legend to clarify this.

      Comment:

      Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.

      Response____:

      We thank the reviewer and will examine the pictures and if need be will replace them with more representative pictures.

      Comment____:

      Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.

      Response____:

      We thank the reviewer for pointing this out. We will add the quantifications of NEUN+/WWOX+.

      Comment____:

      Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).

      Response____:

      We thank the reviewer; we will fix the figures and corresponding titles and legends.

      Comment:

      Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.

      Response____:

      We thank and agree with the observation; we will correct it.

      Comment____:

      Line 353: reference needs to be added for "our earlier findings”.

      Response____:

      We thank the reviewer, and we will re-phrase to clarify.

      Comment____:

      Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.

      Response____:

      We thank the reviewer and agree with their observation and would like to clarify that as we are not aware of any relevant literature examining the relationship between WWOX and MYC in non-tumor settings, we refrained from drawing any conclusions in any one direction without further experimental exploration. Nonetheless, we will re-phrase the sentences to draw clearer conclusions.

      Comment____:

      Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.

      Response____:

      • *We thank the reviewer for their comment. We agree that further clarification is needed regarding the functional involvement of Wnt signaling. While we have previously shown that Wnt is inappropriately activated in WWOX-KO, WOREE, and SCAR12 organoids (PMID: 34268881), and demonstrated Wnt activation in RGs via our scRNA-seq data (Figure 4I), we recognize that direct perturbation experiments would strengthen this aspect. In light of this, we will examine the levels of Wnt target genes in our transcriptomic data to provide more direct evidence of Wnt signaling involvement and its functional relevance in the context of WWOX deficiency.

      Comment:

      Line 473: "at X concentration" needs to be correct to specify the concentration used.

      Response:

      We thank the reviewer for noticing this missing information and we will add the final puromycin concentration (1 mg/ml) .

      Comment____:

      Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?


      Response____:

      We thank the reviewer for raising this concern. To clarify, approval was obtained prior to the commencement of experimentation. The sentence should read: "Skin biopsies and blood samples were obtained with informed consent, under the approval of the Kaplan Medical Center Helsinki Committee," indicating that the study was conducted in full compliance with ethical requirements, with prior approval from the committee.

      Comment____:

      Line 525: which orbital shaker and which speed was used?

      Response____:

      We thank the reviewer and will add orbital shaker details and speed.

      Comment____:

      Line 537: what is GC in GC/ul?

      Response____:

      We thank the reviewer and clarify that this is the accepted units for viral load. GC is Genome Copies, and this is often used in qPCR assays to estimate the amount of viral genetic material in a sample. It is often used interchangeably with vg/µL (Viral Genome per microliter).

      Comment____:

      Line 629: samples were centrifgues at which speed and for how long?

      Response____:

      We thank the reviewer and will fix to include details about centrifugation.

      Comment____:

      Line 639: "All primer sequence" should be plural.

      Response____:

      We thank the reviewer and will correct the typing mistake.

      Referees cross-commenting

      All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

      General assessment:

      The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

      • Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
      • Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
      • Absence of functional validation for MYC's contribution, making its proposed role unclear. Advance:

      This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance.

      Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations.

      Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

      Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

      Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

      Response:

      We thank the reviewer for their valuable feedback and for acknowledging the strengths of our study. We agree with the reviewer that additional validation and replication are needed to strengthen our conclusions. We acknowledge the limitations in experimental design, and we are committed to enhancing the reproducibility of our findings. We also appreciate the reviewer's comments on the study's conceptual advancements, which we believe offer new insights into WWOX's role in neurodevelopmental disorders.

      We are confident that with the additional experiments outlined, our study will provide valuable contributions to understanding WWOX-related syndromes. Thank you again for your thoughtful suggestions.

      __ __


      Reviewer 4:

      Summary

      The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression.

      My concerns are:

      Comment____:

      1.Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.

      Response____:

      We thank the reviewer for their thoughtful comment. We would like to clarify that we do not suggest that only radial glia cells are affected. As mentioned in both the current manuscript and our previous work (Steinberg et al., EMBO Mol. Med. 2021, and Repudi et al., Brain, 2021), other cell populations, including neurons and oligodendrocytes, are also impacted by WWOX deficiency. WWOX is widely expressed in the mature brain, and we are actively investigating whether these effects are cell-autonomous. In this study, we focus on WWOX in RGs due its high expression and possible importance in maintaining RG homeostasis. We will further clarify this point in the revised manuscript.

      Comment____:

      Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.

      Response____:

      We thank the reviewer for their suggestion and agree that performing proteomic analysis on RG populations will strengthen our understanding of the underlying mechanism, however, the experiment itself was attempted and proved to be technically challenging at this size, and for now is beyond the scope of this paper.

      Comment____:

      WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

      Response____:

      We thank the reviewer for their insightful suggestion regarding the validation of neuronal heterotopia in WWOX-deficient cells through real-time migration experiments. While we recognize the potential value of this approach for investigating the behavior of WWOX-deficient radial glia cells, we believe that such experiments would extend beyond the scope of the current study. However, we are considering them as part of our future research to further explore the impact of WWOX deficiency on cell migration and neuronal positioning. Thank you again for your valuable input.

      Significance

      The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

      Response____:

      We thank the reviewer for their thoughtful and constructive feedback. We especially appreciate the suggestions regarding the broader involvement of additional cell types and the importance of exploring radial glia function through real-time migration assays. These insights will help us refine the focus and interpretation of our findings, and we will address the relevant clarifications and improvements in the revised manuscript.

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

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      Referee #4

      Evidence, reproducibility and clarity

      The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression. My concerns are:

      1. Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.
      2. Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.
      3. WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

      Significance

      The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

      Major comments:

      The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below. A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary. Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches. Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible. Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months). Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions. There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity. Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult. Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes. Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed. To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies. Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above). First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim. Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript. Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text. Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified. As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.

      Minor comments:

      • One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations.
      • For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing.
      • Line 116 and 394, reference Steinberg et al is not formatted.
      • Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization.
      • In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.
      • Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.
      • Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.
      • Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.
      • Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.
      • Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.
      • Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?
      • Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?
      • Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.
      • Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.
      • Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.
      • Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).
      • Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.
      • Line 353: reference needs to be added for "our earlier findings"
      • Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.
      • Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.
      • Line 473: "at X concentration" needs to be correct to specify the concentration used.
      • Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?
      • Line 525: which orbital shaker and which speed was used?
      • Line 537: what is GC in GC/ul?
      • Line 629: samples were centrifgues at which speed and for how long?
      • Line 639: "All primer sequence" should be plural

      Referees cross-commenting

      All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

      Significance

      General assessment:

      The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

      • Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
      • Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
      • Absence of functional validation for MYC's contribution, making its proposed role unclear.

      Advance:

      This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance. Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations. Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

      Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

      Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, Steinberg et al aim to elucidate the role of WWOX in human neurogenesis and model WOREE and SCAR12 syndromes which are rare neurodevelopmental disorders. They chose to investigate its function in human brain organoids after generating WWOX KO and patient-derived iPSC lines. Their major finding is that radial glial cells, the main neural progenitor population during corticogenesis, are affected. Via single-cell-RNA-sequencing, they try to decipher the perturbed molecular mechanisms identifying MYC, a proto-oncogene, as a major player. At the end of their study, they proceed to gene therapy restoration and suggest that this could become a potential therapeutic intervention for WOREE and SCAR12 syndromes. The study aims to elucidate major cellular and molecular mechanisms that modulate neurodevelopment and neurodevelopmental disorders. Although sc-RNA-seq could potentially be of great interest and unravel major mechanisms, the authors do not follow this part, but only discuss potential future avenues. Here are some suggestions that could be useful to the authros.

      Major comments:

      • A big part of the paper focuses on generating the iPSCs and characterizing the generated brain organoids and gene restoration of the phenotype via restoration of the WWOX gene expression (Fig.1, Fig.6, Fig.S1, Fig.S8, Fig.S10 and potentially Fig.S9 - this figure is not included) however, this has already been done by the same authors (first and last authors) in a previous publication. What are the differences in the line that have been generated in previous publication (Steinberg et al 2021, EMBO Mol. Med.)? If there are differences, the authors should make a thought comparison and explain why they generated different lines. If there is no difference, the authors should reduce to minimum this part and place it to supplementary.
      • Fig.1E: in the pictures shown, the majority of the Satb2+ cells are colocalized with SOX2. Although a small portion of neurons have been shown from many studies that in brain organoids are co-localized to SOX2, in the pictures depicted this percentage is big. Also in ctrl condition the VZ-CP like areas are not easily recognized. The authors should check if this co-localization is a more general phenotype and if not choose more representative pictures.
      • Information about the number of organoids per batch used in each figure is not included. This needs to be added for each experiment. Data (at least the majority of them) should be collected from brain organoids from at least two batches.
      • The expression of WWOX in cortical development has been shown in the previous publication. Although sc-RNA data are validating the previous data and are adding more information, these data should be put as supplementary. Besides, in Fig.3G where authors aim to compare WWOX expression to MYC that fits nicely with their results depicting MYC as the most affected gene in KO and mutant line, when one looks at the WWOX expression only it seems that its expression is higher in CP that VZ. This is contrary to the conclusion that WWOX is mainly characterizes RGs. Why is that? Authors should at least discuss this.
      • In this study, authors show that progenitors are reduced in WWOX-KO organoids, however in the previous publication SOX2 population is not majorly affected. Why are there such differences? Given that RGs are the main population affected as authors propose in this study, these differences must be at least discussed. Similar comments regarding neurons: in previous publication there is a minimal reduction of neurons in WWOX-KO brain organoids, while here authors describe major differences.
      • Data from sc-RNA-seq analysis highlighting MYC as major differentially regulated gene are very interesting and seem to be key to the molecular pathway affected as authors suggest. Authors also validate this with immunostainings in brain orgnaoids. However, in Fig.3J MYC expression in ctrl is not depicted, even though in the respective graph it seems that 20% of SOX2+ cells co-express MYC. Please choose a more representative picture.
      • One of the main findings in this study is the cell cycle changes observed in WWOX-KO and mutant organoids. Given that the major novelty of the publication is the cellular and molecular mechanism implicated in WOREE and SCAR12 syndromes, authors should perform additional experiments towards this direction. One suggestion would be to perform stainings in brain organoids using markers of the different cell cycle phases (eg. KI67, cyclin a, BrdU/EdU, ph3). Also treatment of organoids with different BrdU/EdU chase experiments would be important so as to measure exactly the length of each cell cycle phase.
      • Regarding the molecular cascade, is WWOX directly affecting MYC of Wnt genes? Do they have information on upstream and downstream factors in the affected molecular pathway?
      • Restoration of phenotype via reinsertion of WWOX gene has already been done in the previous publications by the same authors. But what about MYC? Is MYC manipulation able to rescue the phenotype?
      • Finally, MYC association to ribosome biogenesis as mentioned by the authors in discussion is very interesting. The authors should consider investigating this direction, as it will be a great addition to the mechanisms that regulate WOREE and SCAR12 syndromes which is the main focus of this study.

      Minor comments:

      • Line 115: authors say that the data they discuss are found in Fig.S2A, maybe they mean Fig.S1A?
      • Fig.S9 is missing, in the current version this Fig is the same with Fig.S10. Please change it.

      Significance

      This study is the continuation of a previous publication the authors have published. The topic is very interesting and novel especially in modelling neurodevelopmental disorders in a human context, however, given that the main phenotype has already been published, the authors should include more effort in the molecular cascade. Clinical interventions if the molecular cascade is described would be of great importance to the field

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones. The major issue is related to the overall model the authors seem to build based on their data - or at least the overall model the reader may get from the paper. This model suggests that the loss / decrease in WWOX levels in RGs leads to Myc overexpression, that in turn affects the cell cycle and prevents neuronal differentiation. This model is highly attractive, but is probably incomplete, in the sense that it does not fully recapitulate the complicated picture. Indeed, all three types of mutated WWOX COs (WWOX k/o, WOREE patient-derived organoids, and SCAR12 patient-derived organoids) demonstrate strong - but equal levels of Myc upregulation. Yet the under-differentiation in each of these three types is different, as described above, and the disease manifestations among WOREE vs. SCAR12 patients are also different. Thus, another player (in addition to Myc) must be at place, that is differentially affected by the partial null mutations in WOREE and missense mutations in SCAR12. This point - ideally to be addressed experimentally - should be at least faced directly by the authors in the Discussion. Perhaps they can already point to such additional players based on their transcriptomics analysis.

      The minor issues are as follows.

      1. It would be useful if a table (perhaps supplementary) describing the details of the WWOX mutations in all the COs models studied in this paper were presented.
      2. For the new WOREE individual with complex genetics in WWOX: it is not clear why any WWOX protein is still present in this patient in Fig. S1D (please give an explanation or speculation); it is not clear which tissue was used for the Western blot in Fig. S1D; the data in Fig. S1D need to be quantified.
      3. Western blot, quantified, should be performed on all COs under study, to compare the WWOX expression levels. Please also change the immunofluorescence shown in Fig. 1B (e.g. show WWOX in a different color), as the figure provided shows WWOX poorly in wild-type CO, and it is not clear how much it is removed in the mutant organoids. Why should there be no signal in the SCAR12 COs?

      Significance

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

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      Reply to the reviewers

      Reviewer #1

      __Evidence, reproducibility and clarity __

      The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well-designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

      __Reply:__We thank the reviewer for this general comment which gives us the opportunity to better explain the scope of our work. The purpose and focus of this work are not to test the pathological relevance of skin fibroblasts to GSD1a pathology. We do not claim that skin fibroblasts are involved in GSD1a pathogenesis. It is also not a developmental work claiming to uncover GSD1a pathogenic axis throughout embryonic development. As a matter of fact, since skin fibroblasts originate from the mesoderm embryonic germ layer and hepatocytes develop from the endoderm embryonic germ layer, it would even be unlikely that the pathological phenotype found in skin fibroblasts directly contributes to GSD1a pathology in model mice or in patients. Indeed, we are not aware of any dermatological contribution to GSD1a pathology in patients. However, our results suggest that in addition to the established and mutated organ (liver in the liver-specific G6pc knockout mouse model), other, relatively less studied, patho-mechanisms in distal tissues may also contribute to GSD1a pathology. Notably, this work is also not testing a therapeutic modality for GSD1a. Our work uses GSD1a disease models as a tool for demonstrating, or reviving, the concept of epigenomic landscape (Waddington, 1957): Different cell phenotypes, such as healthy and diseased, are established by innate metabolic differences between their respective cell environments, which impose epigenetic changes generating these different phenotypes. In this respect, our manuscript has a similar message to the one in the recently published paper Korenfeld et al (2024) Nucleic Acids Res 53:gkae1161. doi: 10.1093/nar/gkae1161: The Kornfeld et al paper shows that intermittent fasting generates an epigenetic footprint in PPARα-binding enhancers that is "remembered" by hepatocytes leading to stronger transcriptional response to imposed fasting by up-regulation of ketogenic pathways. In the same way, the diseased GSD1a status imposes metabolic changes, as detailed here, leading to permanent epigenetic changes, also described here, which are "remembered" by GSD1a fibroblasts and play a major role in the transcription of pathogenic genes in these patient's cells. This in turn is how the diseased state is preserved, even in cells not expressing the G6Pase mutant, which is the direct cause of the disease. We added this perspective to the Discussion to better highlight the key takeaway from our manuscript.Naturally, research such as ours with a claim on biological memory would involve ex vivo experiments where tissues are isolated from their in-situ environments and tested for preservation of the original in situ phenotype. The few in vivo experiments we performed (Fig. 5) are mainly aimed at demonstrating that not only the phenotype, but also therapy response is "remembered" ex vivo: In the same way that the G6PC-loss-of-function liver responded positively to GHF201 therapy in situ, ex vivo cells not expressing G6PC also responded positively to the same therapy. This observation only demonstrates further support for "memorization" of the disease phenotype by cell types not expressing the mutant: Both the diseased phenotype and response to therapy were preserved ex vivo.Lastly, while interesting, validation of our findings in vivo (as suggested by the reviewer) is not related to the scope of this manuscript. Such experiments, using the liver-targeted G6pc knockout mouse model, are the follow-up story, which is related to the origin of inductive signals that cause the curious and novel phenotype mechanism in GSD1a fibroblasts described in this manuscript. The scope and volume of such research constitute a novel manuscript.

      Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.

      __Reply:__We thank the reviewer for this important comment, we agree that it is important to note the dietary regimen assigned to the cohort of patients described in this study. We added an explanation to the manuscript on patient's diets as shown below.Briefly, all patients besides patient 6894 were treated with the recommended dietary regimen for GSD1a as explained in Genereviews (Bali et al (2021)). This dietary treatment (now added to the Methods section in the manuscript) allows to maintain normal blood glucose levels, prevent secondary metabolic derangements, and prevent long-term complications. Specifically, this dietary treatment includes- nocturnal nasogastric infusion of a high glucose formula in addition to usual frequent meals during. By constantly maintaining a nearly normal level of blood glucose, this treatment causes a remarkable decrease, although not normalization, of blood lactate, urate and triglyceride levels, as well as bleeding time values. A second layer in the treatment includes the use of uncooked starch in the dietary regimen to allow maintenance of a normal blood glucose levels for long periods of time. Patient 6894 did not tolerate well the uncooked cornstarch and therefore was treated with a tailored dietary treatment planned by metabolic disease specialists and dedicated certified dieticians highly experienced with the management of pediatric and adult patients with GSDs and other inborn errors of metabolism. The biopsies of patients were taken in the range of 3 month to several years from receiving the aforementioned dietary regimen.Importantly, the strict metabolic diet imposed on GSD1a patients might influence the observed phenotype described throughout the manuscript. This concept aligns with our claim that the GSD1a skin cells are affected by the dysregulated metabolism in patients in comparison to healthy individuals. Interestingly, while patient 0762 harbors a mutation in the SI gene in addition to the G6PC mutation and patient 6894 did not receive the same dietary regimen as other patients (as explained above), all patients do show similar disease related phenotypes, perhaps highlighting the role of an early programing process that affected these cells due to the severe metabolic aberrations presented in this disease from birth.One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

      One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

      __Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A and accordingly also edited the relevant text in the Results section.

      Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.

      __Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A as mentioned above.

      Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.

      __Reply:__We thank the reviewer for this comment. To clarify, the results shown in Figure S2A left are based on PAS assay, directly quantifying glycogen in cells with and without GHF201 treatment. We have now added HC glycogen levels as requested above. Regarding N, this is explained in Methods: In imaging experiments N was determined based on wells from the experiments done in three independent plates following the rationale that each well is independent from the others and reflects a population of hundreds of cells as previously described in (Lazic SE, Clarke-Williams CJ, Munafò MR (2018) What exactly is 'N' in cell culture and animal experiments?. PLOS Biology 16(4):e2005282. https://doi.org/10.1371/journal.pbio.2005282, Gharaba S, Sprecher U, Baransi A, Muchtar N, Weil M. Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status. Neurobiol Dis. 2024 Oct 15;201:106667. doi: 10.1016/j.nbd.2024.106667. Epub 2024 Sep 14. PMID: 39284371.). Figure S2A right shows the glucose quantification experiment that we think the reviewer is referring to. Glucose increase is normally concomitant with glycogen reduction and we therefore show these results in support of the glycogen reduction results. These glucose results are part of our metabolomics results done on the same cells (Figure 6), where glucose is one of the metabolites analyzed. This metabolomics analysis was repeated three times; therefore, N is 3. In summary, these results show that GHF201 directly contributes to glycogen reduction in GSD1a fibroblasts and concomitantly increases glucose levels.

      Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiment to confirm this.

      __Reply:__Assuming the reviewer's comment is related to Figure S2A right, glucose levels are only shown to validate the glycogen reduction results (also see point 4): When glycogen levels are reduced, especially by inhibition of glycogen synthesis, glucose levels are supposed to concomitantly rise, being spared as an indirect substrate of glycogen synthesis. There is no proof, and as a matter of fact we also do not assume, that the GHF201-mediated reduction in glycogen levels is a result of increased glycophagy: Glycophagy has been described in cell types with high glycogen turnover, e.g., muscle and liver cells, not fibroblasts. Additionally, glycophagy is a glycogen-selective process implicating STBD1 whose expression in skin fibroblasts is negligible (https://www.proteinatlas.org/ENSG00000118804-STBD1/tissue).On the other hand, glycogen in GSD1a does not accumulate in lysosomes. It is built up in the cytoplasm (Hicks et al (2011) Ultrastr Pathol 35: 183-196; Hannah et al (2023) Nat Rev Dis Primers DOI: 10.1038/s41572-023-00456-z). Therefore, we do not believe that GHF201 reduced glycogen by enhancing glycophagy. As we show, GHF201 activated several key catabolic pathways. It is more likely that activation of one of these pathways, the AMPK pathway, inhibited glycogen synthesis via phosphorylation and ensuing inhibition of glycogen synthase. Alternatively, excessive cytoplasmic glycogen might enter lysosomes by bulk autophagy, or microautophagy (not by glycophagy) and GHF201 might induce lysosomal glycogenolysis by alpha glucosidase as an established lysosomal activator (Kakhlon et al (2021)). However, since, as explained, the mechanism of action of GHF201 is not the topic of this manuscript and therefore we did not dwell more into that.

      Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?

      __Reply:__We thank the reviewer for highlighting this important topic. OCR, measured in Fig. 2B, is an indirect measure of ATP production. Therefore, changes in OCR only measure the capacity of the mitochondria to produce ATP, and not the direct quantity of ATP. Other factors might influence ATP production, e.g., substrate availability and the activity of other metabolic pathways. On the other hand, the ATP Rate Assay (Figure 2h), provides a real-time direct measurement of ATP levels incorporating coupling efficiency and P/O ratio assumptions. Therefore, these two measurements do not necessarily match. We will add this information to the relevant segment in the text to clarify why OCR is reduced and mitochondrial ATP production increased in GSD1a cells.

      Why do GSD1a fibroblasts show reduced glycolytic ATP (Figure 2h) despite increased glycolysis and glycolytic capacity (Fig 2J-K)?

      __Reply:__We thank the reviewer for highlighting this important topic. ECAR measures medium acidification and thus reflects the production of lactic acid, which is a byproduct of glycolysis. However, medium acidification is also influenced by other factors that can acidify the extracellular environment, especially CO2 production which can originate from the intramitochondrial Krebs cycle which produces reductive substrates for mitochondrial respiration, or OCR. Moreover, the buffering capacity of the Seahorse mito stress assay medium might mask changes in lactic acid production, leading to an underestimation of glycolytic activity. On the other hand, glycolytic ATP production measured by the ATP rate assay directly quantifies the rate of ATP production from glycolysis. Notably, there is a major difference between ECAR and the ATP rate assay: The ATP rate assay is less sensitive to variations in buffering capacity than ECAR measurements. This is because the ATP rate assay relies on inhibitor-driven changes in OCR and ECAR, rather than absolute pH values.Teleologically, as indicated, the increased ECAR in GSD1a cells represents a known compensatory response to deficient ATP production which is stimulation of glycolysis (Figure 2i). To test the success of this known compensatory attempt, we applied the real-time ATP rate assay, but as explained they do not report the same entities. We will add this information to the relevant segment in the text to clarify how reduced glycolytic ATP can be co-observed with increased glycolytic capacity.

      The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.

      Reply:__We thank the reviewer for raising this topic. Number of samples per experiment is reported in the Methods section. As for the age range, patients age was matched to healthy controls to account for age differences and experiments were performed under similar passages range. This procedure allowed us to control for technical differences between samples that might arise due to different passages and ages. Importantly, the cohort of samples used in this manuscript included GSD1a patients with different ages further implying the strength of the observed disease phenotype found in patients' cells which exists regardless of the different age and gender of patients. The HC samples were chosen to match age and gender and passages were used in the recommended range (L. Hayflick,The limited in vitro lifetime of human diploid cell strains,Experimental Cell Research,Volume 37, Issue 3,1965,Pages 614-636, änzelmann S, Beier F, Gusmao EG, Koch CM, Hummel S, Charapitsa I, Joussen S, Benes V, Brümmendorf TH, Reid G, Costa IG, Wagner W. Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites. Clin Epigenetics. 2015 Mar 4;7(1):19. doi: 10.1186/s13148-015-0057-5. PMID: 25763115; PMCID: PMC4356053., Magalhães, S.; Almeida, I.; Pereira, C.D.; Rebelo, S.; Goodfellow, B.J.; Nunes, A. The Long-Term Culture of Human Fibroblasts Reveals a Spectroscopic Signature of Senescence. Int. J. Mol. Sci. __2022, 23, 5830. https://doi.org/10.3390/ijms23105830). Finally, for the error bars, assuming the reviewer is addressing this for all experiments, this means that results are consistent across each compared group and reflects robustness of the results. Further, to ensure statistical robustness we used bootstrapping, 95% confidence intervals and other statistical methodologies that were designed to increase the validity of the conclusions drawn from different experiments.

      Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.

      __Reply:__GHF201 reduced liver glucose-6-phosphate (G6P) with p-/-* mice livers and their normalization by GHF201.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      General comments: the authors propose a very intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

      __Reply:__We thank the reviewer for this comment and will address each comment raised.

      The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.

      __Reply:__We concur with this comment and accept that this is a general limitation of the study. We added a reservation clause at the beginning of the Discussion section.

      The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.

      __Reply:__We thank the reviewer for this important comment. We have toned down the language correlating the regulation of gene expression between fibroblasts and liver in GSD1a. We have also alluded to the key metabolic difference between fibroblasts and liver - glycogen levels and turnover - in the second paragraph of the Discussion. We are aware that if our deep analyses were conducted on a different tissue with different basal metabolism the results might have been different. However, the GSD1a-pathogenic findings in fibroblasts suggest that they might also contribute to pathology in situ, perhaps by modulating the expression of functionally redundant genes.

      For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.

      __Reply:__We thank the reviewer for pointing this important topic. Previously, we used the 72 h condition (48 h starvation followed by 24 h glucose supplementation) to attain two goals: generation of glycogen burden by excessive glucose re-uptake after glucose starvation and induction of basal autophagy by serum starvation so as to sensitize detection of the action of the autophagic activator GHF201 on a background of already induced autophagy. As stated, this 72 h condition was used previously in other GSD cell models (Kakhlon et al (2021) - GSDIV, Mishra et al (2024) - GSDIII, GSDII - in preparation), so we decided to use it in this work as well to enable cross-GSD comparison of GHF201 efficacy in GSD cell models. Moreover, as shown in Figure 1, the largest differences between HC and GSD1a fibroblasts, especially in lysosomal and mitochondrial features, were observed at the 72 h time condition. We therefore used this condition in all other fibroblasts experiments presented in this manuscript. Our ultimate aim was to test whether the metabolic reprograming induced in situ by the patients' diseased state before culturing generates stable epigenetic modifications withstanding seclusion from the original in situ environment. Thus, using the non-physiological 72 h condition, after the fibroblasts were cultured in full media remote from the in situ environment, can only confirm the stability and environment-independence of these metabolically-driven epigenetic modulations. We now provide this justification at the beginning of the Results section.

      In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.

      __Reply:__We thank the reviewer for highlighting this important topic. We will add this information to the methods segment. Throughout the manuscript, p https://doi.org/10.1080/00031305.2018.1529624, Cumming, G. (2013). The New Statistics: Why and How. Psychological Science, 25(1), 7 29. https://doi.org/10.1177/0956797613504966 (Original work published 2014)). Along with the p values we presented all data points in each comparison and added bootstrap mediated 95 % confidence intervals as well. Since our sample size was small, we chose to focus on effect sizes, to use a higher p value threshold and to implement various advanced methodologies that allowed us to find important biological patterns.

      In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.

      __Reply:__We thank the reviewer for highlighting this issue. We added glycogen levels of HC to Figure 2SA as requested. Expectedly, glycogen levels are similar between HC and GSD1a fibroblasts because neither wild type G6PC1 in HC, or mutated G6PC1 in GSD1a fibroblasts is expressed. We have now corrected the manuscript text suggesting that glycogen is accumulated in GSD1a fibroblasts and rephrased the text to express the more versatile state where epigenetic modulation could be mediated by different metabolic perturbations according to the expression profile: G6PC1 mutant expressers (notably liver and kidney cells) could inhibit p-AMPK by glycogen accumulation, while non-expressers could inhibit p-AMPK by lowering NAD+. Text changes related to this new concept are found in the Results section "Exploring epigenetics as a phenotypic driver in GSD1a fibroblasts by ATAC-seq analysis" and in the Discussion section "Metabolic-driven, disease-associated programming of cell memory."

      Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.

      __Reply:__We thank the reviewer for this comment allowing us to clarify the reasoning behind the selected methods for the main markers identification. Throughout the manuscript we employed both Western blot and immunofluorescence experiments. We believe that immunofluorescence present as a more robust and efficient method for the following reasons: i. It allows to focus on proteins in their native state; ii. Immunofluorescence allows to observe proteins in relation to their location in the cells (for example TFs in nuclei area); iii. Immunofluorescence allows to focus on each cell and exclude cells which are dead, stressed or with a low viability characteristic; iv. Immunofluorescence allows to generate much more data. For the following reasons, the main proteins explored in this work we used immunofluorescence, in each immunofluorescence experiment we added a control for the secondary antibody alone, verifying the signal is related to the antibodies only. This information can be added if requested. Importantly, some of the antibodies used were recommended for immunofluorescence and not for Western blot. As the reviewer requested, we now provide western blot results for proteins that produced a signal with the antibodies in Western blots, all markers mentioned except TFEB were added to Figure S3 d.

      The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.

      Reply: We strongly agree with this comment and have therfore tested for the effect of GHF201 on H3K27 acetylation levels as shown in Fiugre 3f and on the deacetylase -SIRT-1 as shown in Figure 3e, Figure S3d and representative images in Figure S2b.

      The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

      Reply:

      We thank the reviewer for raising this topic, since there were thousands of differentially expressed genes and we cannot mention all we focused on the most important ones that comprise key pathways we wanted to highlight as described in the Results section. We have now linked in the Results section examples of PGC1α and TFEB target genes that were reduced due to lower levels of these transcription factors in GSD1a, as compared to HC cells. Importantly, a full list of the genes from the RNA-seq experiment can be found in Table S3. Genes regulated by TFEB contain the CLEAR (Coordinated Lysosomal Expression and Regulation) motif. Two notable genes regulated by CLEAR binding TFs such as TFEB, which are very important biologically, are cathepsin L and S (Figure 6A right) both of which were reduced in GSD1a and are now elaborated in the Results section referring to Figure 6a right. Additionally, Table S3 shows differentially expressed genes in GSD1a cells where there are many other lysosomal related genes that are downmodulated in GSD1a, we now added another important example, ATP6V0D2 to the Discussion as the reviewer suggested. As for PGC1alpha, a notable gene whose expression is up-modulated by PGC1alpha, which is down-modulated in GSD1a, is ALDH1A1 (Figure 6a right). In addition, we have now added PPARG and its coactivators alpha and beta to the discussion as requested by the reviewer, these genes are shown in Table S3 and are downmodulated in GSD1a. Finally, the transcriptional effect of PGC1alpha and TFEB is also mentioned in the Discussion within the cell phenotyping section, where we describe the deep impact of dysregulation of NAD+/NADH-Sirt-1-TFEB regulatory axis on the cell phenotype at all the levels described in the manuscript.

      Please revise the following sentences as the statements made are not adequately supported by the provided data a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

      __Reply:__If one ranks treatments according to NAD+/NADH (Figure 3c) and according to cytotoxicity (Figure 3d left) and cell confluence (Figure 3d right), then the mentioned correlation can be supported. ATP availability is compromised by gramicidin, yet gramicidin, which also increased NAD+/NADH, reduced cytotoxicity and enhanced cell confluence.

      b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells."

      __Reply:__Our data (Figure 3c) show that GHF201 increased NAD+/NADH both alone and with gramicidin.

      Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility.

      __Reply:__We agree and have corrected the Figure as requested.

      The reference list (n=160) is probably too long for a research article.

      __Reply:__The number of references reflect the length and depth of the manuscript and we believe that each reference merits its place. We agree that the number of references is large but we are not sure which criteria to use to exclude some references and to reduce them to a more acceptable number that we assume would be determined by the publishing journal.

      The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions. Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

      __Reply:__We thank the reviewer for this comment and for raising the important comments that allowed us to improve our manuscript, please see our reply to point 1.

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      Referee #2

      Evidence, reproducibility and clarity

      General comments: the authors propose a vey intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

      1. The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.
      2. The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.
      3. For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.
      4. In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.
      5. In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.
      6. Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.
      7. The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.
      8. The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

      Minor points

      1. Please revise the following sentences as the statements made are not adequately supported by the provided data

      a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

      b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells." 2. Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility. 3. The reference list (n=160) is probably too long for a research article.

      Significance

      The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions.

      Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

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      Referee #1

      Evidence, reproducibility and clarity

      Major Comments:

      1. Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.
      2. One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.
      3. Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.
      4. Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.
      5. Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiemnt to confirm this.
      6. Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?
      7. Why do GSD1a fibroblasts show reduced glycolytic ATP (Fig. 2H) despite increased glycolysis and glycolytic capacity (Fig. 2J-K)? The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.
      8. Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.
      9. Fig. 5B-C - The authors should explain how GHF201 reduces glucose-6-P levels. Additionally, they should demonstrate whether GHF201 activates lysosomal pathways and induces autophagy in the liver of G6pc knockout mice, as claimed in the fibroblast experiments.

      Significance

      The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

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      Reply to the reviewers

      1. General Statements [optional]

      We* thank all three Reviewers for appreciating our work and for sharing constructive feedback to further enhance the quality of our work. It is really gratifying to read that the Reviewers believe that this work will be of interest to broad audience and will be suitable for a high profile journal. Further, the experiments suggested by the reviewers will add value to the work and will substantiate our findings. It is important to highlight that we have already performed most of the suggested experiments except a couple of experiments that we have plan to carry out during full revision. Please find below the details of experiments performed and planned to address the reviewers comments. *

      2. Description of the planned revisions

      Reviewer #1

      Comment 6. In Figure 6A, B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitination IP if you show the whole blot? It should.

      Reviewer #2

      Comment 5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.

      Reviewer #3

      Comment 3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?

      Response: We thank the Reviewer 1, 2 and 3 regarding their query on the co-immunoprecipitation assays performed for studying Orai3 ubiquitination. The reviewers are asking for ubiquitination status of Orai3 and the potential sites for Orai3 ubiquitination. To address these comments, we are planning to perform co-immunoprecipitation assays with mutated Orai3 with mutations of potential ubiquitination sites. We have already performed bioinformatic analysis and it revealed presence of three potential ubiquitination sites on Orai3: K2 (present on N-terminal region), K274 and K279 (present on C-terminal region). We would mutate these lysine residues on Orai3 protein via site-directed mutagenesis and check the Orai3 ubiquitination status. These experiments will answer the question raised by Reviewers and strengthen the Orai3 ubiquitination data.

      Please refer to below diagrammatic illustration showing potential ubiquitination sites on Orai3:

      Reviewer #2

      Comment 7. Also, all the imaging and pull down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.

      Response: We understand the concern raised by Reviewer 2 regarding direct or indirect interaction of MARCH8 and Orai3. Hence, we are planning to perform co-immunoprecipitation assays in which we delete the MARCH8 interacting domain in Orai3 protein and check the for direct interaction of these proteins. Bioinformatic analysis and literature survey have highlighted two possible MARCH8 interacting domains in Orai3. The first domain is present in 2nd loop region, present between the 2nd and 3rd transmembrane domains at the LMVXXXL (AA113-120) motif and the second domain is present at the GXXXG (AA235-239) motif, present in the 3rd loop region of Orai3. We will remove these domains from Orai3 protein individually and check its effect on MARCH8 interaction. These experiments will provide conclusive evidence of direct interaction between Orai3 and MARCH8.

      Please refer to below diagrammatic illustration displaying potential MARCH8 binding sites on Orai3:

      3. Description of the revisions that have already been incorporated in the transferred manuscript


      Reviewer #1

      Comment 1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.

      Response: We thank the Reviewer for thinking that our work is both impressive and important. Further, we understand the Reviewer’s point that transcription and proteolysis may be co-regulated for other proteins. However, our extensive literature search did not resulted in such scenarios. Therefore, to best of our knowledge, we are revealing for the first time that same transcription factor regulates both transcription and protein degradation of the same target in a context dependent manner in a single study. In case, Reviewer would still recommend to modify the text in abstract and introduction, we would do it.

      Comment 2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.

      Response: We would like to thank the Reviewer for highlighting this point. We have effectively addressed this comment by changing the nomenclature of NFAT2 to NFATc1 throughout the manuscript text and figures.

      Comment 3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

      Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated.

      Response: We thank the Reviewer for raising this highly relevant concern. In this revised manuscript, we have addressed this comment by performing several additional experiments. The new data provided in the revised manuscript corroborates our earlier results. Indeed, this data has notably strengthen our work.

      In the revised manuscript, we performed ChIP assay where we increased the number of sonication cycles to 35 so as to make sheared chromatin of around 100 bp. Next, we designed primers to amplify individual NFATc1 binding sites on Orai3 promoter, but due to close proximity of the NFATc1 binding sites, we could design two primer sets. The primer first set to amplify the -1017 bp binding site and the second set to amplify the -990 and -920 bp. Further, as suggested by the Reviewer, we performed immunoprecipitation with the four isoforms of NFAT. Our results show that only NFATc1 pulldown shows significant enrichment of Orai3 promoter with both the primer sets as compared to the IP mock samples and other NFAT isoforms (Figure 1J). Hence, our data reveals that only NFATc1 binds to these predicted sites on the Orai3 promoter and it doesn’t show a preference among these binding sites.

      Further, as suggested by the Reviewer, we mutated the Orai3 promoter in luciferase vector with deletions of the individual NFATc1 binding sites and also cloned a truncated Orai3 promoter with no NFATc1 binding sites into the luciferase vector. The luciferase assays with these mutant and truncated promoters show that upon co-expression of NFATc1, the luciferase activity of the mutant Orai3 promoter with deletion of individual NFATc1 binding site is significantly reduced in comparison to wild type Orai3 promoter. Furthermore, the maximum decrease in luciferase activity was seen with the truncated Orai3 promoter with no NFATc1 binding sites (Figure 1I). These results show that NFATc1 binds to the predicted binding sites on Orai3 promoter. Taken together, the additional ChIP assays with the four isoforms of NFAT and luciferase assays with mutated & truncated Orai3 promoters validates the transcriptional regulation of Orai3 by NFATc1.

      Comment 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful.

      Response: We really appreciate the feedback given by Reviewer 1. To strengthen our manuscript, we have addressed this comment by performing experiments in 2 cell lines/condition of pancreatic cancer. This new data in the revised manuscript provides substantial evidence for the dichotomous regulation of Orai3 by NFATc1.

      In the revised manuscript, we carried out NFATc1 overexpression and NFAT inhibition via VIVIT studies in three additional cell lines: BXPC-3 (non-metastatic), ASPC-1 (invasive) and SW1990 (metastatic). The results in these cell-lines support our earlier findings as both overexpression of NFATc1 and VIVIT mediated NFAT inhibition leads to transcriptional upregulation of Orai3 in BXPC-3 (non-metastatic) (Figure S3A, D), ASPC-1 (invasive) (Figure S3G, J) and SW1990 (metastatic) (Figure S3M, P). These results are similar to our earlier data from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cells. Further, NFATc1 overexpression leads to an increase in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3B, C) and a decrease in Orai3 protein levels in ASPC-1 (invasive) (Figure S3H, I) and SW1990 (metastatic) (Figure S3N, O). Moreover, VIVIT transfection leads to a decrease in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3E, F) and an increase in Orai3 protein levels in ASPC-1 (invasive) (Figure S3K, L) and SW1990 (metastatic) (Figure S3Q, R). The findings in these cell lines recapitulates the data obtained earlier from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cell lines. Therefore, this new data supports our conclusion regarding the dichotomous regulation of Orai3 by NFATc1 across the three conditions of pancreatic cancer.

      Comment 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained.

      Response: We thank the Reviewer for asking this question. In our manuscript, we performed NFATc1 inhibition studies using VIVIT and siRNA-mediated NFATc1 knockdown. Both of these assays show increase in Orai3 mRNA levels in all non-metastatic, invasive and metastatic pancreatic cancer cell lines. To understand if the increase in Orai3 mRNA levels is via transcriptional regulation, we performed luciferase assay which showed that VIVIT mediated NFAT inhibition leads to increase in luciferase activity suggesting the binding of other transcription factors on the Orai3 promoter. To corroborate this hypothesis, in our revised manuscript, we performed luciferase assay in wild type Orai3 promoter and truncated Orai3 promoter with no NFATc1 binding sites. NFAT inhibition via VIVIT transfection led to an increase in luciferase activity in both wild type and truncated Orai3 promoter (Figure S2A). Hence, removal of NFATc1 binding sites had no significant effect on luciferase activity suggesting that apart from NFATc1, other endogenous transcription factors are involved in regulating Orai3 transcription. We have not identified all the transcription factors that can modulate Orai3 upon NFAT inhibition as it is beyond the scope of this study. We sincerely hope the Reviewer 1 would be satisfied with this additional data.

      Reviewer #2

      Comment 1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.

      Response: We apologize to the Reviewer for not highlighting the NFAT2 (NFATc1) loss of function data effectively. Actually, in the __Figure 3 __and __Supplementary Figure 2 __of the original manuscript, we showed VIVIT mediated NFAT inhibition and siRNA induced NFATc1 silencing data to provide the evidence that endogenous NFATc1 regulates Orai3.

      Comment 2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.

      Response: As suggested by the Reviewer, in the revised manuscript, we overexpressed the four NFAT isoforms: NFATc2, NFATc1, NFATc4 & NFATc3 and checked Orai3 mRNA levels. qRT-PCR analysis shows that overexpression of NFATc1 results in the highest and significant increase in Orai3 mRNA levels compared to the empty vector and other NFAT isoforms (Figure 1F). This data corroborates the western blot data of NFAT isoforms overexpression highlighting the transcriptional regulation of Orai3 by NFATc1.

      Comment 3. Fig. S3D, E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.

      Response: The survival analysis of pancreatic cancer patients with low MARCH3 and MARCH8 levels shows that around 30% of patients with low MARCH3 levels survived for 5.5 years, whereas in case of MARCH8 30% of patients with high MARCH8 levels survived for >7.5 years. Hence high MARCH8 expression in pancreatic cancer patients provided significant survival advantage compared to high MARCH3 levels. Therefore, in the text, we meant that compared to MARCH3, higher MARCH8 levels correlate with better survival. As suggested by the Reviewer, we have modified the text to make this point clearer.

      Comment 4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example, compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cell lines. This needs to be addressed and differences explained.

      Response: We understand the concern raised by Reviewer 2 regarding calcium imaging experiments in MiaPaCa-2 cell line. Therefore, in the revised manuscript, we repeated calcium imaging experiments in MiaPaCa-2 and updated the representative traces as well as quantitative analysis (Figure 2D, E, 3D, E, 4H, I, S2L, M). Further, we have discussed this point in the text of the manuscript.

      Comment 6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.

      Response: As recommended by the Reviewer, in the revised manuscript, we have indicated the region used for intensity profile generation by drawing a line in the representative image (Figure 6D). Further, we have updated the methodology of colocalization microscopy with details of the size of the pinhole and imaging mode.

      Comment 8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So, it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.

      Response: We would like to thank the Reviewer for this highly relevant suggestion. In our revised manuscript, we carried out transwell migration assays with MARCH8 overexpression as well as MARCH8 knockdown in CFPAC-1 (metastatic) cells. Our data shows that stable lentiviral knockdown of MARCH8 increased the number of migrated CFPAC-1 cells compared to shNT CFPAC-1 cells while MARCH8 overexpression decreased the number of migrated CFPAC-1 cells compared to empty vector control cells (Figure 9F, G). Therefore, as pointed out by the Reviewer, MARCH8 overexpression lowers Orai3 levels in metastatic pancreatic cancer cells and hinders their metastatic potential.

      Comment 9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.

      Response: As suggested, we have updated the summary figure (Figure 10) showing higher Orai3 protein levels in the metastatic side.

      Comment 10. Please show all full WBs in the supplementary data.

      Response: As recommended by the Reviewer, we have provided all full western blots in a supplementary file (Supplementary File 1).

      Reviewer #3


      Comment 1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.

      Response: As suggested by Reviewer 3, in the revised manuscript, we have provided quantitative analysis of Orai3 and MARCH8 co-localization. Further, we have stated the labels used in the co-localization experiment in the figure legend of the revised manuscript. Unfortunately, we could not perform FRET assay between Orai3 and MARCH8 due to limited resources. Instead, as discussed in the planned revisions section, we are planning to perform co-immunoprecipitation assay with mutated Orai3 protein in which the MARCH8 interacting domains are deleted to investigate direct interaction of Orai3 and MARCH8. We believe that Reviewer 3 will be satisfied with this experiment.

      Comment 2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract.

      Response: As recommended by the Reviewer, in the revised manuscript, we have introduced the use of pancreatic cancer cells at the beginning of the abstract.

      Comment 4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

      Response: We thank the reviewer for asking this interesting question. There is only one report on Orai1’s role in pancreatic cancer. It was suggested that Orai1 can contribute to apoptotic resistance of pancreatic cancer cells (Kondratska et al. BBA-Molecular Cell Research, 2014). However, only one cell line i.e. PANC-1 was used in this study. While our earlier work and other studies have demonstrated that Orai3 drives pancreatic cancer metastasis (Arora et al. Cancers, 2021) and proliferation (Dubois et al. BBA-Molecular Cell Research, 2021) respectively. Therefore, emerging literature suggests that both Orai1 and Orai3 can contribute to different aspects of pancreatic cancer progression. But whether Orai1 and Orai3 form heteromers in pancreatic cancer cells remains unexplored. Further, we believe that the degradation machinery and the underlying molecular mechanisms would be analogous for both Orai3 homomers and heteromers. Nonetheless, the rate of degradation may differ for Orai3 homomers and heteromers as literature suggests that usually proteins are more stable in large heteromeric protein complexes.

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      Referee #3

      Evidence, reproducibility and clarity

      The study by Raju et al. demonstrated that NFAT2 drives both Orai3 transcription and protein degradation. They find a clearly distinct mechanism between non-metastatic cancerous and metastatic cells. While in non-metastatic cells NFAT2 drives Orai3 transcritpion and increases Orai3 expression, in invasive and metastatic cells degradation of Orai3 is driven. They find a physical interaction of MARCH8 with Orai3 resulting in degradation. This degradation is not happening in non-metastatic cells as MARCH8 promotor is highly methylated. This study is highly interesting for a broad readerships and provides a solid basis for the development of novel therapeutic strategies for cancer treatment. Before publication the authors should address a few minor comments.

      1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.
      2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract
      3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?
      4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

      Significance

      The authors highlight and decode a dual role of NFAT2 in controling Orai3 expression, which is highly interestingly to gain insight in different states of cancer cells (non-metastatic, metastatic). The findings form a great basis for a deeper understanding of potential therapeutic targets.

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      Referee #2

      Evidence, reproducibility and clarity

      Raju et al. presents a nice comprehensive study of the differential regulation of Orai3 at the transcriptional and stability levels in metastatic versus non-metastatic pancreatic cancer (PC) cells. They convincingly show that NFAT2 regulates Orai3 transcription in all PC cells but interestingly, in the metastatic PC cells NFAT2 also upregulates the expression of MARCH8 an E3 ubiquitin ligase that targets Orai3 for lysosomal degradation. The MARCH8 locus is hypermethylated in the non-metastatic cell line, thus preventing MARCH8 upregulation in those cells. The data is convincing and complementary. I only a few suggestions below.

      Specific Comments:

      1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.
      2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.
      3. Fig. S3D,E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.
      4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cells lines. This needs to be addressed and differences explained.
      5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.
      6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.
      7. Also all the imaging and pull down down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.
      8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.
      9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.
      10. Please show all full WBs in the supplementary data.

      Significance

      SIgnificant and relevant study that will be of great interest to the cancer and calcium signaling fields.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript entitled, "NFAT2 drives both Orai3 transcription and protein degradation by harnessing the differences in epigenetic landscape of MARCH8 E3 ligase" offers an extensive study of how Orai3 levels are controlled during pancreatic cancer progression. The central hypothesis is that NFAT2 stimulates both Orai3 and MARCH8 transcription, resulting in both Orai3 transcription and degradation. They further establish that MARCH8 expression/Orai3 degradation is epigenetically regulated in PDAC, with a progressive loss of methylation during cancer progression leading to increased Orai3 transcription, stability and Ca2+ entry.

      Overall, I'm certain that there is new information to be learned here. However, as detailed below, the manuscript makes a number of general claims about what happens during PDAC progression, but this is based on only one cell line per disease state. While they should not be expected to do a complete analysis in more cell lines, a demonstration that Orai3 and MARCH8 expression are correlated with disease progression in a panel of cell lines and/or on the TCGA database would increase enthusiasm considerably. In addition, although I found the work with MARCH8 to be highly convincing, the fact that NFAT2 knockdown increased rather than reduced Orai3 transcription does not support the central hypothesis. The explanation that this results from compensation is not very meaningful; that NFAT2 drives Orai3 transcription is in the title of the paper. These observations clearly demonstrate that this relationship is more complicated than suggested. Finally, there are a number of missing controls and unclear aspects to the authors' ChIP data that could help explain some of these discrepancies.

      Specific Comments:

      1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.
      2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.
      3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

      Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated. 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful. 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained. 6. In Figure 6A,B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitinization IP if you show the whole blot? It should.

      Significance

      My expertise is in calcium signaling, particularly within the context of disease states. I currently have a PDAC study in its late stages, but I have worked more with melanoma.

      Issues about significance were raised in my comments above; generalization of these observations requires the appropriate use of a panel of cell lines and/or TCGA usage. In addition, some observations require additional investigation for confidence; necessary to achieve significance.

      The extent of the advance is quite reasonable for a high profile paper in this field, should the issues I and the other reviewers raise be formally and thoroughly addressed.

      Given that the study crosses lines between signaling, cancer, epigenetics, transcription and ubiquitination, I think that it is of potential interest to a general audience.

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      Reply to the reviewers

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

      __* SUMMARY

      This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

      MAJOR AND MINOR COMMENTS *__

      Overall Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.

      This information is now provided in the figure legends (numbers of cells analyzed and/or numbers of embryos) except for data in Figure 5, which are presented in a new Supplementary Table

      Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

      We have used SD throughout the study. This information has now been added in figure legends.

      Results 2

      ____A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.

      We now cite a recent review on spinal cord development (Saade and E. Marti, Nature Reviews Neuroscience, 2025) to illustrate this point

      The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.

      We have now reformulated this paragraph as follows: "At E3, the transcript was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside (Saade and Marti, 2025)). One day later, at E4, this salt and pepper expression was still detected in the ventricular zone, while it markedly increased in the region of the mantle zone that is immediately adjacent to the ventricular zone. This region is enriched in nascent neurons on their way to differentiation that are still HuC/D negative. In contrast, the transcript was completely excluded from the more basal region of the mantle zone, where mature HuC/D positive neurons accumulate.

      It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.

      Thank you for the suggestion. We have now added a dotted line that separates the ventricular zone from the mantle zone at E3 and E4 in Figure 2A

      Reference should be included for pRb expression dynamics.

      This section has been rewritten in response to comments from Reviewer #3, and now contains several references regarding pRb expression dynamics. See detailed response to Reviewer #3 for the new version

      Could the Myc tag insertion approach disrupt protein function or turnover? ____Why was the insertion target site at the C terminus chosen?

      The first reason was practical: at the time when we decided to generate a KI in Cdkn1c, we had already generated several successful KIs at C-termini of other genes, in particular using the P2A-Gal4 approach (see Petit-Vargas et al, 2024), and had not yet experimented with N-terminal Gal4-P2A. We therefore decided to use the same approach for Cdkn1c.

      We also chose to target the C-terminus to avoid affecting the active CKI domain which is located at the N-terminus.

      Nevertheless, the C-terminal targeting may have an impact on the turnover: it has been described that CDK2 phosphorylation of a Threonin close to the C-terminus of Cdkn1c leads to its targeting for degradation by the proteasome from late G1 (Kamura et al, PNAS, 2003; doi: 10.1073/pnas.1831009100). We can therefore not rule out that the addition of the Myc tags close to this phosphorylation site modulates the dynamics of Cdkn1c degradation. We note, however, that we observed little overlap between the Cdkn1c-Myc and pRb signals in cycling progenitors, suggesting that Cdkn1c is effectively degraded from late G1.

      OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?

      Although it could be done, we have not attempted to do this for CDKN1c because our current experience of endogenous tagging of several genes with a similar expression level (based on our scRNAseq data) and nuclear localization (Hes5, Pax7) with a fluorescent reporter shows that the fluorescent signal is extremely low or undetectable in live conditions; Therefore we favored the multi-Myc tagging approach, and indeed we find that the Myc signal in progenitors is also very low even though it is amplified by the immunohistology method; this suggests that most likely, the only signal that would be detected -if any- with a fluorescent approach would be the peak of expression in newborn neurons.

      In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?

      The reviewer refers to the control gRNA condition in panel C, that shows that two small patches of GFP-positive cells are visible in the whole spinal cord of this particular embryo.

      Technically, the origin of these "background" cells could be multiple. A spontaneous legitimate insertion at the CDKN1c locus by homologous recombination is possible, although we tend to think it is unlikely, given the extremely short length of the arms of homology; illegitimate insertions of the Myc-P2A-Gal4 cassette at off-target sites of the control gRNA is a possibility. Alternatively, a low-level leakage of Gal4 expression from the donor vector could lead to a detectable nls-GFP expression in a few cells via Gal4-UAS amplification.

      In any case, these cells are observed at a very low frequency (1 or 2 patches of cells/embryo) relative to the signal obtained in presence of the CDKN1c gRNA#1 (probably several thousand positive cells per embryo). This suggests that if similar "background" cells are also present in presence of the CDKN1c gRNA, they would not significantly contribute to the signal, and would not impact the interpretation.

      In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

      It is indeed possible that the CDKN1c protein is more stable than the transcript in newborn neurons and remains detectable in the mantle zone after the mRNA disappears. In Gui et al, 2006, where they use an anti-CDKN1c antibody to label the protein in mouse spinal cord transverse sections at E11.5 (Figure 1B), a few positive cells are also visible basally. They could correspond to neurons that have not yet degraded CDKN1c, although it is unclear in the picture whether these cells are really in the mantle zone or in the adjacent dorsal root ganglion; we note that a similar differential expression dynamics between mRNA and protein has been described for Tis21/Btg2 in the developing mouse cortex, where the protein, but not the mRNA, is detected in some differentiated bIII-tubulin-positive neurons (Iacopetti et al, 1999).

      However, related to our response above to a previous comment from the same reviewer, we cannot rule out the possibility that the Myc tags modulate the turnover of CDKN1c protein and slow down the dynamics of its degradation in differentiating neurons.

      We have added a sentence to indicate the presence of these cells: "In addition, a few Myc-positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript."

      Results

      It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).

      We did not quantify the level of mRNA reduction, it was just evaluated by eye. The reason for choosing shRNA1 for the whole study was dictated by 1) the fact that we more consistently saw (by eye) a reduction in the signal on the electroporated side with this construct than with the other shRNAs, and 2) that the effect on neurogenesis was also more consistent.

      We will perform additional experiments to provide some quantitation of the shRNA effect, as this is also requested by Reviewer #3.

      As our Cdkn1c KI approach offers a direct read-out of the protein levels in the ventricular and mantle zones, and since our shRNA strategy of "partial knock-down" is based on the idea that the shRNA effect should be more complete in progenitors expressing Cdkn1c at low levels than in newborn progenitors that express the protein at a higher level, we propose to validate the shRNA in the Cdkn1c-Myc knock-in background, by comparing the Myc signal intensity between control and Cdkn1c shRNA conditions

      Figure panels are not currently cited in order. Citation or figure order could be changed.

      We have now added a common citation of the panels referring to analyses at 24 and 48 hours after electroporation (now Figure 3A-F), allowing us to display the experimental data on the figure according to the timing post electroporation, while the text details the phenotype at the later time point first.

      The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.

      We have added images in a revised version of the Figure 3, as requested

      A supplementary figure showing the Caspase3 experiment should be added.

      We have added data showing Caspase3 experiments in Supplementary Figure 3D

      OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

      We agree with the reviewer that direct tracking is the most direct method for the identification of pairs of sister cells. However, it remains technically challenging, and the added value compared to the retrospective identification would be limited, while requiring a great workload, especially considering the many different experimental conditions that we have explored in this study.

      Results 4

      How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.

      We have not done these quantifications in the original version of the study. We will add a quantification of the signal intensity in the ventricular and mantle zones for the revised version of the manuscript, as also requested by reviewer #3.

      In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.

      All images in the figure are single Z confocal images. Images in Column 2 (showing both electroporated sides of the same tube) were acquired with a 20x objective, whereas the insets shown in Columns 1 and 3 are 100x confocal images. 100x images on both sides were acquired with the same acquisition parameters, and the display parameters are the same for both images in the figure. The signal intensity can therefore be compared directly between columns 1 and 3.

      We have modified the legend of the Figure to indicate these points: "The insets shown in Columns 1 and 3 are 100x confocal images acquired in the same section and are presented with the same display parameters".

      In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

      Our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

      We have modified the manuscript to elaborate on our interpretation of this result: "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3H)."

      Results 5 ____The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.

      Thank you for pointing this out. We have modified the sentence in the main text.

      "We found that the proportion of pRb positive progenitors having entered S phase (EdU positive cells) was significantly higher at all time points examined more than 4h30 after FT injection in the Cdkn1c knock-down condition compared to the control population (Figure 5D)"

      OPTIONAL Could CyclinD1 activity be directly assessed?

      This is an interesting suggestion. For example, using the fluorescent CDK4/6 sensor developed by Yang et al (eLife, 2020; https://doi.org/10.7554/eLife.44571) in a CDKN1c shRNA condition would represent an elegant experimental alternative to complement our rescue experiments with the double CDKN1c/CyclinD1 shRNA. However, we fear that setting up and calibrating such a tool for in vivo usage in the chick embryo represents too much of a challenge for incorporation in this study.

      General ____Scale bars missing fig s1c s4d.

      Thanks for pointing this out. Scale bars have been added in the figures and corresponding legends

      OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.

      OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?

      We agree that it will be interesting and important that our findings are replicated in other species, experimental systems, and even tissues, or by alternative experimental approaches. Nevertheless, it is probably beyond the scope of this study.

      A model cartoon to summarise outcomes would be useful.

      We thank the reviewer for the suggestion. We will propose a summary cartoon for the revised version of the manuscript.

      Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

      Positivity or negativity was decided by eye. However, for each experiment, we ensured that all images of perturbed conditions and the relevant controls were analyzed with the same display parameters and by the same experimenter to guarantee that the criteria to determine positivity or negativity were constant.

      Reviewer #1 (Significance (Required)):

      SIGNIFICANCE

      Strengths: This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

      • Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
      • A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
      • Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
      • Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
      • Mechanistic insight by examining downstream target CyclinD1.
      • Clearly presented with useful illustrations throughout.
      • Logic is clear and examination thorough.
      • Conclusions are warranted on the basis of their findings. ____Limitations ____T____his study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.

      Some aspects of quantification require explanation in order for the experiments to be replicated.

      It is imperative that precise sample sizes are included for all experiments presented.

      Advance: ____First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.

      Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.

      Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

      Audience:

      Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

      Please define your field of expertise with a few keywords to help the authors contextualize your point

      Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

      The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

      Major comments

      I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed?

      As already stated in our response to a similar question from reviewer #1, our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

      At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN).

      Regarding the results of Pax7 overexpression presented in figure 4D (now Figure 4E in the revised version), we had made the choice to merge PN and NN values in the main text to focus on the neurogenic transition from PP to PN/NN collectively. We agree with this reviewer, as well as with reviewer #1, that it should be more detailed and better discussed. We therefore propose to modify the paragraph as follows (and as already indicated above in the response to reviewer #1):

      "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that Cdkn1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of Cdkn1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal Cdkn1c shRNA approach (see Figure 3F, now 3H)."

      Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c?

      It is likely that a proportion of cells that would normally have done a NN division are pushed to a direct differentiation that bypasses their last division in the Pax7-CDKN1c condition, and that they contribute to the general increase in neuron production observed in our quantification 48hae (Figure 3F -previously 3C). However, these cases would not contribute to the increase in the NN quantification in pairs of sister cells 6 hours after division at 24hae (Figure 4E - previously 4D), because by design they would not incorporate FlashTag. The rise in NN is therefore the result of a PN to NN conversion.

      Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

      These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification

      « We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 6A)."

      * * Minor comments

      Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

      Some studies suggest that HuC/D can, under certain conditions, be observed in the nucleus of neurons. However, HuC/D is a RNA binding protein whose localization is mainly expected to be cytoplasmic. In our experience (Tozer et al, 2017), and in other publications using the antibody in the chick spinal cord (see, for example, le Dreau et al, 2014), it is observed in the cell body of differentiated neurons, as in the current manuscript.

      Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

      This has now been modified in the figures.

      Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis? Or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

      We will modify the introduction and discussion in several instances, in order to address the above suggestions and we will:

      • add references to its role in other contexts and/or species.

      • expand the discussion on the cross talk between neurogenic factors and CDK inhibitors in other cellular contexts.

      • add a dedicated paragraph in the discussion to answer reviewer#2's questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages?

      Reviewer #2 (Significance (Required)):

      The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

      ____I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

      My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

      __*

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

      Summary: In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely.

      By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

      This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

      This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

      Major comments:

      1.-The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions.

      However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified.

      Throughout their comments on the manuscript, this reviewer raises several points regarding the characterization of pRb expression in our model and of our use of this marker in our study. We take these comments into account and propose to expand on pRb characteristics in the first occurrence of pRb as a marker of cycling cells in the manuscript. The modifications rely on:

      • the quotation of several studies showing that phosphorylation of Rb is regulated during the cell cycle, and that "it is not detectable during a period of variable length in early G1 in several cell types (Moser et al, 2018;Spencer et al, 2013; Gookin et al, 2017), including neural progenitors in the developing chick spinal cord (Molina et al, 2022). Apart from this absence in early G1, pRb is detected throughout the rest of the cell cycle until mitosis".

      • a more detailed description of our own characterization of pRb dynamics in a synchronous cohort of cycling cells, which reveals a similar heterogeneity in the timing of the onset of Rb phosphorylation after mitosis. This description was initially shown in supplementary figure 3 and will be transferred to a new supplementary figure 2 to account for the fact that it will now be cited earlier in the manuscript.

      Regarding the specific question the "suitability (of pRb) as a neurogenic division marker": we do not directly "use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors", but we use Rb phosphorylation to discriminate between progenitors (pRb+) and neurons (pRb-) identity in pairs of sister cells to retrospectively identify the mode of division of their mother.

      Given that Rb is unphosphorylated during a period of variable length after mitosis (see references above), pRb is not a reliable marker of ALL cycling progenitors. We developed an assay to identify the timepoint (the maximal length of this "pRb-negative" phase) after which Rb is phosphorylated in all cycling progenitors (new Supplementary Figure 2). This assay relies on a time course of pRb detection in cohorts of FlashTag-positive pairs of sister cells born at E3. This time course experiment allowed us to identify a plateau after which the proportion of pRb-positive cells in the cohort remains constant. From this timepoint, this proportion corresponds to the proportion of cycling cells in the cohort. Rb phosphorylation therefore becomes a discriminating factor between cycling progenitors (pRb+) and non-cycling neurons (pRb-).

      We are confident that this provides a solid foundation for the determination of the identity of pairs of sister cells in all our Flash-Tag based assays, which retrospectively identify the mode of division of a progenitor on the basis of the phosphorylation status of its daughter cells 6 hours after division.

      We propose to modify the main text to describe the strategy and protocol more explicitly, by introducing the sentence highlighted in yellow in the following paragraph where the paired-cell analysis is first introduced (in the section on CDKN1c knock-down):

      "This approach allows to retrospectively deduce the mode of division used by the mother progenitor cell. We injected the cell permeant dye "FlashTag" (FT) at E3 to specifically label a cohort of progenitors that undergoes mitosis synchronously (Baek et al., 2018; Telley et al., 2016 and see Methods), and let them develop for 6 hours before analyzing the fate of their progeny using pRb immunoreactivity (Figure 3D). Our characterization of pRb immunoreactivity in the tissue had established beforehand that 6 hours after mitosis, all progenitors can reliably be detected with this marker (Supplementary Figure 2, Methods). Therefore, at this timepoint after FT injection, two-cell clones selected on the basis of FT incorporation can be categorized as PP, PN, or NN based on pRb positivity (P) or not (N) (see Methods, new Figure 3G and new Supplementary Figures 2 and 4)."

      We also modified accordingly the legend to Supplementary Figure 2 (previously Supplementary Figure 3, which describes the identification of the plateau of pRb.

      Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c.

      In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components.

      We agree with the reviewer that Rb phosphorylation may be a direct or indirect target of Cdkn1c activity, and exploring the molecular aspects of the cellular and developmental phenomena that we describe in our manuscript would represent an interesting follow up study.

      ____A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

      To complement our analyses of the modes of division, we propose to use a positive marker to assess neural identity in parallel to the absence of pRb within pairs of cells. This approach may be the most meaningful in the gain of function context (Pax7 driven expression of Cdkn1c) because in this context, the time-point to reach the plateau of Rb phosphorylation used in our FT-based assay may indeed be delayed. On the opposite, in the context of loss of functions, the plateau may be reached earlier, which would have no effect on this assay.

      2.-Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division.

      This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.

      The reviewer probably mistyped and meant 6-hour post injection, which is the duration that we use for paired cell tracking. We would like to emphasize that in addition to the FlashTag label, we benefit from the electroporation reporter to assess clonality. Altogether, we combine 5 criteria to define a clonal relationship :

      • 2 cells are positive for Flash Tag
      • The Flash Tag intensity is similar between the 2 cells
      • The 2 cells are positive for the electroporation reporter
      • The electroporation reporter intensity is similar between the two cells
      • the position of the two cells is consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995;__; __Loulier et al, 2014): they are found on a shared line along the apico-basal axis, and share the same Dorso-Ventral and Antero-Posterior position . This combination is already described in the Methods section. We propose to modify the paragraph to include the sentence highlighted in yellow in the text below;

      "Cell identity of transfected GFP positive cells was determined as follows: cells positive for pRb and FT were classified as progenitors and cells positive for FT and negative for pRb as neurons. In addition, a similar intensity of both the GFP and FT signals within pairs of cells, and a relative position of the two cells consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995; Loulier et al, 2014) were used as criteria to further ascertain sisterhood. This combination restricts the density of events fulfilling all these independent criteria, and can confidently be used to ensure a robust identification of pairs of sister cells."

      3.- The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.

      - "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation.

      We have entirely remodeled this section, which describes the expression of Myc-tagged Cdkn1c relative to pRb and now provide several references that describe the generally accepted view that pRb is specific of cycling cells, regulated during the cell cycle, and in particular absent in early G1. We also remove the mention of the "Restriction point" in the main text to avoid any confusion on the timing of phosphorylation, as the notion of restriction point is not useful in our study. The section now reads as follows:

      "To ascertain that Cdkn1c is translated in neural progenitors, we used an anti-pRb antibody, recognizing a phosphorylated form of the Retinoblastoma (Rb) protein that is specifically detected in cycling cells (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) , including neural progenitors of the developing chick spinal cord (Molina et al., 2022). In the ventricular zone of transverse sections at E4 (48hae), we detected triple Cdkn1c-Myc/GFP/pRb positive cells (arrowheads in Figure 2B), providing direct evidence for the Cdkn1c protein in cycling progenitors. We also observed many double GFP/pRb positive cells that were Myc negative (arrowheads in Figure 2B). The observation of UAS-driven GFP in these pRb-positive cells is evidence for the translation of Gal4 and therefore provides a complementary demonstration that the Cdkn1c *transcript is translated in progenitors. The absence of Myc detection in these double GFP/pRb positive cells also suggests that Cdkn1c/Cdkn1c-Myc stability is regulated during the cell cycle. *

      Finally, we observed double Myc/GFP-positive cells that were pRb-negative (Figure 2B; asterisks). One characteristic of Rb phosphorylation as a marker of cycling cells is a period in early G1 during which it is not detectable, as described in several cell types (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) including chick spinal cord neural progenitors (Molina et al., 2022). Using a method that specifically labels a synchronous cohort of dividing cells in the neural tube, we similarly observed a period in early G1 during which pRb is not detectable in some progenitors at E3 (See Supplementary Figure 2 and Methods). Hence, the double Myc/GFP positive and pRb negative cells may correspond to progenitors in early G1. Alternatively, they may be nascent neurons whose cell body has not yet translocated basally (see Figure 2C). Finally, we observed a pool of GFP positive/pRb negative nuclei with a strong Myc signal in the region of the mantle zone that is in direct contact with the ventricular zone (VZ), corresponding to the region where the transcript is most strongly detected (see Figure 2A). This pool of cells with a high Cdkn1c expression likely corresponds to immature neurons exiting the cell cycle and on their way to differentiation (Figure 2B; double asterisks). In addition, a few Myc positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript.

      In summary, our dual Myc and Gal4 knock-in strategy which reveals the history of Cdkn1c transcription and translation confirms that Cdkn1c is expressed at low level in a subset of progenitors in the chick spinal neural tube, as previously suggested (Gui et al., 2007; Mairet-Coello et al., 2012). In addition, the restricted overlap of Cdkn1c-Myc detection with Rb phosphorylation suggests that in progenitors, Cdkn1c is degraded during or after G1 completion. "

      This section will again be remodeled in a future revised version of the manuscript, in which we will add quantifications of Myc levels, as requested by Reviewer 1 above, and also by Reviewer #3 below.

      Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).

      In the original version of the manuscript, the section describing the dynamics of CDKN1c-Myc in the KI experiments presented in Figure 2 relied on the idea that the dynamics of pRb in chick spinal progenitors is similar to what I described in other tissues and cell types, without providing any references to substantiate this fact. Actually, Molina et al provide a characterization of pRb in combination with their cell cycle reporter and conclude that pRb negative progenitors are in G1 ("We also verified that phospho-Rb- and HuC/D-negative cells were in G1 by using our FUCCI G1 and PCNA reporters"). We will now cite this reference to support our claim. In addition, our characterization of Rb progressive phosphorylation in the synchronic Flash-Tag cohort of newborn sister cells provides a complementary demonstration that a fraction of the progenitors are pRb-negative when they exit mitosis (i.e. in early G1). This analysis was initially only introduced in the supplementary Figure 3, as support for the section that presents the Paired-cell assay used in Figure 3. We propose to introduce the data from Supplementary Figure 3 earlier in the manuscript (now Supplementary Figure 2), in order to better introduce the reader with the dynamics of pRb in cycling cells in our model. This will better support our description of the Cdkn1c-Myc dynamics in relation with pRb. We therefore propose to reformulate this whole section as follows.

      - It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.

      - It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).

      These are two interesting suggestions. To complement our data with guide #1, we have performed Myc-immunostaining experiments on transverse sections in the context of guide #3, showing exactly the same pattern of Myc signal, with low expression in the VZ, and a peak of signal in the part of the mantle zone that is immediately touching the VZ. This confirms the specificity of the spatial distribution of the Cdkn1c-Myc signal. These data have been added in a revised version of Supplementary Figure 1.

      We will perform the suggested quantifications using guides #1 and #3, which both show a good KI efficiency. We do not think it is useful to do these experiments with guide #2, whose efficiency is much lower, and which would lead to a very sparse signal.

      - The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).

      We will perform these experiments to validate guide cutting efficiency using the Tide method (Brinkman et al, 2014)

      - In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.

      We will perform genomic PCR experiments to confirm in-frame insertion of the Myc tags at the Cdkn1c locus

      4.- In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

      The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c.

      Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression.

      All the predicted isoforms in the chick genome contain the sequence targeted by shRNA1, which is located in the CKI domain, the region of the protein that is most conserved between species. Besides, all the isoforms annotated in the mouse and human genomes also contain the region targeted by shRNA1. We are therefore confident that shRNA1 should target all chick isoforms.

      A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2).

      This approach (qRT-PCR on sorted cells) would enable us to focus solely on electroporated cells, but it would result in an averaged quantification of Cdkn1c depletion. In order to obtain additional information on the shRNA-dependent decrease in Cdkn1C in the different neural cell populations (progenitor versus differentiating neuron), we propose an alternative approach consisting in monitoring the level of Cdkn1c protein, assessed through Cdkn1c-Myc signal in knock-in cells, in the presence versus absence of Cdkn1c shRNA.

      - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons.

      We have addressed the case of pRb dynamics in progenitors above and added a reference documented pRb expression during the cell cycle of chick neural progenitors (Molina et al, 2022).

      Regarding Sox2 persistence: we consistently detect a small fraction of double positive Sox2+/HuC/D+ cells in chick spinal cord transverse sections. We have shown that this marker of differentiating neurons (HuC/D) only becomes detectable more than 8 hours after mitosis in newborn neurons at E3 (Baek et al, 2018), indicating that Sox2 protein can persist for up to at least 8 hours in newborn neurons.

      We now cite a paper showing that a similar persistence of Sox2 protein is reported in differentiating neurons of the human neocortex, where double Sox2/NeuN positive cells are frequently observed in cerebral organoids (Coquand et al, Nature Cell Biology 2024__)__

      - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption?

      We do not think that these numbers are equal for both conditions, and we did not formulate this assumption. We only indicate (in the methods section) that this undefined/undetermined population (based on negativity for both markers) is a mix of two possible cell types. However, we do not offer any interpretation of the CDKN1c phenotypes based on the changes in this population. Indeed, our interpretation of the knock-down phenotype is solely based on the increase in pRb-positive and decrease in HuC/D-positive cells, which both suggest a delay in neurogenesis. We understand from the reviewer's comment that depicting an "undefined" population on the graph may cause some confusion. We therefore propose to present the data on pRb and HuC/D in different graphs, rather than on a combined plot, and to remove the reference to undefined cells in Figure 3, as well as in Figures 4 and 5 depicting the gain of function and double knock-down experiments. We have implemented these changes in updated versions of the figures.

      - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe?

      We have now performed experiments with an anti phospho Histone 3 antibody, which labels mitotic cells, at 24 and 48 hours post electroporation. We do not see any ectopic mitoses upon Cdkn1c knock-down with this marker, and we have produced a Supplementary Figure with these data. This is consistent with the fact that we also do not see ectopic pRb or Sox2 positive cells in the mantle zone in the knock-down experiments. These data (pH3 and Sox2) have been added in the new Supplementary Figure 3E and F.

      We have now modified the main text to include these data:

      "In the context of a full knock-out of Cdkn1c in the mouse spinal cord, a reduction in neurogenesis was also observed, which was attributed to a failure of prospective neurons to exit the cell cycle, resulting in the observation of ectopic mitoses in the mantle zone (Gui et al, 2007). In contrast with this phenotype, using an anti phospho-Histone3 antibody, we did not observe any ectopic mitoses 24 or 48 hours after electroporation in our knock-down condition (Supplementary Figure 3E-F). This is consistent with the fact that we also do not observe ectopic cycling cells with pRb (Figure 3A and D) and Sox2 (Supplementary Figure 3E-F) antibodies. We therefore postulated that the reduced neurogenesis that we observe upon a partial Cdkn1c knock-down may result from a delayed transition of progenitors from the proliferative to neurogenic modes of division."

      - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye.

      - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells?

      The key criterion for cells that are not directly touching each other is that their relative position corresponds to the classical "radial" organization of clones in this tissue (Leber and Sanes, 1995__; __Loulier et al, Neuron, 2014). In other words, we make sure that they are located on a same apico-basal axis, as is the case for the NN clone presented on the figure. As stated above in our response to major comment #2, we have modified the Methods section accordingly.

      Can they provide further image examples of different types of clones?

      We now provide additional examples in a new Supplementary Figure 4

      - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity?

      The plateau for Rb phosphorylation in progenitors is reached before 6 hours post mitosis at E3. At the same age, we have previously shown (Baek et al, PLoS Biology 2018) in a similar time course experiment in pairs of FT+ cells that the HuC/D signal is not detected in newborn neurons 8 hours after mitosis. HuC/D only starts to appear between 8 and 12 hours, and still increases between 8 and 16 hours. The plateau would therefore be very delayed for HuC/D compared to pRb. This long delay in the appearance of this « positive » marker of neural differentiation is the main reason why we chose to use Rb phosphorylation status for the analysis of synchronous cohorts of pairs of sister cells, because pRb becomes a discriminating factor much earlier than HuC/D after mitosis.

      - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?

      We have carried out similar experiments at E2, showing a plateau of 95% of pRb-positive cells in the FT-positive population (see graph on the right). This provides a retrospective estimate of the mode of division of the mother cells at this stage (roughly 90% of PP and 10% of PN) which is consistent with the vast majority of PP divisions described by Saade et al (2013, see Figure S1) at this stage.

      5.- In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is:

      - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024.

      We have now performed Pax7 immunostainings on transverse sections at 24 and 48 hours post electroporation, both with the Pax7-CDKN1c-Gal4 and with the Pax7-Gal4 control constructs. We present these data in the new supplementary figure 7. In both conditions, we find that the Pax7 protein is still present in KI-positive cells. We observe a modest increase in Pax7 signal intensity in these cells, suggesting either that the insertion of exogenous sequences stabilizes the Pax7 transcript, or that the C-terminal modification of Pax7 protein with the P2A tag increases its stability. This does not affect the interpretation of the CDKN1c overexpression phenotype, because we used the Pax7-Gal4 construct that shows the same modification of Pax7 stability as a control for this experiment. We have introduced this comment in the legend of Supplementary Figure 7.

      - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level?

      Cdkn1c transcription is regulated by multiple transcription factors and non-coding RNAs (see for example Creff and Besson, 2020, or Rossi et al, 2018 for a review). To our knowledge, these studies focus more on the regulation of Cdkn1c global expression than on the regulation of its levels during cell cycle progression. Although it is very likely that transcriptional regulation contributes, post-translational regulation, and in particular degradation by the proteasome, is also a key factor in the cell cycle regulation of Cdkn1c activity

      If so, how does this differ from the promoter activity of Pax7?

      The transcriptional regulation of Pax7 and Cdkn1c is probably controlled by different regulators, since their expression profiles are very different. Regardless of the mechanisms that control their expression, the rationale for choosing Pax7 as a driver for Cdkn1c expression was that Pax7 expression precedes that of Cdkn1c in the progenitor population, and that it disappears in newborn neurons, when that of Cdkn1c peaks. This provided us with a way to advance the timing of Cdkn1c expression onset in proliferative progenitors.

      - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B.

      We will carry out experiments similar to those shown in Figure 2B in order to characterise the dynamics of Cdkn1c in a context of overexpression, in relation to pRb.

      In addition, we will include a more precise quantification of the "misexpressed" compared to "endogenous" Cdkn1c -Myc levels, as already mentioned in the answer to a request by reviewer1.

      6.-In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons.

      - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845).

      The Nowakowski non linear regression method has been used often in the literature in the same tissue, and is generally used to calculate fixed values for Tc, Ts, etc... This method is based on several selective criteria, and in particular the assumption that "all of the cells have the same cycle times". Yet, many studies have documented that cell cycle parameters change during the transition from proliferative to neurogenic modes of division during which our analysis is performed; live imaging data in the chick spinal cord have illustrated very different cell cycle durations at a given time point (see Molina et al). We therefore think that the proposed formulas do not reflect the heterogenous reality of neural progenitors of the embryonic spinal cord. However, the cumulative approach described by Nowakowski is useful to show qualitative differences between populations (e.g. a global decrease of the cycle length, like in our comparison between control and shRNA conditions). For these reasons, we prefer to display only the raw measurements rather than the regression curves.

      - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why?

      Le Dreau et al count the EdU+ proportion of cells in the total population of electroporated cells located in the VZ (which includes progenitors, but also future neurons that have been labelled during the previous cycles -at least for the time points after 2hours- and have not yet translocated to the mantle zone), whereas we only consider pRb+ progenitors in the analysis. In addition, the experiments are not performed at the same developmental stage. Altogether, this may account for the different curves obtained in our study.

      - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

      We will perform cumulative EDU incorporation experiments similar to that shown in Figure 5D to measure G1 length for the cdkn1c-sh - ccnd1-sh knock down double conditions, as well as in the Ccnd1 knock down condition alone.

      Minor comments

      __*Introduction:

      • The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. ____Nature Communications 2020).*__

      We will modify the introduction in several instances, in order to address suggestions by Reviewers #2 (see above) and #3, in particular to expand the description of the role of Cdkn1c during cortical development

      1) Transcriptional signature of the neurogenic transition (Figure 1).

      - In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).

      We have now listed the genes used to determine the progenitor and neuron score in the main text of the result section

      - Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).

      We have now added the detail of what 'filtering' means in the diagram

      - In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.

      We have reworded this sentence, adding a reference to the expression profile of Tis 21. The paragraph now reads as follows:

      « However, Cdkn1c expression is maintained longer and transiently peaks at high levels after Tis21 expression is switched off. Given that Tis21 is no more expressed in neurons (Iacopetti et al, 1999), this suggests that Cdkn1c expression is transiently upregulated in nascent neurons before fading off in more mature cells. »

      - "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.

      We have now added references linking the function and/or expression profile of these genes to the neurogenic transition: Dll1 (Henrique et al., 1995), the bHLH transcription factors Hes6 (Fior and Henrique, 2005), NeuroG1 and NeuroG2 (Lacomme et al., 2012; Sommer et al., 1996) and the coactivator Gadd45g (Kawaue et al., 2014).

      - There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

      - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper.

      We have corrected the colour code errors in Figure 1c and Supp Figure 3B (now changed to Supplementary Figure 5 in the modified revision)

      ____It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors.

      We have so far refrained from performing the suggested combined analysis based on cell cycle and cell type scores, as the "neurogenic progenitor population" (based on neurogenic progenitor score values) in which Cdkn1c expression is initiated represents a small number of cells in our scRNAseq, and felt that the significance of such an analysis is uncertain. We will perform this analysis in the revised version

      2) Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2).

      - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2).

      The scale bar is actually valid for the whole panel A. The E2 section in the original figure appeared as "large" as the E3 section along the DV axis probably because the cutting angle was not perfectly transverse at E2, artificially lengthening the section. In a new version of the figure, we have replaced the E2 images with another section from the same experiment. The scale bar remains valid for the whole panel.

      - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A.

      We have now added a diagram for the knock-in strategy in Figure 2B, and modified the legend of the figure accordingly.

      - Indicate hours post-electroporation. Indicate which guide is used in the main text.

      We have now added the post-electroporation timing and guide used in the main text.

      3) Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3).

      - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed.

      - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region?

      __We have modified this sentence as follows: "__Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal two thirds of the neural tube, except for the Pax7-Cdkn1c misexpression analysis, which was performed in the more dorsal Pax7 domain."

      This is valid both for the whole population and clonal analyses

      - Figure 3. Would have a better flow if 3C preceded 3A and 3B.

      We have modified the Figure accordingly.

      - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification.

      We have modified the Figure accordingly

      - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B.

      This explanation corresponds indeed to Figure 5A. We have corrected this mistake in the new version of the manuscript.

      4) Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).

      - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain.

      We have added references to the expression profile of Pax7 in the dorsal neural tube (Jostes et al, 1990). In addition, the new Supplementary Figure 7 shows anti-Pax7 staining that confirm this expression pattern at E3 and E4

      - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells.

      As stated in the response to Major Point 5 above, we will perform a quantification based on Myc immunofluorescence to compare endogenous Cdkn1c expression versus Cdkn1c expression upon overexpression.

      - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe.

      We have added in the main text that the quantification was performed 48hae.

      - Legend of figure 4D should indicate that the quantification has been done 24hpe.

      We have added the timing of quantification in the legend of Figure 4D.

      - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment.

      These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification:

      « We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)."

      - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections.

      This whole section has been modified in response to a question from reviewer 1. The new version does not contain percentages in the main text, and reads as follows:

      « Using the FlashTag cohort labeling approach described above, we traced the fate of daughter cells born 24 hae. We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3F). Overall, these data show that inducing a premature low-level expression of Cdkn1c in cycling progenitors is sufficient to accelerate the transition towards neurogenic modes of division. »

      - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study.

      The three guides listed in the original Supplementary Figure 4C correspond to the guides that we tested in Petit-Vargas et al. 2024. In this study, we only used the most efficient of these three guides. We have modified Figure 4C by quoting only this guide.

      5) The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5)

      - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing

      We have included references related to the activity of the CyclinD1/CDK4-6 complex in the developing CNS, and the antagonistic activities of CyclinD1 and Cdkn1c in this model

      - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation in the developing CNS (Lobjois et al, 2004, 2008, Lange 2009, Gui et al 2007), and is inhibited by Cdkn1c (Gui et al, 2007)."

      - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G.

      We have added the experimental set-up information in Figure 5.

      - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

      The analyses were carried out on two thirds of the neural tube (dorsal 2/3), excluding the ventral zone, as specified above (and in the Methods section)

      - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G.

      - For Figure 4C and D, it would be valuable to add images to illustrate the quantification.

      We have added images:

      • in Supplementary Figure 7C to illustrate what is quantified in Figures 4C (now 4C and 4D);
      • In Figure 5E to illustrate what is quantified in Figure 5D
      • In Supplementary Figure 8B to illustrate what is quantified in Figure 5G (now Figure 5H and 5I) Regarding the requested images for Figures 4D and 5F, they correspond to the same types of images already shown in Figure 3E. Since we have now added several additional examples of representative pairs of each type of mode of division in the new Supplementary Figure 4, we do not think that adding more of these images in figures 4 and 5 would strengthen the result of the quantifications.

      Discussion:

      - "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).

      We have now included the references suggested by the reviewer.

      - "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

      We propose to reformulate this paragraph in the discussion as follows to take this remark into account

      "This allows us to re-interpret the role of Cdkn1c during spinal neurogenesis: while previously mostly considered as a binary regulator of cell cycle exit in newborn neurons, we demonstrate that Cdkn1c is also an intrinsic regulator of the transition from the proliferative to neurogenic status in cycling progenitors. This occurs through a change in their mode of division, and our double knock-down experiments suggest that the onset of Cdkn1c expression may promote this change by counteracting a CyclinD1/CDK6 complex dependent mechanism."

      Other comments:

      - To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.

      We have modified the figures to systematically show the electroporated side of the neural tube on the same side of the image for single electroporations.

      ____- Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.

      This information is now provided in the figure legends (numbers of cells analysed and/or numbers of embryos), except for data in Figure 5, which are presented in a new Supplementary Table 1.

      All experiments were performed on vibratome sections, except for in situ hybridization experiments, which were performed on cryostat sections. This last information was already indicated in the relevant figure legends

      - Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.

      - There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.

      We have now homogenized the nomenclature in the figures.

      - "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

      In the original version of the manuscript, the anti-Sox2 antibody was not used; we have now added experiments using this antibody in the modified version of the manuscript; this sentence in the Methods thus remains unchanged.

      Reviewer #3 (Significance (Required)):

      __*Significance:

      In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. *__

      The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

      The strengths of the study include:

      The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

      - The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.

      - The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

      The aspects to improve:

      - The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.

      The sequencing dataset has been deposited in NCBI's Gene Expression Omnibus database. It is currently under embargo, but will be made available upon acceptance and publication of the peer reviewed manuscript. Access is nonetheless available to the reviewers via a token that can be retrieved from the Review Commons website.

      The following information will be added in the final manuscript.

      Data availability

      Single cell RNA sequencing data have been deposited in NCBI's Gene Expression Omnibus (GEO) repository under the accession number GSE273710, and are available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273710."

      - The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed. - Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

      - Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.

      - The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.

      - The presentation of the existing literature could be more up to date.

      - The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

      Field of expertise of the reviewer: neural development, cell biology, embryology.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely. By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

      This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

      This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

      Majors comments:

      1. The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions. However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified. Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
      2. Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division. This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.
      3. The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.
        • "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation. Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).
        • The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).
        • In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.
        • It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.
      4. It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).
      5. In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

      The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c. Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression. A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2). - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons. - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption? - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe? - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye. - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells? Can they provide further image examples of different types of clones? - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity? - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?. 5. In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is: - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024. - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level? If so, how does this differ from the promoter activity of Pax7? - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B. 6. In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons. - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845). - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why? - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

      Minor comments

      Introduction:

      • The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. Nature Communications 2020).

      • Transcriptional signature of the neurogenic transition (Figure 1).

        • In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).
        • Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).
        • In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.
        • "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.
        • There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

      It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors. 2. Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2). - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2). - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A. - Indicate hours post-electroporation. Indicate which guide is used in the main text. 3. Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3). - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed. - Figure 3. Would have a better flow if 3C preceded 3A and 3B. - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification. - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region? - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper. - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B. 4. Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).<br /> - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain. - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells. - For Figure 4C and D, it would be valuable to add images to illustrate the quantification. - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe. - Legend of figure 4D should indicate that the quantification has been done 24hpe. - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment. - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections. - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study. 5. The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5) - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G. - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G. - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

      Discussion:

      • "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).
      • "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

      Other comments:

      • There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.
      • To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.
      • Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.
      • Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.
      • "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

      Significance

      In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

      The strengths of the study include:

      The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

      • The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.
      • The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

      The aspects to improve:

      • The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.
      • The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed.
      • Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
      • Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.
      • The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.
      • The presentation of the existing literature could be more up to date.
      • The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

      Field of expertise of the reviewer: neural development, cell biology, embryology.

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      Referee #2

      Evidence, reproducibility and clarity

      The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

      The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

      Major comments

      I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed? At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN). Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c? Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

      Minor comments

      Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

      Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

      Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

      Significance

      The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

      I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

      My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

      Major and Minor Comments:

      Overall

      • Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.
      • Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

      Results 2

      • A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.
      • The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.
      • It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.
      • Reference should be included for pRb expression dynamics.
      • Could the Myc tag insertion approach disrupt protein function or turnover?
      • Why was the insertion target site at the C terminus chosen?
      • OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?
      • In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?
      • In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

      Results 3

      • It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).
      • Figure panels are not currently cited in order. Citation or figure order could be changed.
      • The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.
      • A supplementary figure showing the Caspase3 experiment should be added.
      • OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

      Results 4

      • How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.
      • In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.
      • In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

      Results 5

      • The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.
      • OPTIONAL Could CyclinD1 activity be directly assessed?

      General

      • Scale bars missing fig s1c s4d.
      • OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.
      • OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?
      • A model cartoon to summarise outcomes would be useful.
      • Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

      Significance

      Strengths:

      This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

      • Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
      • A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
      • Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
      • Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
      • Mechanistic insight by examining downstream target CyclinD1.
      • Clearly presented with useful illustrations throughout.
      • Logic is clear and examination thorough.
      • Conclusions are warranted on the basis of their findings.

      Limitations

      • This study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.
      • Some aspects of quantification require explanation in order for the experiments to be replicated.
      • It is imperative that precise sample sizes are included for all experiments presented.

      Advance:

      • First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.
      • Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.
      • Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

      Audience:

      Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

       Please define your field of expertise with a few keywords to help the authors contextualize your point Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

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

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      Reply to the reviewers

      Manuscript number: RC-2025-02860

      Corresponding author(s): Duncan, Sproul

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

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

      1. General Statements [optional]

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

      We thank the reviewers for recognizing that our work contributes 'both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development' and their insightful suggestions to improve the manuscript. We note that the reviewers suggest that the data are 'comprehensive', 'well-controlled', 'rigorously done' and 'diligently analysed'.

      Our planned revisions focus on further elucidating the broader implications of our findings for partially methylated domain formation in cancer, the effects of the methylation changes we observe on gene expression and the potential mechanisms underpinning the formation of the hypermethylated domains we observe.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      We have reproduced the reviewer's comments in their entirety and highlighted them in blue italics.

      February 21, 2025*RE: Review Commons Refereed Preprint #RC-2025-02860 *

      *Kafetzopoulos *

      DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A

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

      The DNA methylation landscape is frequently altered in cancers, which may contribute to genome misregulation and cancer cell behavior. One phenomenon is the emergence of "partially methylated domains (PMDs)": intermediately methylated regions of the genome that are generally heterochromatic and late replicating. The prevailing explanation is that the DNA methyltransferase, DNMT1, is not able to maintain DNAme levels at late replicating sites in proliferating cancer cells. This could result in genome instability. In this study, Kafetzopoulos and colleagues interrogated this possibility using a common laboratory colorectal cancer cell line (HCT116). Additionally, they utilized a DNMT1 mutant line that they refer to as a knockout, even though, more accurately, it is a hypomorphic truncation. They performed several genomic assays, such as whole genome bisulfite sequencing, ChIP and repli-seq, in order to assess the effect of reduced DNMT1 activity. While expectedly, global DNAme levels are decreased, they discovered a subset of PMDs gain DNA methylation, which they term hyperPMDs. There seems to be no impact on DNA replication timing, but the authors did go on to show that the de novo DNA methyltransferase, DNMT3Α, is likely responsible for this counterintuitive increase in DNAme levels.

      *Reviewer #1 (Significance (Required)): *

      Overall, I found the data well-presented and diligently analyzed, as we have come to expect from the Sproul group. However, I am somewhat at a loss to understand both the rationale for the experimental set-up and the meaning of the results. The HCT116 cell line is already transformed but was treated as though it was a wild-type control. I was more curious to see how the PMD chromatin state and replication compare to a healthy cell.

      We focused on the comparison between WT and DNMT1 KO cells as we wanted to understand the role of DNMT1 in maintaining the organisation of the cancer methylome. We agree that, strictly, this could differ from its role in normal cells. However, we are unaware of a suitable cell line to test the consequences of DNMT1 KO in normal colon cells and testing this in vivo would be beyond the time-scale of a manuscript revision.

      To further understand the relevance of our findings in the context of carcinogenesis, we propose to analyse data derived from normal and cancerous colon tissue in the revised manuscript. Preliminary analysis shows that HCT116 PMDs are hypomethylated in a colorectal tumour but not in the normal colon (revision plan figure 1). This suggests that HCT116 cells are a model that can be used to understand PMD formation in tumours and we will extend this analysis in the revised manuscript. We will also add discussion of the caveat that DNMT1 may function differently in normal tissues and cancer cells.

      Note, revision plan figure 1 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 1. HCT116 PMDs are hypomethylated in colorectal tumours. Heatmaps and pileup plots of HCT116, normal colon and colorectal tumour DNA methylation levels for HCT116 PMDs (n=546 domains) and HMDs (n=558 domains). DNA methylation levels are mean % mCpG. PMDs and HMDs are aligned and scaled to the start and end points of each domain and ranked based on their mean methylation levels in HCT116 cells. Colon and tumour data re-analysed from a previous publication (Berman et al 2011, PMID: 22120008).

      Moreover, the link between late replication and PMDs would indicate that a DNMT1 gain-of- function line would potentially be more interesting: could more increased DNMT activity rescue the PMDs, and how would this impact the chromatin and replication states? Perhaps this is not trivial to create; I do not know if simply overexpressing DNMT1 and/or UHRF1 could act as a gain-of-function.

      We agree with the reviewer that a DNMT1 overexpression or a gain-of-function mutation cell line would be interesting to analyse and potentially informative as to the mechanism of PMD formation. However, as the reviewer notes, this is a complex experiment that could require the overexpression of partners such as UHRF1 or generation of an unknown gain-of-function mutation. In addition, the full dissection of the implications of this separate experimental strategy would entail the repetition of the majority of our experiments in DNMT1 KO cells. Instead, in the revised manuscript, we will focus on a related experiment suggested by reviewer 2 and ask whether re-expression of DNMT1 rescues DNA methylation patterns DNMT1 KO cells.

      Nevertheless, the appearance of hyperPMDs was a curious finding worth publishing. However, it is unclear what the biological relevance is. There is no effect on replication timing, and no assessment on cell behavior (eg, proliferation assays).* In other words, is DNMT3A performing some kind of compensatory action, or is it just a curiosity? Below in the significance section, I have highlighted some additional specific points *

      PMDs are important to study because cancer-associated hypomethylation is believed to drive carcinogenesis through genomic instability (Eden et al 2003, PMID: 12702868). However, the mechanisms underpinning their formation remain unclear. At present the predominant hypothesis is that PMDs emerge in heterochromatin because their late replication timing leaves insufficient time for re-methylation following DNA replication (Zhou et al 2018, PMID: 29610480 and Petryk et al 2021, PMID: 33300031). We believe that our observations of hypermethylated PMDs in DNMT1 KO cells provides important evidence contrary to this hypothesis because they disconnect domain-level methylation patterns from the replication timing program. Our work instead suggests that the localization of de novo DNMTs plays a key role in the formation of PMDs by protecting euchromatin from hypomethylation.

      To further explore this hypothesis, we propose to analyze data derived from tumours in our revised manuscript to understand the degree to which our findings are reflected in vivo. As shown above, our preliminary analysis suggests that HCT116 cell PMDs are also hypomethylated in a colorectal tumour but not the normal colon (revision plan figure 1). We will also analyze how the changes in methylome affect gene expression using our RNA-seq data.

      - Why were DNMT3A and 3B transgenes used for ChIP instead of endogenous proteins? I know the authors cited work justifying this strategy, but this still merits explanation. Also, the expression level of transgenes compared to the endogenes was not shown (neither protein nor RNA level).

      DNMT3A and B transgenes were used because antibodies against the endogenous proteins are not suitable for ChIP. Furthermore, performing these experiments using endogenously tagged proteins, required generating 3 knock-in tagged lines (we have already generated HCT116 cells with tagged DNMT3B, Masalmeh et al 2021, PMID: 33514701).

      We have previously shown that our constructs do indeed result in overexpression of DNMT3B compared to endogenous protein in this system (Masalmeh et al 2021, PMID: 33514701). However, our previous results also demonstrate that overexpressed DNMT3B recapitulates the localization of the endogenously tagged protein to the genome (Taglini et al 2024, PMID: 38291337). Others have similarly demonstrated that ectopically expressed DNMT3A and DNMT3B can be used to understand their localization on the genome (Baubec et al 2015, PMID: 25607372 and Weinberg et al 2019, PMID: 31485078).

      To address this point, we propose to add further justification of our approach and discussion of this potential limitation to a revised version of the manuscript.

      - The DNMT3A binding profile appears as though it is on the edges of the PMDs and fairly depleted within (Fig 4A,D). Could the authors comment on this?

      This is an interesting point. We note that although mean DNMT3A signal is indeed higher at the edges of hypermethylated PMDs than inside these domains, its levels are both above background and the levels observed in HCT116 cells. As suggested by reviewer 3, this could be consistent with H3K36me2 and DNMT3A spreading in from the boundaries of hypermethylated PMDs in DNMT1 KO cells. We propose to add discussion of this possibility to the revised version of the manuscript.

      - A more compelling experiment would be to assess the loss of DNMT3A genetically. How would this affect PMD DNA methylation? Maybe in this case there would be an effect on replication timing. Could a KO or KD (eg, siRNA) strategy be employed to assess this on top of either the HCT116 or DNMT1 KO.

      As the reviewer suggests, functional experiments aimed at understanding the role of DNMT3A in our system are likely to be informative. We therefore propose to include such experiments in a revised version of the manuscript.

      - What is the major H3K36me2 methylatransferase in these cells? Could an Nsd1 KO or KD strategy be used, for example, to show that indeed H3K36 methylation is required for HyperPMDs? This would complement the DNMT3A experiment above.

      H3K36 methylation is thought to be deposited in the mammalian genome by at least 8 different methyltransferase enzymes, NSD1, NSD2, NSD3, ASH1L, SETD2, SETMAR, SMYD2 and SETD3 (Wagner and Carpenter 2023, PMID: 22266761). To understand which of these might be responsible for the deposition of H3K36me2 in hypermethylated PMDs, we have examined their expression in HCT116 and DNMT1 KO cells using our RNA-seq data. This suggests that 5 of these enzymes are highly expressed in HCT116 cells and their expression levels are similar in DNMT1 KO cellsrevision plan figure 2). The other 3 putative methyltransferases have lower expression levels and, although SMYD2 is significantly upregulated in DNMT1 KO cells, its expression remains low (revision plan figure 2). It is currently unclear whether SMYD2 is a bona fide H3K36 methyltransferase (Wagner and Carpenter 2023, PMID: 22266761). We also note that in a recent study, cells lacking NSD1, NSD2, NSD3, ASH1L and SETD2 had no detectable H3K36 methylation, although expression levels of SMYD2 were not reported (Shipman et al, 2024. PMID: 39390582). Based on this analysis, it is therefore unclear which enzyme(s) might be responsible for H3K36me2 deposition in hypermethylated PMDs and delineation of this enzyme would require multiple perturbation and sequencing experiments. We therefore suggest that assessing the consequences of knocking out H3K36me2 methyltransferase activity on hypermethylated PMDs is beyond the scope of a manuscript revision. We propose to include discussion of the expression of the different H3K36me2 depositing enzymes in the revised manuscript.

      Note, revision plan figure 2 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 2. HCT116 cells express multiple H3K36 methyltransferases. Barplot of mean expression levels for putative mammalian H3K36 methyltransferases in HCT116 and DNMT1 KO cells. Expression levels are counts per million (CPM) derived from RNA-seq. Mean expression levels are derived from 9 and 4 independent cultures of HCT116 and DNMT1 KO cells respectively.

      - Based on Figure 2C, it seems that a general predictive pattern of hyperPMDs is H3K9me3-enriched and H3K27me3-depleted. Is this an accurate interpretation? Given the authors' expertise in the relationship between DNMT3A and polycomb, could they perhaps give an explanation for this phenomenon?

      The reviewer is correct. In HCT116 cells, those PMDs that become hypermethylated in DNMT1 KO cells are marked by H3K9me3 and are H3K27me3-depleted (except at their boundaries). DNMT3A is recruited to polycomb-marked regions associated with H3K27me3 through interaction of its N-terminal region with H2AK119ub. However, this mark is depleted from hypermethylated-PMDs in DNMT1 KO cells (current manuscript Figure S5D) meaning that this pathway of recruitment is unlikely to explain DNMT3A's localisation to these regions in DNMT1 KO cells. This is discussed in the current manuscript:

      We and others have reported that DNMT3A is also recruited to the polycomb-associated H2AK119ub mark through its N-terminal region (Chen et al, 2024; Gretarsson et al, 2024; Gu et al, 2022; Wapenaar et al, 2024; Weinberg et al, 2021). However, we do not observe the polycomb-associated H3K27me3 mark, which is generally tightly correlated with H2AK119ub (Ku et al, 2008), at hypermethylated PMDs suggesting that H2AK119ub does not play a role in the recruitment of DNMT3A to these regions.

      Furthermore, DNMT3A's localisation is predominantly driven by its PWWP-dependent H3K36me2 recruitment pathway unless its PWWP domain is mutated (Heyn et al 2019, PMID: 30478443, Sendžikaitė et al 2019, PMID: 31015495, Kibe et al 2021, PMID: 34048432 and Weinberg et al, 2021, PMID: 33986537). Our observations of DNMT3A at hypermethylated PMDs marked by H3K36me2 is therefore consistent with previous findings. We propose to discuss this point in the revised manuscript.

      - This is a minor point, but calling the DNMT1 mutant a "KO" seemed a bit misleading, as it is a truncation mutant. Perhaps there is a more accurate way to describe this line.

      We propose to amend the manuscript to reflect this point as suggested by the reviewer. To ensure our responses are consistent with the reviewer comments we continue to refer to this line as DNMT1 KO cells in our revision plan.

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      *In this study, Kafetzopoulos et al. investigated the role of DNMT1-mediated methylation maintenance in cancer partially methylated domains (PMDs) using DNMT1 knockout HCT116 colorectal cancer cells. They used a range of sequencing-based approaches, including whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and replication timing sequencing (Repli-seq), to define the dynamics of DNA methylation loss and gain in PMDs during DNA synthesis. Interestingly, they demonstrate that specific PMDs marked by H3K9me3 undergo a gain of DNA methylation in DNMT1-deficient HCT116 cells. This increase in methylation is associated with the loss of H3K9me3, an enrichment of H3K36me2, and the recruitment of DNMT3A. These findings suggest that de novo methyltransferase activity plays a critical role in determining which genomic regions become PMDs in cancer. *

      *The authors use a comprehensive and well-controlled set of sequencing-based techniques. While the sequencing depth for DNA methylation is somewhat limited, the inclusion of multiple biological replicates strengthens the reliability of the data. The study effectively integrates multiple layers of epigenomic information, providing a nuanced view of PMD regulation in the context of DNMT1 loss. *

      *Overall, this paper provides valuable insights into the epigenetic regulation of PMDs in cancer, and its conclusions are well supported by the data. It significantly advances our understanding of how DNMT1 loss reshapes the epigenome and highlights the interplay between de novo and maintenance methylation mechanisms in cancers. *

      ------------------------------------------------------------------------------

      *Reviewer #2 (Significance (Required)): *

      General assessment

      -The main strength of the study lies in the clear presentation of the data, which follows a cohesive and well-defined storyline.

      *-The authors demonstrate that both hypomethylated and hypermethylated domains occur at the late replication stage. They further investigate the dynamics of histone modifications and DNA methylation, focusing on the acquisition and loss of these marks, particularly in relation to DNMT3A and DNMT3B. *

      Limitation

      -Although the study is compelling, its primary limitation is the correlative nature of most of the data. While the high-level representations (e.g., tracks, heat maps) are convincing, the study would have been more informative if it had explored the impact of these changes on a specific set of genes or regions critical to cancer initiation and progression. For example, in the DNMT1 knockout model, how does the loss of H3K9me3, the gain of H3K36me2, and the recruitment of DNMT3A in hypermethylated PMDs affect the expression of key genes involved in colorectal cancer?

      To understand how the remodeling of DNA methylation and chromatin structure in DNMT1 KO cells affects gene expression, we propose to include an analysis of our RNA-seq data in the revised manuscript. We will also cross reference these results and our ChIP-seq with lists of colorectal cancer genes.

      Additional experiments that could provide deeper insights

      -Cross-validation in other cancer cell lines would have enable to define if these signatures are observed beyond HCT116.

      As the reviewer suggests, we propose to undertake analyses of additional samples in the revised manuscript to understand how our findings relate to domain-level methylation patterns beyond HCT116 cells. As noted above in response to reviewer 1, our preliminary analysis suggests our findings are relevant for primary colorectal tumours (revision plan figure 1).

      -Are the observed signatures permanent, or could they be reversed by reinstating the full activity of DNMT1? Since DNMT1 might be dysregulated but never completely deleted.

      To address this suggestion, we propose to include the results of a DNMT1 rescue experiment in the revised manuscript.

      -Use knockdown and overexpression experiments to track the dynamics and occurrence of these molecular events over time, providing insight into the progression and reversibility of epigenetic changes.

      This is an interesting suggestion. As the reviewer suggests, we propose to analyse data derived from time-course experiments to understand the dynamics of changes in different genomic compartments following perturbation of DNMT1.

      Advances

      -The study provides new insights into the establishment of PMD types in colorectal cancer cell lines.

      -These findings contribute both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development.

      Audience:

      -This study will appeal to a broad audience, from researchers primarily focused on epigenetics and cancer biology to those interested in the mechanistic underpinnings of DNA methylation and its role in cancer progression. It will also be relevant to those exploring therapeutic strategies targeting epigenetic regulators in cancer.

      We thank the reviewer for their kind comments on our manuscript.

      ------------------------------------------------------------------------------

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

      Summary:*Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells. *

      General comments:The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C- D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

      The reviewer makes an interesting point about the potential for H3K27me3 to act as a boundary preventing H3K36me2 spread into PMDs. Multiple studies have shown that H3K36me2 restricts H3K27me3 deposition in the genome (Streubel et al 2018, PMID: 29606589, Shirane et al 2020, PMID: 32929285 and Farhangdoost et al 2021, PMID: 3362635). The structural nature of this inhibitory effect has also been resolved, demonstrating that the PRC2 catalytic subunit, EZH2 directly binds H3K36 and this is inhibited when the residue is methylated (Jani et al 2019, PMID: 30967505, Finogenova et al 2020, PMID: 33211010 and Cookis et al 2025, PMID: 39774834). The effect of H3K27me3 on H3K36me2 is less well characterised. However, previous work has suggested that inhibiting EZH2 leads to elevated H3K36me2 being established on newly replicated chromatin (Alabert et al 2020, PMID: 31995760). Expression of the EZH2-inhibiting oncohistone H3.3K27M has also been reported to lead to increased H3K36me2 dependent on NSD1/2 in diffuse intrinsic pontine gliomas (DIPG) (Stafford et al 2018, PMID: 30402543 and Yu et al 2021, PMID: 34261657). However, this increase was not reported by an independent study of H3.3K27M DIPG cells (Harutyunyan et al 2020, PMID: 33207202) and the molecular basis of the effect of H3K27me3 on H3K36me2 remains unclear.

      As the reviewer suggests, we propose to explore the relationship between H3K27me3 and H3K36me2 further in a revised manuscript along with the including further discussion of previous findings in this area.

      Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

      The relationship between H3K36me2 and H3K9me3 is far less explored than that of H3K27me3 and H3K36me2. Interestingly, we note that a recent study reported that depletion of H3K36me2 results in H3K9me3 re-distribution suggesting that H3K9me3 is restricted by H3K36me2 (Padilla et al 2024, DOI: 10.1101/2024.08.10.607446, also cited in the original manuscript).

      To understand this relationship further, we therefore propose to explore the relationship between H3K9me3 and H3K36me2 in our datasets as part of revised manuscript along with including additional discussion of relevant experimental findings.

      In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

      In accordance with the reviewer's suggestion, we propose to re-organise the revised manuscript to make it easier to follow.

      Specific Comments/Questions:

      • An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context**
* As suggested by the reviewer, we will amend the manuscript to include an expanded discussion of the DNMT1 truncation present in the cell line.

      • Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?**
*

      While differences between cancer cell line and tumour methylation patterns have previously been noted (for example Anne Rogers et al 2018, PMID: 30559935), we have previously demonstrated that HCT116 cells recapitulate CpG island methylation patterns observed in colorectal tumours (Masalmeh et al 2021, PMID: 33514701). As stated above in response to reviewer 1, we have now examined the methylation status of HCT116 PMDs in a colorectal tumour. This analysis shows that HCT116 PMDs have reduced methylation levels in a colorectal tumour but not in the normal colon (revision plan figure 1). We propose to extend this analysis of colorectal tumour samples and add them to the revised manuscript to address this point.

      Regarding the expression of DNMTs in colorectal tumours, DNMT1 is ubiquitously expressed to our knowledge. DNMT3B is reported to be overexpressed in 15-20% of cases of colorectal cancer, often as a result of amplification (Nosho et al 2009, PMID: 19470733, Ibrahim et al 2011, PMID: PMID: 21068132, Zhang et al 2018, PMID: 30468428 and Mackenzie et al 2020, PMID: 32058953). DNMT3A expression in colorectal tumours is less studied but one report suggests upregulation in at least some tumours (Robertson et al 1999, PMID: 10325416 and Zhang et al 2018, PMID: 30468428). We propose to add additional discussion of DNMT expression in colorectal cancer to the revised manuscript to clarify the degree to which our results reflect methylation regulation in primary colorectal tumours.

      • Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.**
*

      As part of our work towards revising the manuscript, we have undertaken blots of DNMT3A in our cell lines. This shows that DNMT3A levels in DNMT1 KO cells are similar to those in HCT116 cells which (revision plan figure 3). We propose to include this in the revised manuscript alongside a similar analysis of DNMT3B. We will also include an analysis of T7-DNMT3A and T7-DNMT3B levels to understand whether they are expressed to similar levels in HCT116 and DNMT1 KO cells.

      Note, revision plan figure 3 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 3. DNMT3A protein levels are similar in HCT116 and DNMT1 KO cells. Left, representative DNMT3A Western blot. Right, bar plot quantifying relative DNMT3A levels. The bar height indicates the mean levels observed in protein extracts from 3 independent cell cultures. Individual points indicate the level of each replicate.

      • Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time?*

      The reviewer makes an interesting point with regard to a potential alteration of DNMT3A residence at hypermethylated PMDs. Given that ChIP-seq signal is affected by residence time (Schmiedeberg et al 2009, PMID: 19247482), it is possible that our findings could reflect this rather than increased DNMT3A localisation. We propose to add discussion of this point as a limitation of the current study to the manuscript.

      It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).*
*

      As the reviewer suggests, we will include the data on HMDs to the main Figure 4 and include enrichments at all PMDs in the supplementary figures.

      • It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.*

      We had shown a single locus for consistency and to not overcomplicate figures which already contain multiple panels. As the reviewer suggests, we will add additional loci in the supplementary figures of our revised manuscript. We had also included the chromosome co-ordinates in the figures. In the revised version we will ensure that the precise co-ordinates are included in the legends.

      • The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.**
*

      We thank the reviewer for this point and we propose to examine the quantification of the ChIP-seq without normalizing to input to ensure that uneven input signal does not substantially contribute to our results.

      • Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.*

      We will clarify the explanation of n numbers in the revised manuscript.

      *Reviewer #3 (Significance (Required)): *

      This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

      We thank the reviewer for their insightful comments and believe that our proposed revisions will further clarify the points they raise.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      We have not yet incorporated revisions into the manuscript.

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

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      As stated in our responses to the reviewer comments above, we plan to address all comments. However, we suggest that two experiments proposed by the reviewers are beyond the scope of a manuscript revision and we will instead address these comments in the following manner:

      Analysis of a DNMT1 gain-of-function line (Reviewer 1). As suggested by the reviewer such a line is non-trivial to generate. It would also require extensive profiling of this new line to fully understand its implications for our findings. We therefore believe it is outwith the scope of a manuscript revision. Instead, we propose to address this comment by undertaking the related experiment suggested by Reviewer 2 and perform a DNMT1 rescue experiment in the DNMT1 KO line. Analysis of H3K36me2 methyltransferase knockout cells (Reviewer 1). Our preliminary analysis suggests that HCT116 cells express multiple H3K36 methyltransferases and that their expression does not vary greatly in DNMT1 KO cels (revision plan figure 2). This means that it is unclear which enzyme(s) might be responsible for depositing H3K36me2 in hypermethylated PMDs. Delineation of this would require the generation and analysis of multiple knockouts and we suggest it is therefore outwith the scope of a manuscript revision. To address this point we will instead include discussion of the spectrum of H3K36 methyltransferases expressed in our cells in the revised manuscript as detailed in the specific response above.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells.

      General comments:

      The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C-D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

      Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

      In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

      Specific Comments/Questions:

      1. An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context.
      2. Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?
      3. Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.
      4. Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time? It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).
      5. It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.
      6. The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.
      7. Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.

      Significance

      This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

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      Referee #2

      Evidence, reproducibility and clarity

      In this study, Kafetzopoulos et al. investigated the role of DNMT1-mediated methylation maintenance in cancer partially methylated domains (PMDs) using DNMT1 knockout HCT116 colorectal cancer cells. They used a range of sequencing-based approaches, including whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and replication timing sequencing (Repli-seq), to define the dynamics of DNA methylation loss and gain in PMDs during DNA synthesis. Interestingly, they demonstrate that specific PMDs marked by H3K9me3 undergo a gain of DNA methylation in DNMT1-deficient HCT116 cells. This increase in methylation is associated with the loss of H3K9me3, an enrichment of H3K36me2, and the recruitment of DNMT3A. These findings suggest that de novo methyltransferase activity plays a critical role in determining which genomic regions become PMDs in cancer.

      The authors use a comprehensive and well-controlled set of sequencing-based techniques. While the sequencing depth for DNA methylation is somewhat limited, the inclusion of multiple biological replicates strengthens the reliability of the data. The study effectively integrates multiple layers of epigenomic information, providing a nuanced view of PMD regulation in the context of DNMT1 loss.

      Overall, this paper provides valuable insights into the epigenetic regulation of PMDs in cancer, and its conclusions are well supported by the data. It significantly advances our understanding of how DNMT1 loss reshapes the epigenome and highlights the interplay between de novo and maintenance methylation mechanisms in cancers.

      Significance

      General assessment

      • The main strength of the study lies in the clear presentation of the data, which follows a cohesive and well-defined storyline.
      • The authors demonstrate that both hypomethylated and hypermethylated domains occur at the late replication stage. They further investigate the dynamics of histone modifications and DNA methylation, focusing on the acquisition and loss of these marks, particularly in relation to DNMT3A and DNMT3B.

      Limitation

      • Although the study is compelling, its primary limitation is the correlative nature of most of the data. While the high-level representations (e.g., tracks, heat maps) are convincing, the study would have been more informative if it had explored the impact of these changes on a specific set of genes or regions critical to cancer initiation and progression. For example, in the DNMT1 knockout model, how does the loss of H3K9me3, the gain of H3K36me2, and the recruitment of DNMT3A in hypermethylated PMDs affect the expression of key genes involved in colorectal cancer?

      Additional experiments that could provide deeper insights

      • Cross-validation in other cancer cell lines would have enable to define if these signatures are observed beyond HCT116.
      • Are the observed signatures permanent, or could they be reversed by reinstating the full activity of DNMT1? Since DNMT1 might be dysregulated but never completely deleted.
      • Use knockdown and overexpression experiments to track the dynamics and occurrence of these molecular events over time, providing insight into the progression and reversibility of epigenetic changes.

      Advances

      • The study provides new insights into the establishment of PMD types in colorectal cancer cell lines.
      • These findings contribute both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development.

      Audience:

      • This study will appeal to a broad audience, from researchers primarily focused on epigenetics and cancer biology to those interested in the mechanistic underpinnings of DNA methylation and its role in cancer progression. It will also be relevant to those exploring therapeutic strategies targeting epigenetic regulators in cancer.
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      Referee #1

      Evidence, reproducibility and clarity

      The DNA methylation landscape is frequently altered in cancers, which may contribute to genome misregulation and cancer cell behavior. One phenomenon is the emergence of "partially methylated domains (PMDs)": intermediately methylated regions of the genome that are generally heterochromatic and late replicating. The prevailing explanation is that the DNA methyltransferase, DNMT1, is not able to maintain DNAme levels at late replicating sites in proliferating cancer cells. This could result in genome instability. In this study, Kafetzopoulos and colleagues interrogated this possibility using a common laboratory colorectal cancer cell line (HCT116). Additionally, they utilized a DNMT1 mutant line that they refer to as a knockout, even though, more accurately, it is a hypomorphic truncation. They performed several genomic assays, such as whole genome bisulfite sequencing, ChIP and repli-seq, in order to assess the effect of reduced DNMT1 activity. While expectedly, global DNAme levels are decreased, they discovered a subset of PMDs gain DNA methylation, which they term hyperPMDs. There seems to be no impact on DNA replication timing, but the authors did go on to show that the de novo DNA methyltransferase, DNMT3Α, is likely responsible for this counterintuitive increase in DNAme levels.

      Significance

      Overall, I found the data well-presented and diligently analyzed, as we have come to expect from the Sproul group. However, I am somewhat at a loss to understand both the rationale for the experimental set-up and the meaning of the results. The HCT116 cell line is already transformed but was treated as though it was a wild-type control. I was more curious to see how the PMD chromatin state and replication compare to a healthy cell. Moreover, the link between late replication and PMDs would indicate that a DNMT1 gain-of-function line would potentially be more interesting: could more increased DNMT activity rescue the PMDs, and how would this impact the chromatin and replication states? Perhaps this is not trivial to create; I do not know if simply overexpressing DNMT1 and/or UHRF1 could act as a gain-of-function. Nevertheless, the appearance of hyperPMDs was a curious finding worth publishing. However, it is unclear what the biological relevance is. There is no effect on replication timing, and no assessment on cell behavior (eg, proliferation assays). In other words, is DNMT3A performing some kind of compensatory action, or is it just a curiosity? Below in the significance section, I have highlighted some additional specific points

      • Why were DNMT3A and 3B transgenes used for ChIP instead of endogenous proteins? I know the authors cited work justifying this strategy, but this still merits explanation. Also, the expression level of transgenes compared to the endogenes was not shown (neither protein nor RNA level).
      • The DNMT3A binding profile appears as though it is on the edges of the PMDs and fairly depleted within (Fig 4A,D). Could the authors comment on this?
      • A more compelling experiment would be to assess the loss of DNMT3A genetically. How would this affect PMD DNA methylation? Maybe in this case there would be an effect on replication timing. Could a KO or KD (eg, siRNA) strategy be employed to assess this on top of either the HCT116 or DNMT1 KO.
      • What is the major H3K36me2 methylatransferase in these cells? Could an Nsd1 KO or KD strategy be used, for example, to show that indeed H3K36 methylation is required for HyperPMDs? This would complement the DNMT3A experiment above.
      • Based on Figure 2C, it seems that a general predictive pattern of hyperPMDs is H3K9me3-enriched and H3K27me3-depleted. Is this an accurate interpretation? Given the authors' expertise in the relationship between DNMT3A and polycomb, could they perhaps give an explanation for this phenomenon?
      • This is a minor point, but calling the DNMT1 mutant a "KO" seemed a bit misleading, as it is a truncation mutant. Perhaps there is a more accurate way to describe this line.
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      Reply to the reviewers

      We thank the reviewers for their feedback on our paper. We have taken all their comments into account in revising the manuscript. We provide a point-by-point response to their comments, below.

      Reviewer #1

      Major comments:

      The manuscript is clearly written with a level of detail that allows others to reproduce the imaging and cell-tracking pipeline. Of the 22 movies recorded one was used for cell tracking. One movie seems sufficient for the second part of the manuscript, as this manuscript presents a proof-of-principle pipeline for an imaging experiment followed by cell tracking and molecular characterisation of the cells by HCR. In addition, cell tracking in a 5-10 day time-lapse movie is an enormous time commitment.

      My only major comment is regarding "Suppl_data_5_spineless_tracking". The image file does not load. It looks like the wrong file is linked to the mastodon dataset. The "Current BDV dataset path" is set to "Beryl_data_files/BLB mosaic cut movie-02.xml", but this file does not exist in the folder. Please link it to the correct file.

      We have corrected the file path in the updated version of Suppl. Data 5.

      Minor comments:

      The authors state that their imaging settings aim to reduce photo damage. Do they see cell death in the regenerating legs? Is the cell death induced by the light exposure or can they tell if the same cells die between the movies? That is, do they observe cell death in the same phases of regeneration and/or in the same regions of the regenerating legs?

      Yes, we observe cell death during Parhyale leg regeneration. We have added the following sentence to explain this in the revised manuscript: "During the course of regeneration some cells undergo apoptosis (reported in Alwes et al., 2016). Using the H2B-mRFPruby marker, apoptotic cells appear as bright pyknotic nuclei that break up and become engulfed by circulating phagocytes (see bright specks in Figure 2F)."

      We now also document apoptosis in regenerated legs that have not been subjected to live imaging in a new supplementary figure (Suppl. Figure 3), and we refer to these observations as follows: "While some cell death might be caused by photodamage, apoptosis can also be observed in similar numbers in regenerating legs that have not been subjected to live imaging (Suppl. Figure 3)."

      Based on 22 movies, the authors divide the regeneration process into three phases and they describe that the timing of leg regeneration varies between individuals. Are the phases proportionally the same length between regenerating legs or do the authors find differences between fast/slow regenerating legs? If there is a difference in the proportions, why might this be?

      Both early and late phases contribute to variation in the speed of regeneration, but there is no clear relationship between the relative duration of each phase and the speed of regeneration. We now present graphs supporting these points in a new supplementary figure (Suppl. Figure 2).

      To clarify this point, we have added the following sentence in the manuscript: "We find that the overall speed of leg regeneration is determined largely by variation in the speed of the early (wound closure) phase of regeneration, and to a lesser extent by variation in later phases when leg morphogenesis takes place (Suppl. Figure 2 A,B). There is no clear relationship between the relative duration of each phase and the speed of regeneration (Suppl. Figure 2 A',B')."

      Based on their initial cell tracing experiment, could the authors elaborate more on what kind of biological information can be extracted from the cell lineages, apart from determining which is the progenitor of a cell? What does it tell us about the cell population in the tissue? Is there indication of multi- or pluripotent stem cells? What does it say about the type of regeneration that is taking place in terms of epimorphosis and morphallaxis, the old concepts of regeneration?

      In the first paragraph of Future Directions we describe briefly the kind of biological information that could be gained by applying our live imaging approach with appropriate cell-type markers (see below). We do not comment further, as we do not currently have this information at hand. Regarding the concepts of epimorphosis and morphallaxis, as we explain in Alwes et al. 2016, these terms describe two extreme conditions that do not capture what we observe during Parhyale leg regeneration. Our current work does not bring new insights on this topic.

      Page 5. The authors mention the possibility of identifying the cell ID based on transcriptomic profiling data. Can they suggest how many and which cell types they expect to find in the last stage based on their transcriptomic data?

      We have added this sentence: "Using single-nucleus transcriptional profiling, we have identified approximately 15 transcriptionally-distinct cell types in adult Parhyale legs (Almazán et al., 2022), including epidermis, muscle, neurons, hemocytes, and a number of still unidentified cell types."

      Page 6. Correction: "..molecular and other makers.." should be "..molecular and other markers.."

      Corrected

      Page 8. The HCR in situ protocol probably has another important advantage over the conventional in situ protocol, which is not mentioned in this study. The hybridisation step in HCR is performed at a lower temperature (37˚C) than in conventional in situ hybridisation (65˚C, Rehm et al., 2009). In other organisms, a high hybridisation temperature affects the overall tissue morphology and cell location (tissue shrinkage). A lower hybridisation temperature has less impact on the tissue and makes manual cell alignment between the live imaging movie and the fixed HCR in situ stained specimen easier and more reliable. If this is also the case in Parhyale, the authors must mention it.

      This may be correct, but all our specimens were treated at 37˚C, so we cannot assess whether hybridisation temperature affects morphological preservation in our specimens.

      Page 9. The authors should include more information on the spineless study. What been is spineless? What do the cell lineages tell about the spineless progenitors, apart from them being spread in the tissue at the time of amputation? Do spineless progenitors proliferate during regeneration? Do any spineless expressing cells share a common progenitor cell?

      We now point out that spineless encodes a transcription factor. We provide a summary of the lineages generating spineless-expressing cells in Suppl. Figure 6, and we explain that "These epidermal progenitors undergo 0, 1 or 2 cell divisions, and generate mostly spineless-expressing cells (Suppl. Figure 5)."

      Page 10. Regarding the imaging temperature, the Materials and Methods state "... a temperature control chamber set to 26 or 27˚C..."; however, in Suppl. Data 1, 26˚C and 29˚C are indicated as imaging temperatures. Which is correct?

      We corrected the Methods by adding "with the exception of dataset li51, imaged at 29{degree sign}C"

      Page 10. Regarding the imaging step size, the Materials and Methods state "...step size of 1-2.46 µm..."; however, Suppl. Data 1 indicate a step size between 1.24 - 2.48 µm. Which is correct?

      We corrected the Methods.

      Page 11. Correct "...as the highest resolution data..." to "...at the highest resolution data..."

      The original text is correct ("standardised to the same dimensions as the highest resolution data").

      Page 11. Indicate which supplementary data set is referred to: "Using Mastodon, we generated ground truth annotations on the original image dataset, consisting of 278 cell tracks, including 13,888 spots and 13,610 links across 55 time points (see Supplementary Data)."

      Corrected

      p. 15. Indicate which supplementary data set is referred to: "In this study we used HCR probes for the Parhyale orthologues of futsch (MSTRG.441), nompA (MSTRG.6903) and spineless (MSTRG.197), ordered from Molecular Instruments (20 oligonucleotides per probe set). The transcript sequences targeted by each probe set are given in the Supplementary Data."

      Corrected

      Figure 3. Suggestion to the overview schematics: The authors might consider adding "molting" as the end point of the red bar (representing differentiation).

      The time of molting is not known in the majority of these datasets, because the specimens were fixed and stained prior to molting. We added the relevant information in the figure legend: "Datasets li-13 and li-16 were recorded until the molt; the other recordings were stopped before molting."

      Figure 4B': Please indicate that the nuclei signal is DAPI.

      Corrected

      Supplementary figure 1A. Word is missing in the figure legend: ...the image also shows weak...

      Corrected

      Supplementary Figure 2: Please indicate the autofluorescence in the granular cells. Does it correspond to the yellow cells?

      Corrected

      Video legend for video 1 and 2. Please correct "H2B-mREFruby" to "H2B-mRFPruby".

      Corrected

      Reviewer #2

      Major comments:

      MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

      This paper describes a technical advance. While previous work (Alwes et al. 2016) established some key elements of our live imaging approach, we were not at that time able to record the entire time course of leg regeneration (the longest recordings were 3.5 days long). Here we present a method for imaging the entire course of leg regeneration (up to 10 days of imaging), optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining in cuticularised adult legs (an important technical breakthrough in this experimental system), which we combine with live imaging to determine the fate of tracked cells. We have revised the abstract and introduction of the paper to point out these novelties, in relation to our previous publications.

      In the abstract we explain: "Building on previous work that allowed us to image different parts of the process of leg regeneration in the crustacean Parhyale hawaiensis, we present here a method for live imaging that captures the entire process of leg regeneration, spanning up to 10 days, at cellular resolution. Our method includes (1) mounting and long-term live imaging of regenerating legs under conditions that yield high spatial and temporal resolution but minimise photodamage, (2) fixing and in situ staining of the regenerated legs that were imaged, to identify cell fates, and (3) computer-assisted cell tracking to determine the cell lineages and progenitors of identified cells. The method is optimised to limit light exposure while maximising tracking efficiency."

      The introduction includes the following text: "Our first systematic study using this approach presented continuous live imaging over periods of 2-3 days, capturing key events of leg regeneration such as wound closure, cell proliferation and morphogenesis of regenerating legs with single-cell resolution (Alwes et al., 2016). Here, we extend this work by developing a method for imaging the entire course of leg regeneration, optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining of gene expression in cuticularised adult legs, which we combine with live imaging to determine the fate of tracked cells."

      MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

      The primary goal of this work was to establish a robust method for continuous long-term live imaging of regeneration, but we do appreciate that a more quantitative analysis would add value to the data we are presenting. We tried to address this request in three steps:

      First, we examined whether clear temporal patterns in cell division, cell movements or other cellular features can be observed in an accurately tracked dataset (li13-t4, tracked in Sugawara et al. 2022). To test this we used the feature extraction functions now available on the Mastodon platform (see link). We could discern a meaningful temporal pattern for cell divisions (see below); the other features showed no interpretable pattern of variation.

      Second, we asked whether we could use automated cell tracking to analyse the patterns of cell division in all our datasets. Using an Elephant deep learning model trained on the tracks of the li13-t4 dataset, we performed automated cell tracking in the same dataset, and compared the pattern of cell divisions from the automated cell track predictions with those coming from manually validated cell tracks. We observed that the automated tracks gave very imprecise results, with a high background of false positives obscuring the real temporal pattern (see images below, with validated data on the left, automated tracking on the right). These results show that the automated cell tracking is not accurate enough to provide a meaningful picture on the pattern of cell divisions.

      Third, we tried to improve the accuracy of detection of dividing cells by additional training of Elephant models on each dataset (to lower the rate of false positives), followed by manual proofreading. Given how labour intensive this is, we could only apply this approach to 4 additional datasets. The results of this analysis are presented in Figure 4.

      MC 3. The authors describe the challenges faced by their described approach: Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

      Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

      In the revised manuscript we have added data on the tracking performance of Elephant in relation to imaging depth in Suppl. Figure 3. These data confirm our original statement (which was based on manual tracking) that nuclei are more challenging to track in deeper layers.

      We point to these new results in two parts of the paper, as follows: "A smaller proportion of cells are trackable in the deeper layers (see Suppl. Figure 3)", and "Our results, summarised in Table 1A, show that the detection of nuclei can be enhanced by doubling the z resolution at the expense of xy resolution and image quality. This improvement is particularly evident in the deeper layers of the imaging stacks, which are usually the most challenging to track (Suppl. Figure 3)."

      MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

      We think that similar trade-offs will apply to all our datasets because tracking performance is constrained by the same features, which are intrinsic to our system; e.g. by the crowding of nuclei in relation to axial resolution, or the speed of mitosis in relation to the temporal resolution of imaging. We therefore do not see a clear rationale for repeating this analysis. On a practical level, our existing image datasets could not be subsampled to generate the various conditions tested in Table 1, so proving this point experimentally would require generating new recordings, and tracking these to generate ground truth data. This would require months of additional work.

      A second, related question is whether Elephant would perform equally well in detecting and tracking nuclei across different datasets. This point has been addressed in the Sugawara et al. 2022 paper, where the performance of Elephant was tested on diverse fluorescence datasets.

      Reviewer #3

      Major comments:

      The authors should clearly specify what are the key technical improvements compared to their previous studies (Alwes et al. 2016, Elife; Konstantinides & Averof 2014, Science). There, the approaches for mounting, imaging, and cell tracking are already introduced, and the imaging is reported to run for up to 7 days in some cases.

      In Konstantinides and Averof (2014) we did not present any live imaging at cellular resolution. In Alwes et al. (2016) we described key elements of our live imaging approach, but we were never able to record the entire time course of leg regeneration. The longest recordings in that work were 3.5 days long.

      We have revised the abstract and introduction to clarify the novelty of this work, in relation to our previous publications. Please see our response to comment MC1 of reviewer 2.

      While the authors mention testing the effect of imaging parameters (such as scanning speed and line averaging) on the imaging/tracking outcome, very little or no information is provided on how this was done beyond the parameters that they finally arrived to.

      Scan speed and averaging parameters were determined by measuring contrast and signal-to-noise ratios in images captured over a range of settings. We have now added these data in Supplementary Figure 1.

      The authors claim that, using the acquired live imaging data across entire regeneration time course, they are now able to confirm and extend their description of leg regeneration. However, many claims about the order and timing of various cellular events during regeneration are supported only by references to individual snapshots in figures or supplementary movies. Presenting a more quantitative description of cellular processes during regeneration from the acquired data would significantly enhance the manuscript and showcase the usefulness of the improved workflow.

      The events we describe can be easily observed in the maximum projections, available in Suppl. Data 2. Regarding the quantitative analysis, please see our response to comment MC2 of reviewer 2.

      Table 1 summarizes the performance of cell tracking using simulated datasets of different quality. However only averages and/or maxima are given for the different metrics, which makes it difficult to evaluate the associated conclusions. In some cases, only 1 or 2 test runs were performed.

      The metrics extracted from each of the three replicates, per dataset, are now included in Suppl. Data 4.

      We consistently used 3 replicates to measure tracking performance with each of the datasets. The "replicates" column label in Table 1 referred to the number of scans that were averaged to generate the image, not to the replicates used for estimating the tracking performance. To avoid confusion, we changed that label to "averaging".

      OPTIONAL: An imaging approach that allows using the current mounting strategy but could help with some of the tradeoffs is using a spinning-disk confocal microscope instead of a laser scanning one. If the authors have such a system available, it could be interesting to compare it with their current scanning confocal setup.

      Preliminary experiments that we carried out several years ago on a spinning disk confocal (with a 20x objective and the CSU-W1 spinning disk) were not very encouraging, and we therefore did not pursue this approach further. The main problem was bad image quality in deeper tissue layers.

      Minor comments:

      The presented imaging protocol was optimized for one laser wavelength only (561 nm) - this should be mentioned when discussing the technical limitations since animals tend to react differently to different wavelengths. Same settings might thus not be applicable for imaging a different fluorescent protein.

      In the second paragraph of the Results section, we explain that we perform the imaging at long wavelengths in order to minimise photodamage. It should be clear to the readers that changing the excitation wavelength will have an impact for long-term live imaging.

      For transferability, it would be useful if the intensity of laser illumination was measured and given in the Methods, instead of just a relative intensity setting from the imaging software. Similarly,more details of the imaging system should be provided where appropriate (e.g., detector specifications).

      We have now measured the intensity of the laser illumination and added this information in the Methods: "Laser power was typically set to 0.3% to 0.8%, which yields 0.51 to 1.37 µW at 561 nm (measured with a ThorLabs Microscope Slide Power Sensor, #S170C)."

      Regarding the imaging system and the detector, we provide all the information that is available to us on the microscope's technical sheets.

      The versions of analysis scripts associated with the manuscript should be uploaded to an online repository that permanently preserves the respective version.

      The scripts are now available on gitbub and online repositories. The relevant links are included in the revised manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      Çevrim et al. provide a method for confocal live imaging of the entire leg regeneration process in the crustacean Parhyale. This approach allows imaging for up to 10 days with cellular resolution. Subsequently, the data can be used for cell tracking and inferring lineage relationships. The authors explore strategies for doing so efficiently, as well as the associated tradeoffs. They also provide modifications to the HCR RNA FISH protocol that allow applying it in their system to asses marker gene expression at the imaging endpoints (and cross-referencing it with the live imaging data).

      Major comments

      • The authors should clearly specify what are the key technical improvements compared to their previous studies (Alwes et al. 2016, Elife; Konstantinides & Averof 2014, Science). There, the approaches for mounting, imaging, and cell tracking are already introduced, and the imaging is reported to run for up to 7 days in some cases.
      • While the authors mention testing the effect of imaging parameters (such as scanning speed and line averaging) on the imaging/tracking outcome, very little or no information is provided on how this was done beyond the parameters that they finally arrived to.
      • The authors claim that, using the acquired live imaging data across entire regeneration time course, they are now able to confirm and extend their description of leg regeneration. However, many claims about the order and timing of various cellular events during regeneration are supported only by references to individual snapshots in figures or supplementary movies. Presenting a more quantitative description of cellular processes during regeneration from the acquired data would significantly enhance the manuscript and showcase the usefulness of the improved workflow.
      • Table 1 summarizes the performance of cell tracking using simulated datasets of different quality. However only averages and/or maxima are given for the different metrics, which makes it difficult to evaluate the associated conclusions. In some cases, only 1 or 2 test runs were performed.
      • OPTIONAL: An imaging approach that allows using the current mounting strategy but could help with some of the tradeoffs is using a spinning-disk confocal microscope instead of a laser scanning one. If the authors have such a system available, it could be interesting to compare it with their current scanning confocal setup.

      Minor comments

      • The presented imaging protocol was optimized for one laser wavelength only (561 nm) - this should be mentioned when discussing the technical limitations since animals tend to react differently to different wavelengths. Same settings might thus not be applicable for imaging a different fluorescent protein.
      • For transferability, it would be useful if the intensity of laser illumination was measured and given in the Methods, instead of just a relative intensity setting from the imaging software. Similarly,more details of the imaging system should be provided where appropriate (e.g., detector specifications).
      • The versions of analysis scripts associated with the manuscript should be uploaded to an online repository that permanently preserves the respective version.

      Significance

      As the authors point out, live imaging the entirety of a regeneration process can provide valuable insights but is not easy to perform. Many organisms cannot be easily mounted or immobilized for a sufficiently long time, and the imaging conditions might be too stressful. The manuscript provides methods for overcoming these issues in Parhyale, an interesting and tractable arthropod model system for limb regeneration. Additional tools to analyze the acquired movies and cross-reference them with stainings at the endpoint are also provided. As such, it will be a valuable resource for researchers investigating regeneration in this this organism, or in similar settings where parts of the workflow can be adapted. In the present form, however, it is unclear what are the major improvements to previous work and the key steps to achieve them. Similarly, presenting a more detailed analysis of the already acquired data would not only serve to showcase the improved protocols but also make the study of interest to the broader regeneration community.

      My expertise: post-embryonic development, whole-body regeneration, cell specification

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors describe a workflow for preparing and imaging leg regeneration in the marine crustacean Paryhale hawaiensis. The method relies on a heatshock-inducible fluorescent histone reporter and captures the regenerating limbs of a mid-sized adult. The authors expand on the prior work that established this transgenic line (Wolff et al. 2018), determined optimal spatial and temporal sampling rates (Alwes et al. 2016), and established the use of an incremental deep learning framework to perform robust automated cell tracking (Sugawara et al. 2022), by jointly optimizing the heatshock induction, imaging parameters, tracking implementation, and integrating in situ end-point analysis to capture the entirety of the regeneration process over an incredibly long window (up to 10 days).

      Major comments

      MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

      MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

      MC 3. The authors describe the challenges faced by their described approach:

      Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

      Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

      MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

      Significance

      As a microscopist and practitioner of large-scale timelapse image acquisition and analysis, my general assessment is that the integration of such complex and data intensive experiments is non-trivial. The study's primary strengths include: 1. Novel capabilities for continuous recording and analysis of limb regeneration in a crustacean model where previous approaches were limited to piecemeal analyses. 2. The assessment of variability in the regeneration timecourse enabled by this approach. And 3. The integration of in situ endpoint analysis enabling retrospective analysis of cell lineage and terminal fate. The study's primary limitation is the lack of quantitative analysis of the resulting datasets, what this reviewer feels is one of the most promising capabilities afforded by this approach. The primary advances described in this manuscript are twofold. First, incremental optimization of imaging and image analysis approaches enabling continuous long-term imaging and robust cell tracking. Second, the potential for the integrated assessment of cellular scale behaviors and tissue level events during regeneration alongside analysis of cell fate endpoints that can be aligned to the time lapse data with cellular precision. This work will be of interest to a somewhat specialized audience, especially given the methods-intensive focus of the manuscript, particularly to microscopists, researchers interested in the biology of regeneration who may be interested in using the method, and developmental or cell biologists working with Paryhale who might benefit from adopting the long-term imaging protocols for other questions. Aligning with my expertise, my review focuses principally on the data analysis and tracking performance characterization aspects of the manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Çevrim et al. presents a live-imaging method that covers the entire regeneration process of crustacean legs at a resolution that allows for cell tracking and identification of cells based on their gene expression profiles.

      Previous work by this group imaged and described the early events of leg regeneration in the crustacean Parhyale hawaiensis (Alwes et al., 2016). Parhyale was also used in the current study because it meets important criteria for live imaging studies: the leg cuticle is optically transparent and does not change in size prior to molting. The authors first recorded 22 time-lapse movies using confocal microscopy, starting immediately after amputation and ending with the fully regenerated leg. They used the movies to describe the entire regeneration of the leg and divided the process into three phases: 1) wound closure, 2) cell proliferation and morphogenesis, and 3) differentiation. There is variability in the duration of these phases, but by highlighting cellular and morphological differences, the authors were able to distinguish the phases from each other. The authors then used one of the 22 time-lapse movies to sub-sample and test the influence of different imaging parameters (z-spacing, time interval, and image quality) on the reliability of cell tracking and the time required for proofreading of the cell lineages, while keeping the phototoxicity on the embryo as low as possible. They achieved this for the upper tissue layer (top 30 µm). For combined semi-automated and manual cell tracking, they used an improved version of the Mastodon add-on Elephant. This cell-tracking software, previously published by the group (Sugawara et al., 2022), now has a backtracking function. It runs in Fiji and is open source. Finally, the regenerated leg used for cell tracking was fixed to perform in situ hybridisation against a gene called spineless. The spineless expressing cells were then aligned with the corresponding cells in the time-lapse movie. This allowed them to associate these cells with cell lineages and identify the progenitor cells at the time of leg amputation. To achieve this, they had to establish the HCR in situ protocol for adult legs. A previous study by the group provided them with transcriptomic data (Sinigaglia et al., 2022), which can inform them about the potential cell types in the leg for future studies.

      In summary, this study presents a method to image the complete leg regeneration process at a spatial and temporal resolution, which allows for cell tracking and the addition of molecular information to the cells in the leg.

      Major comments:

      The manuscript is clearly written with a level of detail that allows others to reproduce the imaging and cell-tracking pipeline. Of the 22 movies recorded one was used for cell tracking. One movie seems sufficient for the second part of the manuscript, as this manuscript presents a proof-of-principle pipeline for an imaging experiment followed by cell tracking and molecular characterisation of the cells by HCR. In addition, cell tracking in a 5-10 day time-lapse movie is an enormous time commitment.

      My only major comment is regarding "Suppl_data_5_spineless_tracking". The image file does not load. It looks like the wrong file is linked to the mastodon dataset. The "Current BDV dataset path" is set to "Beryl_data_files/BLB mosaic cut movie-02.xml", but this file does not exist in the folder. Please link it to the correct file.

      Minor comments:

      The authors state that their imaging settings aim to reduce photo damage. Do they see cell death in the regenerating legs? Is the cell death induced by the light exposure or can they tell if the same cells die between the movies? That is, do they observe cell death in the same phases of regeneration and/or in the same regions of the regenerating legs?

      Based on 22 movies, the authors divide the regeneration process into three phases and they describe that the timing of leg regeneration varies between individuals. Are the phases proportionally the same length between regenerating legs or do the authors find differences between fast/slow regenerating legs? If there is a difference in the proportions, why might this be?

      Based on their initial cell tracing experiment, could the authors elaborate more on what kind of biological information can be extracted from the cell lineages, apart from determining which is the progenitor of a cell? What does it tell us about the cell population in the tissue? Is there indication of multi- or pluripotent stem cells? What does it say about the type of regeneration that is taking place in terms of epimorphosis and morphallaxis, the old concepts of regeneration?

      Page 5. The authors mention the possibility of identifying the cell ID based on transcriptomic profiling data. Can they suggest how many and which cell types they expect to find in the last stage based on their transcriptomic data?

      Page 6. Correction: "..molecular and other makers.." should be "..molecular and other markers.."

      Page 8. The HCR in situ protocol probably has another important advantage over the conventional in situ protocol, which is not mentioned in this study. The hybridisation step in HCR is performed at a lower temperature (37˚C) than in conventional in situ hybridisation (65˚C, Rehm et al., 2009). In other organisms, a high hybridisation temperature affects the overall tissue morphology and cell location (tissue shrinkage). A lower hybridisation temperature has less impact on the tissue and makes manual cell alignment between the live imaging movie and the fixed HCR in situ stained specimen easier and more reliable. If this is also the case in Parhyale, the authors must mention it.

      Page 9. The authors should include more information on the spineless study. What been is spineless? What do the cell lineages tell about the spineless progenitors, apart from them being spread in the tissue at the time of amputation? Do spineless progenitors proliferate during regeneration? Do any spineless expressing cells share a common progenitor cell?

      Page 10. Regarding the imaging temperature, the Materials and Methods state "... a temperature control chamber set to 26 or 27˚C..."; however, in Suppl. Data 1, 26˚C and 29˚C are indicated as imaging temperatures. Which is correct?

      Page 10. Regarding the imaging step size, the Materials and Methods state "...step size of 1-2.46 µm..."; however, Suppl. Data 1 indicate a step size between 1.24 - 2.48 µm. Which is correct?

      Page 11. Correct "...as the highest resolution data..." to "...at the highest resolution data..."

      Page 11. Indicate which supplementary data set is referred to: "Using Mastodon, we generated ground truth annotations on the original image dataset, consisting of 278 cell tracks, including 13,888 spots and 13,610 links across 55 time points (see Supplementary Data)."

      p. 15. Indicate which supplementary data set is referred to: "In this study we used HCR probes for the Parhyale orthologues of futsch (MSTRG.441), nompA (MSTRG.6903) and spineless (MSTRG.197), ordered from Molecular Instruments (20 oligonucleotides per probe set). The transcript sequences targeted by each probe set are given in the Supplementary Data."

      Figure 3. Suggestion to the overview schematics: The authors might consider adding "molting" as the end point of the red bar (representing differentiation).

      Figure 4B': Please indicate that the nuclei signal is DAPI.

      Supplementary figure 1A. Word is missing in the figure legend: ...the image also shows weak...

      Supplementary Figure 2: Please indicate the autofluorescence in the granular cells. Does it correspond to the yellow cells?

      Video legend for video 1 and 2. Please correct "H2B-mREFruby" to "H2B-mRFPruby".

      Mette Handberg-Thorsager, Jan Huisken

      Significance

      The optimisation of the imaging and cell tracking parameters presented in this study allows the authors to follow the cells throughout leg regeneration, a milestone that will increase the attractiveness of Parhyale for regeneration studies. The authors have also shown that it is possible to add a cell ID to the cells using HCR in situ staining, which will allow them to decipher the gene-regulatory-networks during cell specification and differentiation in future studies.

      Why and how some animals regenerate are fascinating questions in biology. The process of regeneration is therefore being studied in many organisms. A key point is the development of methods to do so, in particular the development of cell lineage tracing techniques combined with molecular profiling of cells to understand the origin of the newly formed cells. In the case of Parhyale, Wolff et al. (2018) traced the cells during leg development. Previous work by the Averof group (Alwes et al. 2016) described the initial phases of leg regeneration. Therefore, the present manuscript represents an important step towards a full understanding of the complete leg regeneration process and the cells that contribute to the reformation of this tissue.

      The potential audience interested in this manuscript is broad because of the variety of tools presented in this manuscript (microscopy, software development, cell tracking, and HCR) and the biological question behind the manuscript (cellular and molecular contribution to leg regeneration). This includes scientists working in live imaging, regeneration biology, and software developers.

      My field of expertise:

      Animal development and regeneration.

      Live imaging (including long term) using spinning disk confocal microscopy and light sheet microscopy.

      Cell tracking using Mastodon and TGMM.

      Limited expertise:

      Software development.

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      Reply to the reviewers

      Manuscript number: RC- 2025-02880

      Corresponding author(s): Monica, Gotta

      1. General Statements [optional]

      We thank the reviewers for their useful comments that will improve our manuscript and make it clearer. We agree with Reviewer 1 that SDS-22 has more general functions in cellular processes by maintaining GSP-1/-2 levels, rather than only regulating cell polarity. We have now modified our conclusion in the text (all changes are highlighted in yellow) and we hope that it is now more clear and better explained. Below we address the reviewer’s comments one by one and indicate how we have or will address the comments in the final version. We expect the revisions to take 2-3 months.

      2. Description of the planned revisions

      Major comments

      Reviewer 1

      (1) Overall, the evidence supporting the core finding that SDS-22 is required for normal GSP-1/2 levels is strong and well documented. The experiments were performed well and controls, statistics, replicates were appropriate. Our only slight reservation was whether the effect of sds-22(RNAi) on stability may be overstated due to the use of GFP fusions to GSP-1/2 for this analysis. The authors note these alleles are hypomorphic, potentially raising the possibility that GFP tags destabilise the proteins and make them more prone to degradation. Ideally this would be repeated with an untagged allele via Western (e.g. Peel et al 2017 for relevant antibodies).

      We thank the reviewer for the general comments. To address this important point on the protein levels we have requested GSP-1 and GSP-2 antibodies reported in Peel et al and Tzur et al (Peel et al, 2017; Tzur et al, 2012). The published GSP-1 antibody has been used in western blot, and the GSP-2 antibody has been used in both immunostaining and Western blot analysis. Despite our efforts, we were not able to detect GSP-2 neither on western blots nor on immunostainings with the aliquot we have received. On the opposite, GSP-1 antibodies worked well on western blot. We have already measured the GSP-1 levels in SDS-22 depleted embryos (N=2, see below) and we observed reduced levels, confirming our initial result. However, as the reviewer rightly pointed out, the levels are reduced by 20% (rather than about 50% as in the GFP strain), suggesting that indeed the GFP fusion does contribute to the instability. We will measure GSP-1 levels in at least an additional sds-22(RNAi) experiment and in sds-22(E153A) embryos.

      Left, Western Blot of embryonic extracts from N2 in ctrl(RNAi) and sds-22(RNAi) embryos. Tubulin is used as a loading control. Right, Fold change of GSP-1 normalized to Tubulin levels. N = 2.

      Since we could not detect endogenous GSP-2 with the antibodies we have received, we will generate an OLLAS-tagged GSP-2 strain. OLLAS is a commonly used tag consisting of 14 amino acids (Park et al, 2008), with an additional 4 amino acids as a linker. The tag is much smaller than mNeonGreen, which consists of approximately 270 amino acids. We will then measure the GSP-2 levels using the ollas antibody in sds-22(RNAi) embryos. We will also cross this strain with sds-22(E153A) and measure OLLAS::GSP-2 levels in this mutant. If this strain is not embryonic lethal, as in the case of the mNG::gsp-2; sds-22(E153A) (Fig EV6A), it will also suggest that ollas::gsp-2 does not behave as hypomorph.

      These data will complement the data shown in Fig 6.

      (2) The role for SDS-22 in polarity is rather weak. Both the SDS-22 depletion phenotypes and the ability of SDS-22 depletion to suppress pkc-3(ts) polarity phenotypes are modest (and weaker in than GSP-2 depletion). For example, the images in Figure 1B appear striking, but from Movie S1 it is clear that this isn't a full rescue as PAR-2 is initially uniformly enriched on the cortex (rather than mostly cytoplasmic) and it is never fully cleared. In the movie, the clearance at the point of pronuclear meeting is very modest. Quantitation might be helpful here (i.e. as in Figure 3G). As the authors state, it seems that SDS-22 does not have a specific role in polarity beyond the general effect on GSP-1/2 levels. This does not undermine the core message of the paper, but we would recommend downplaying the conclusions with respect to contributing to polarity establishment. For example "...suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos" implies a regulatory role for SDS-22 in polarity, but we would interpret it as simply helping reduce aberrant degradation of GSP-1/2 and this impacts a variety of cellular processes including polarity.

      We agree with the reviewer that the rescue of pkc-3ts polarity defects by SDS-22 depletion is not as strong as GSP-2 depletion, and as suggested, we have re-quantified the phenotype, as we did in Fig 3G, as shown below in Fig 1C.

      This has replaced Fig.1 in the manuscript.

      Accordingly, we have clarified this in the text in several locations. We have added “partial” rescue in many places and modified conclusions in the results and discussion. The changes are all highlighted and the major ones are also below:

      From Result Line 119-121, page 5:

      “In contrast, depletion of SDS-22 resulted in PAR-2 localization being restricted to the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

      To: Result Line 122-125, page 5

      “In contrast, depletion of SDS-22 resulted in PAR-2 localization being enriched in the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B,C) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

      • *

      From Result Line 172-175, page 7:

      “Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2 in polarity establishment.”

      To: Result Line 178-181, page 7

      “Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, partially suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2.”

      From Result Line 256-257, page 10:

      “suggesting that the interaction of SDS-22 with the PP1 phosphatases is important for polarity establishment.”

      To: Result Line 264-265, page 10

      “suggesting that the interaction of SDS-22 with the PP1 phosphatases contributes to polarity establishment”

      • *

      From Result Line 311-313, page 12:

      To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases.

      To: Result Line 319-322, page 12

      To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases, as shown by phospho-histone H3 (Ser10) levels. This suggests that SDS-22 plays a general role in regulating GSP-1 and GSP-2, which is not specific to cell polarity.

      From Result Line 391-392, page 15:

      In summary, our results show that SDS-22 maintains the levels of GSP-1 and GSP-2 by protecting them

      392 from proteasome mediated degradation.

      To: Result Line 402-403, page 15

      In summary, these data show that SDS-22 is important to maintain the levels of GSP-1 and GSP-2 by protecting them from proteasome mediated degradation.

      We have also rephrased our conclusion according to Reviewer 1’s suggestion.

      From Introduction Line 95-101, Page 4:

      Here we show that SDS-22 depletion rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C), similarly to the depletion of GSP-2. Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish SDS-22 as a regulator of PAR polarity establishment in the C. elegans one-cell embryo and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024).

      To: Introduction Line 96-101, Page 4

      *Here we show that SDS-22 depletion partially rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C). Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish that SDS-22 contributes to cell polarity by regulating GSP-1/-2 levels and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024). *

      From Discussion Line 417-420, page 17:

      Depletion of SDS-22, or mutation of its E153 residue (E153A) important for SDS-22-PP1 interaction resulted in reduced GSP-1/-2 protein levels, decreased dephosphorylation of a PP1 substrate, and a smaller PAR-2 domain, suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos.

      To: Discussion Line 426-429, page 17

      *Here we find that a conserved PP1 regulator, SDS-22, when depleted, results in a smaller PAR-2 domain and can partially rescue the polarity defects of a pkc-3(ne4246) mutant. We demonstrate that SDS-22 contributes to the activity of GSP-1/-2 by protecting them from proteasomal degradation and maintaining their protein levels. *

      Add new discussion to Discussion Line 429-432, page 17:

      Taken together, our data suggest that the role of SDS-22 in polarity is indirect via the regulation of GSP-1/-2 levels. In support of this, SDS-22 depletion results in broader GSP-1/-2 dependent phenotypes such as increased Phospho-H3 (Ser10) (Fig 5) and centriole duplication defects in later-stage embryos (Peel et al., 2017).

      • *

      (3) Specificity of SDS-22 effects on polarity. SDS-22 (or GSP-1/2) depletion is likely to have effects on many pathways. We wondered whether some of the polarity phenotypes may not be specifically due to changes in the PAR-2 phosphorylation cycle as implied.

      One candidate is the actomyosin cortex. It was noticeable that control and sds-22 embryos were different: In Movies S1, S2, and S3 control embryos show either stronger or more persistent cortical ruffling or pseudocleavage furrows. This is also visible in Figure 3A. Is it possible that disruption of SDS-22 reduces cortical flows (time, intensity or duration) and could this explain the small reduction in anterior PAR-2 spreading and thus the slightly smaller domain size measured in Figures 1B and 3A.

      We have noticed that SDS-22 depletion results in less ruffling and reduced pseudocleavage furrows. To properly address this question we should have a condition in which we can rescue the cortical flow reduction in the SDS-22 depletion and measure the PAR-2 domain. Since we do not know how SDS-22 reduces the flows, we could not come up with a clean experiment to address this issue and are happy to have suggestions.

      We believe that the most rigorous way to address this issue, as reviewer 1 points out, is to clearly address this limitation in the text. We have now added this in the discussion:

      Discussion Line 463-466, page 18:

      Consistent with GSP-2 reduced levels, SDS-22 depleted or E153A mutant embryos also have a smaller PAR-2 domain. However, since these embryos also show reduced cortical ruffling (Movie EV1,2) and are smaller (Fig EV2C) we cannot exclude that these two phenotypes also contribute to the smaller size of the PAR-2 domain.

      • *

      A potentially related issue could be embryo size. sds-22 embryos generally seem to be smaller than wild-type (e.g. Figure 1B(left), 4A(left column), and particularly EV3). Is this consistently true? Could cell size effects change the ability of embryos to clear anterior PAR-2 domains as described in EV3? Klinkert et al (2018, biorXiv) note that reducing the size of air-1(RNAi) embryos reduces the frequency of bipolar PAR-2 domains.

      Quantification of perimeter of embryos at pronuclear meeting in live zygotes. Sample size (n) is indicated in the graph, each dot represents a single embryo and mean is shown. N = 5. The P value was determined using two-tailed unpaired Student’s t test.

      We quantified the perimeter of the embryos and as seen by quantification, there is a weak but significant decrease of size in the absence of SDS-22, and in SDS-22(E153A) mutant, as shown above. We have now added the data of the RNAi in the supplementary information and mentioned it in the results.

      Results Line 129, page 5:

      SDS-22 depleted embryos also displayed a smaller size (Fig EV2C).

      Klinkert et al reported that reducing the size of air-1(RNAi) embryos by depletion of ANI-2, a homolog of the actomyosin scaffold protein anillin, reduces the frequency of bipolar PAR-2 domains (Klinkert et al, 2018). In the image shown in the paper on bioRxiv, the PAR-2 domain appears small but there are no quantifications and these data have been removed from the published paper.

      From published data, a smaller embryo size does not appear to correlate with smaller PAR-2 domain. Chartier et al show that depletion of ANI-2 reduces embryo size without changing the relative anterior PAR-6 domain (Chartier et al, 2011), thereby suggesting that the posterior PAR-2 domain should not change either. In addition, Hubatsch et al reported that small embryos depleted of ima-3 tend to have larger PAR-2 domains, whereas larger embryos depleted of C27D9.1 exhibit smaller PAR-2 domains (Hubatsch et al, 2019), which is the opposite of what we see. We do not believe that the smaller PAR-2 domain is the important message of our paper. Our main question was whether PAR-2 was cortical or not and since GSP-2 had a smaller domain, we decided to quantify the PAR-2 domain length in the different RNAi conditions and mutants. Since RNAi of C27D9.1 which makes embryos bigger, results in a small PAR-2 domain, again we do not know how to experimentally address this question, unless the reviewer has a suggestion. As for the point above, we will clearly highlight this limitation in the discussion (see our reply to the previous point, now it is in Discussion Line 463-466, page 18).

      We would stress that these comments relate to interpreting the polarity phenotypes and do not undermine the core finding that SDS-22 stabilises GSP-1/2.

      We thank the reviewer and we hope that by performing the experiments mentioned above and by changing the text, their comments are properly addressed.

      Reviewer 2

      Major comment: Consistent with the model that PP1 activity is reduced in the absence of SDS-22, the authors show that a surrogate PP1 target (phospho-histone H3) becomes hyper-phosphorylated. To strengthen the study, the authors could consider performing an OPTIONAL experiment (see below) of assaying the phosphorylation status of PAR-2 itself, as this is proposed to be the target of both PKC-3 and PP1, and represent the mechanism of PAR-2 polarization.

      We thank the reviewer for this comment and also for pointing out that there is technical difficulty in the proposed experiment.

      We have already attempted to address this point without success in Calvi et al (Calvi et al, 2022), using western blot analysis (see below). For this we used the GFP::PAR-2 strain and used a GFP antibody (shown below in the left panel), as none of the anti-PAR-2 antibodies (neither the ones produced by us nor the ones produced by other laboratories) were working on western blot. We observed several bands of GFP::PAR-2 but were not able to determine if these represented phosphorylated forms or to compare the ratio of phosphorylated to unphosphorylated PAR-2. We did use λ-PPase in the embryonic extracts but we did not always observe a clear difference. We show three experiments below.

      Left, __Western blots of gfp::par-2 embryonic extract in the presence or absence of λ-PPase (+/- PhosSTOP) and probed with anti-GFP and anti-Tubulin antibodies. Right,__ Representative images of fixed embryos with indicated genotypes at one-, two- and four-cell stages. DNA (DAPI) is gay. Scale bars, 5 μm. Anterior is to the left and posterior to the right.

      One possible explanation is that the role of GSP-1/-2 in PAR-2 dephosphorylation is specific to the very early embryos. As shown in the right panel above, despite PAR-2(RAFA) remaining cytoplasmic in one- and two-cell embryos due to lack of binding to GSP-1/-2, it can localize to internal cortices in four-cell stage embryos, similarly to the control and suggesting that in later embryos other mechanisms are intervening. One limitation of our Western Blot is that it is not possible to isolate only early embryos, which are a minority in a mixed population of embryos. This may mask difference of phosphorylation status of PAR-2 in the early stages.

      For the revision, we plan to blot PAR-2 using GFP antibody in gfp::par-2 embryo lysates, with both control and sds-22(RNAi) treatment. We will also compare the GFP::PAR-2 bands between gfp::par-2 and gfp::par-2; sds-22(E153A) mutant samples. We are not very hopeful and our failures with gsp-1/2 RNAi (unpublished) are why we did not try with SDS-22 but it is definitely worth giving it a go and we will.

      As for Hao et al (Hao et al, 2006) the result was quite clear. In this paper however, the authors used a transgene strain of PAR-2. We have never tried to use a transgene (the proteins are usually overexpressed) but we can deplete SDS-22 in a PAR-2 transgene as well and see if a difference is observed.



      Reviewer 3

      Major comments: major issues affecting the conclusions

      Overall, the authors' conclusions are supported by their data. The data and methods are presented clearly, with appropriate replicates and statistics. Here I propose two experiments to strengthen the link between some of their data and their claims. These experiments could take a month or two to complete.

      Experiment 1

      It would be helpful if the authors could show that blocking the proteasome in the zygote restores GSP-1/-2 levels in the absence of SDS-22 or even better in the SDS-22(E153A) mutant. This would provide more direct evidence to support their claim that SDS-22 regulates polarity by protecting PP1 from proteasomal degradation. While they are currently conducting this experiment in the germline, they cannot assess polarity there. However, in the zygote, they would be able to examine the PAR-2 domain (polarity). To do this, the authors could permeabilise the embryos and apply a proteasome inhibitor.

      This would be a straightforward experiment if we were using culture cells. One problem with the set up is that much of the protein of the one-cell embryo is inherited from the egg and the reduction in SDS-22 depletion or mutant happens already in the germline (Fig 6-7). Even if the proteasome is inhibited in embryos, the whole division process only takes 20 minutes and we wonder whether the timing will be sufficient to inhibit the proteasome, produce more protein and rescue the phenotype. We will try, as only this will tell us.

      One alternative approach would be to apply the proteasome inhibitor to adult worms in liquid culture for several hours before dissection. This would aim to inhibit degradation in the germline, therefore allowing us to test whether GSP-1/-2 levels are restored in the embryos with SDS-22 disruption. However, proteasome inhibition in the germline impairs oogenesis (Shimada et al, 2006), suggesting that we might incur in the same problem (unless we succeed in timing the inhibition).

      One additional experiment that we will try is to deplete other proteasomal subunits that result in a lower level or proteasomal activity reduction. As reported by Fernando et al (Fernando et al, 2022), depletion of RPN-9, -10, or -12 impairs proteasomal activity, but worms remain fertile.

      Quantification of mNG::GSP-2 and GFP::GSP-1fluorescence intensity in rpn-12, rpn-9, and rpn-10(RNAi) normalized to ctrl(RNAi). Mean is shown and error bars indicate SD. Dots in graphs represent individual embryo measurements and sample size (n) is indicated inside the bars in the graph. N = 1.

      So far, our data suggest that the GSP-1/-2 levels are weakly but significantly increased in the embryos (16.8% for GSP-2 and 12.5% for GSP-1) following RPN-12 depletion (see above). We will co-deplete RPN-12 and SDS-22 to assess if the protein levels of GSP-1/-2 are rescued. We will also deplete RPN-12 in gfp::gsp-1; sds-22(E153A) strains to test if GSP-1 levels are rescued. We cannot measure GSP-2 levels in mNG::GSP-2; sds-22(E153A) because they are embryonic lethal (see details below in the reply to minor comments of Reviewer 3).

      Left, Representative midsection images of gfp::gsp-1 and gfp::gsp-1;sds-22(E153A) embryos in ctrl(RNAi) and rpn-12(RNAi).__ Right, __Quantification of GFP::GSP-1 intensity levels. N = 1.

      Our preliminary data showed that similar to germlines (Fig 7G-I), RPN-12 depletion in gfp::gsp-1; sds-22(E153A) rescued the reduction of GSP-1 levels in embryos (shown above). We will perform two additional experiments to quantify GSP-1 levels.

      We will also test if the smaller PAR-2 domain in sds-22(E153A) mutant is rescued by RPN-12 depletion. With these experiments, we aim to answer if proteasome inhibition rescues the reduced levels of GSP-1/-2 and thereby rescues the reduced PAR-2 domain when SDS-22 is depleted or mutated.

      Experiment 2

      The posterior localization of PAR-2 after co-RNAi of GSP-1 and SDS-22 contrasts with the absence of PAR-2 at the cortex when both GSP-1 and GSP-2 are depleted. This difference may be due to the partial reduction of GSP-2 levels when SDS-22 is depleted, compared to the more substantial reduction of GSP-2 upon GSP-2 RNAi. Have the authors considered combining full depletion of GSP-1 with partial depletion of GSP-2 to see if PAR-2 remains present and localized to the posterior? This experiment could help clarify the discrepancy between the phenotypes and further support the role of SDS-22 in regulating GSP-2 protein levels. Additionally, by titrating PP1, the authors may be able to determine the minimum amount of PP1 needed to establish the PAR-2 domain.

      We will try this experiment but, assuming we find a condition in which we can fully deplete GSP-1 and only half of GSP-2, one problem is that it is impossible to control the levels of both GSP-1 and 2 and measure the PAR-2 domain in the same embryos (which would be the most rigorous way to perform the experiment so that we know the amount of depletion and correlate with the PAR-2 domain length). The only thing we can do is the same depletion time in the 3 different strains (the mNG::gsp-2, the gfp::gsp-1 and the gfp::par-2) and assume that the depletion will work the same in the three different strains.

      • *

      Minor comments

      Reviewer 1

      Minor Points

      • The link between lethality and polarity of the zygote is not always obvious and whether they are connected (or not) could probably be made clearer. Indeed, the source of lethality is unclear, particularly given that loss of SDS-22 on its own strongly impacts lethality with minimal effects on polarity (at least in the zygote).

      In many cases, we have reported embryonic lethality as information, not with a precise scope to correlate the lethality with the phenotype. We apologize for the lack of clarity. We know that embryonic lethality is normally associated with severe polarity defects. As example, in the par-2(RAFA) mutant and in the pkc-3ts mutant at temperatures around 24-25°C cortical polarity is lost, embryos divide symmetrically and synchronously and die (Calvi et al., 2022; Rodriguez et al, 2017) and many more references for the PAR mutants (Kemphues et al, 1988; Kirby et al, 1990; Morton et al, 1992). We and others have also shown that depletion of GSP-2 can rescue the lethality of pkc-3(ts) but only at a semipermissive temperature when there is still residual PKC-3 activity (Calvi et al., 2022; Fievet et al, 2013). As our aim was to identify the regulator of GSP-2, we tested the potential regulators by RNAi in the pkc-3(ts), with the assumptions that a regulator, similar to GSP-2, would rescue the pkc-3(ts) polarity defects and lethality. As it turns out, SDS-22 is not a canonical regulator of GSP-2. The partial rescue of the polarity defects is most likely the result of the fact that SDS-22 lowers the level of GSP-2. However, SDS-22 is probably involved in many other functions that involve GSP-1 and GSP-2 (as shown for example:(Beacham et al, 2022; Peel et al., 2017)) and it is embryonic lethal. We do not know, however, whether the embryonic lethality is the results of the sum of the various functions of SDS-22 or it is due to a specific function.

      To clarify it better, we have now explained the connection between polarity and lethality in the text,

      From Result Line 111-114, page 5:

      We first asked whether depletion of any of these three regulators suppress the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (in which PKC-3 is partially active, temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated), similar to depletion of the catalytic subunit GSP-2.

      To Results Line 111-117, page 5:

      *When the temperature sensitive mutant pkc-3(ne4246) is grown at semi-permissive temperature, the residual PKC-3 activity is not sufficient to exclude PAR-2 from the anterior cortex. These embryos cannot establish polarity and die. Depletion of the catalytic subunit GSP-2 in this strain suppresses PAR-2 mislocalization and the resulting polarity defects, thereby rescuing embryonic lethality. We first asked whether depletion of any of these three identified regulators suppresses the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated) , similar to depletion of GSP-2. *

      From Result Line 241-242, page 10:

      We next asked whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

      To Results Line 223-224, page 9:

      Because of this, we decided to test whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

      • Formally, the conclusion that reduced GSP-1/2 in SDS-22 depletion conditions is due to increased proteasomal degradation is not shown directly as there is no data on rates just steady-state levels. We agree that the genetic data is strongly suggestive of this model and it is consistent with work of other labs. Thus this is the most likely scenario, but could in principle reflect reduced expression that is balanced by reduced degradation.

      We agree with the reviewer. To address this point, we will perform RT-PCR analysis to measure the gene expression levels of gsp-1 and gsp-2 from control, SDS-22 depletion and sds-22(E153A) embryos.

      • It is interesting that sds-22(E153A) caused a stronger decrease in oocyte GSP-1 levels than sds-22(RNAi) (Fig 7). The authors may want to comment on this result.

      As we performed depletion of SDS-22 by RNAi feeding from L4 stage, we might not see strong reduction of GSP-1 in oocytes compared to that in sds-22(E153A) mutant, which carries an endogenous mutation of SDS-22 throughout the life cycle.

      Left, Representative images of gfp::gsp-1 germlines in ctrl(RNAi) and sds-22(RNAi), comparing to gfp::gsp-1; sds-22(E153A); ctrl(RNAi). __Right, __Quantification of GFP::GSP-1 intensity levels in the cytoplasm and nucleus of -1 and -2 oocytes. N = 1.

      To address this point we have performed an experiment where we have depleted SDS-22 starting from L1s. As shown above, RNAi feeding of SDS-22 from L1 stage showed a similar reduction of GSP-1 (16.1% in the cytoplasm; 24.6% in the nucleus) as in gfp::gsp-1; sds-22(E153A), which was stronger comparing to feeding from L4 (8.8% in the cytoplasm; 17.4% in the nucleus, Fig 7D-E). This supports our hypothesis that the difference shown in Fig 7D-I might result from a relative short RNAi depletion of SDS-22 from L4 stage comparing to endogenous SDS-22(E153A) mutation. This experiment was done only once and will be repeated. If confirmed, we will add a sentence in the text. As RNAi feeding of SDS-22 from L1 stage impairs the formation of germlines, we will keep the protocol using SDS-22 RNAi feeding in L4 worms for other experiments in this study.

      • "At polarity establishment, the PP1 phosphatases GSP-1/-2 dephosphorylate PAR-2 allowing its cortical posterior accumulation." This statement, possibly inadvertently, implies temporal regulation, which has not been shown.

      We have changed the sentence, as suggested by the reviewer:

      To Introduction Line 59-60, page 3:

      The PP1 phosphatases GSP-1/-2 dephosphorylate PAR 2 allowing its cortical posterior accumulation and embryo polarization.

      • It would be ideal if the authors could explicitly state how they define pronuclear meeting. For example in Figure 1B, the embryos look like they are a few minutes past pronuclear meeting (e.g. compared to Figure 3), but maybe the pronuclei tend to meet more centrally in these conditions? Given that PAR-2 clearance is changing in time in some of these cases (based on looking at the movies), staging needs to be very accurate to get the best comparisons.

      We apologize for the lack of clarity. Pronuclear meeting is defined when the two pronuclei first contact each other.

      As noted by Reviewer 1, it is true that the pronuclei in pkc-3ts mutant tend to meet more centrally compared to control embryos. The same finding was also observed on PKC-3 inhibition (through depletion, mutation or inhibitor treatment) by Rodriguez et al (Rodriguez et al., 2017). In addition, Kirby et al reported that mutations in the anterior PAR complex lead to the mislocalization of the pronuclei, causing them to meet more in the center (Kirby et al., 1990). We now specify this in the Material and Methods.

      Add in Material and Methods Line 633-635, page 22:

      *The stage of pronuclear meeting is defined when the two pronuclei first contact each other. In pkc-3(ne4246) embryos, the two pronuclei exhibited a tendency to meet more centrally compared to controls (Fig 1B, Movie EV1), as shown in (Kirby et al, 1990; Rodriguez et al, 2017). *

      As Reviewer 1 mentioned, accurate staging is crucial, as PAR-2 clearance can vary over time. The measurements were done in the first frame where pronuclei touch each other. However, in Fig. 1B we had shown one pkc-3ts; sds-22(RNAi) embryo one frame (10 seconds) later. We have now corrected this (see the updated Figure 1B).

      • In the interests of data-availability, upon publication the authors would deposit the raw mass spec data underlying Figure EV1.

      The reviewer is right, this was forgotten. We have now added as supplementary material the Dataset EV1 and EV2.

      Reviewer 3

      Minor comments: important issues that can confidently be addressed

      In the introduction (line 83), it's unclear what reconciles the contradictory data. I also have difficulty understanding this point in the discussion (line 435).

      We apologize for the lack of clarity and have now modified the text:

      From Introduction Line 82-84, page 4:

      This underscores the complex roles of SDS22 in regulating PP1 function and reconciling the contradictory data obtained in vivo and in vitro (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

      To Introduction Line 81-85, page 4:

      These two recent findings suggest that while SDS-22 is required for the biogenesis of PP1 holoenzymes, its removal is essential to have an active PP1. This dual role of SDS-22 explains how SDS22 behaves as an inhibitor in biochemical assays in vitro but as an activator in vivo (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

      From Discussion Line 435-436, page 17:

      These data reconcile the contradictory in vivo and in vitro observations.

      To Discussion Line 447-451, page 17:

      Given that SDS-22 both stabilizes PP1 levels and inhibits its activity, this dual role clarifies the apparent contradiction: while SDS-22 is essential for PP1 activity in vivo (because it is essential for the biogenesis/stability), it inhibits PP1 activity in vitro (as it needs to be removed to have an active PP1), while in vivo it is removed by p97/Valosin resulting in active PP1.

      Additionally, in the results section (line 389), it's not clear why the gonads cannot be studied in the strain with dead embryos. Are the gonads also altered in a way that prevents their observation?

      We explained this in the material and methods part (Line 583-584, 588-592), page 21.

      To clarify it better in the main text, we have now modified

      Results Line 377-378, page 15:

      Since depletion of these subunits results in worms with very little to no progeny (Fernando et al., 2022)

      Results Line 396-401, page 15:

      *Since we use the embryonic lethality phenotype of the mNG::gsp-2; sds-22(E153A) strain to recognize the homozygote sds-22(E153A), this precluded the possibility to analyze the germlines of homozygote mNG::gsp-2; sds-22(E153A) worms depleted of RNP-6.1 or RPN-7, as these worms do not have progenies (Fernando et al., 2022) and we therefore cannot distinguish the sds-22(E153A) homozygote from the sds-22(E153A) heterozygote (see material and methods for details). *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • *

      We have re-quantified the data in Fig 1B and displayed as in Fig 1C.

      We have double checked our data and corrected Fig 3G.

      We have modified the text to address many of the comments of the reviewer about clarity and rigor.

      We have added supplementary information Fig EV2C and Dataset EV1 and EV2.

      Other experiments performed are still preliminary and only shown in this revision letter.

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

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      We believe with the reply, the text changes and the experiments that we have proposed and started, we will address all comments of the reiewers.

      • *

      References

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      Calvi I, Schwager F, Gotta M (2022) PP1 phosphatases control PAR-2 localization and polarity establishment in C. elegans embryos. J Cell Biol 221

      Chartier NT, Salazar Ospina DP, Benkemoun L, Mayer M, Grill SW, Maddox AS, Labbe JC (2011) PAR-4/LKB1 mobilizes nonmuscle myosin through anillin to regulate C. elegans embryonic polarization and cytokinesis. Curr Biol 21: 259-269

      Fernando LM, Quesada-Candela C, Murray M, Ugoaru C, Yanowitz JL, Allen AK (2022) Proteasomal subunit depletions differentially affect germline integrity in C. elegans. Front Cell Dev Biol 10: 901320

      Fievet BT, Rodriguez J, Naganathan S, Lee C, Zeiser E, Ishidate T, Shirayama M, Grill S, Ahringer J (2013) Systematic genetic interaction screens uncover cell polarity regulators and functional redundancy. Nat Cell Biol 15: 103-112

      Hao Y, Boyd L, Seydoux G (2006) Stabilization of cell polarity by the C. elegans RING protein PAR-2. Dev Cell 10: 199-208

      Hubatsch L, Peglion F, Reich JD, Rodrigues NT, Hirani N, Illukkumbura R, Goehring NW (2019) A cell size threshold limits cell polarity and asymmetric division potential. Nat Phys 15: 1075-1085

      Kemphues KJ, Priess JR, Morton DG, Cheng NS (1988) Identification of genes required for cytoplasmic localization in early C. elegans embryos. Cell 52: 311-320

      Kirby C, Kusch M, Kemphues K (1990) Mutations in the par genes of Caenorhabditis elegans affect cytoplasmic reorganization during the first cell cycle. Dev Biol 142: 203-215

      Klinkert K, Levernier N, Gross P, Gentili C, von Tobel L, Pierron M, Busso C, Herrman S, Grill SW, Kruse K et al (2018) Aurora A depletion reveals centrosome-independent polarization mechanism in C.elegans. bioRxiv: 388918

      Morton DG, Roos JM, Kemphues KJ (1992) par-4, a gene required for cytoplasmic localization and determination of specific cell types in Caenorhabditis elegans embryogenesis. Genetics 130: 771-790

      Park SH, Cheong C, Idoyaga J, Kim JY, Choi JH, Do Y, Lee H, Jo JH, Oh YS, Im W et al (2008) Generation and application of new rat monoclonal antibodies against synthetic FLAG and OLLAS tags for improved immunodetection. J Immunol Methods 331: 27-38

      Peel N, Iyer J, Naik A, Dougherty MP, Decker M, O'Connell KF (2017) Protein Phosphatase 1 Down Regulates ZYG-1 Levels to Limit Centriole Duplication. PLoS Genet 13: e1006543

      Rodriguez J, Peglion F, Martin J, Hubatsch L, Reich J, Hirani N, Gubieda AG, Roffey J, Fernandes AR, St Johnston D et al (2017) aPKC Cycles between Functionally Distinct PAR Protein Assemblies to Drive Cell Polarity. Dev Cell 42: 400-415 e409

      Shimada M, Kanematsu K, Tanaka K, Yokosawa H, Kawahara H (2006) Proteasomal ubiquitin receptor RPN-10 controls sex determination in Caenorhabditis elegans. Mol Biol Cell 17: 5356-5371

      Tzur YB, Egydio de Carvalho C, Nadarajan S, Van Bostelen I, Gu Y, Chu DS, Cheeseman IM, Colaiacovo MP (2012) LAB-1 targets PP1 and restricts Aurora B kinase upon entrance into meiosis to promote sister chromatid cohesion. PLoS Biol 10: e1001378

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: your understanding of the study and its conclusions

      This is a follow up on Gotta's lab paper, which shows that the PP1 catalytic subunits GSP-1 and GSP-2 are involved in the polarization of the C. elegans zygote (10.1083/jcb.202201048). Here, the authors report that SDS-22, an interactor of PP1, regulates PP1 function in the zygote. Depleting SDS-22, similar to depleting GSP-2, rescues the polarity defects caused by the inactivation of aPKC in the zygote. This suggests that SDS-22 plays a role in promoting GSP-2's function in polarity. The mechanism behind this may involve SDS-22 protecting GSP-1 and GSP-2 from degradation by the proteasome.

      Major comments: major issues affecting the conclusions

      Overall, the authors' conclusions are supported by their data. The data and methods are presented clearly, with appropriate replicates and statistics. Here I propose two experiments to strengthen the link between some of their data and their claims. These experiments could take a month or two to complete.

      Experiment 1

      It would be helpful if the authors could show that blocking the proteasome in the zygote restores GSP-1/-2 levels in the absence of SDS-22 or even better in the SDS-22(E153A) mutant. This would provide more direct evidence to support their claim that SDS-22 regulates polarity by protecting PP1 from proteasomal degradation. While they are currently conducting this experiment in the germline, they cannot assess polarity there. However, in the zygote, they would be able to examine the PAR-2 domain (polarity). To do this, the authors could permeabilise the embryos and apply a proteasome inhibitor.

      Experiment 2

      The posterior localization of PAR-2 after co-RNAi of GSP-1 and SDS-22 contrasts with the absence of PAR-2 at the cortex when both GSP-1 and GSP-2 are depleted. This difference may be due to the partial reduction of GSP-2 levels when SDS-22 is depleted, compared to the more substantial reduction of GSP-2 upon GSP-2 RNAi. Have the authors considered combining full depletion of GSP-1 with partial depletion of GSP-2 to see if PAR-2 remains present and localized to the posterior? This experiment could help clarify the discrepancy between the phenotypes and further support the role of SDS-22 in regulating GSP-2 protein levels. Additionally, by titrating PP1, the authors may be able to determine the minimum amount of PP1 needed to establish the PAR-2 domain.

      Minor comments: important issues that can confidently be addressed

      In the introduction (line 83), it's unclear what reconciles the contradictory data. I also have difficulty understanding this point in the discussion (line 435). Additionally, in the results section (line 389), it's not clear why the gonads cannot be studied in the strain with dead embryos. Are the gonads also altered in a way that prevents their observation?

      Referees cross-commenting

      Overall, I agree with the other reviewers' comments. The suggested experiments would help strengthen the connection between SDS-22 and cell polarity, as well as its role in relation to the proteasomal-mediated degradation of GSP-1/-2 and its impact on cell polarity. These experiments seem feasible and could provide stronger support for the authors' claims about these regulatory mechanisms. Alternatively, the authors may consider moderating some of their conclusions if these experiments are not conducted.

      Significance

      General assessment: strengths and limitations

      This study enhances our understanding of how phosphatases regulate cell polarity, specifically in the C. elegans zygote, a key model system for studying cell polarity. The study could be further strengthened by the experiments mentioned above. Additionally, see the comment on how to increase the impact of the work (Audience section).

      Advance: compare the study to existing published knowledge This study is the first to characterize the role of SDS-22 in the polarization of the C. elegans zygote. As the authors discuss, their results align with and complement existing knowledge of SDS-22 in other cell types. Together with the literature, this work highlights the complexity of PP1 regulation, suggesting that different PP1 outcomes may be achieved by combining SDS-22 with various PP1 co-regulators.

      Audience that will be interested or influenced by this research

      These results will be of interest to scientists studying cell signalling and cell polarity. There is currently strong focus on understanding the regulation of phosphatases. In cell polarity research, the spatial regulation of phosphatases is particularly important for understanding the asymmetric activation of signalling pathways. SDS-22 does not appear to control the spatial localization or activity of PP1, but rather its overall protein levels. As the authors note in the discussion, this suggests that other factors may be involved in the polarization of PP1 signalling. In supplementary figure S1, the authors provide a volcano plot showing candidate PP1 interactors. Providing the list of positive hits would increase the impact of the study and benefit the research community. It would also help explain why the authors chose to follow up on SDS-22 in this study. Furthermore, this could advance the identification of factors involved in the polarization of PP1 signalling.

      My expertise

      Cell polarity, cell signalling, embryo development.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: The authors present a logical and clear set of data that support the model that SDS-22 is an important regulator of cell polarity via its ability to stabilize Protein Phosphatase 1 (PP1).

      The authors use a clever combination of genetic manipulations and quantitative imaging to show that loss of SDS-22 phenocopies loss of PP1, in that PAR-2 polarization is restored in nascent C. elegans zygotes following inactivation of PKC-3. Rescue of PAR-2 polarization also occurs when the authors mutate a conserved residue in SDS-2 that is predicted to form electrostatic interactions with PP1, suggesting that SDS-22 acts via PP1. Inactivation of SDS-22 results in decreased levels of PP1, and the authors provide evidence that this is via proteasomal degradation by showing that PP1 levels are restored by knockdown of proteasomal subunits.

      Overall the manuscript is well-written, the experiments rigorous, and the methods and data likely to be reproducible.

      Major comment: Consistent with the model that PP1 activity is reduced in the absence of SDS-22, the authors show that a surrogate PP1 target (phospho-histone H3) becomes hyper-phosphorylated. To strengthen the study, the authors could consider performing an OPTIONAL experiment (see below) of assaying the phosphorylation status of PAR-2 itself, as this is proposed to be the target of both PKC-3 and PP1, and represent the mechanism of PAR-2 polarization.

      Referees cross-commenting

      In principle I agree with many of the thoughtful comments by the other reviewers. They point out many potential areas for both enhancing the strength of the findings and including a more nuanced interpretation of the results. However, I also feel that the experiments proposed to deal with their concerns might not be so straightforward to pursue for unforeseen technical reasons and may actually take substantially longer than anticipated. The same is true for my proposed experiment to assess phosphorylation status of PAR-2, which is why I have indicated it as optional. I ask the other reviewers to consider if any of their proposed experiments might also be considered optional. I also thank them for their critical assessments of the paper! They were helpful for me and I'm sure will also be for the authors.

      Significance

      This study brings clarity to the contentious role of SDS-22 by showing that it appears to promote PP1 activity by counteracting the phosphatase degradation process in vivo. This complements a previously hypothesized function of SDS-22, while contrasting with other proposed functions of SDS-22 as a regulator of PP1 localization or stimulator of PP1 degradation. Thus, the authors' discoveries in C. elegans represent a significant advance in our understanding of protein phosphatase regulation, a long-standing question in biology and a central process in all cellular systems. The study also points to potential mechanisms for modulating phosphatase activity in other contexts, across different organisms and disease states. Basic science researchers will be interested in the findings, with potential to attract additional interest from physiologists and even drug designers.

      One limitation of the study is that the authors use PAR-2 polarization as a readout of PKC-3 and PP1 activity without showing directly that PAR-2 phosphorylation status is changing in response to their genetic manipulations, including SDS-22 inactivation. The PAR-2 membrane localization is thought to be inhibited by PKC-3-dependent phosphorylation and promoted by PP1-dependent dephosphorylation. Is there a possibility of examining whether PAR-2 phosphorylation is elevated in SDS-22 RNAi or mutant animals? Previously, Hao et. al., 2006; doi.org/10.1016/j.devcel.2005.12.015. showed in Figure 2 that PAR-2 runs as a doublet band on western blots with the phosphorylated form of PAR-2 appearing to correlate with the slightly higher molecular weight band. This was used to infer the ratio of phosphorylated to dephosphorylated PAR-2. I'm wondering if it might be possible for the authors to perform a similar analysis of their existing GFP::PAR-2? It appears from their previous paper on PP1 regulation of PAR-2 polarization (Calvi et. al., 2022; doi: 10.1083/jcb.202201048.) that they might also be detecting a similar doublet (Figure S5F and associated source file), so perhaps it is a doable experiment? Regardless, this is not an essential experiment as the study is already significant and rigorous!

      I am a cell biologist that uses C. elegans to understand the function of conserved protein complexes that regulate the development and function of animal tissues.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This work from Li et al. identifies a role for SDS-22 in maintaining normal levels of the PP1 subunits GSP-1/2 in C. elegans. Prior work in the lab identified a role for the catalytic PP1 subunits GSP-1/2 in opposing the phosphorylation of of the polarity protein PAR-2 by the polarity kinase PKC-3(aPKC). To identify potential regulatory subunits in this process, they performed IP-MassSpec on GSP-2 and pulled out a number of potential regulatory subunits, including SDS-22. While polarity was the primary motivation for the study, their subsequent analysis did not point to a specific function in cell polarity, but rather pointed to a general effect on stabilising levels of GSP-1/2 against potential proteasome-mediated degradation. Consistent with this hypothesis, SDS-22 depletion or mutation of the SDS-22-GSP-1/2 interaction partially recapitulated the phenotypes of GSP-1/2 depletion including increased phosphorylation of histone H3. Consistent with reduced PP1 activity, there were modest effects on polarity as seen in GSP-2 depleted embryos, including slightly reducing PAR-2 domain size and partially restoring PAR-2 asymmetry in embryos carrying a temperature sensitive pkc-3 mutation.

      Major Comments:

      1. Overall, the evidence supporting the core finding that SDS-22 is required for normal GSP-1/2 levels is strong and well documented. The experiments were performed well and controls, statistics, replicates were appropriate. Our only slight reservation was whether the effect of sds-22(RNAi) on stability may be overstated due to the use of GFP fusions to GSP-1/2 for this analysis. The authors note these alleles are hypomorphic, potentially raising the possibility that GFP tags destabilise the proteins and make them more prone to degradation. Ideally this would be repeated with an untagged allele via Western (e.g. Peel et al 2017 for relevant antibodies).
      2. The role for SDS-22 in polarity is rather weak. Both the SDS-22 depletion phenotypes and the ability of SDS-22 depletion to suppress pkc-3(ts) polarity phenotypes are modest (and weaker in than GSP-2 depletion). For example, the images in Figure 1B appear striking, but from Movie S1 it is clear that this isn't a full rescue as PAR-2 is initially uniformly enriched on the cortex (rather than mostly cytoplasmic) and it is never fully cleared. In the movie, the clearance at the point of pronuclear meeting is very modest. Quantitation might be helpful here (i.e. as in Figure 3G). As the authors state, it seems that SDS-22 does not have a specific role in polarity beyond the general effect on GSP-1/2 levels. This does not undermine the core message of the paper, but we would recommend downplaying the conclusions with respect to contributing to polarity establishment. For example "...suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos" implies a regulatory role for SDS-22 in polarity, but we would interpret it as simply helping reduce aberrant degradation of GSP-1/2 and this impacts a variety of cellular processes including polarity.
      3. Specificity of SDS-22 effects on polarity. SDS-22 (or GSP-1/2) depletion is likely to have effects on many pathways. We wondered whether some of the polarity phenotypes may not be specifically due to changes in the PAR-2 phosphorylation cycle as implied.

      One candidate is the actomyosin cortex. It was noticeable that control and sds-22 embryos were different: In Movies S1, S2, and S3 control embryos show either stronger or more persistent cortical ruffling or pseudocleavage furrows. This is also visible in Figure 3A. Is it possible that disruption of SDS-22 reduces cortical flows (time, intensity or duration) and could this explain the small reduction in anterior PAR-2 spreading and thus the slightly smaller domain size measured in Figures 1B and 3A.

      A potentially related issue could be embryo size. sds-22 embryos generally seem to be smaller than wild-type (e.g. Figure 1B(left), 4A(left column), and particularly EV3). Is this consistently true? Could cell size effects change the ability of embryos to clear anterior PAR-2 domains as described in EV3? Klinkert et al (2018, biorXiv) note that reducing the size of air-1(RNAi) embryos reduces the frequency of bipolar PAR-2 domains.

      We would stress that these comments relate to interpreting the polarity phenotypes and do not undermine the core finding that SDS-22 stabilises GSP-1/2.

      Minor Points

      • The link between lethality and polarity of the zygote is not always obvious and whether they are connected (or not) could probably be made clearer. Indeed, the source of lethality is unclear, particularly given that loss of SDS-22 on its own strongly impacts lethality with minimal effects on polarity (at least in the zygote).
      • Formally, the conclusion that reduced GSP-1/2 in SDS-22 depletion conditions is due to increased proteasomal degradation is not shown directly as there is no data on rates just steady-state levels. We agree that the genetic data is strongly suggestive of this model and it is consistent with work of other labs. Thus this is the most likely scenario, but could in principle reflect reduced expression that is balanced by reduced degradation.
      • It is interesting that sds-22(E153A) caused a stronger decrease in oocyte GSP-1 levels than sds-22(RNAi) (Fig 7). The authors may want to comment on this result.
      • "At polarity establishment, the PP1 phosphatases GSP-1/-2 dephosphorylate PAR-2 allowing its cortical posterior accumulation." This statement, possibly inadvertently, implies temporal regulation, which has not been shown.
      • It would be ideal if the authors could explicitly state how they define pronuclear meeting. For example in Figure 1B, the embryos look like they are a few minutes past pronuclear meeting (e.g. compared to Figure 3), but maybe the pronuclei tend to meet more centrally in these conditions? Given that PAR-2 clearance is changing in time in some of these cases (based on looking at the movies), staging needs to be very accurate to get the best comparisons.
      • In the interests of data-availability, upon publication the authors would deposit the raw mass spec data underlying Figure EV1.

      Referees cross-commenting

      We also generally agree with the comments of the other reviewers.

      Our only concern with the main conclusion regarding GSP stabilization of GSP-1/2 is the impact of the gfp tags. Given that antibodies exist and have been used in Peel et al 2017 for exactly this purpose in C. elegans embryos, this does not seem excessively burdensome in our view and would strengthen the paper.

      The remainder of our concerns can likely be addressed by modifications to the text and/or adding a caveats/limitations section to their discussion. As we noted, these mostly relate to the magnitude and specificity of the impact of SDS-22 on polarity and PAR-2 phosphorylation, which in our view is rather peripheral to the core conclusion (i.e. that SDS-22 stabilizes GSP-1/2).

      Significance

      Overall, this is a careful and well-executed study identifying a conserved role for SDS-22 in stabilising PP1 catalytic subunits in C. elegans embryos and shows that this can broadly impact PP1 activity in this system. A mechanistic role for SDS-22 in PP1 function was recently demonstrated in (Cao et al, 2024), where it was shown to stabilise nascent catalytic subunits, but also in subunit recycling (Kuetsch et al 2024). The data here suggest this role in stabilisation PP1 subunits is broadly relevant.

      These data are also consistent with prior work from the lab demonstrating the role of PP1 in C. elegans zygote polarity. It adds to previous reports that compromised PP1 activity can impact cell polarity and further highlights the importance of considering regulation of protein phosphatases in cell polarity pathways. That said, the impact on polarity is rather modest, likely reflecting a general requirement for SDS-22 in supporting optimal PP1 activity rather than any specific role in polarity.

      Field of expertise: cell polarity, cell and developmental biology.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time

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      Referee #3

      Evidence, reproducibility and clarity

      The study by Yasmin & colleagues tackles an important question, what is the molecular nature of specificity that arises from otherwise highly similar proteins. In this case, they focus on two proteins with epigenetic activity, DNMT3A and DNMT3B, using a functional readout of their ability to methylate DNA in a model that specifically requires DNMT3B function at a subset of the genome, i.e. DNMT3B-dependent regions. This includes characterizing the role of DNMT3B in these regions in stem cell-to-embryoid body differentiation experiments, using genomic assays to probe DNA methylation dynamics. By removing DNMT3B and ectopically expressing a variety of sophisticated mutants, the authors attempt to show the protein domains required for specificity. However, several questions remain about the strength of the data to support the claims, particularly with respect to the ectopically expressed mutant DNMT3B proteins.

      Major comments:

      1. The strength of this study is in the very nice addback strategy for probing DNMT3B specificity, where the designed mutants seem highly useful to ask critical questions. However, the stability of the mutant proteins (i.e. cellular expression levels) and question of protien levels in the nucleus are insufficient evidence for the conclusions stated in the paper. With the exception of the Dnmt3b1-KI clones (top panel fig 3B), it seems like most mutants are not expressed at wildtype levels. How much of the results are driven by differences in expression, relative to the wildtype protein? While this a technically challenging problem, there are various methods to establish roughly matched expression such as integration into a stronger locus for expression or tuning the promoter sequence for expression of a construct. Given the mutants are key for the main conclusions of the study, this seems critical to address, though would substantially increase effort required for the paper.
      2. Characterisation of the datasets supporting effects seems lacking in several instances. For example, the text states that DMNT3B null cells behave similarly to wildtype cells but supporting data (FigS2A-C) or that Dnmt3b1-KI and Dnmt3b3-KI behave normally with respect to differentiation (FigS4C), seem insufficient evidence for this, with largely summary plots supporting the statements. Similarly, several of the MBDseq datasets seem discordant, such as FigS2G or FigS4D(right panel) where the x-y axis for scatterplots are clearly not equivalent suggesting global effects on the data. The authors should also clearly demonstrate the levels of DNMT3A throughout their EB timecourse for mutant lines, where this seems especially important given their readout is DNA methylation dynamics.
      3. An optional analysis that could support the claims of the paper would be to contrast the effect sizes in their cellular model with existing datasets that profile DNA methylation dynamics in vivo, where these have been captured at early developmental levels. This would nicely show that their functional readout in relation to normal processes.

      Minor comments:

      1. Several figures require addressing, listed here:
        • Fig1B the points are not so legible when overplotted, consider reducing the size of the datapoint circles or turning into "*" representations.
        • Fig4I seems not to have a figure legend.
        • FigS2G should be represented as a square and not as a rectangle, as this visually condenses on axis relative to the other.
        • FigS3A is unclear, could more be added to the legend to describe what exactly is the schematic representing?
        • FigS3D the axis seems not aligned with the barplot positioning?
      2. The Dnmt3b-PAS-KI clone 1 does not seem to well-cluster with the 2nd and 3rd clone, could this be a clonal effect at the global level?
      3. The text states (page 7, third paragraph) that in the two differentiation models the identity of the CGIs that exhibit different dynamics largely match, though no direct comparison (i.e. delta-delta effect) is show, rather a summary plot of either is presented side-by-side. This seems insufficient evidence of the statement, and a direct comparison of the fold changes would help.
      4. The clonal effect sizes would benefit from more quantitative comparisons throughout the manuscript, broken down to raw data. For example, the statement in page 8 paragraph two that the effects on independent clones were fully consistent is show from largely a PCA plot, which seems incomplete evidence that replicates behave consistently. More transparent analysis of clonality from the raw data would be helpful for the reader.
      5. The statement in the discussion that the authors experimental system affords 'homogeneous and highly synchronised onset and progression of XCI", but it seems unclear from the data provided in the manuscript that cells exhibit differentiation in a synchronized manner. Softening this statement seems apt here.

      Significance

      The question of specificity is highly important, not just to the field of epigenetics and DNA methylation where this study is particularly relevant, but also to a broader audience. Many of our cells proteins are highly homologous but have nevertheless highly divergent activities. Molecular explanations of specificity are therefore critical to understand phenotype and how traits can be acquired through gene paralogue evolution. Here, by focusing on a particularly apt example, the similar DNMT3A/B proteins, this study offers a nice breakdown with the potential to tie back the results to locus specific activity in the genome. The strongest aspect is the comparison of sophisticated mutants in a matched experimental setting, however, the experiments do not seem sufficient to support the broad conclusions of the study. From a genomics standpoint, the experimental setup is impressive, but requires additional work to show that matched expression levels of wildtype/mutant proteins still maintains the phenotypes reported.

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      Referee #2

      Evidence, reproducibility and clarity

      DNA methylation controls gene expression and genome stability during development and in healthy adult cells. It is frequently abnormal in diseases including cancer. Therefore, there is a clear impetus for the community to better understand the mechanisms that underlie DNA methylation patterns in mammalian genomes, including how the mark is deposited on specific regions. Of particular interest are CpG islands, which correspond to the majority of promoters, and are typically devoid of methylation. However, in specific cases, including during differentiation and X chromosome inactivation, CpG islands acquire extensive DNA methylation in a de novo methylation process. There are 2 de novo DNA methyltransferases expressed in the embryo: DNMT3A and DNMT3B. They are globally similar, but only DNMT3B contributes to de novo DNA methylation during X inactivation (Gendrel 2012, from the authors' lab). The question of this paper is: why is that?

      The model system used by the authors is female mouse ES cells, which during differentiation in vitro inactivate one of their X chromosomes. They use a hybrid line to distinguish parental alleles, and a genetic trick to ensure that the same chromosome is inactivated in all cells. Figure 1 validates the system, showing that CGIs on the inactivated X acquire DNA methylation during the differentiation process into EBs, along with some autosomal CGIs (this is done by MBD-seq). Some are fast to gain methylation, some slower. A similar analysis is carried out during differentiation into NPCs.

      The authors then move on to functional experiments by knocking out DNMT3B in their system. The KO clones are extensively characterized (Fig 2 and S2). They lack DNMT3B but retain DNMT3A levels similar to WT. However, they fail to methylate the majority of CGIs during X inactivation, confirming that DNMT3B (and not DNMT3A) is the principal actor in this process.

      The next question is: which domain(s) of DNMT3B is/are involved in this function. For this, the authors rescue their KO clones with cDNAs encoding different isoforms of DNMT3B, namely 3B1 (active) and 3B3 (inactive). They found that 3B1 fully rescued proper DNA methylation and gene expression during differentiation, whereas 3B3 had no effect (Fig 3 and S4).

      Having found that 3B1 expression fully rescues the DNMT3B KO, they move on to a more precise delineation of the important domains (Fig 4). Domain-swapping experiments show that the catalytic domain itself does not contribute to the specificity of DNMT3B (see my note on this experiment below). A similar strategy is then employed to test the contribution of the PWWP and ADD domains to 3B function. I did not find this part very clear. My understanding is that the swapped rescue construct has some activity on gene bodies even before differentiation. I gather from the lower part of Panel 4F that the PAS construct mostly fails to rescue DNA methylation during differentiation, but I am a little confused by the phrasing. It would be very easy to solve this with a summary graphic.

      Lastly, they move on to an examination of the Nter part of DNMT3B, using their domain swapping/rescue approach (Figures 5 and S6). A first experiment suggests that the Nter of 3A cannot substitute for the Nter of 3B (see note below). A second set of experiments shows that the presence of residues 1-218 is necessary for DNMT3B to function, and that smaller deletions within this region also inactivate the protein (see notes below).

      The paper is very well written. The figures are clear. The experiments are well controlled and correctly interpreted, except for the points below.

      Fig 4A: it is looking like the DNMT3B1-3A-Cat rescue construct is vastly overexpressed relative to the endogenous protein. This is surprising as the DNMT3B1 rescue construct is not (Figure 3B). What is the reason for this? Is a different promoter or rescue method being used? I feel that the overexpression level weakens the conclusion that the catalytic domain of 3A can perfectly replace that of 3B.

      Fig 4G: similarly, the two different DNMT3B-PAS-KI clones show widely different levels of DNMT3B expression. Are they both used to generate the data of Fig 4H? A third rescue clone is shown in Fig S5B. What experiment(s) was it used for?

      The clarity of the section concerning DNMT3B-PAS-KI clones can be improved easily.

      Fig 5B: the DNMT3a2(N)3b-KI clones show 3B expression levels that are ~10% of endogenous. I find it hard to conclude from this that the Nter of 3A cannot replace the Nter of 3B. Unless the authors can show that a 10% level of 3B expression is enough to fully rescue the KO of 3B.

      Fig S6D: same comment for the DeltaA and DeltaB construct. I am not seeing the data for DeltaC. In the absence of expression data, the methylation data for this mutant are not interpretable.

      Fig 5B. Is it clear that the Delta 1-218 protein is nuclear? What about the Delta A-E mutants?

      I suggest that the authors add the following paper to their reference list: Wapenaar EMBO Rep 2024 PMID: 39528729

      Significance

      I feel that the target audience is somewhat specialized. In addition, the novelty of the paper is diminished by the existence of published papers, in particular: DNMT3B PWWP mutations cause hypermethylation of heterochromatin. Taglini F, ..., Sproul D. EMBO Rep. 2024 Mar;25(3):1130-1155. doi: 10.1038/s44319-024-00061-5. Epub 2024 Jan 30. PMID: 38291337

      This paper uses a different system (human cancer cells), but arrives at the same conclusion, ie the Nter of DNMT3B is necessary for de novo DNA methylation. In addition, it shows that the Nter interacts with HP1.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      In this study the authors used a previously established mESCs iXist-ChrX cell line to investigate the mechanisms underpinning developmentally regulated CGI methylation through differentiation into embryoid bodies and MBD-seq profiling. They show that their system recapitulates developmentally regulated DNA methylation at CGIs on the X chromosome and autosomes before using a knockout and rescue system to determine that this is dependent on the DNMT3B1 isoform. Through domain swap experiments, they then go on to suggest that this requires the PWWP-ADD domain and that the N-terminal region of DNMT3B1.

      Major comments:

      • Are the key conclusions convincing?
      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
      • Are the data and the methods presented in such a way that they can be reproduced?
      • Are the experiments adequately replicated and statistical analysis adequate?

      Overall, this is an interesting study that provides insights into the role of different domains of DNMT3B in establishing DNA methylation patterns during development. However, it suffers from poor annotation and description throughout. More specific comments are:

      1. The manuscript provides some details as to the replication of experiments but fails to show the replicate data in the vast majority of cases. Instead, representative data is presented alongside global correlations for some experiments. However, global correlations can mask differences between replicates. The data from all replicates should be shown in the manuscript and clear details provided regarding the replication strategy in the methods and figure legends. For example, were the different knock-in clones generated from independent DNMT3B knockout clones? For individual experiments this would be a minor point however, collectively this is a major point. It is particularly important given the variation highlighted in point 2 below.
      2. Different DNMT3B knock-in clones show high variability in expression levels. Have the authors investigated whether the discrepancy in protein levels contributes to the differences in methylation patterns seen? A non-comprehensive list of examples is given in the minor comments section.
      3. There is variation in the level of expression of different DNMT3B constructs detected by Western blot. Could this be caused by differences in protein stability? It would be helpful to see an assessment of protein stability to determine whether this contributes to the variable expression. For example, the DNMT3B3-KI has lower levels than the DNMT3B1-KI (Figure 3B) and this could potentially contribute to the differences in DNA methylation observed.
      4. The study lacks statistical tests to support the conclusions drawn from the analysis of the sequencing data. For example, are the differences in CGI methylation between DNMT3B1-KI and DNMT3B3-KI statistically significant? For individual analyses this would be a minor comment but given the lack throughout the study, this classes as a major comment.
      5. Chromatin marks play major roles in DNMT3A and DNMT3B recruitment (Tibben & Rothbart 2024) and the N-terminal region, PWWP and ADD domains have direct or indirect chromatin reading activity. However, the manuscript does not detail the chromatin environment of the CGIs studied. This could potentially be addressed through experiments, analysis of existing data or discussion.
      6. As the authors state in their discussion, MBD-seq may only detect very dense methylation. This could potentially obscure lower levels of DNA in some conditions. Analysis of a few loci by alternative methods, such as targeted bsPCR or EM-PCR would help support key results and rule out the possibility that some of the rescue constructs are able to partially rescue DNA methylation patterns.
      7. While some expression data is shown, there is currently no investigation as to whether the different DNMT3B domain swap constructs have impact on transcriptional silencing on Xi/autosomal sites.
      8. The text relating to the section on the PWWP-ADD domain is very brief and currently unclear. Expanding this section and specifying which data are derived from ES cells vs differentiated cells would help to clarify. We also suggest that it would be clearer to move this data to the main figure and to move the results of the catalytic domain experiments, which are negative, to the supplementary.
      9. The authors suggest that PWW-ADD domain region of DNMT3B is required for developmental methylation of Xi and autosomal CGIs. However, there is no further dissection as to whether this requirement is due to the PWWP, ADD or the intervening region.
      10. Throughout the text autosomal and Xi CGIs are both analysed. The introduction highlights SMCHD1 as important in methylating CGIs on Xi but that PCGF6 complex for the autosomal targets. This suggests separate mechanisms target DNMT3B to these loci. However, based on the results presented here, these two different types of targets have similar DNMT3B1 domain requirements. It would be interesting to see discussion with regard to this point in the manuscript.

      Minor comments:

      • Specific experimental issues that are easily addressable.
      • Are prior studies referenced appropriately?
      • Are the text and figures clear and accurate?
      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
      • Introduction: Methylation of tumour suppressor gene CGI promoters is mentioned alongside examples of developmental CGI methylation. It would be useful to place rare event in context. Methylation of CGIs is common in cancer but very few of these correspond to tumour suppressor genes. It would also be useful to discuss how DNMT3B might be involved in these events.
      • Introduction: The authors mention the DNMT3A1's N-terminal region recruits it to H2AK119ub1. It would also be useful to discuss recent work on the N-terminal of DNMT3B which can bind HP1-alpha to mediate H3K9me3 recruitment (Taglini et al 2024). This paper is currently only cited in the discussion.
      • Throughout the manuscript DNMT3B1 is referred to as the catalytic isoform, however it is not the only catalytic isoform of DNMT3B.
      • Page 5: 'the presence of non-catalytic isoforms, notably DNMT3L and DNMT3B3'. This statement incorrectly suggests DNMT3L is a non-catalytic isoform of DNMT3B.
      • The authors refer to the protein complex as heterodimers when referring to a DNMT3B -DNMT3L or DNMT3B3-DNMT3A complex. However, the consensus of structural studies is that they form tetramers.
      • Marker sizes are not included on blots with the exception of Figure 3B.
      • Western blots are cropped closely. It would be useful if full blots were shown in the supplementary particularly given the presence of extra bands in some blots and different DNMT3B isoforms.
      • Details about the Western blot methods (eg visualisation and antibodies) are missing.
      • It would be useful if the size of different groups was annotated in plots. This is given in Figure 1D for example but not in Figure 2E.
      • The authors show data confirming DNMT3B knockout by western blot. However, they do not provide details of the strategy for generating the knockout (ie vector used, sgRNAs, screening process). Could the authors also provide additional details as to whether there is any sequencing to confirm the results on the knockout?
      • Page 9: The authors state "Since Dnmt3b-/- cells have normal levels of DNMT3A", but show no data to support this statement. This is particularly relevant as they have generated new DNMT3B knockout clones for this study so they cannot be assumed to behave similarly to previous studies.
      • Figure 1B: There is poor PCA clustering between replicates at some timepoints, particularly Day 8.
      • Figure 1G: Different colours are used for the different timepoints in this figure. We are unclear if this is deliberate.
      • Page 7: The authors state "... the two categories largely matched between the two differentiation systems (Figure S1C-G)". It is difficult to draw this interpretation from the data presented as no explicit overlap is shown.
      • Figure 2E: MBD-seq peaks for DNMT3B-independent loci in WT sample have a dip in the middle of the peak (also seen in Figure 5F). Could the authors explain why this might be and why it only appears in some experiments?
      • Clarification as to whether the DNMT3B -dependent and -independent loci are located on chromosome X or autosomes (e.g.: Figure 2D, E).
      • Figure S2C: the chromatin RNA-seq is not explained in the text or figure.
      • Figure S2E: Suggests WT is one of 5 replicates. The authors should show all replicates.
      • Figure S2H: What genes are included in the metaplot?
      • DNMT3B rescue knock-in clones. As shown in figure 2A, there are two different Dnmt3b-/- cell line clones. Could the authors clarify whether all the CRISPR KI clones are produced from the same parental Dnmt3b-/- cell line clone?
      • Figure 3B: The two clones shown for Dnmt3b3-KI have variable expression. Do the individual replicates for the Dnmt3b3-KI clones show similar methylation patterns?
      • Figure 3E: in the MBD-seq metaplots, there is a peak present at -/+ 4kb. What are these peaks and why do they appear at 4kb distance? Similar peaks are seen in other metaplots.
      • In figure 3F, the signal for both Dnmt3b1-KI and Dnmt3b3-KI at DNMT3B-independent CGIs is higher than in the KO. This suggests that these may not be DNMT3B independent but this point is unaddressed in the text.
      • Figure S3A: it is currently unclear what has been modelled in this figure, adding labels of what has been plotted along the x- and y- axis may aid in understanding.
      • Figure 4A: Dnmt3b1-3a-Cat-KI appears very highly expressed. Is the WT shown the endogenous protein? Could this higher expression be because the chimeric protein is more stable than DNMT3B1? There are also multiple bands on this blot.
      • Plots panels are inconsistently ordered, e.g.: Figure 3F is dependent then independent. 4F is independent then dependent.
      • Figure 4G: the expression level of the Dnmt3b-PAS-KI varies significantly between the clones shown. There are also two bands on the blot, both for the wt and KI. Clarify if WT is endogenous.
      • Figure 4H: The figure lacks a legend to indicate the scale of the colour density used.
      • Figure 4F,H: Could the authors clarifying what data (clones and number of replicates) are presented in the representative plots. Does the different protein levels between the clones result in any differences in DNA methylation?
      • Page 12: The authors cite Boyko et al when discussing potential differences between the ADD domains of DNMT3A and B. However, they do not cite the study of Lu et al., 2023 (https://doi.org/10.1093/nar/gkad972) which reaches a different conclusion.
      • Figure S4A: The position of the 750 residue is inconsistent across the isoforms in this schematic.
      • Figure 5A: Schematic suggests the chimeric protein is DNMT3A2(N)-3B. However, DNMT3A2 lacks the N terminal region so presumably this should be DNMT3A1(N)-3B. This applies other figure panels using this construct.
      • Figure 5B: Many lanes on this blot are unlabelled and it would be useful to clarify what these extra lanes show.
      • Figure 5C: For Dnmt3a2(N)3b-KI the levels of methylation appear to be lower than Dnmt3b-/- and it would be useful to understand why this might be the case.
      • Figure 5D: would be helpful to indicate which CGIs are DNMT3B dependent and independent.
      • Figure 5F: Dnmt3a2(N)3b-KI data not included for the autosomal peaks
      • Figure 5G, H: It is difficult to see if there are any differences between the deletions in this heatmap. For example, it appears that levels of methylation on autosomal DNMT3B-dependent loci are very similar between the KO and rescue constructs. ∆D also appears to have a lesser effect than the other deletions on the Xi CGIs. A more quantitative representation of the data would help with interpretation.
      • S5E has different colour scale to other heatmaps. Red is low and in other heatmaps red is high.
      • Figure S6C: sequence conservation is shown for primates. However as mouse Dnmt3b is used throughout the paper, including the mouse NT would be a useful comparison. This is particularly relevant given that the NT is the region that varies the most between mouse and human proteins (Molaro et al 2020 https://doi.org/10.1093/molbev/msaa044 ).
      • Figure S6D: There is variable expression levels between the clones of the different deletions. The deletion ∆C is also not shown in this figure meaning that no data is shown to support the statement that it is unstable.
      • It would be useful to clarify in the text that "deletion of residues 98-146" corresponds to ∆C. It is also unclear why MBD-seq data for this deletion was included if it is unstable.
      • Discussion, page 15: The authors propose that DNMT3B could directly bind to H3K9me3. However, a study they cite, Taglini et al., 2024 (Figure S8C, D), suggests this is not the case.
      • Discussion: When discussing regulatory element methylation on Xi. An uncited statement is included: 'This observation may help to explain a prior observation that loss of DNMT3B1 alone does not result in significant de-repression of Xi during embryogenesis'. However this model appears contradictory to the observation that DNMT1 is not required for Xi silencing, given that DNMT1 KO embryos would be expected to have very low DNA methylation (eg Sado et al 2000, https://doi.org/10.1006/dbio.2000.9823 and Sado et al 2004, https://doi.org/10.1242/dev.00995).

      Referee cross-commenting

      Having reviewed the comments of the other reviewers, we agree that they are very similar and we have no issues with them. We note that Reviewer 3 notes that considering nuclear protein levels is important in the context of this study and we agree that this is an important additional consideration that we did not consider in our review.

      Significance

      The manuscript is an interesting study on the role of DNMT3B in X inactivation and development. It will be of interest to scientists who work on these fundamental processes. In addition, given the roles of DNA methylation in gene regulation, cancer, aging and disease more generally the findings are likely to be of interest to many others.

      Our expertise is in epigenetics and its regulation in disease, with a specific focus on DNA methylation and DNMTs.

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      Reply to the reviewers

      Revision Plan (Response to Reviewers)

      1. General Statements [optional]

      Response: We are pleased the reviewers appreciate the power of this novel proteomics methodology that allowed us to uncover new depths on the complexity of the ribosome ubiquitination code in response to stress. We also appreciate that the reviewers think that this is a "very timely" study and "interesting to a broad audience" that can change the models of translation control currently adopted in the field. Characterizing complex cellular processes is critical to advance scientific knowledge and our work is the first of its kind using targeted proteomics methods to unveil the integrated complexity of ribosome ubiquitin signals in eukaryotic systems. We also appreciate the fairness of the comments received and below we offer a comprehensive revision plan substantially addressing the main points raised by the reviewers. According to the reviewers' suggestions, we will also expand our studies to two additional E3 ligases (Mag2 and Not4) known to ubiquitinate ribosomes, which will create an even more complete perspective of ubiquitin roles in translation regulation.

      2. Description of the planned revisions

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

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

              __Response: __We appreciate the evaluation of our work and that the power of our proteomics method established a good foundation for the study. We also understand the reviewer's concerns and we will detail below a plan to enhance quantification and increase systematic comparisons. The experiments presented here were conducted with biological replicates, but in several instances, we focused on presence and absence of bands, or their pattern (mono vs poly-ub) because of the semi-quantitative nature of immunoblots. We will revise the figures and present their quantification and statistical analyses. In additional, we did not intend to use these stressors interchangeably, but instead, to use select conditions to highlight the complexity the stress response. In particular, we followed up with H2O2 *versus* 4-NQO because both chemicals are considered sources of oxidative stress. Even though it is unfeasible to compare every single stress condition in every strain background, in the revised version, we will include additional controls to increase the cohesion of the narrative, and expand the comparison between MMS, H2O2, and 4-NQO, as suggested. Details below.
      

      To strengthen the work, the following revisions are essential:

      R1.1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.

              __Response: __As requested, we will display quantification and statistical analysis of the suggested and new immunoblots that will be conducted during the revision period.
      

      R1.3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.

              __Response: __We will follow the reviewers' suggestion and redesign the analysis to increase consistency and prioritize data under identical conditions. To increase confidence in the mRNA data analysis, we intend to perform follow up experiments and analyze protein abundance of *ARG proteins* and *CTT1 *under different conditions. The remaining data using non-parallel comparisons will be moved to supplemental material and de-emphasized in the final version of the manuscript.
      

      R1.4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

              __Response: __To ensure a better comparison across strains and conditions, we will re-run several experiments and focus on our main stress conditions. Specifically:
      
      • 3D: We plan to re-run this experiment and include MMS

      • 3E: We plan to perform the same panel of experiments in rad6D ,and display WT data as main figure.

      • 4A-B: We plan to perform translation output (HPG incorporation) experiments with MMS as suggested

      • 4C: We plan to re-run blots for p-eIF2a under MMS for improved comparison.

      Reviewer #1 (Significance (Required)):

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6. It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted.

      Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

              __Response: __We value the positive evaluation of our work. We also appreciate the notion that it meaningfully expands the knowledge on the complexity of the ribosome ubiquitination code, challenges the current models of translation control, and conducted well-performed, and nicely controlled experiments. To address the main concern of the reviewer, we will expand our work by studying two additional enzymes involved in ribosome ubiquitination (Mag2 and Not4) and provide a more comprehensive picture of this integrated system. Specifically, we will generate yeast strains deleted for *MAG2* and *NOT4*, and evaluate their impact in ribosome ubiquitination under our main conditions of stress. We will investigate the role of these additional E3s in translation output (HPG incorporation), and in inducing the integrated stress response via phosphorylated eIF2α and Gcn4 expression. Additional follow up experiments will be performed according to our initial results.
      

      Reviewer #2 (Significance (Required)):

      In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience. However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

      My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

              __Response: __We appreciate the comments and the balanced view that studies like ours will still be impactful and contribute to a number of fields in multiple and meaningful ways. With the new experiments proposed here, and used of additional mutants and strains, we intend to propose and provide a more unified model that explain this complex and dynamic relationship.
      

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

      Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

      In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

      Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

      Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

      Major Comments:

      I consider the additional experiments essential to support the claims of the paper.

      R3.1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.

              __Response: __We understand the importance of the suggested experiment. We have already requested and kindly received strains expressing these mutations, which will reduce the time required to successfully address this point. We will perform our translation and ISR assays such as the one referred by the reviewer in Figs. 4A-C and 5E, and results will determine the role of individual ribosome ubiquitination sites in translation control.
      

      R3.2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

              __Response: __As was also requested by reviewer 1 and discussed above (point R1.1), we will conduct quantification and display statistical analyses for our immunoblots. In addition, we will re-run the aforementioned experiments to improve quantification following the reviewers' request (same gel & diluted control samples).
      

      Reviewer #3 (Significance (Required)):

      • General assessment:

      Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

      • Advance:

      In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

      • Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

      • The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

      __ Response:__ We appreciate that our work will be valuable to a number of fields in protein dynamics and that our method advances the field by measuring ribosome ubiquitination relatively and stoichiometrically in response to stress.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Response: All requested changes require experiments and data analyses, and a complete revision plan is delineated above in section #2.

      • *

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

      • *

      R1.2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.

              __Response: __Although we understand the interest on the proposed result for consistency, this is the only requested experiment that we do not intend to conduct. Because of the lack of overall ubiquitination of ribosomal proteins in *rad6**D* in response to H2O2 (e.g., Silva et al., 2015, Simoes et al., 2022), we believe that this PRM experiment in unlikely to produce meaningful insight on the ubiquitination code. In this context, we expected that sites regulated by Hel2 will be the ones largely modified in rad6*D *and we followed up on them via immunoblot. Moreover, this experiment would not be time or cost-effective, and resources and efforts could be used to strengthen other important areas of the manuscript, such as including the E3's Mag2 and Not4 into our work.
      
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      Referee #3

      Evidence, reproducibility and clarity

      Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

      In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

      Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

      Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

      Major Comments:

      I consider the additional experiments essential to support the claims of the paper.

      1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.
      2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

      Significance

      General assessment:

      Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

      Advance:

      In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

      Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

      The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6.

      It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted. Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

      Significance

      In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience.

      However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

      My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

      To strengthen the work, the following revisions are essential:

      1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.
      2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.
      3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.
      4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

      Significance

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

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      Reply to the reviewers

      The response appears in a PDF document, which will be easier to read than plain text

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      Referee #3

      Evidence, reproducibility and clarity

      This article investigates the phenomenon of intracellular protein agglomeration. The authors distinguish between agglomeration and aggregation, both physically characterising them and developing a simple but elegant assay to differentiate the two. Using microscopy and structural analysis, this research demonstrates that unlike aggregates, agglomerates retain their folded structures (and are not misfolded), and do not colocalise with chaperones or interact with the proteostasis machinery which targets and breaks down misfolded proteins. The inert nature of agglomerates was further confirmed in fitness assays, though they were observed to disrupt the yeast proteome. Overall, agglomerated proteins were described and characterised, and shown to be largely neutral in vivo.

      The claims and conclusions were well supported by the data. Microscopy and CD spectra (previously published) were used to confirm the nature of agglomerates and to rule out colocalistion with proteostasis machinery. This was confirmed by testing ubiquitination.

      The fitness of yeast cells carrying enzymatically-inactive agglomerates was assayed by generating growth curves over 24 hours. The growth rate and doubling time were taken from these growth curves as a proxy for relative fitness. The authors mention not wanting to mask differences in lag, log or stationary phases between mutants. This could be achieved by using the area under each growth curve, rather than growth rate or doubling time alone. No further experimentation would be needed, and area under the curve may provide a more holistic metric to measure fitness by.

      The results indicate that agglomerates confer a slight fitness advantage. The authors do not speculate on a reason for this. I would be interested to know why they thought this might be.

      Referees cross-commenting

      I have read the reports from the other reviewers and agree with their comments.

      Significance

      Protein filamentation is observed across the tree of life, and contributes greatly to cell structure and organisation (Wagstaff, J., Löwe, J. Prokaryotic cytoskeletons: protein filaments organizing small cells. Nat Rev Microbiol 16, 187-201 (2018).). Recent work in this field has shown that self-assembly is also important for enzyme function (S. Lim, G. A. Jung, D. J. Glover, D. S. Clark, Enhanced Enzyme Activity through Scaffolding on Customizable Self-Assembling Protein Filaments. Small 2019, 15, 1805558.). Previous work from several of these authors demonstrated that the ability of a protein to filament is subject to selection (Garcia-Seisdedos H, Empereur-Mot C, Elad N, Levy ED. Proteins evolve on the edge of supramolecular self-assembly. Nature. 2017 Aug 10;548(7666):244-247. doi: 10.1038/nature23320. Epub 2017 Aug 2. PMID: 28783726.). It has become increasingly clear that protein assemblies are ubiquitous, evolvable and perhaps overlooked in research.

      This research explores a specific type of filamentation, named agglomeration, unique in that the protein which assemble are not misfolded (Romero-Romero ML, Garcia-Seisdedos H. Agglomeration: when folded proteins clump together. Biophys Rev. 2023;15: 1987-2003.). This is particularly of biomedical interest due to its role in disease, such as sickle cell anaemia (J. Hofrichter, P.D. Ross, & W.A. Eaton, Kinetics and Mechanism of Deoxyhemoglobin S Gelation: A New Approach to Understanding Sickle Cell Disease*, Proc. Natl. Acad. Sci. U.S.A. 71 (12) 4864-4868, https://doi.org/10.1073/pnas.71.12.4864 (1974).) The current research adds to the field by specifically exploring agglomerates in the most detailed methodology to date.

      The novelty of this research lies especially in two areas; (1) establishing a method for distinguishing between aggregation and agglomeration, and (2) the finding that agglomerates are largely innocuous in vivo. The method established for defining agglomerates is simple, elegant and well-described in this paper's methods. The authors then probe cellular responses to agglomeration via both proteostasis machinery and cellular fitness. They noted no disruption to fitness and observed little targeting of agglomerates by chaperones. The experiments were thorough, conclusive, and resulted in interesting findings.

      The inertia of this type of protein filament is unexpected; agglomerates are large and have been associated with disease. The results of this study, however, indicate that agglomerates are non-toxic and well-tolerated in vivo. The authors speculate that agglomerates may have evolved in a non-adaptive process, which is evolutionary very interesting. They also posit that these results could lead to synthetic biology applications such as a tracking expression or as a molecular sensor. This work is of great interest and impact both in cell biology, biomedicine and in-vivo biology.

      Personal note: I come from a background of enzyme evolution and have viewed the work in this light.

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      Referee #2

      Evidence, reproducibility and clarity

      This is a very interesting paper investigating the fitness and cellular effects of mutations that drive dihedral protein complex into forming filaments. The Levy group have previously shown that this can happen relatively easily in such complexes and this paper now investigates the cellular consequences of this phenomenon. The study is very rigorous biophysically and very surprisingly comes up empty in terms of an effect: apparently this kind of self-assembly can easily be tolerated in yeast, which was certainly not my expectation. This is a very interesting result, because it implies that such assemblies may evolve neutrally because they fulfill the two key requirements for such a trajectory: They are genetically easily accessible (in as little as a single mutation), and they have perhaps no detrimental effect on fitness. This immediately poses two very interesting questions: Are some natural proteins that are known to form filaments in the cell perhaps examples of such neutral trajectories? And if this trait is truly neutral (as long as it doesn't affect the base biochemical function of the protein in question), why don't we observe more proteins form these kinds of ordered assemblies.

      I have no major comments about the experiments as I find that in general very carefully carried out. I have two more general comments:

      1. The fitness effect of these assemblies, if one exists, seems very small. I think it's worth remembering that even very small fitness effects beyond even what competition experiments can reveal could in principle be enough to keep assembly-inducing alleles at very low frequencies in natural populations. Perhaps this could be acknowledged in the paper somewhere.
      2. The proteins used in this study I think were chosen such that they do not have an important function in yeast that could be disrupted by assembly This allows the effect of the large scale assemblies to be measured in isolation. If I deduced this correctly, this should probably be pointed out agin in this paper (I apologise if I missed this).
      3. The model system in which these effects were tested for is yeast. This organism has a rigid cell wall and I was wondering if this makes it more tolerant to large scale assemblages than wall-less eukaryotes. Could the authors comment on this?

      Minor points:

      In Figure 2D, what are the fits? And is there any analysis that rules out expression effects on the mutant caused by higher levels of the wild-type? The error bars in Figure 2E are not defined.

      Significance

      This is a remarkably rigours paper that investigates whether self-assembly into large structures has any fitness effect on a single celled organism. This is very relevant, because a landmark paper from the Levy group showed that many proteins are very close in genetic terms to forming such assemblies. The general expectation I think would have been that this phenomenon is pretty harmful. This would have explained why such filaments are relatively rare as far as we know. This paper now does a large number of highly rigours experiments to first prove beyond doubt that a range of model proteins really can be coaxed into forming such filaments in yeast cells through a very small number of mutations. Its perhaps most surprising result is that this does not negatively affect yeast cells.

      From an evolutionary perspective, this is a very interesting and highly surprising result. It forces us to rethink why such filaments are not more common in Nature. Two possible answers come to mind: First, it's possible that filamentation is not directly harmful to the cell, but that assembling proteins into filaments can interfere with their basic biochemical function (which was not tested for here).

      Second, perhaps assembly does cause a fitness defect, but one so small that it is hard to measure experimentally. Natural selection is very powerful, and even fitness coefficients we struggle to measure in the laboratory can have significant effects in the wild. If this is true, we might expect such filaments to be more common in organisms with small effective population sizes, in which selection is less effective.

      A third possibility is of course that the prevalence of such self-assembly is under-appreciated. Perhaps more proteins than we currently know assemble into these structures under some conditions without any benefit or detriment to the organism.

      These are all fascinating implications of this work that straddle the fields of evolutionary genetics and biochemistry and are therefore relevant to a very wide audience. My own expertise is in these two fields. I also think that this work will be exciting for synthetic biologists, because it proves that these kinds of assemblies are well tolerated inside cells.

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      Referee #1

      Evidence, reproducibility and clarity

      In this work, the authors used yeast cell as a model system to study the abovementioned question. They established a model protein system using fluorescently labeled proteins that can form both agglomerates and aggregates. Using imaging experiments, they arguably showed that agglomerates do not colocalize with the proteostasis machinery, echoing what was observed by proteomics results. The proteomics results after pull down assay to study the interactome revealed that agglomerate-size-dependent changes were dependent on the cell-wall and plasma-membrane proteins. On the other hand, as expected, the misfolded proteins (aggregates) showed heavy involvement of proteostasis network components.

      Although the experiments still lack some controls and failed to support some of the conclusions, I found this work is a nice complement of the field to emphasize the point that "aggregates" and "agglomerates" are two different states, which is often mistaken by lots of researchers in recent years, in particular with the membraneless organelles (LLPS). I support its publication after the authors may consider the following suggestions and make necessary improvement.

      Major concerns:

      My major concern was raised by the lack of evidence to support the model system's folding state in the cell. 1. In Figure 1 and 2, I found the evidence to distinguish the folded state of proteins in the cells was limited. The concept of using hybrid imaging technique to prove the folding state is not a common experiment. The description of Figure 2 was very limited. I am sure the general audience can be convinced that the model proteins were actually folded and form agglomeration. 2. In addition, for mutants formed aggregates, the authors may consider to perform fractionation or crosslinking or native page experiment to show the evidence of protein misfolding and aggregation. 3. Have the authors considered to use FRAP assay to distinguish "aggregates" and "agglomerates" states in the cell? Does each of the state display different dynamics in the cell?

      Minor concerns:

      1. In Figure 3, it is very interesting to see such patten. I wonder why some of the chaperones were not responsive to misfolded proteins but some were very addicted to proteostasis. Could you elaborate more on this point? Are they chaperone sensitive, namely selective to 60/10, 70/40 or 90 system?
      2. In Figure 6, I suggest to add GO analysis and KEGG analysis to distinguish pathways and functional mismatch between "aggregates" and "agglomerates" interactomics.
      3. The quantity control for the proteomics studies is needed, namely the reproducibility of 3 repeats?
      4. This may beyond the scope of this work. I am interested whether the authors could point out whether similar works can be done in mammalian cells. What is the model system for mammalian cell that can form "agglomerates".

      Referees cross-commenting

      I read through the other two reviewers' comments, which I found reasonable. It seems like all reviewers agreed that this work is of enough significance for the field only with several technical concerns.

      Significance

      The submitted manuscript emphasized on a very important but often misleading concept: "aggregates" and "agglomerates" are two different states of protein structures in the cell with distinct physiological roles. However, these two states are of very similar phenotype: punctate structure in the cell. While the proteostasis network has been well-established for its central role of protein quality control and coping with misfolded and aggregated proteome, the authors attempted to profile the mechanism and physiological impact of mutation-induced folded-state protein filamentation, namely a model of "agglomerates". Such overarching goal of this work clearly pointed out the novelty of this work. Clearly, this is a new angle and aspect remained to be clarified for the field.

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      Reply to the reviewers

      Manuscript number: RC-2024-02465

      Corresponding author(s): Saravanan, Palani

      1. General Statements

      We would like to thank the Review Commons Team for handling our manuscript and the Reviewers for their constructive feedback and suggestions. In our revised manuscript, we have addressed and incorporated all the major suggestions of the reviewers, and we have also added new significant data on the role of Tropomyosin in regulation of endocytosis through its control over actin monomer pool maintenance and actin network homeostasis. We believe that with all these additions, our study has significantly gained in quality, strength of conclusions made, and scope for future work.

      2. Point-by-point description of the revisions

      Reviewer #1

      Evidence, reproducibility and clarity

      There are 2 Major issues -

      Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.

      Response: We would like to clarify that all mNG-Tpm constructs used in our study contain a 40 amino-acid (aa) flexible linker between the N-terminal mNG fluorescent protein and the Tpm protein as per our earlier published study (Hatano et al., 2022). During initial optimization, we have also experimented with linker length and the 40aa-linker length works optimally for clear visualization of Tpm onto actin cable structures in budding yeast, fission yeast (both S. pombe and S. japonicus), and mammalian cells (Hatano et al., 2022). These constructs have also been used since in other studies (Wirshing et al., 2023; Wirshing and Goode, 2024) and currently represents the best possible strategy to visualize Tpm isoforms in live cells. In our study, we characterized these proteins for functionality and found that both mNG-Tpm1 and mNG-Tpm2 were functional and can rescue the synthetic lethality observed in Dtpm1Dtpm2 cells. During our study, we observed that mNG-Tpm1 expression from a single-copy integration vector did not restore full length actin cables in Dtpm1 cells (Fig. 1B, 1C). We hypothesized that this could be a result of reduced binding affinity of the tagged tropomyosin due to lack of normal N-terminal acetylation which stabilizes the N-terminus. The 40aa linker is unstructured and may not be able to neutralize the charge on the N-terminal Methionine, thus, we tried to insert -Ala-Ser- dipeptide which has been routinely used in vitro biochemical studies to stabilize the N-terminal helix and impart a similar effect as the N-terminal acetylation (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) by restoring normal binding affinity of Tpm to F-actin (Monteiro et al., 1994; Greenfield et al., 1994). We observed that addition of the -Ala-Ser- dipeptide to mNG-Tpm fusion, indeed, restored full length actin cables when expressed in Dtpm1 cells, performing significantly better in our in vivo experiments (Fig. 1B, 1C). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may stabilize the N-terminus of Tpm and allow normal head-to-tail dimer formation (Greenfield et al., 1994; Monteiro et al., 1994; Frye et al., 2010). We have discussed this in our new Discussion section (Lines 350-372). Since, the addition of -AS- dipeptide was referred to as "acetyl-mimic (am)" in a previous study (Alioto et al., 2016), we continued to use the same nomenclature in our study. Now as per your suggestions and to be more accurate, we have renamed "mNG-amTpm" constructs as "mNG-ASTpm" throughout the study to not confuse or claim that -AS- addition mimics acetylation. In any case, we have not seen any other ill effect of -AS- dipeptide introduction in addition to our 40 amino acid linker suggesting that it can also be considered part of the linker. Although, we agree with the reviewer that biochemical characterization of the effect of linker would be important to determine, we strongly believe that it is currently outside the scope of this study and should be taken up for future work with these proteins. Our study has majorly aimed to understand the functionality and utility of these mNG-Tpm fusion proteins for cell biological experiments in vivo, which was not done earlier in any other model system.

      My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

      Response: We agree with the reviewer that N-terminal tagging of tropomyosin may have effects on its function, but these constructs represent the only fluorescently tagged functional tropomyosin constructs available currently while C-terminal fusions are either non-functional (we were unable to construct strains with endogenous Tpm1 gene fused C-terminally to GFP) or do not localize clearly to actin structures (See Figure R1 showing endogenous C-terminally tagged Tpm2-yeGFP that shows almost no localization to actin cables). To our knowledge, our study represents a first effort to understand the question of spatial sorting of Tpm isoforms, Tpm1 and Tpm2, in S. cerevisiae and any future developments with better visualization strategies for Tpm isoforms without compromising native N-terminal modifications and function will help improve our understanding of these proteins in vivo. We have also discussed these possibilities in our new Discussion section (Lines 391-396).

      Significance

      This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly. The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

      Response: __Our study explores multiple facets of Tropomyosin (Tpm) biology. The lack of functional tagged Tpm has been a major bottleneck in understanding Tpm isoform diversity and function across eukaryotes. In our study, we characterize the first functional tagged Tpm proteins (Fig. 1, Fig. S1) and use them to answer long-standing questions about localization and spatial sorting of Tpm isoforms in the model organism S. cerevisiae (Fig. 2, Fig. 3, Fig. S2, Fig. S3). We also discover that the dual Tpm isoforms, Tpm1 and Tpm2, are functionally redundant for actin cable organization and function, while having gained divergent functions in Retrograde Actin Cable Flow (RACF) (Fig. 4, Fig. 5A-D, Fig. S4, Fig. S5, Fig. S6). We have now added new data on role of global Tpm levels controlling endocytosis via maintenance of normal linear-to-branched actin network homeostasis in S. cerevisiae (Fig. 5E-G)__. We respectfully differ with the reviewer on their assessment of our study and request the reviewer to read our revised manuscript which discusses the significance, limitations, and future perspectives of our study in detail.

      Reviewer #2

      Evidence, reproducibility and clarity

      This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

      Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

      1. Functionality of the acetyl-mimic tagged tropomyosin constructs: The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

      __Response: __We would like to thank the reviewer for his feedback and suggestions. Based on the suggestions, we have now more accurately described the growth rescue observed by expression of mNG-ASTpm1 in Dtpm1 cells in the revised text. We have also removed the use of "completely functional" to describe mNG-Tpm functionality and corrected any errors in Figure citations in the revised manuscript.

      As per reviewers' suggestion, we have now tested rescue of synthetic lethality of Dtpm1Dtpm2 cells by expression of all mNG-Tpm variants and we find that all of them are capable of restoring the viability of Dtpm1Dtpm2 cells when expressed under their native promoters via a high-copy plasmid (pRS425) (Fig. S1E) but only mNG-Tpm1 and mNG-ASTpm1 restored viability of Dtpm1Dtpm2 cells when expressed under their native promoters via an integration plasmid (pRS305) (Fig. S1F). These results clearly suggest that while both mNG-Tpm1 and mNG-Tpm2 constructs are functional, Tpm1 tolerates the presence of the N-terminal fluorescent tag better than Tpm2. These observations now enhance our understanding of the functionality of these mNG-Tpm fusion proteins and will be a useful resource for their usage and experimental design in future studies in vivo.

      It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

      Response: __We agree with the reviewer's observation and for the sake of clarity and accuracy, we have now renamed "mNG-amTpm" with "mNG-ASTpm". The use of -AS- dipeptide is very routine in studies with Tpm (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) and its addition restores normal binding affinities to Tpm proteins purified from E. coli (Monteiro et al., 1994). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may help neutralize the impact of a freely protonated Met on the alpha-helical structure and stabilize the N-terminus helix of Tpm and allow normal head-to-tail dimer formation (Monteiro et al., 1994; Frye et al., 2010; Greenfield et al., 1994). Consistent with this, we also observe a highly significant improvement in actin cable length when expressing mNG-ASTpm as compared to mNG-Tpm in Dtpm1 cells, suggesting an improvement in function probably due to increased binding affinity (Fig. 1B, 1C). We have also discussed this in our answer to Question 1 of Reviewer 1 and the revised manuscript (Lines 350-372)__.

      __ Localization of Tpm1 and Tpm2:__Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

      Response: __We thank the reviewer for this observation and their suggestions. We agree that relative concentrations of functional Tpm1 and Tpm2 in cells may influence the extent of their localizations. As per the reviewer's suggestion, we have now conducted our quantitative analysis in cells lacking endogenous Tpm1 and only expressing mNG-ASTpm1 from an integrated plasmid copy at the leu2 locus and the data is presented in new __Figure S3. We compared Tpm-bound cable length (Fig. S3A, S3B) __and Tpm-bound cable number (Fig. S3A, S3C) along with actin cable length (Fig. S3D, S3E) and actin cable number (Fig. S3D, S3F) in wildtype, Dbnr1, and Dbni1 cells. Our analysis revealed that mNG-ASTpm1 localized to actin cable structures in wildtype, Dbnr1, and Dbni1 cells and the decrease observed in Tpm-bound cable length and number upon loss of either Bnr1 or Bni1, was accompanied by a corresponding decrease in actin cable length and number upon loss of either Bnr1 or Bni1. Thus, this analysis reached the same conclusion as our earlier analysis (Fig. 2) that mNG-ASTpm1 does not show preference between Bnr1 and Bni1-made actin cables. mNG-ASTpm2 did not restore functionality, when expressed as single integrated copy, in Dtpm1Dtpm2 cells (new results in __Fig. S1E, S1F, S5A) thus, we could not conduct a similar analysis for mNG-ASTpm2. This suggests that use of mNG-ASTpm2 would be more meaningful in the presence of endogenous Tpm2 as previously done in Fig. 2D-F.

      We have now also performed additional yeast mating experiments with cells lacking bnr1 gene and expressing either mNG-ASTpm1 or mNG-ASTpm2 and the data is shown in new Figure 3. From these observations, we observe that both mNG-ASTpm1 and mNG-ASTpm2 localize to the mating fusion focus in a Bnr1-independent manner (Fig. 3B, 3D) and suggests that they bind to Bni1-made actin cables that are involved in polarized growth of the mating projection. These results also add strength to our conclusion that Tpm1 and Tpm2 localize to actin cables irrespective of which formin nucleates them. Overall, these new results highlight and reiterate our model of formin-isoform independent binding of Tpm1 and Tpm2 in S. cerevisiae.

      In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

      Response: __We thank the reviewer for pointing this out. Our data and analysis do not suggest that Tpm1 and Tpm2 show any preference for decoration of cables in either mother or bud compartment. As per the reviewer's suggestion, we have now quantified the ratio of mean mNG fluorescence in the bud to the mother (Bud/Mother) and the data is shown in __Figure. S2G. The bud-to-mother ratio was similar for mNG-ASTpm1 and mNG-ASTpm2 in wildtype cells, and the ratio increased in Dbnr1 cells and decreased in Dbni1 cells for both mNG-ASTpm1 and mNG-ASTpm2 (Fig. S2G). __This is consistent with the decreased actin cable signal in the mother compartment in Dbnr1 cells and decreased actin cable signal in the bud compartment in Dbni1 cells (Fig. S2A-D). Thus, our new analysis shows that both mNG-ASTpm1 and mNG-ASTpm2 have similar changes in their concentration (mean fluorescence) upon loss of either formins Bnr1 and Bni1 and show similar ratios in wildtype cells as well, suggesting no preference for binding to actin cables in either bud or mother compartment. The preference inferred by the reviewer seems to be a bias of the current representative images and thus, we have replaced the images in __Fig. 2A, 2D to more accurately represent the population.

      The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

      __Response: __We did not use LifeAct for our analysis as LifeAct is known to cause expression-dependent artefacts in cells (Courtemanche et al., 2016; Flores et al., 2019; Xu and Du, 2021) and it also competes with proteins that regulate normal cable organization like cofilin. Use of LifeAct would necessitate standardization of expression to avoid such artefacts in vivo. Also, phalloidin staining provides the best staining of actin cables and allows for better quantitative results in our experiments. The use of LifeAct along with mNG-Tpm would also require optimization with a red fluorescent protein which usually tend to have lower brightness and photostability. However, during the revision of our study, a new study from Prof. Goode's lab has developed and optimized expression of new LifeAct-3xmNeonGreen constructs for use in S. cerevisiae (Wirshing and Goode, 2024). Thus, a similar strategy of using tandem copies of bright and photostable red fluorescent proteins can be explored for use in combination with mNG-Tpm in the future studies.

      __ Complementation of tpm1∆ by Tpm2:__

      I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue.

      The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

      Response: __We thank the reviewer for pointing this out. We would like to clarify that in our RT-qPCR experiments, the primers were chosen within the Tpm1 and Tpm2 gene and do not distinguish between transcripts from endogenous or plasmid copy. We have now mentioned this in the Materials and Methods section of the revised manuscript. So, they represent a relative estimate of the total mRNA of these genes present in cells. We were consistently able to detect ~19 fold increase in Tpm2 total mRNA levels as compared to wildtype and ∆tpm1 cells (Fig. S4D) when tpm2 was expressed from a high-copy plasmid (pRS425). This increase in Tpm2 mRNA levels was accompanied by a rescue in growth (Fig. S4A) and actin cable organization (Fig. S4B) of ∆tpm1 cells containing pRS425-ptpm2TPM2. When tpm2 was expressed from a low-copy number centromeric plasmid (pRS316), we detected a ~2 fold increase in Tpm2 transcript levels when using the tpm1 promoter and no significant change was detected when using tpm2 promoter (Fig. S4E)__. We have made sure that these results are accurately described in the revised manuscript.

      As per the reviewer's suggestion, we have now conducted a more extensive analysis to ascertain the expression levels of Tpm2 in our experiments and the data is now presented in new Figure S5. We used mNG-ASTpm1 and mNG-ASTpm2 to rescue growth of ∆tpm1 (Fig. S5A) and correlated growth rescue with protein levels using quantified fluorescence intensity (Fig. S5B, S5C) and western blotting (anti-mNG) (Fig. S5D, S5E). We find that ∆tpm1 cells containing pRS425-ptpm1mNG-ASTpm1 had the highest protein level followed by pRS425-ptpm2 mNG-ASTpm2, pRS305-ptpm1mNG-ASTpm1, and the least protein levels were found in pRS305-ptpm2 mNG-ASTpm2 containing ∆tpm1 cells in both fluorescence intensity and western blotting quantifications (Fig. S5C, S5E). Surprisingly, we were not able to detect any protein levels in ∆tpm1 cells containing pRS305-ptpm2 mNG-ASTpm2 with western blotting (Fig. S5D) which was also accompanied by a lack of growth rescue (Fig. S5A). This most likely due to weak expression from the native Tpm2 promoter which is consistent with previous literature (Drees et al., 1995). Taken together, this data clearly shows that the rescue observed in ∆tpm1 cells is caused due to increased expression of mNG-ASTpm2 in cells and supports our conclusion that increase in Tpm2 expression leads to restoration of normal growth and actin cables in ∆tpm1 cells.

      __ Specific function of Tpm2:__

      The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

      Response: __We agree with the reviewer and as per the reviewer's suggestion, we have performed another experiment which include wildtype, ∆tpm2 cells containing empty pRS316 vector or pRS316-ptpm2TPM1 or pRS316-ptpm1TPM1. We find that RACF rate increased in ∆tpm2 cells as compared to wildtype and was restored to wildtype levels by exogenous expression of Tpm2 but not Tpm1 (Fig. S6E, S6F). Since, actin cables were not detectable in ∆tpm1 cells, we measured RACF rates in ∆tpm1 cells expressing Tpm1 or Tpm2 from a plasmid copy, which restored actin cables as shown previously in __Fig. 5A-C. We observed that RACF rates were similar to wildtype in ∆tpm1 cells expressing either Tpm1 or Tpm2 (Fig. S6E, S6F), suggesting that Tpm1 is not involved in RACF regulation. Taken together, these results suggest a specific role for Tpm2, but not Tpm1, in RACF regulation in S. cerevisiae, consistent with previous literature (Huckaba et al., 2006).

      Minor comments: __1.__The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

      Response: We thank the reviewer for pointing this out. We have now repeated the drop test multiple times (Fig. R2), but we see similar growth rates as the drop test already presented in Fig. S4A. __At this point, it would be difficult to ascertain the basis of this difference observed at 23{degree sign}C and 30{degree sign}C, but a recent study that links leucine levels to actin cable stability (Sing et al., 2022) might explain the faster growth of these ∆tpm1 cells containing a leu2 gene carrying high-copy plasmid. However, there is no effect on growth rate at 37{degree sign}C which is consistent with other spot assays shown in __Fig. S1D, S4F, S5A.

      Significance

      I am a cell biologist with expertise in both yeast and actin cytoskeleton.

      The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

      We thank the reviewer for their positive assessment of our work and the constructive feedback that has greatly improved the quality of our study. After addressing the points raised by the reviewer, we believe that our study has significantly gained in consolidating the major conclusions of our work.

      **Referees cross-commenting**

      Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

      Response: __We acknowledge the reviewer's point about the effect of Ala-Ser dipeptide and would request the reviewer to refer to our response to Reviewer 1 (Question 1) for a more detailed discussion on this. We have also extensively addressed the question of Tpm2 expression levels as suggested by the reviewer (new data in __Figure S5) which has further strengthened the conclusions of our study.

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

      Summary:__ The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

      Major and Minor Comments: 1. The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

      Response: __We agree with the reviewer's suggestions and have now updated our Materials and Methods section to describe the image analysis pipelines used in more detail. We have also added examples of quantification procedure for actin cable length/number and mitochondrial morphology as an additional Supplementary __Figure S7. Briefly, the following pipelines were used:

      • Actin cable length and number analysis: This was done exactly as mentioned in McInally et al., 2021, McInally et al., 2022. Actin cables were manually traced in Fiji as shown in __ S7A__, and then the traces files for each cell were run through a Python script (adapted from McInally et al., 2022) that outputs mean actin cable length and number per cell.
      • Mitochondria morphology: Mitochondria Analyzer plug-in in Fiji was used to segment out the mitochondrial fragments. The parameters used for 2D segmentation of mitochondria were first optimized using "2D Threshold Optimize" to find the most accurate segmentation and then the same parameters were run on all images. After segmentation of the mitochondrial network, measurements of fragment number were done using "Analyze Particles" function in Fiji. An example of the overall process is shown in __ S7B.__ As per the reviewer's suggestion, we have now included the description of the statistical test used in the Figure Legends of each Figure in the revised manuscript. We have used One-Way Anova with Tukey's Multiple Comparison test, Kruskal-Wallis test with Dunn's Multiple Comparisons, and Unpaired Two-tailed t-test using the in-built functions in GraphPad Prism (v.6.04).

      **Referees cross-commenting**

      I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

      Response: __We acknowledge the reviewer's concern about the Ala-Ser dipeptide and would request them to refer our earlier discussion on this in response to Reviewer 1 (Question 1) and Reviewer 2 (Question 2). We would also request the reviewer to refer to our answer to Reviewer 2 (Question 6) where we have extensively addressed the question of Tpm2 expression levels and their effect on rescue of Dtpm1 cells. This data is now presented as new __Figure S5 in our revised manuscript.

      Reviewer#3 (Significance (Required)):

      The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

      The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

      As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

      We thank the reviewer for their positive outlook and assessment of our study. We have incorporated all their suggestions, and we are confident that the revised manuscript has significantly improved in quality due to these additions.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

      Major and Minor Comments:

      The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

      Referees cross-commenting

      I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

      Significance

      The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

      The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

      As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

      Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

      Functionality of the acetyl-mimic tagged tropomyosin constructs:

      The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

      It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

      Localization of Tpm1 and Tpm2:

      Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

      In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

      The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

      Complementation of tpm1∆ by Tpm2:

      I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue. The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

      Specific function of Tpm2:

      The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

      Minor comments:

      The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

      Referees cross-commenting

      Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

      Significance

      I am a cell biologist with expertise in both yeast and actin cytoskeleton.

      The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

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      Referee #1

      Evidence, reproducibility and clarity

      There are 2 Major issues:

      1. Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.
      2. My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

      Significance

      This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly.

      The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

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      Reply to the reviewers

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      Referee #3

      Evidence, reproducibility and clarity

      Lupu et al have identified a role for the RNA binding protein SRSF3 in epicardial development. In a well written manuscript containing many experiments the authors show that this protein is required at different time points in epicardial development to control a range of processes, in particular cell proliferation. This advances understanding of the complex roles of this RNA binding protein in the heart - and raises an important message about how incomplete Cre recombination needs to be considered in interpreting conditional mutant phenotypes. The following points should be addressed.

      1. SRSF3 is known to play essential developmental roles in the myocardium where it regulates capping of transcripts involved in contraction. This point should be mentioned in addition to roles in proliferation. To facilitate understanding, the authors should say more about the subset of cardiomyocytes labelled by Gata5-Cre. For example, is this the result of stochastic activation of the transgene or is a specific subset of cells labelled? How much of the myocardium is targeted?
      2. The authors show failure of ventricular compaction at E13.5 using Wt1-CreERT2 and go on to assess proliferation in epicardial cells. As epicardial-derived signals are known to promote compact myocardial growth, they should also show whether there are indirect defects in proliferation in compact layer myocardium that might explain the non-compaction phenotype. The authors should also indicate if any of the large number of genes bound by SRSF3 encode known or potential pro-proliferative signals from the epicardium or EPDCs to the myocardium and potentially validate their altered expression in mutant hearts.
      3. The rescue by expansion of non-recombined cells is a most interesting aspect of this study. Can the authors see any such outcompeting in the explant experiments (for example in Figure 2)? Do the authors consider this to be an exclusively in vivo competition phenomenon? Given the known roles of Myc in cell competition can the authors use their single cell transcriptomic data to score Myc expression levels in cells from Srsf3 iKO hearts or determine if Myc transcripts are bound by SRSF3?
      4. The authors suggest that this rescue occur by upregulation of Srsf3 in non-recombined cells. It would be helpful to provide additional lines of evidence supporting the hypothesis that SRSF3 expression is upregulated due to hypoxia. Do the CLIP experiments reveal whether SRSF3 binds to it's own transcript?
      5. The authors imply that SRSF3 may regulate Ccnd1 mRNA stability. Can the authors directly evaluate this point? Please clarify if this gene is also affected in the knock-down experiments in MEC1 cells.
      6. Please brighten the immunofluorescence panels in Figure 1 to more clearly show nuclear labelling and tissue structure.
      7. Given the broad roles of SRSF3 is the adjective key necessary in the title?

      Significance

      This ms advances understanding of the complex roles of this RNA binding protein in the heart - and raises an important message about how incomplete Cre recombination needs to be considered in interpreting conditional mutant phenotypes. This study would be of interest to reseachers in the fields of heart development and RNA-protein interactions. Although there are a number of major points to be addressed, these could be potentially dealt with rapidly.

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      Referee #2

      Evidence, reproducibility and clarity

      In this study, Lupu et al. analyzed the role of the RNA-binding protein SRSF3 for epicardial development. The authors found that Srsf3 is highly expressed in the proepicardial organ and during early stages of epicardial layer formation. Conditional inactivation of SRSF3 in the proepicardial organ stage using a Gata5-Cre driver line resulted in defective formation of the epicardium, accompanied by a proliferation arrest of the proepicardium, resulting in embryonic lethality at E12.5. In contrast, epicardial-specific Srsf3 deletion at later stages using the inducible Wt1CreERT2 line caused a less severe phenotype indicated by impaired coronary vasculature formation, reduced cardiac compaction, and myocardial hypoxia. Mosaic recombination yielded a small population of epicardial cells that upregulate Srsf3, hyperproliferate and compensate for the depleted Srsf3 negative lineage. Single-cell RNA sequencing of control and epicardial Srsf3 knock out hearts, combined with infrared CLIP to map SRSF3 binding sites in the transcriptome identified a number of putative SRSF3 targets involved in mitotic cell cycle control. Among others, SRSF3 binds directly to transcripts encoding key regulators of proliferation, such as Cyclin D1, and senescence, including MAP4K4. The authors conclude that SRSF3 exerts different functions in processing of RNAs, including splicing.

      Overall, this is a well-written and well-organized manuscript, describing interesting findings in the field of epicardial development. However, the mechanistic part is not overly strong. The authors detected some moderate changes in the distribution of different Map4k4 splicing isoforms after knockdown of Srsf3 in an immortalized epicardial cell line but did not go any deeper. The cause for the reduced presence of transcripts for the SRSF3-target Ccnd1 after knockdown of Srsf3 remains enigmatic.

      Significance

      The authors raise a number of speculations why remaining Srsf3-expressing cell start to hyperproliferate after inactivation of Srsf3 but it does not become clear which mechanism is critical. How do non-targeted epicardial cells in the mosaic recombination model sense the loss of SRSF3 knock out cells, resulting in hyperproliferation and enhanced Srsf3 expression? Is a loss of lateral inhibition, e.g. by activated YAP/TAZ, causative for enhanced proliferation of the remaining epicardial cells and an elevated expression level of WT1 and SRSF3? Immunofluorescence staining and/or qRT-PCR of YAP/TAZ and TEADs might provide an answer.

      Is the elevated expression of Srsf3 in non-targeted epicardial cells due to enhanced transcription and/or by altered post-transcriptional processes? How does this observation fit to previous reports indicating that Srsf3 overexpression promotes inclusion of an autoregulatory cassette exon (exon 4) containing a premature (in-frame) stop codon in Srsf3, thereby confining this SRSF3 isoform to nonsense mediated decay (NMD) (doi: 10.1093/emboj/16.16.5077, doi: 10.1186/gb-2012-13-3-r17, doi: 10.1038/srep14548, doi: 10.1161/CIRCRESAHA.118.31451)? In contrast, Srsf1 as well as PTBP1/2 have been previously reported to regulate Srsf3 expression by promoting exon 4 skipping. The authors should perform RNA seq and/or qRT-PCRs validation to check the inclusion of Srsf4 Exon4 as well as Srsf1 and PTBP1/2 expression levels in control and knock out epicardial cells.

      It remains unclear by which mechanisms (alternative splicing, alternative polyadenylation, miRNA processing, or others) SRFS3 mainly exerts its function in the embryonic epicardial lineage. The selection and validation of Map4k4 as a splicing target is not based on an unbiased splicing analysis. In my opinion it is mandatory to provide a full assessment of splicing changes in Srsf3-deficient cells, either by long-range sequencing or by analysis of exon-junction reads.

      Likewise, it is completely enigmatic what SRFS3 does to Ccnd1 transcripts. Does SRFS3 increase the half-life of Ccnd1, does it impact trafficking? At least, the authors have to determine changes in the half-life of Ccnd1 after depletion of SRFS3.

      An unbiased bioinformatics analysis addressing alternative splicing, alternative polyadenylation, and mRNA processing is necessary. Ideally, primary epicardial cells should be used and not an immortalized epicardial cell lines. It is well known, that splicing in cell lines differs substantially from splicing in primary cells.

      I am not convinced that the moderate changes of different Map4k4 splicing isoforms after knockdown of Srsf3 are really responsible for the rather drastic phenotype. Additional experiments are needed to prove a decisive function of a shift in Map4k4 splicing isoforms for hyperproliferation of epicardial cells.

      The authors claim that inactivation of Srsf3 inhibits cell proliferation and causes a senescence-like phenotype. The claim for acquisition of senescence is solely based on transcriptional changes. No attempts were made to visualize an increase of senescent cells in Srsf3-mutant embryos. The authors need to perform SA-bGAL assays or use other techniques to analyse the appearance of senescent cells in the mutants.

      Fig. 2E indicates that WT1 positive / tdTom negative epicardial cell population is enriched in a specific region of the pre-epicardial organ from Srsf3KOs. However it is not clear whether these cells proliferate. The authors should quantify Ki67positive cells in both the WT1-positive / tdTom-positive and the WT1positive / tdTom-negative epicardial population.

      In the headline on page 6, the authors stated that "SRSF3 depletion in the PEO results in impaired ... migration of epicardial progenitor cells", which they deduced from the reduced outgrowth of ventricular epicardial explants. However, the reduced outgrowth from the PEO could be caused by both, reduced proliferation and/or reduced migration. Therefore, the authors should provide additional data clearly indicating reduced migration, e.g. by blocking transcription. Scratch assays of SRFS3 knockout/knockdown vs. control epicardial cells would strengthen the analysis, Is there a change in the GO term "regulation of migration"?

      To prove the reduced proliferation ratio in Figure 4B, quantification of Cyclin D1 positive cells in both SRSF3 positive and negative cells is required.

      Minor issues

      Abstract line 12: a full stop is missing at the end of the sentence.

      Figure 1A: E11.5, figure label 'DAPI WT1' is missing.

      Page 8: Bracket in front of Fig. 4B is missing

      Page 8: G2M phase change uniformly to G2/M phase

      Page 9: 'Srsf3-depleted hearts also demonstrated an increased abundance of epicardial cells with upregulated expression of genes associated with quiescence, such as Clu48, and senescence, for example Map4k4, Tmem30a and Pofut249 (Fig. 4F)'. The sentence is misleading, implying Srfs3 inactivation in all cardiac cell types ('Srsf3-depleted hearts').

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The study by Irina Lupu and colleagues highlights SRSF3 as a key regulator of epicardial development by regulating epicardial cell proliferation. This was demonstrated via two murine knockout models; the first to assimilate the role SRSF3 plays in epicardial formation as a whole, and the second to address its importance post a pivotal maturation point. Through scRNA sequencing and irCLIP, several SRSF3 targets were ascertained and identified as cell cycle regulators. Those epicardial cells that did not lose SRSF3 compensated the loss of some of their mates by increasing SRSF3 expression and over-proliferating. Overall, the paper is interesting and the conclusions are largely supported by the provided data.

      Major comments:

      1. Authors claim that a "reduction in SRSF3 expression levels coincided with the downregulation of WT1 in the epicardium". This was evidenced by immunofluorescence imaging (figure 1A). I suggest conducting a qRT-PCR to quantify Wt1 expression over time, similar to the experiment they performed in figure 1B.
      2. A western blot depicting SRSF3 protein production in controls compared to the knockout model may provide stronger evidence of its depletion (figure 1E).
      3. Authors state that they were unable to directly identify the absence of exons 2 and 3 in individual cells. Please provide evidence that exons 2 and 3 have been knocked out, at least by performing a qRT-PCR.
      4. To prove the functional implication in the observed phenotype of the identified SRSF3 targets, please interfere with Map4k4 activity or expression and check whether the defective epicardial cell proliferation is reverted. This should be done at least in vitro, ideally in vivo.

      Minor comments:

      1. Several minor typos and spacing issues were observed. Please correct.
      2. It would be good for the reader if the authors would simplify their rationale for the use of the two mouse models. It is slightly convoluted and not easy to follow.
      3. In figure 4, it is recommended to add a stacked bar plot to represent the percentage of each cell cluster/population after 4A. This would help the reader
      4. Figure 4B. It is confusing for the reader to understand the fact that the majority of tdTomato+ sorted cells in Srsf3 iKO keep expressing Srsf3. Including the quantification of the image could help.

      Significance

      The paper will be of interest to readers in the field of cardiology, embryology and molecular biology. It will advance the field especially in the study of the development of the epicardium. The models are sophisticated and the experiments carefully performed.

      My field is molecular cardiology, with interest in RNA-binding proteins.

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      Reply to the reviewers

      Manuscript number: RC-2024-02835

      Corresponding author: Eglantine, Heude


      RESPONSE TO REVIEWERS


      Minor changes include:

      - concerns of Reviewer #1 with additional work to complete a new Supp. Fig. 3.

      - some text modifications to avoid ambiguity and misunderstanding, and to address the concerns of Reviewers #1 and #2.

      - addition of three references to develop some aspects of the discussion following the suggestions from Reviewer #3.



      Reviewer #1

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

      This manuscript investigates the role of Dlx5/6 in development of the ear and vocal tract components. They generate a new conditional mouse knockout of Dlx5/6 using a Sox10 driver, which deletes functional forms of these factors in the otic placode (the primordium of the entire inner ear) and in neural crest cells, which contribute to the middle and outer ear, the otic capsule, as well as the craniofacial skeleton and cartilage including that of the larynx, and tendons.

      The authors describe the ear and craniofacial phenotypes in these mutants using marker analysis on sections, some whole mounts and microCT imaging. They confirm previously described ear and craniofacial phenotypes, and add additional information on tongue, thyroid and hyoid cartilages, and associated mesoderm-derived muscles. Finally, they assess innervation finding subtle changes innervations patterns.

      They conclude that Dlx5/6 are required for normal development of auditory and vocal structures and suggest that these factors coordinate both systems and that this is important for their co-evolution.

      The experiments presented are straightforward phenotypic analysis, which is well presented with high quality imaging. However, it is not clear how well the conclusions are supported by the data because

      i) The number of animals analysed and showing the reported phenotypes is not clearly stated; some figure legends list some n numbers, but it is sometimes ambiguous whether they refer to controls or experimental specimen. In other cases, n=2 is too low to draw firm conclusions.

      We thank the reviewer for highlighting this point and for giving us the opportunity to clarify it.

      The number of specimens mentioned correspond to the number of mutants analyzed versus the equivalent number of controls. For most figures, at least 3 mutants and 3 corresponding controls have thus been studied independently, unless for the microCT analysis (n=2 each condition) that however complete histological data on sections (n=6 each condition) presented at equivalent stage. Our data showing highly penetrant and reproducible results, the n numbers appear significant in support of our conclusions. To avoid ambiguity, we have systematically completed the n numbers with the mention 'each condition' in the figure legends.

      ii) The nature of the controls is not stated, so we cannot know what is compared. Are controls WT, heterozygous for Dlx5 and -6, single KOs, single hets?

      The lack of clarity in methods and controls does not allow others to reproduce the findings easily.

      To address this concern, we have now added the genotype of controls in the Materials and Methods section. For all experiments, the controls of mutant specimens were from the same littermate. We included as control genotypes the specimens heterozygous for the Cre and/or flox or homozygous for the flox only (genotypes equivalent to breeders), which we had previously validated as showing a normal phenotype compared to non-genetically modified 'wild-type' specimens. This observation is consistent with what had been described previously in constitutive inactivated Dlx5/6 mouse models, for which the development of heterozygous animals was unaffected by the mutation (e.g. Heude et al. 2010 PNAS).


      Based on the fairly limited data presented here, the authors make some far-reaching suggestions that are not supported by experimental findings. For example, they propose that their study points to "co-adaptation of the effector and receptor organs during acoustic communication diversification in land vertebrates" and that Dlx5/6 play a role in this process. This idea appears to be the main motivation for the current study, however, there is little evidence to support such conclusions.

      Likewise, they suggest that a "common Sox10-Dlx5/6-BMP signaling axis coordinates the morphogenesis of both CNCC and otic placode derivatives for the proper formation of the vocal and auditory complex". There is no evidence provided that Sox10 controls any of the other genes and functional experiment related to Sox10 are not carried out, while the Dlx5/6-BMP link has been established previously.

      Overall, the authors need to improve numbers, experimental information and controls, and given the descriptive nature of the manuscript they should refrain from wide-ranging conclusions.

      We regret that Reviewer #1 took our discussion points as conclusions, which was not our purpose. Rather, Reviewer #3 stated that 'The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion' (see below). We believe that the Discussion section is the appropriate space to formulate hypotheses based on experimental results, mainly while speculating on evolutionary aspects, that cannot be technically tested but are still conceptually of interest.

      Concerning the Sox10-Dlx5/6-BMP axis, we agree with Reviewer #1 that we do not provide evidences that this common genetic program regulates the formation of vocal and auditory systems. We have added additional nuance to the text to avoid ambiguity, and we expect that our propositions will inspire future research on the issues raised.

      The analysis could also be strengthened by focusing on the novel aspects, providing a developmental time series as to when phenotypes are first observed combined with marker analysis e.g. looking at different compartments of the otic vesicle, cell types etc., and providing higher magnification images to describe the phenotypes (e.g. those in figure 1) will strengthen the paper and might provide some novelty.

      Reviewer #1 (Significance (Required)):

      The authors present a new conditional knock out line for Dlx5/6. The major limitation of the study is that the data presented largely confirm what has already been published including the regulation of BMP pathway components and non-cell autonomous effects on muscle. As far as I am aware, the only new additions to the literature are the analysis of the cartilages and tongue.

      The phenotypic analysis is minimal, there is no developmental time series and no other functional experiments to address some of the suggestions made by the authors.

      My expertise is in ear formation, and I am aware of the craniofacial literature.

      The novelty of our study is based on the generation of a new conditional mouse model to address the pleiotropic role of Dlx5/6 in coordinating the development of both the vocal tract and auditory components, an aspect that has never been considered before.

      To this end, we have combined a variety of high-resolution imaging techniques to achieve an unprecedented analysis of critical markers of placode, neural crest and mesoderm derivatives (including a neural crest lineage analysis). We initially selected key early embryonic (E10.5-E11.5), fetal (E14.5) and perinatal (E16.5-E17.5) stages, but we agree with Reviewer #1 that an additional stage is relevant in our context to complete this developmental time series. We have now added in a new Supp. Fig. 3 including the analysis of a late embryonic stage (E12.5). In particular, our results show that primary chondrogenic condensations are affected at the level of the vocal tract and auditory compartments. Furthermore, our data reveal that the differentiation defects of the masticatory and tongue musculature occur at the transition between early (E11.5) and late (E12.5) embryonic stages. We are grateful to Reviewer #1 for his comments, which allowed us to present these new and interesting results.

      Reviewer #2


      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Sanchez-Garrido and colleagues analyze the consequences of Dlx5/6 deletion on cranial neural crest- and otic placode-derived structures development. The authors show that Dlx5/6 inactivation causes broad defects of the outer, middle and inner ear as well as malformations of the jaw, pharynx and larynx musculoskeletal systems. The authors conclude that Dlx5/6 play an important role in the regulation of vocal and auditory systems development in mammals.

      Reviewer #2 (Significance (Required)):

      The work is well-presented and illustrated, with appropriate description of methods. The role of this gene family in regulating craniofacial structures is not completely novel. The analysis is largely descriptive providing little mechanistic insights. The authors propose a possible link between Dlx5/6 activity and BMP signaling as the culprit for the defects observed in the mutants, however this is not supported experimentally. The work is viewed as too preliminary at this stage.


      We are appreciative of the feedback from Reviewer #2. Our aim was to gain insight into the genetic basis of the coordinated development of the vocal tract and hearing complex. As mentioned above for Reviewer #1, we have added modifications to the discussion to nuance and develop our propositions. We hope that our study will help to further develop some of the aspects we have explored.

      Reviewer #3

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

      This is a very good paper presenting results examining the role of DLX5/6 in the formation of ear and vocal tract structures by means of mutant mice. I believe the evidence offered in this paper in favor of such a role is strong and well presented.

      Reviewer #3 (Significance (Required)):

      I think this is a very good study, contributing to our knowledge of the action of DLX5/6, giving us insights into the genes' role in ear and vocal tract structures, critical for vocalization and the pairing of auditory and oral capacities. The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion.

      I would encourage the authors to consider making mention of Gokhman et al's (2021) work, where genes similarly impacting craniofacial and oral capacities are discussed: https://www.nature.com/articles/s41467-020-15020-6

      I would also have liked the authors to cite work on GLI3 and larynx formation (given that both GLI3 and HAND2 control DLX5 and 6) and play an important role in Neural Crest-related processes: https://elifesciences.org/articles/77055 / https://elifesciences.org/articles/56450

      In this context, the authors mention work on the role of Dlx5/6 in GABAergic neurons. Do the authors believe there to be a connection (mediated by the SHH pathway) between the ganglionic eminence and neural crest development, or are these two findings only convergent at the level of the phenotype?

      We are grateful to Reviewer #3 for his positive assessment of our study and for his very relevant suggestions of works to consider in the context of our research. This has allowed us to complete and develop exciting points in our discussion, which we hope will stimulate further lines of research regarding the diversification of acoustic communication abilities during mammalian evolution, including the emergence of speech in humans.

      To answer the reviewer's question, we do not believe that there is a link between the ganglionic eminences and neural crest derivatives, which develop independently in two embryonic compartments under the regulation of distinct genetic programs. However, the observation that Dlx5/6 expression in the brain regulates socialization and vocalization behaviors in adult mice suggest that the genes have a broader pleiotropic role in acoustic communication, both during development and in the adult at the cognitive level.

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      Referee #3

      Evidence, reproducibility and clarity

      This is a very good paper presenting results examining the role of DLX5/6 in the formation of ear and vocal tract structures by means of mutant mice. I believe the evidence offered in this paper in favor of such a role is strong and well presented.

      Significance

      I think this is a very good study, contributing to our knowledge of the action of DLX5/6, giving us insights into the genes' role in ear and vocal tract structures, critical for vocalization and the pairing of auditory and oral capacities. The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion.

      I would encourage the authors to consider making mention of Gokhman et al's (2021) work, where genes similarly impacting craniofacial and oral capacities are discussed. https://www.nature.com/articles/s41467-020-15020-6

      I would also have liked the authors to cite work on GLI3 and larynx formation (given that both GLI3 and HAND2 control DLX5 and 6) and play an important role in Neural Crest-related processes: https://elifesciences.org/articles/77055 https://elifesciences.org/articles/56450 In this context, the authors mention work on the role of Dlx5/6 in GABAergic neurons. Do the authors believe there to be a connection (mediated by the SHH pathway) between the ganglionic eminence and neural crest development, or are these two findings only convergent at the level of the phenotype?

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

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Sanchez-Garrido and colleagues analyze the consequences of Dlx5/6 deletion on cranial neural crest- and otic placode-derived structures development. The authors show that Dlx5/6 inactivation causes broad defects of the outer, middle and inner ear as well as malformations of the jaw, pharynx and larynx musculoskeletal systems. The authors conclude that Dlx5/6 play an important role in the regulation of vocal and auditory systems development in mammals.

      Significance

      The work is well-presented and illustrated, with appropriate description of methods. The role of this gene family in regulating craniofacial structures is not completely novel. The analysis is largely descriptive providing little mechanistic insights. The authors propose a possible link between Dlx5/6 activity and BMP signaling as the culprit for the defects observed in the mutants, however this is not supported experimentally. The work is viewed as too preliminary at this stage.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This manuscript investigates the role of Dlx5/6 in development of the ear and vocal tract components. They generate a new conditional mouse knockout of Dlx5/6 using a Sox10 driver, which deletes functional forms of these factors in the otic placode (the primordium of the entire inner ear) and in neural crest cells, which contribute to the middle and outer ear, the otic capsule, as well as the craniofacial skeleton and cartilage including that of the larynx, and tendons.

      The authors describe the ear and craniofacial phenotypes in these mutants using marker analysis on sections, some whole mounts and CT imaging. They confirm previously described ear and craniofacial phenotypes, and add additional information on tongue, thyroid and hyoid cartilages, and associated mesoderm-derived muscles. Finally, they assess innervation finding subtle changes innervation patterns.

      They conclude that Dlx5/6 are required for normal development of auditory and vocal structures and suggest that these factors coordinate both systems and that this is important for their co-evolution. The experiments presented are straightforward phenotypic analysis, which is well presented with high quality imaging. However, it is not clear how well the conclusions are supported by the data because

      i) The number of animals analysed and showing the reported phenotypes is not clearly stated; some figure legends list some n numbers, but it is sometimes ambiguous whether they refer to controls or experimental specimen. In other cases, n=2 is too low to draw firm conclusions.

      ii) The nature of the controls is not stated, so we cannot know what is compared. Are controls WT, heterozygous for Dlx5 and -6, single KOs, single hets?

      The lack of clarity in methods and controls does not allow others to reproduce the findings easily. Based on the fairly limited data presented here, the authors make some far-reaching suggestions that are not supported by experimental findings. For example, they propose that their study points to "co-adaptation of the effector and receptor organs during acoustic communication diversification in land vertebrates" and that Dlx5/6 play a role in this process. This idea appears to be the main motivation for the current study, however, there is little evidence to support such conclusions. Likewise, they suggest that a "common Sox10-Dlx5/6-BMP signaling axis coordinates the morphogenesis of both CNCC and otic placode derivatives for the proper formation of the vocal and auditory complex". There is no evidence provided that Sox10 controls any of the other genes and functional experiment related to Sox10 are not carried out, while the Dlx5/6-BMP link has been established previously. Overall, the authors need to improve numbers, experimental information and controls, and given the descriptive nature of the manuscript they should refrain from wide-ranging conclusions. The analysis could also be strengthened by focusing on the novel aspects, providing a developmental time series as to when phenotypes are first observed combined with marker analysis e.g. looking at different compartments of the otic vesicle, cell types etc., and providing higher magnification images to describe the phenotypes (e.g. those in figure 1) will strengthen the paper and might provide some novelty.

      Significance

      The authors present a new conditional knock out line for Dlx5/6. The major limitation of the study is that the data presented largely confirm what has already been published including the regulation of BMP pathway components and non-cell autonomous effects on muscle. As far as I am aware, the only new additions to the literature are the analysis of the cartilages and tongue. The phenotypic analysis is minimal, there is no developmental time series and no other functional experiments to address some of the suggestions made by the authors.

      My expertise is in ear formation, and I am aware of the craniofacial literature.

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      Reply to the reviewers

      The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

      The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others).

      The BioID list summarises interactions of proteins present across five datasets and among two cell lines, HEK293T and Jurkat T cells. As the reviewer pointed out, these stringent criteria excluded proteins originally present in the BioID datasets. Indeed, the original datasets across the cell lines include a wide range of nucleoporins Nup37, Nup43, Nup53, Nup85, Nup107, Nup188, and Nup205. Apart from this, several other proteins were consistently found on the BioID of the basket nucleopore that had been previously found in the literature, namely transcription-related and export-related proteins, as the reviewer can depict on Fig 1 C.

      To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account when interpreting/discussing their data.

      Before conducting the DamID experiment, we validated the peripheral localization of the constructs used here. We provide here the images showing the distribution of the two nucleoporins

      Figure R1: Immunofluorescent images obtained for Nup153 and TPR in Jurkat cells prior to BioID experiment.

      The authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

      The reviewer is right; the role of DDX39b/UAP56 goes beyond pre-mRNA splicing. This is indeed involved in posttranscriptional maturation and export from the nucleus to the cytoplasm of cellular RNAs. Originally identified as a helicase involved in pre-mRNA splicing, UAP56 has been shown to facilitate the formation of the A complex during spliceosome assembly. This is the reason why we included it in our study. Additionally, DDX39b/UAP56 has been found to be critical for interactions between components of the exon junction and transcription and export complexes to promote the loading of export receptors, while more recently has also been identified as a DNA:RNA helicase involved in the resolution of R-loops (PMID:32439635). At present, we indeed cannot distinguish between multiple functions of this protein at the NPC, and this is now acknowledged in our manuscript.

      • Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done. We would like to clarify better how we selected the antibodies for the control PLA experiments. In PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML, is also an anti-mouse; therefore, the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore, we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities. Moreover, the conclusion that there’s a pool of splicing machinery associated with the NPC is sustained on several pieces of evidence accumulated through other experiments, not only PLA. We have supporting evidence from super-resolution microscopy, coIPs as well as the referred PLA.

      Figure R2: Control PLA assay with anti-AQR (rabbit) and Nucleophosmin (B23)(mouse) antibodies.

      • Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. Nup153/TPR are peripherally located at the most outer shells (0, 40% of signal and 1, 60% of signal). Consistently, based on figure 2d, Nup153:DDX39b is similarly distributed (0, 40% of signal and 1, 60% of signal) and the vast majority of the Nup153:AQR and Nup153:SF31A1 signal is also present in these two shells (20% and 40%). Although our super-resolution microscopy excludes the presence of internal Nup153 staining we cannot exclude that PLA potentially increases the signal of a possible internal Nup153/splicing interaction due to the rolling circle amplification reaction. However, as referred above this is not where primarily our interaction is occurring.

      • The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. The reviewer is right to point out that the study focuses on determining the role of Nup153/TPR in recruiting the splicing machinery to the NPC. As TPR is docked to the NPC through Nup153 (PMID: 12802065; 39127037) we investigated Nup98 and performed internal controls to show that 1) Nup98 wasn’t disrupted by shNup153 (Fig below) and technically 2) Nup98 was used in the shNup153 studies because of the availability of a reliable mouse antibody that could be coupled with the various rabbit antibodies used previously for SF3A1, DDX39, XAB2, and AQR in PLA experiments' for which mouse counterparts do not exist and would therefore hinder the continuation of the study as explained above. For TPR only a reliable rabbit antibody exists that works in our hands and therefore we wouldn’t have been able to perform the PLA experiments shown here

      Figure R3: Nup98 staining in wild type and shNup153 depleted Jurkat cells. In (a) co-immunofluorescence between AQR and Nup98 showing predominant positioning of Nup98 in the nuclear periphery (at the NPC). (b) Nup98 staining at the periphery persists also in shNup153 depleted cells, indicating that this Nup can be used as an NPC marker in Nup153 depleted conditions.

      When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC).

      We agree with the reviewer that expanding our observations to other peripherally located genes would be interesting; however, most of the other known peripheral genes are LAD-associated and mostly not expressed. While it was not our intention to claim that splicing at the periphery is specific only for Nup153-bound genes, we had obviously focused on Nup153-bound genes to understand the dynamics between Nup153/splicing machinery interactions. As stated above, other peripherally located genes within LADs are repressed.

      It Is out of the scope of this study to understand other relevant splicing hubs, as the reviewer knows splicing can occur throughout the nucleus at different sites (outside speckles). However, we do understand the reviewer's point of view, and to include more controls as requested by this and other reviewers, we have designed primers for additional non-Nup153 bound genes, and these additional experiments will be included in the manuscript.

      Figure R4: Preliminary data showing splicing of other Non-Nup153 Bound genes upon shNup153

      The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

      Measurement of the distance of FISH signals to the nuclear periphery for each probe (ie transript) performed in n>100 cells were represented graphically in Fig. 5f. We then compared total number of signals for each probe, obtained in shCtrl and shNup153 cells, and represented them graphically in Fig 5g; representative images shown in Fig 5g are those that were measured in Fig5g and represent the accumulation of the signal in cells upon shNup153 (not necessarily all at the nuclear periphery).

      We hope that this clarifies better what is represented.

      Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

      We will take into consideration this comment and will expand the section in the revised manuscript

      Minor comments:

      Western in Figure 3c does not represent well the quantification in 3e.

      Figure S3 is mislabelled (pannel h is panel g).

      __Reviewer #1 (Significance (Required)): __

      Reviewer #1 (Significance (Required)):

      The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

      We are grateful to the reviewer for highlighting the importance and relevance of our work for a broad audience. We now provide additional experimental evidence that will hopefully aid in substantiating our overall conclusions, as suggested by the reviewer.

      __ Reviewer 2:__

      Summary:

      In this manuscript, using a combination of proximity labelling, immunoprecipitations and imaging, the authors report a physical interaction between splicing factors (SFs) and the nuclear basket of nuclear pore complexes (i.e. NUP153). Using DamID, they further identify a set of NUP153-bound genes characterized by long, GC-poor introns. Finally, based on molecular analyses for a set of candidate loci, they report that inactivation of NUP153 triggers a (modest) reduction of intron splicing, which may specifically affect NUP153-bound genes.

      Major comments:

      • The BioID experiments (Fig. 1) lack proper controls. Proteins biotinylated by NUP-BirA fusions need to be compared with those modified upon expression of a control BirA protein, as has been done previously, especially when other NUPs were used as baits in BioID experiments (PMID: 24927568, to be cited). This control fusion should ideally be targeted to the same compartment (i.e. the nucleus or the nuclear side of the nuclear envelope). In our experimental setting, we have opted to use an unrelated protein tagged with BirA (Lck-BirA) rather than BirA-only control. The peripheral membrane proteins of the Src family kinase (SFK) Lck and its GPF tagged version (LckN18.GFP) localize predominantly at the plasma membrane (PMID: 29588370), whereas the GFP only (non-biothinylated) shows a broader nuclear distribution. All MS-detected proteins from the Lckn18-BirA and GFP negative control experiments were excluded. Moreover, we have analysed carefully the published data of nuclear transporter receptors binding to the NPC and the respective controls (BirA alone or the shuttling NLS_NES_Dendra with C or N terminal tags) (PMID: 29254951), and we did not find that any of these protein controls interact with the proteins of the splicing machinery.

      • Here, the chosen controls are inappropriate as the authors are probing interactions between NPC proteins (NUP153/TPR) and proteins restricted to a different nuclear compartment (e.g., nucleophosmin in the nucleolus). We have used two different controls in our PLA experiments - initially, we used B23 for nucleolus stain and then PML protein, major component of PML NBs, as PML can be found scattered throughout the nucleus and sometimes even resides at the nuclear periphery. Both of these controls showed negligible amounts of PLA spots in all our experiments. We take the opportunity to clarify that in PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML is also an anti- mouse; therefore the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities.

      • Would a control soluble, diffusible nucleoplasmic protein be detected in the vicinity of the NPC and sometimes colocalized with Nups? Precisely for this purpose we have used PML protein, that can be found both disperse in nucleoplasm as well as in PML NBs.

      • In order to assess these possibilities, the authors should perform their immunoprecipitation on extracts treated with benzonase, thus abrogating DNA- and RNA-dependent interactions. We have performed this experiment assessing the binding of Nup153 with the components of the IBC and observed that Nup153 interaction with these splicing factors is DNA or RNA independent, with some factors being more affected than others (probably passively recruited by protein-protein interactions with their splicing counterparts).

      __Figure R5: __RNA or DNA do not affect the interaction between Nup153 and splicing proteins. Lysates from HEK293T cells transfected with eGFP-Nup153 or eGFP were treated with RNase or DNase or left untreated prior to Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2, and DDX39B. The graph shows quantified bands of the bound fractions, normalized to the input and untreated control from one experiment.

      NUP153 inactivation appears to have a modest effect on splicing (Fig. 5; S6), which is poorly characterized here. It is also unclear whether this effect is direct or caused by side consequences of the depletion of this nucleoporin (e.g., changes in nucleocytoplasmic exchanges or gene expression).

      We have indeed asked if the presence of splicing components at the periphery could be a consequence of protein trafficking. To address whether nucleocytoplasmic exchange has a role in these associations, we have pharmacologically inhibited nuclear import by ivermectin (IVM). ​​IVM has been shown to block the importin-α/β-mediated nuclear import by directly interacting with karyopherin importin-α.(PMID: 30826604). HEK293T cells transfected with eGFPNup153 or eGFP alone were treated with IVM for 2hr (24hrs post transfection). As biochemical fractionation demonstrated (data not shown) there were slightly decreased protein levels of AQR, DDX39B and SF3A1 in the nuclear insoluble fraction. However, we barely observed any decrease in interactions between Nup153 and the splicing components we tested, indicating that the interaction with the spliceosomal components is not a consequence of nucleocytoplasmic exchange.

      Figure R6: Association of splicing components with Nup153 is not only due to nuclear import. HEK293T cells transfected with eGFP-Nup153 or eGFP and treated with import inhibitor IVM or DMSO were analyzed with Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2 and DDX39 B. Quantified bands of the bound fractions were normalized to the input fraction and DMSO control and the results of two experiments were shown in the graph.

      Related to the gene expression levels, we have not observed any significant changes in total expression levels of tested genes, probed with designed exonic primers (as indicated with blue arrows in Figure 5a). Additional control genes will be added as suggested by this and other reviewers.

      • To confirm the specificity of the effects of NUP153 depletion on the splicing of NUP153-bound genes, the authors need to provide additional splicing measurements for several genes that are bound by NUP153 "in the nucleoplasm" (e.g. excluded from their analysis by the cutoff of proximity to LAD borders, line 192) and for other "non-NUP153" genes (beyond the unique control shown in Fig. S6a).

      We acknowledge the comment of the reviewer that our work will benefit from additional controls. We are currently designing primers and probes to amplify additional regions, Nup153 bound and non-LAD proximal or non-Nup153 bound; Please also see the comment below.

      - From the few examples provided, it is difficult to evaluate the type of splicing events affected by NUP153 inactivation. Are they uniquely intron retention events? The authors should analyze available RNA-seq data obtained from NUP153-depleted cells (PMID:32917881) to characterize the types of alternative splicing events that are impacted by NUP153.

      In Aksenova et al, only 28 differentially expressed genes were detected during rapid degron Nup153 depletion (2h). With this small number of genes, it is highly unlikely we would be able to perform a statistically significant and detailed analysis. Importantly, the depletion was performed in colorectal adenocarcinoma cell line (DLD-1), whereas here we are reporting on T lymphocytes. Based on the analysis which we performed and explained below, there seem to be significant cell type specific differences in Nup153 associations, as already reported by others (PMID: 27807035; 32451376; 28919367).

      Splicing dynamics and speckle localization propensity have been proposed to depend on the overall GC content and the overall average intron size by several studies (PMID: 39413186; 38720076; 35182478; 22832277; 35182477). Prompted by our observation that Nup153 genes have longer than average introns and lower than average GC content (Figure 4f and 4g), we analyzed the data from HeLa cells, where genes were classified into groups A, B and C based on their speckle localization and dynamics (PMID 39413186). We intersected our Nup153 genes with the list of ABC genes from HeLa cells and found that 43 out of our 461 protein-coding genes were represented among non-speckle enriched group C genes, with the lowest GC content and longest average intron length.

      Figure R7: Nup153 genes comparison to the A_B_C genes from Wu J et al study. GC content and number of introns, used to classify the identified genes from HeLa cells are plotted on X and Y axis. A genes in Red, B in green or C in turquoise from HeLa cells were compared with Nup153 genes from Jurkat cells in the graph on the left. Nup153 genes are represented as triangles. A subgroup of Nup153 genes, classified as C group genes (long introns with high GC content and spliced away from speckles) are shown as turquoise triangles. Graph on the right shows total pool of Nup153 genes in violet (not identified in HeLa cells) and a subgroup of C Nup153 genes as turquoise. The list of Nup153 C genes is shown below.

      query

      entrezgene

      name

      symbol

      ENSG00000168615

      8754

      ADAM metallopeptidase domain 9

      ADAM9

      ENSG00000139154

      121536

      AE binding protein 2

      AEBP2

      ENSG00000112249

      10973

      activating signal cointegrator 1 complex subunit 3

      ASCC3

      ENSG00000176788

      10409

      brain abundant membrane attached signal protein 1

      BASP1

      ENSG00000153956

      781

      calcium voltage-gated channel auxiliary subunit alpha2delta 1

      CACNA2D1

      ENSG00000153113

      831

      calpastatin

      CAST

      ENSG00000134371

      79577

      cell division cycle 73

      CDC73

      ENSG00000188517

      84570

      collagen type XXV alpha 1 chain

      COL25A1

      ENSG00000182158

      64764

      cAMP responsive element binding protein 3 like 2

      CREB3L2

      ENSG00000109861

      1075

      cathepsin C

      CTSC

      ENSG00000153904

      23576

      dimethylarginine dimethylaminohydrolase 1

      DDAH1

      ENSG00000139734

      81624

      diaphanous related formin 3

      DIAPH3

      ENSG00000102580

      5611

      DnaJ heat shock protein family (Hsp40) member C3

      DNAJC3

      ENSG00000173852

      23333

      dpy-19 like C-mannosyltransferase 1

      DPY19L1

      ENSG00000151914

      667

      dystonin

      DST

      ENSG00000165891

      144455

      E2F transcription factor 7

      E2F7

      ENSG00000138829

      2201

      fibrillin 2

      FBN2

      ENSG00000115414

      2335

      fibronectin 1

      FN1

      ENSG00000075420

      64778

      fibronectin type III domain containing 3B

      FNDC3B

      ENSG00000114861

      27086

      forkhead box P1

      FOXP1

      ENSG00000090615

      2802

      golgin A3

      GOLGA3

      ENSG00000196591

      3066

      histone deacetylase 2

      HDAC2

      ENSG00000071794

      6596

      helicase like transcription factor

      HLTF

      ENSG00000145012

      4026

      LIM domain containing preferred translocation partner in lipoma

      LPP

      ENSG00000065833

      4199

      malic enzyme 1

      ME1

      ENSG00000087053

      8898

      myotubularin related protein 2

      MTMR2

      ENSG00000145555

      4651

      myosin X

      MYO10

      ENSG00000061676

      10787

      NCK associated protein 1

      NCKAP1

      ENSG00000185630

      5087

      PBX homeobox 1

      PBX1

      ENSG00000113448

      5144

      phosphodiesterase 4D

      PDE4D

      ENSG00000163110

      10611

      PDZ and LIM domain 5

      PDLIM5

      ENSG00000070087

      5217

      profilin 2

      PFN2

      ENSG00000152952

      5352

      procollagen-lysine,2-oxoglutarate 5-dioxygenase 2

      PLOD2

      ENSG00000106772

      158471

      prune homolog 2 with BCH domain

      PRUNE2

      ENSG00000173482

      5797

      protein tyrosine phosphatase receptor type M

      PTPRM

      ENSG00000164292

      22836

      Rho related BTB domain containing 3

      RHOBTB3

      ENSG00000067900

      6093

      Rho associated coiled-coil containing protein kinase 1

      ROCK1

      ENSG00000112701

      26054

      SUMO specific peptidase 6

      SENP6

      ENSG00000154447

      57630

      SH3 domain containing ring finger 1

      SH3RF1

      ENSG00000187164

      57698

      shootin 1

      SHTN1

      ENSG00000198887

      23137

      structural maintenance of chromosomes 5

      SMC5

      ENSG00000116754

      9295

      serine and arginine rich splicing factor 11

      SRSF11

      ENSG00000152818

      7402

      utrophin

      UTRN

      Table R1: List of Nup153 genes that are characterized as C group of genes.

      Only two Nup153 gene were found among A and B genes (Serine and arginine rich splicing factor 11 SRSF11 among A, and Phosphodiesterase 4D PDE4D among B genes). Despite the cell type specific expression and splicing patterns it is worth noting that we find Nup153 genes enriched among C group genes that are spliced out of speckles. We are currently probing the splicing of some of these genes, and these data will be added to the list of control genes.

      Considering all these new observations related to the Nup153 splicing events and the general interest and relevance of our initial observations, a new dedicated study will have to be designed to tackle all these important questions that go beyond these current findings


      Minor comments:

      • Several studies have shown that the nuclear basket contributes to a splicing quality control process preventing the nuclear export of improperly spliced transcripts, both in yeast and mammalian cells (PMID:14718167, 19127978, 24452287, 25845599, 22253824, 22661231). These studies have to be mentioned and discussed here.

      • Line 31: "movement of active genes towards the NPC would be favorable for their transcription and export ". Please rephrase: "...transcription and mRNA export".

      • Line 163: "NUP153 plays a role in harboring splicing machinery". Please rephrase.

      • Line 200-202: Fig. 4d and 4e (instead of S4d and S4e)

      • Line 186 and beyond: All conclusions about NUP153-bound genes (e.g., "Majority of NUP153 bound genes are proximal to LADs and expressed") are not accurately phrased since the authors selected NUP153-bound genes with a cutoff of proximity to LAD borders. The conclusions are thus only valid for a subpopulation of NUP153-bound regions located in the vicinity of LADs.

      • Line 292: "transport Nups less likely interact with splicing machinery". The term "transport Nup" is not correct. Does this mean "nuclear transport receptors"? Or "FG-Nups" (which interact with NTRs)?

      All the comments will be addressed in the revised version of the manuscript

      Reviewer #2 (Significance (Required):

      It is increasingly recognized that NPCs are involved in a number of cellular processes beyond nucleo-cytoplasmic transport and, in particular, contribute to several genomic functions. In this context, the identification of a physical and functional interaction between NPCs and the splicing machinery could be of conceptual interest in the NPC field, and more generally, in cell and genome biology, although it needs to be (i) carefully controlled and validated in view of the strong limitations mentioned above, and (ii) discussed in line with the known links between the nuclear basket and splicing quality control (see minor comments). This coupling would be particularly relevant for genes that have been shown to be positioned at NPCs during transcriptional activation, in line with the "gene gating" model mentioned by the authors.

      We greatly appreciate these insightful comments and suggestions, and the time and effort that this reviewer invested in critical reading of our manuscript. We will certainly take the points into account as we revise the manuscript. Specifically, we will carefully address the concerns related to the NPC-splicing interaction, ensuring that the experimental validation is robust and well-controlled, and we will further discuss the connection between the nuclear basket and splicing quality control in the context of our findings.

      Once again, thank you for your thoughtful and constructive feedback.

      Reviewer #3

      The authors discovered that the splicing machinery and nuclear baskets are sometimes in close proximity using Nup153 as a representative for the nuclear basket. They characterize this interaction using several different methods and propose that NUP153 is required to assemble the splicing machinery on genes that are transcribed in the nuclear periphery, which would supporting the gene gating model.

      The manuscript is well written and structured and the experiments are carefully conducted and analyzed.

      We thank the reviewer for the appreciation of our work. We have addressed all the major points here and will amend the manuscript text according to the suggestions.

      Reviewer #3 (Significance (Required)):

      The impression that I get from this manuscript is that we are looking at rather rare events with a small effect size. A definitive proof that the splicing machinery really assembles in the vicinity of NPCs docked via NUP153 is lacking. To assist in the revision process I will raise some questions to discuss but also propose some additional experiments to substantiate the claims.

      1. It is not clear what NUP153 really binds to and which domain is important. The experiments shown suggest proximity and indirect interactions (Co-IPs), but it is not clear whether NUP153 binds to DNA, RNA or a specific splicing factor. The bioID experiment might label splicing factors because they are cargos that pass through the pores during import, or it labels splicing factors that remain bound to spliced mRNPs during mRNP export. For example DDX39b, also called UAP56 is an important subunit of the TREX complex and involved the final packaging of mRNPs at the NPC. In my opinion, this protein is not a good choice. Also, the negative control that was used in the experiments is a potassium channel in the plasmamembrane, which can exclude that signal occurs by chance. But it would have been better to use a nuclear protein as control to exclude these possibilities.

      In line with a rare event, the Co-IP signals are very weak and barely higher than the GFP control. They should be repeated in the presence of RNase to confirm that this interaction occurs on nascent RNA during splicing and not e.g. to recycle or reroute splicing factors or during import.

      We acknowledge all the points, some of them already brought to our attention by other reviewers, that we tried to address throughout our response here, and we will also incorporate our answers in the revised manuscript. In particular, we have already provided some evidence related to the role of DNA, RNA, as shown above in Figure R5 (Response to R2). We have also addressed the effect of nuclear-cytosolic transport by using IVM and described these results in Figure R6, showing that splicing factors interact with Nup153 even when cytosolic transport is blocked with IVM. We have also commented on the use of controls and on the additional control analysis that we performed (also mentioned in more detail in response to R2)

      Moreover, we are also further trying to understand the binding of Nup153 to the splicing components. Intron Binding Complex, recently shown to be crucial for the activation of the spliceosome due to the activity of its helicase AQR (PMID: 37165190) is one of the protein complexes that we found bound to the NPC basket. We are interested in different functions of this helicase and have created the previously described mutant that has been shown to be defective in splicing. We have probed the interaction of Nup153 to this mutant, which we also characterized for its splicing inefficiency, and observed that Nup153 interacts with the splicing competent AQR, whereas the interaction with the splicing mutant seems to be less efficientThis set of additional data strengthens the bulk of data present here, details of which remain still to be further elucidated in an additional follow-up study.

      __Figure R8 __Lysates from HEK 293T cells, transiently transfected with eGFP-NUP153 and AQR-His-FLAG or AQRK829A-His-FLAG were probed in co-IP experiments. Western blot of the co-IP performed with Dynabeads for His-tagged proteins; input (IN), unbound (U) and bound (B) fractions are shown. Densitometric analysis of the co-IP where eGFP-NUP153 intensity was quantified, bound fractions were normalized to input samples, and the results are expressed relative to the AQR-His-FLAG control (n = 2). pENTER is a control empty plasmid.

      I do not understand why a NUP would be required to recruit or tether splicing factors to peripheral genes. Usually, splicing factors hitchhike on transcribing polymerase II or they are delivered by nuclear speckles which could happen also at the periphery. The authors should co-stain with a nuclear speckle marker to exclude this possibility.

      __Figure R9 __STED microscopy resolves the position of sc-35, marker of splicing speckles with respect to AQR. Jurkat T cells were stained with anti AQR AB (rabbit) and anti - sc35 antibody (mouse) to probe the positioning of splicing speckles with respect to the splicing helicase AQR.

      This is a very interesting remark, which we have addressed through co-staining experiments. Since the Nup153 antibody we used is anti-mouse, like SC-35, we instead co-stained SC-35 with AQR, a representative splicing factor. Our results show that a substantial number of AQR spots are detected away from nuclear speckles and near the nuclear rim, suggesting that a subset of splicing factors localize independently of speckles. While we have not directly stained the nuclear periphery, this pattern is consistent with the idea that splicing factors can be recruited outside of speckle-mediated delivery.

      To further confirm that NPC-associated splicing does not rely on nuclear speckles, we are open to performing additional co-staining between Nup153 and SON, another speckle marker in a followup study. Furthermore, emerging evidence supports off-speckle splicing, particularly for genes with long introns and low GC content (PMID: 39413186, 38720076, 35182478, 22832277, 35182477). Our additional analysis (Figure R7) demonstrates that genes associated with Nup153 share characteristics with known off-speckle spliced genes, suggesting that these genes might be processed outside of speckles due to their transcription and splicing kinetics

      What would be the advantage to splice in the vicinity of the pore? Given that genes with long introns take a long time to be transcribed, splicing would block the pores for hours and would prevent other activities. It would also be possible that splicing does occur at the periphery but NUP153 picks the mRNPs up at a later stage.

      We thank the reviewer for these insightful comments. We would like to add here that there are numerous pores, according to our own estimations around 800 pores in Jurkat cells, which implies that there is also huge heterogeneity of the NPC which is at present largely unexplored. In yeast, some pores are basketless and the assembly of the basket is transcription dependent (PMID: 36220102) - suggesting that there’s more than one pore population. Similarly, looking into the statuses of genes that have been associated with pore, both polycomb-repressed and transcriptionally active genes have been found at pores- again pointing to an heterogeneity. This is a very interesting (and large) question that we pose ourselves and to the NPC field but not something we can address straight away.

      One major drawback of the story is that the authors use very long-term depletion of NUP153 via shRNAs that will definitely screw up the import of many nuclear proteins; and a splicing inhibitor that has broad effects on nuclear architecture. Degron lines of Nup153 exist and should be used to substantiate at least some of the conclusions. Alternatively, a NUP153 mutant without zinc finger or IDR could be used to prevent DNA binding or basket association.

      The reviewer is right, we have for over 2 years tried tirelessly to use the degron Nup153 system established in DLD-1 cells by Dasso’s Lab. This has been unsuccessful. Jurkats are difficult to transfect and more sensitive than other cell lines and they died with our trials. We have therefore used the shNup153 which has been used in X and Y and shown to not interfere with nucleocytoplasmic trafficking.

      However, we understand the reviewers point and since then have tried to use a Nup153 mutant construct, containaing Nup153 N-terminus with and without a zinc finger (McKay et al 2009.), kindly sent by the Ulman Lab. Unfortunately, in our hands, the construct has low levels of GFP:Nup153 expression, not comparable to the ones we used in our coIP experiments and that would make conclusions hard.

      We are now planning the cloning of our GFP:Nup153 construct to produce such plasmids,

      N-terminus with/without the Zinc finger and use it in coIP experiments to understand the importance of the Zn finger domain in the splicing interactions.

      Specific comments: Abstract: suggesting that a fraction of splicing occurs at the NPC. speckle-distant splicing events, it should be nuclear speckles, term not explained Line 20: Super enhancers not introduced

      Line 39: Splicing and the different spliceosomal subcomplexes needs more explanation and introduction to understand the selection of proteins that were used in the study. Line 65: Choice of controls. LckN18 The genes should be written once in full and their choice should be explained better.

      Line 123: The authors state: 'However, we detected a lower number of interacting spots between Nup98/DDX39b than with its Nup153 counterpart; a similar trend is followed between Nup98 and SF3A1 (Fig. S2g), suggesting that the interaction between splicing proteins and Nup98 might be further apart within the NPC structure.

      A more likely explanation is that both proteins are shed from the mRNP at the basket as they are not shuttling with the mRNA and should not enter the pore.

      Line 131: Mention right at the beginning of the sentence which splicing proteins were imaged here.

      Line 141: Nuclear speckles are still not properly introduced. Why is SF3A1 not expected to be in nuclear speckles? It Should be co-stained for nuclear speckle markers. Lane 149: PlaB drastically changes the nuclear architecture. The reduced interactions can have also different reasons. The authors should image the distribution of the investigated factors in the presence of PlaB. Again the Co-IPs should be performed in the presence of RNase to confirm that the observed interactions depend on RNA. Line 181: The requirement of Nup153 to tether the splicing machinery to the NPC is not convincing from the presented data. The knockdown is way too long and the changes are tiny. Wouldn't it be better to use a Nup153 mutant without Zinc knuckle or IDR to show that now the splicing factors interactions are lost? Alternatively degron lines should be used.

      Line 186: I do not understand the logic why NUP153 needs to bind to chromatin to fullfil its function in splicing. It could also bind to RNA with its zinc knuckle or IDRs. The authors should perform iCLIP or RIP to exclude this possibility? I also do not understand the logic to look to look for proximity to repressive LADs as a criterion, while investigating a function of NUP153 in splicing which requests actively transcribing genes. This has to be better motivated. Excluding the nucleoplasmic pool of NUP153 removes important data points that might be functionally relevant. Line 187: The entire paragraph on Dam-ID and all subsequent genome-wide analyses is way too densely written and hard to understand for non-experts. Analysis tools or thresholds are rarely given and it is unclear how the different data sets have been made and by who, what they mean and how they have been integrated. Chromatin patterns and expression profiles are very unspecific terms. Many of the used terms have not been introduced properly. The metaplots show very small differences. In the end I am not sure what we have learned from all the data integration. Is NUP153 bound to DNA, to nucleosomes, to nascent RNA or to splicing factors?

      Lane 232: Unclear what NUP153 introns are. Is the entire gene where NUP153 binds to considered or only the intron with a NUP153 peak.

      Lane 242: Again, the shRNA knockdown performed in this manuscript is way to long to observe a direct effect on splicing at the pore, which occurs at the level of minutes. Degrons should be used here to confirm this observation.

      Line 249: More negative controls are needed for genes not bound by NUP153 for the splicing analysis. RNA-Seq analyzed for intron retention could be helpful.

      All the text and specific comments will be addressed as suggested by the reviewer.

      The experimental part to be included in the revised manuscript is explained in more detail above.

      Reviewer expertise (keywords): nuclear pore complexes, nuclear organization, gene expression, mRNA biology

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The authors discovered that the splicing machinery and nuclear baskets are sometimes in close proximity using Nup153 as a representative for the nuclear basket. They characterize this interaction using several different methods and propose that NUP153 is required to assemble the splicing machinery on genes that are transcribed in the nuclear periphery, which would supporting the gene gating model.

      The manuscript is well written and structured and the experiments are carefully conducted and analyzed.

      Significance

      The impression that I get from this manuscript is that we are looking at rather rare events with a small effect size. A definitive proof that the splicing machinery really assembles in the vicinity of NPCs docked via NUP153 is lacking. To assist in the revision process I will raise some questions to discuss but also propose some additional experiments to substantiate the claims.

      1. It is not clear what NUP153 really binds to and which domain is important. The experiments shown suggest proximity and indirect interactions (Co-IPs), but it is not clear whether NUP153 binds to DNA, RNA or a specific splicing factor. The bioID experiment might label splicing factors because they are cargos that pass through the pores during import, or it labels splicing factors that remain bound to spliced mRNPs during mRNP export. For example DDX39b, also called UAP56 is an important subunit of the TREX complex and involved the final packaging of mRNPs at the NPC. In my opinion, this protein is not a good choice. Also, the negative control that was used in the experiments is a potassium channel in the plasmamembrane, which can exclude that signal occurs by chance. But it would have been better to use a nuclear protein as control to exclude these possibilities. In line with a rare event, the Co-IP signals are very weak and barely higher than the GFP control. They should be repeated in the presence of RNase to confirm that this interaction occurs on nascent RNA during splicing and not e.g. to recycle or reroute splicing factors or during import.
      2. I do not understand why a NUP would be required to recruit or tether splicing factors to peripheral genes. Usually, splicing factors hitchhike on transcribing polymerase II or they are delivered by nuclear speckles which could happen also at the periphery. The authors should co-stain with a nuclear speckle marker to exlcude this possibility.
      3. What would be the advantage to splice in the vicinity of the pore? Given that genes with long introns take a long time to be transcribed, splicing would block the pores for hours and would prevent other activities. It would also be possible that splicing does occur at the periphery but NUP153 picks the mRNPs up at a later stage.
      4. One major drawback of the story is that the authors use very long-term depletion of NUP153 via shRNAs that will definitely screw up the import of many nuclear proteins; and a splicing inhibitor that has broad effects on nuclear architecture. Degron lines of Nup153 exist and should be used to substantiate at least some of the conclusions. Alternatively, a NUP153 mutant without zinc finger or IDR could be used to prevent DNA binding or basket association.

      Specific comments:

      Abstract: suggesting that a fraction of splicing occurs at the NPC. speckle-distant splicing events, it should be nuclear speckles, term not explained

      Line 20: Super enhancers not introduced

      Line 39: Splicing and the different spliceosomal subcomplexes needs more explanation and introduction to understand the selection of proteins that were used in the study.

      Line 65: Choice of controls. LckN18 The genes should be written once in full and their choice should be explained better.

      Line 123: The authors state: 'However, we detected a lower number of interacting spots between Nup98/DDX39b than with its Nup153 counterpart; a similar trend is followed between Nup98 and SF3A1 (Fig. S2g), suggesting that the interaction between splicing proteins and Nup98 might be further apart within the NPC structure. A more likely explanation is that both proteins are shed from the mRNP at the basket as they are not shuttling with the mRNA and should not enter the pore.

      Line 131: Mention right at the beginning of the sentence which splicing proteins were imaged here.

      Line 141: Nuclear speckles are still not properly introduced. Why is SF3A1 not expected to be in nuclear speckles? It Should be co-stained for nuclear speckle markers.

      Lane 149: PlaB drastically changes the nuclear architecture. The reduced interactions can have also different reasons. The authors should image the distribution of the investigated factors in the presence of PlaB. Again the Co-IPs should be performed in the presence of RNase to confirm that the observed interactions depend on RNA.

      Line 181: The requirement of Nup153 to tether the splicing machinery to the NPC is not convincing from the presented data. The knockdown is way too long and the changes are tiny. Wouldn't it be better to use a Nup153 mutant without Zinc knuckle or IDR to show that now the splicing factors interactions are lost? Alternatively degron lines should be used.

      Line 186: I do not understand the logic why NUP153 needs to bind to chromatin to fullfil its function in splicing. It could also bind to RNA with its zinc knuckle or IDRs. The authors should perform iCLIP or RIP to exclude this possibility? I also do not understand the logic to look to look for proximity to repressive LADs as a criterion, while investigating a function of NUP153 in splicing which requests actively transcribing genes. This has to be better motivated. Excluding the nucleoplasmic pool of NUP153 removes important data points that might be functionally relevant.

      Line 187: The entire paragraph on Dam-ID and all subsequent genome-wide analyses is way too densely written and hard to understand for non-experts. Analysis tools or thresholds are rarely given and it is unclear how the different data sets have been made and by who, what they mean and how they have been integrated. Chromatin patterns and expression profiles are very unspecific terms. Many of the used terms have not been introduced properly. The metaplots show very small differences. In the end I am not sure what we have learned from all the data integration. Is NUP153 bound to DNA, to nucleosomes, to nascent RNA or to splicing factors?

      Lane 232: Unclear what NUP153 introns are. Is the entire gene where NUP153 binds to considered or only the intron with a NUP153 peak.

      Lane 242: Again, the shRNA knockdown performed in this manuscript is way to long to observe a direct effect on splicing at the pore, which occurs at the level of minutes. Degrons should be used here to confirm this observation.

      Line 249: More negative controls are needed for genes not bound by NUP153 for the splicing analysis. RNA-Seq analyzed for intron retention could be helpful.

      There is quite some typos and missing words in the text.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, using a combination of proximity labelling, immunoprecipitations and imaging, the authors report a physical interaction between splicing factors (SFs) and the nuclear basket of nuclear pore complexes (i.e. NUP153). Using DamID, they further identify a set of NUP153-bound genes characterized by long, GC-poor introns. Finally, based on molecular analyses for a set of candidate loci, they report that inactivation of NUP153 triggers a (modest) reduction of intron splicing, which may specifically affect NUP153-bound genes.

      Major comments:

      1. The data presented do not convincingly demonstrate a specific interaction between the NPC basket and the splicing machinery, mainly due to the lack of appropriate controls.
        • The BioID experiments (Fig. 1) lack proper controls. Proteins biotinylated by NUP-BirA fusions need to be compared with those modified upon expression of a control BirA protein, as has been done previously, especially when other NUPs were used as baits in BioID experiments (PMID: 24927568, to be cited). This control fusion should ideally be targeted to the same compartment (i.e. the nucleus or the nuclear side of the nuclear envelope).
        • NUP153 immunoprecipitates only very low levels of splicing factors, which are almost indistinguishable from those detected in control pull-downs (see for example AQR or XAB2 signals in blot images and error bars in the quantifications, Fig. 2a). In addition, in these analyses, the high/saturated signals do not allow comparison of the abundance of the proteins of interest in the input samples under different conditions. These limitations also apply to the interpretation of the changes in NUP153-SF association scored upon splicing inhibition (Fig. 3a), which also seems to affect SF abundance in inputs (e.g. AQR, SF3A1).
        • PLA experiments (e.g. Fig. 2b-d) also lack proper control. PLA has been shown to reveal artefactual signals for abundant proteins present in the same compartment (doi.org/10.1101/411355). Here, the chosen controls are inappropriate as the authors are probing interactions between NPC proteins (NUP153/TPR) and proteins restricted to a different nuclear compartment (e.g., nucleophosmin in the nucleolus). An abundant nucleoplasmic protein/epitope should be used as a control in these experiments. In addition, the authors need to show direct immunofluorescence images for each antibody used in PLA assays, in order to verify that the expression levels or localization of their targets are unchanged between conditions (e.g. upon PladB treatment, Fig. S3g, or NUP153 depletion, Fig. 3g).
        • The proximal localization of NUP153 and splicing factors in super resolution microscopy (Fig. 2e-f) is also not properly controlled. Would a control soluble, diffusible nucleoplasmic protein be detected in the vicinity of the NPC and sometimes colocalized with Nups?
        • Since NUP153 is located in the vicinity of peripheral genes (as also shown here through DamID), some of which contain introns, its association with the spliceosome could be indirect, i.e., mediated by DNA. Of note, the association of Mlp1 (the yeast ortholog of TPR) with the splicing factor SF1 has been shown to be mediated by RNAs (PMID:14718167). In order to assess these possibilities, the authors should perform their immunoprecipitation on extracts treated with benzonase, thus abrogating DNA- and RNA-dependent interactions.
      2. NUP153 inactivation appears to have a modest effect on splicing (Fig. 5; S6), which is poorly characterized here. It is also unclear whether this effect is direct or caused by side consequences of the depletion of this nucleoporin (e.g., changes in nucleocytoplasmic exchanges or gene expression).
        • To confirm the specificity of the effects of NUP153 depletion on the splicing of NUP153-bound genes, the authors need to provide additional splicing measurements for several genes that are bound by NUP153 "in the nucleoplasm" (e.g. excluded from their analysis by the cutoff of proximity to LAD borders, line 192) and for other "non-NUP153" genes (beyond the unique control shown in Fig. S6a).
        • From the few examples provided, it is difficult to evaluate the type of splicing events affected by NUP153 inactivation. Are they uniquely intron retention events? The authors should analyze available RNA-seq data obtained from NUP153-depleted cells (PMID:32917881) to characterize the types of alternative splicing events that are impacted by NUP153.

      Minor comments:

      • Several studies have shown that the nuclear basket contributes to a splicing quality control process preventing the nuclear export of improperly spliced transcripts, both in yeast and mammalian cells (PMID:14718167, 19127978, 24452287, 25845599, 22253824, 22661231). These studies have to be mentioned and discussed here.
      • Line 31: "movement of active genes towards the NPC would be favorable for their transcription and export ". Please rephrase: "...transcription and mRNA export".
      • Line 163: "NUP153 plays a role in harboring splicing machinery". Please rephrase.
      • Line 200-202: Fig. 4d and 4e (instead of S4d and S4e)
      • Line 186 and beyond: All conclusions about NUP153-bound genes (e.g., "Majority of NUP153 bound genes are proximal to LADs and expressed") are not accurately phrased since the authors selected NUP153-bound genes with a cutoff of proximity to LAD borders. The conclusions are thus only valid for a subpopulation of NUP153-bound regions located in the vicinity of LADs.
      • Line 292: "transport Nups less likely interact with splicing machinery". The term "transport Nup" is not correct. Does this mean "nuclear transport receptors"? Or "FG-Nups" (which interact with NTRs)?

      Significance

      It is increasingly recognized that NPCs are involved in a number of cellular processes beyond nucleo-cytoplasmic transport and, in particular, contribute to several genomic functions. In this context, the identification of a physical and functional interaction between NPCs and the splicing machinery could be of conceptual interest in the NPC field, and more generally, in cell and genome biology, although it needs to be (i) carefully controlled and validated in view of the strong limitations mentioned above, and (ii) discussed in line with the known links between the nuclear basket and splicing quality control (see minor comments). This coupling would be particularly relevant for genes that have been shown to be positioned at NPCs during transcriptional activation, in line with the "gene gating" model mentioned by the authors.

      Reviewer expertise (keywords): nuclear pore complexes, nuclear organization, gene expression, mRNA biology

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      Referee #1

      Evidence, reproducibility and clarity

      The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

      The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others). To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account interpreting/discussing their data.<br /> The authors should be more precise when describing the role of the different splicing factor identified in the BioID screen and their function in specific steps of splicing, as this is important when claiming that they identify factors acting at all steps of splicing. For example, the authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

      Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done.

      Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC). The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

      Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

      Minor comments:

      Western in Figure 3c does not represent well the quantification in 3e.

      Figure S3 is mislabelled (pannel h is panel g).

      Significance

      The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

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      Reply to the reviewers

      Reply to the Reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Reviewer #1

      1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of CTCF-binding motif sequence and RAD21-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-253, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

      __2. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. __

      Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 435 and 829: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      3. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). We found that the DNA-binding sites of the insulator-associated DBPs were statistically overrepresented in the 5 kb boundary sites more than other DBPs (Fig. 4d). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2. We also observed the directional biases of CTCF, RAD21, and SMC3 by using Micro-C chromatin interaction data as we estimated, but the directionality was more apparent to distinguish the differences between the four directions of FR, RF, FF, and RR using CTCF-mediated ChIA-pet chromatin interaction data (lines 287 and 288).

       I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. *Cell* 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay, and include less long-range interactions due to distance bias. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study.
      
       I discussed other causes in lines 614-622: Another reason for the difference may be that boundary sites are more closely associated with topologically associated domains (TADs) of chromosome than are insulator sites. Boundary sites are regions identified based on the separation of numerous chromatin interactions. On the other hand, we found that the multiple DNA-binding sites of insulator-associated DNA-binding proteins were located close to each other at insulator sites and were associated with distinct nested and focal chromatin interactions, as reported by Micro-C assay. These interactions may be transient and relatively weak, such as tissue/cell type, conditional or lineage-specific interactions.
      
       Furthermore, I have added the statistical summary of the analysis in lines 372-395 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.
      

      4. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 405 - 412: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      6. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

      Reviewer #2

      1. Introduction, line 95: CTCF appears two times, it seems redundant.

      On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

       I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.
      

      3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 501: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

       In Aljahani A et al. *Nature Communications* 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. *Nature Genetics* 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.
      
      I added the following sentence on lines 569-577: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.
      
       FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. *Molecular Cell* 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. *Nucleic acids research* 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 556: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.
      

      7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

       The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. *Nature Genetics* 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. *Proc Natl Acad Sci USA* 2021 ; Ortabozkoyun H et al. *Nature genetics* 2022 ; Wang R et al. *Nature communications* 2022). I have added the following sentences on lines 571-575: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 582-584: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.
      
       As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. *Nature* 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. *Nature Reviews Molecular Cell Biology* 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. *EMBO Journal* 2024). I have added the following sentences on lines 543-547: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 577-582: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.
      
       Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. *Elife* 2024) (Ke W et al. *Elife* 2024) (Fujioka M et al. *PLoS Genetics* 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 559-567: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.
      

      8. Do the authors think that the identified DBPs could work in that way as well?

      The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

       Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. *Nucleic Acids Research* 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. *Cell Reports* 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 554: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.
      

      9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 584-590: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 539 - 543 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

       To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the *Drosophila even skipped *locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. *PLoS Genetics* 2016).
      

      Reviewer #3

      Major Comments:

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 257 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 929 - 931 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 974 - 976 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 356 - 360: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 593-597: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      Minor Comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., ____https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2____or ____https://pubmed.ncbi.nlm.nih.gov/37486787____/). The authors should discuss how that would impact their results.

      The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, although the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

       As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.
      

      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

       Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 623-628: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.
      
       Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
      
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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Osato and Hamada propose a systematic approach to identify DNA binding proteins that display directional binding. They used a modified Deep Learning method (DEcode) to investigate binding profiles of 1356 DBP from GTRD database at promoters (30 of 100bp bins around TSS) and enhancers (200 bins of 10Kb around eSNPs) and use this to predict expression of 25,071 genes in Fibroblasts, Monocytes, HMEC and NPC. This method achieves a good prediction power (Spearman correlation between predicted and actual expression of 0.74). They then use PIQ, and overlap predicted binding sites with actual ChIP-seq data to investigate the motifs of TFs that are controlling gene expression. They find 99 insulator proteins showing either a specific directional bias or minor non-directional bias, corresponding to 23 DBP previously reported to have insulator function. Of the 23 proteins they identify as regulating enhancer promoter interactions, 13 are associated with CTCF. They also show that there are significantly more insulator proteins binding sites at borders of polycomb domains, transcriptionally active or boundary regions based on chromatin interactions than other proteins.

      Major Comments:

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      Minor comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      Referee Cross-Commenting

      I would like to mention that I agree with the comments of reviewers 1 and 2.

      Significance

      General assessment:

      This is the first study to my knowledge that attempts to use Deep Learning to identify insulators and directional biases in binding. One of the limitations is that no additional methods were used to show that these DBP have directional binding bias. It is not necessarily to employ additional methods, but it would definitely strengthen the paper.

      Advancements:

      This is a useful catalogue of potential DNA binding proteins of interest, beyond just CTCF. Some known TFs are there, but also new ones are found.

      Audience:

      Basic research mainly, with particular focus on chromatin conformation and TF binding fields.

      My expertise:

      ML/AI methods in genomics, TF binding models, epigenetics and 3D chromatin interactions.

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      Referee #2

      Evidence, reproducibility and clarity

      In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

      In general, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see my points below). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the following list.

      Also, I encourage the authors to integrate the current presentation of the data with other (published) data about chromatin architecture, to make more robust the claims and go deeper into the biological implications of the current work. Se my list below.

      It follows a specific list of relevant points to be addressed:

      Specific points:

      1. Introduction, line 95: CTCF appears two times, it seems redundant;
      2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
      3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS;
      4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details;
      5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
      6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
      7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
      8. Do the authors think that the identified DBPs could work in that way as well?
      9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
      10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Significance

      In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

      In general, chromatin organization is an important topic in the context of a constantly expanding research field. Therefore, the work is timely and could be useful for the community. The paper appears overall well written and the figures look clear and of good quality. Nevertheless, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see list of specific points). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the above reported points.

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      Referee #1

      Evidence, reproducibility and clarity

      The study by Osato and Hamada aims at computationally identifying a set of novel putative insulator-associated DNA binding proteins (DBPs) via estimation of their contribution to the expression of genes in the same chromosome region of their binding sites (+- 1Mbp from TSS). To achieve this, the authors leverage a deep learning architecture already published via which ChIP-seq peaks of DBPs in the TSS of a given gene are used to predict its expression level in four human cell lines.

      Building on this, the authors used another tool called DeepLIFT to evaluate the weight of each DBP binding site on the final gene expression value. Hence they made the assumption that if a given DBP had an insulator function they could restrict the prediction of the gene's expression to the region included between pairs of that DBP binding sites, and evaluate the pair's motif directionality bias in the distribution of weights. They exemplify their approach's validity by the fact that they can predict the known directionality bias of CTCF/cohesin-bound sites as the highest of the lot, with the F-R orientation of the pairs the most enriched, recapitulating what already known in literature: i.e., that F-R chromatin interaction peaks are the most enriched. In addition, they find several new DBPs showing significant directionality bias; hence they could be candidates for insulation activity. They then provide correlation between these putative insulator binding sites and sites of transition between euchromatin and heterochromatin by independently using histone mark and gene expression datasets. This, of course, is not surprising because (a) there is insulation between regions with heterotypic chromatin identities, and (b) it was already known from the first papers describing insulated chromatin domains that their boundaries were well-enriched for active transcription and transcriptional regulators (e.g., Dixon et al, Nature 2012).

      Finally, they use chromatin interaction (looping) sites to check the overlap between CTCF and all other DBPs and define a subset of putative insulator DBPs not overlapping CTCF peaks, suggesting potentially new insulatory mechanisms. These factors were all known transcriptional activators, but this part of the findings carry most of the novelty in the work and have the potential of opening up new directions for research in chromatin organization.

      Overall, the methodology applied here is adequate, clear, and reproducible. The major issue, in our view, is that the entire manuscript's findings relies on the usage of deepLIFT, a tool which was not benchmarked previously or by the current study. In fact, deepLIFT is public as regards its code, and also appears as a preprint from 2017 on biorXiv and published in the Proceedings of Machine Learning Research conference. Also, this key tool was developed by the Kundaje lab (who produce high quality alogrithms), and not by the authors. Therefore, the manuscript is predominantly based on the execution of existing workflows to publicly-available data. This does not take anything away from the interesting question posed here, but at the same time does not provide the community with any new algorithm/workflow.

      Finally, although I appreciate that the authors are purely computational and have likely no capacity for experimental validation of their claims of new DBPs having insulator roles, I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning. Using this kind of data, effects on gene expression can at least be tested in regard to the authors' predictions. Moreover, in terms of validation, Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As secondary issues, we would point out that:

      • The suggested alternative transcripts function, also highlighted in the manuscript;s abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
      • Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
      • Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Significance

      The scientific novelty of the work lies primarily in the identification of a set of DBPs that are proposed to confer insulator activity genome-wide. This has been long sought after in human data (whilst it is well understood and defined in Drosophila). The authors produce a quantitative ranking of the putative insulation effect of these DBPs and, most importantly, go on to identify a smaller subset that are apparently non-overlapping with anchors of CTCF-cohesin loop anchors; the presence of strong motif orientation biases in many DBPs can also be of broad interest, especially those that cannot be trivially ascribable to the loop extrusion process.

      However, although these findings open the way for speculation on multiple insulation mechanisms via proteins with multiple regulatory functions, the manuscript provide no experimental or computational means to test the proposed roles of these DBPs - and, as such, this limits the potential impact of the work and mostly targets researchers in the field of genome organization that can test these findings. Having said this, if validated, this work can significantly broaden our understanding of how chromatin is organized in 3D nuclear space.

      I typically identify myself to the authors: A. Papantonis, expertise in 3D genome architecture, chromatin biology, and genomics/bioinformatics.

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      Reply to the reviewers

      We thank the reviewers for their thoughtful comments


      Reviewer #1 (Evidence, reproducibility and clarity):

      SUMMARY: The manuscript is well written, with excellent explanation and documentation of experimental approaches. All conclusions are well supported by the data. The discussion is balanced and appropriate. The data, including images and movies, are of high quality and beautifully presented. The experimental design and analysis, including quantification of parameters in the images, is rigorous. Additional rigor is provided by comparing different cell types. The rapalog and iLID dimerization strategies have been described previously, as has their use to recruit kinesin motors to membranous organelles. However, this is the first application of these strategies to recruit motors to intermediate filaments. The evidence that vimentin filaments can be redistributed locally is clear and convincing and offers appealing potential for future experimentation. The redistribution was not fully reversible in all cells, but this is not surprising given the entanglement that must result from the action of motors along the length of these long flexible polymers.

      In terms of the biology of intermediate filaments, the authors show that vimentin redistribution had negligible effect on microtubule or F-actin organization, cell area, or the number of focal adhesions. Depletion of vimentin filaments locally reduced cell stiffness. Both ER and mitochondria segregated with vimentin filaments, but not lysosomes. These findings are consistent with published reports (e.g. comparing vimentin null and wildtype cell lines), but the acute and reversible nature of the motor recruitment strategy is a more elegant experimental approach, and the selectivity of the observed effects is evidence of its specificity. It is interesting that the ER network segregated with vimentin even in the absence of RNF26. While this is not explored further, it points to the potential power of this motor recruitment strategy for future studies on intermediate filament interactions.

      • *

      The following are some major and minor issues, which should all be easy for the authors to address.

      MAJOR COMMENTS:

        • Fig. S1 shows that the Vim-mCherry-FKBP construct coassembles with endogenous vimentin, but similar data for the iLID constructs appears to be lacking. I would like to see data demonstrating the incorporation of the Vim-mCherry-SspB constructs into the vimentin filaments. This should include high magnification images of single filaments in the cytoplasm of the cells.*
      • *

      Response:

      We have included a new Figure 2D, which illustrates the incorporation of the vimentin-mCherry-SspB construct into the vimentin network stained for endogenous vimentin.

        • The authors do not discuss the density of motor recruitment along the filaments. To address this, I'd like to see images showing the extent of recruitment of motors to the filaments using the rapalog and LID strategies. This should include high magnification images of single filaments in the cytoplasm of the cells.*
      • *

      Response:

      We have included new Figure S1B,C and Figure S2A, which illustrate the recruitment of kinesin motors to vimentin filaments upon induction with rapalog or light, respectively, by using super-resolution imaging with an Airyscan microscope. The motors were stained with antibodies against GFP. These data are discussed in the text, lines 126-132 and 165-168.

        • For the experiments on vimentin and keratin organization, the authors do not explain that these proteins form distinct networks and do not coassemble. The authors should show this in the cell types examined. This should also be explained explicitly in the body of the manuscript, though the data could be placed in the supplementary data. This is important because many intermediate filaments can coassemble freely, and coassembled proteins would be expected to segregate together.*
      • *

      Response:

      To address this important comment, we have now included images of vimentin and keratin in the three studied cell types using super-resolution imaging, both for cells expressing vimentin constructs (updated Figure 5) and endogenous filament staining in untransfected cells (updated Figure S4). These images illustrate that vimentin and keratin mostly form distinct filaments in HeLa cells. However, we do observe some degree of co-assembly of vimentin and keratin in COS-7 and U2OS cells. We were really surprised by this observation as, to our knowledge, it has not been clearly documented in the literature. These data help to explain why vimentin pulling causes keratin co-clustering in COS-7 and U2OS cells. We note that in a study where kinesin-1 mediated transport of vimentin and keratin has been previously investigated by the Gelfand lab in RPE1 cells, the two networks also appear to overlap quite strongly (Robert et al, 2019, FASEB J). Since no super-resolution microscopy was performed in that study, potential co-assembly of keratin and vimentin filaments was not discussed. Colocalization and coprecipitation of vimentin and keratin have been also described by Velez-delValle et al. in epithelial cells (Sci Rep 2016). Cell type-specific co-assembly of keratin and vimentin would require more investigation, and we make no strong conclusions about it, but we think that our data illustrate the usefulness of our methodology to address the co-dependence of different types of intermediate filaments.

      MINOR COMMENTS:

        • The authors refer to selecting cells within an "optimized expression range" for their transiently expressed recombinant proteins. They should state the proportion of the cells that met this criterion in their transient transfection experiments as this is important information for other researchers that might wish to use this approach in their own studies*. Response:

      These numbers are now included in lines 137 -142 and 173-176 of the revised paper. For the FRB-FKP system, ~50% of transfected cells could be used for analysis, for the light-induced system, ~40% were in the optimal range.

        • In Fig. 1F there should be a statistical comparison between cells transfected with the Kin14 construct and control (untransfected) cells in the absence of rapalog*
      • *

      Response:

      This comparison has been added.

        • In Fig. 1G there should be a statistical comparison between cells expressing Kin14 and KIF5A in the absence of rapalog.*
      • *

      Response:

      This comparison has been added.

        • The depletion of the ER network in the cell periphery is not evident in Fig. 7B, though the perinuclear accumulation is evident. Perhaps the authors could select another example or explain to the reader what exactly to look for in these images.*
      • *

      Response:

      We note that Figure 7B is a line scan of the image shown in Figure 7A. We assume that the reviewer meant Figure 7C, which is discussed in detail below.

        • In Fig. 7C, the intensity of the mCherry declines markedly over time. This is presumably due to photobleaching but should be explained in the legend.*
      • *

      Response:

      We have now improved Figure 7 by adding additional quantifications of ER and vimentin intensity and distribution in Figures 7D and E. We also extended the corresponding text (lines 288-297), which now reads; “Using the optogenetic tool, we observed that ER sheets and matrices, but not tubules, were pulled along with vimentin, confirming their previously described direct connections (Cremer et al., 2023) (black arrows, Figure 7C; Video S5). Most of the vimentin and ER repositioning occurred within approximately 10 minutes (Figure 7C, D, Video S5). While initially this resulted in a sparser tubular ER network at the cell periphery, over time, the network became denser, with smaller polygonal structures. This effect could also be observed in the ratio of perinuclear to peripheral intensity, where a subset of ER initially follows vimentin to the perinuclear region but then redistributes again towards the cell periphery (Figure 7D). It should be noted that while photobleaching of the ER channel was negligible, there was a 40% reduction in total Vim-mCh-SspB intensity over the course of the experiment due to photobleaching (Figure 7E).”

      • *

      Reviewer #1 (Significance):

      SUMMARY: The authors show that chemical-induced and light-induced dimerization strategies can be used to recruit microtubule motors to vimentin filaments, allowing rapid and reversible experimental manipulation of vimentin filament organization either locally or globally in cells. These strategies provide an experimental approach for investigating the physical interaction of intermediate filaments with organelles and other cytoskeletal component, as well as a method for probing the role of intermediate filaments in cell mechanics, cytoskeletal dynamics, etc. This is a technical improvement over previous experimental strategies, which have relied largely on chronic manipulation such as global disassembly or genetic deletion of intermediate filaments, e.g. comparison of vimentin null and wild type cells.

      The principal weakness of this study is that it offers limited insight into intermediate filament biology. As such, it might be most appropriate for a tools or techniques section of a journal. The dimerization strategies have been reported previously, so that is not new, but the application to intermediate filaments is novel.

      • *

      Response:

      We agree that our paper is primarily of technical nature and thus would be most appropriate for the tools and techniques section of a journal. We also agree that we used motor recruitment strategies that we and others have employed previously. However, we would like to emphasize that the demonstration that the tools work very well for intermediate filaments is entirely novel, as are the observations that these tools can be used to very rapidly alter cell stiffness or probe the links between intermediate filaments and organelles. Most importantly, the intermediate filament field currently lacks rapid specific manipulation strategies, and our tools will allow revisiting many important pending questions in the field. For example, they will allow to distinguish short-term and direct effects of intermediate filaments on cell polarity, adhesion and migration from their function in signaling and gene expression. We also report some new biology, such as evidence of some degree of co-assembly of vimentin and keratin.

      AUDIENCE: This paper will be of interest to cell biologists who study cytoskeletal interactions, particularly the interaction of intermediate filaments with other cellular organelles or cytoskeletal polymers, or the role of intermediate filaments in cellular mechanics.

      REVIEWER EXPERTISE: This reviewer has expertise on the cytoskeleton, cytoskeletal dynamics, and intracellular transport including intermediate filament biology.

      __ __


      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary: The manuscript presents a novel methodology for acute manipulation of vimentin intermediate filaments (IFs) using chemical genetic and optogenetic tools. By recruiting microtubule-based motors to vimentin via inducible dimerization systems, the authors achieve precise temporal and spatial control over vimentin distribution. Apart from the significant advancement in terms of methods development, key findings include:

      * Vimentin's role in organelle positioning: Mitochondria and ER are repositioned with vimentin, while lysosomes are less dependent on its organization.

      * Cytoskeletal interactions: Vimentin clustering minimally impacts actin and microtubule networks in the short term.

      * Cell stiffness: Vimentin repositioning reduces cell stiffness, indicating its significant role in cellular mechanics.

      * Cell-type-specific keratin interactions: The study highlights diverse interactions between vimentin and keratin-8 across cell lines.

      The study demonstrates methodological advancements enabling rapid vimentin manipulation and provides insights into vimentin's interactions with cellular structures.

      A major shortcoming is the unclear narrative, what do the authors want to present? This aspect requires significant attention.

      Response:

      By “unclear narrative” the reviewer meant that we should have provided a more balanced discussion of the insights that could be obtained using our new method compared to previously published literature, and we have modified our narrative accordingly.

      General Comments and Overall Assessment

      The manuscript represents an interesting contribution to the cytoskeletal field, addressing limitations of long-term perturbation methods. The tools developed are innovative, allowing controlled and reversible vimentin reorganization with minimal off-target effects. The findings are robust and provide important insights into the role of vimentin in cellular mechanics and organelle positioning.

      Strengths:

      Methodological novelty with broad applicability - this is the most exciting aspect.

      Comprehensive validation of the tools in multiple cell lines.

      Clear differentiation between vimentin's short- and long-term roles.

      Addressing gaps in understanding vimentin-organelle interactions.

      Limitations:

      * The manuscript is a little bit all over the place. While the method development is clear, the manuscript makes claims way beyond the method development. The message and narrative needs to be improved, and in the respect the whole structure needs an overhaul.

      Response:

      We have carefully modified the manuscript to avoid the impression that we make any claims that go beyond the immediate and quantifiable effects of vimentin repositioning on different cellular structures.

      * Unclear how much the differences in expression levels impact results and reproducibility.

      Response:

      Quantifications of expression levels and their discussion are included in Figures 1G-I, 2G-H, S2B and lines 137-142 and 173-176.

      * Would be good to discuss some findings that are specific to a given experimental cell line. How generalizable are these results?

      Response:

      Cell line-specific findings concerned mostly the co-displacement of keratin together with vimentin, which occurred in COS-7 and U2OS cells but in in HeLa cells. This interesting finding is discussed in the text, lines 246-269 and 375-383 (see also our answers on page 3 above and page 7 below).

      Major Comments

      Evidence and Claims:

      * While the methodological aspect is very strong the balance between presenting a novel method and presenting specific cell biological findings needs to be improved. Now it is quite unclear what the manuscript wants to present.

      * The abstract needs a complete overhaul. From reading the abstract, it is not clear what the manuscript wants to present.

      Response:

      We have modified the abstract to make it more clear that we do not make any general claims on the impact of vimentin on the interactions and functions of different organelles, but rather describe what can be directly observed after the acute displacement of vimentin and which conclusions can be made from these observations.

      Regarding the research findings there are a number of things for the authors to consider. Since the methods aspect is, in the eyes of this reviewer, in focus, I have not stringently assessed the experimental findings. Hence, the comments below are things to be considered in order to make the findings related to IF research stronger:

      • *

      * Cell-specific keratin interactions: The manuscript could benefit from some further validation of the physical interactions between vimentin and keratin-8 across different cell types.

      Response:

      We have improved the images of keratin and vimentin by using super-resolution (Airyscan) microscopy to show that they indeed form distinct filaments in HeLa cells, whereas in COS-7 and U2OS cells, where their co-displacement occurs, they can also incorporate into the same filaments. This observation was very surprising but agrees with the data published by the Gelfand lab on similarity in the distribution pattern and co-transport of vimentin and keratin in RPE1 cells (Robert et al, 2019, FASEB J). Colocalization and coprecipitation of vimentin and keratin has been also described by Velez-delValle at al. in epithelial cells (Sci Rep 2016).

      * Impact on microtubules: The disorganization of stable microtubules in cells expressing KIF5A was attributed to overexpression effects. It would be helpful to include additional controls, such as expressing KIF5A without vimentin constructs, to confirm this claim.

      Response:

      This control has been included in the new Figure S3. We note that this observation fully aligns with data published by another lab (Andreu-Carbó et al, 2024, Nat Comm).

      * ER-vimentin linkages: The observation that ER-vimentin interactions persist in RNF26 knockout cells is intriguing. The manuscript would benefit from a discussion on possible candidates for alternative linkers.

      Response:

      We have added a short discussion (lines 394-398) about the potential involvement of nesprins, such as nesprin-3, because they can connect the nuclear envelope to intermediate filaments, and might also partly participate in ER sheet-IF connections because ER and nuclear membranes are continuous and show some overlap in proteome.

      * Construct variability: Do the authors have some data on how much Expression level differences significantly affect the outcomes (e.g., incomplete recovery)?

      Response:

      We have added a figure (Figure S2B), which shows that incomplete recovery of vimentin clustering does not correlate with protein expression levels and likely depends on other factors, which could possibly be the cell cycle phase or degree of vimentin entanglement after repositioning. This point is discussed in revised text, lines 194-197.


      Reviewer #2 (Significance):

      Significance

      General Assessment: The study represents a significant technical advance in the study of cytoskeletal dynamics. The tools developed address critical limitations of traditional vimentin perturbation methods, allowing for spatiotemporally precise manipulation without long-term effects on gene expression or signaling pathways.

      Novelty:

      This is, to my knowledge, the first demonstration of reversible and acute vimentin repositioning using optogenetics. The study extends understanding of vimentin's short-term mechanical and organizational roles, distinguishing them from compensatory effects observed in knockdown models.

      Audience and Impact: The manuscript will appeal to researchers in cytoskeletal dynamics, cell mechanics, and organelle biology. The tools have broader applicability in studying other cytoskeletal systems and could inspire translational applications, such as investigating the role of vimentin in cancer or fibrosis.

      The reference list provide a relatively representative selection of articles relevant for the article. However, the authors may consider whether there could be relevant information in the relatively recent special edition of Current Opinion in Cell Biology, which focused on IFs, specially featuring vimentin https://www.sciencedirect.com/special-issue/10TFHK2QCKW

      Response:

      We thank the reviewer for this excellent suggestion, and we have included some additional references from this issue.

      Field of Expertise

      I specialize in cell biology, intermediate filaments, post-translational modifications, cytoskeletal dynamics, and advanced microscopy techniques.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary:

      This is an excellent paper describing the use of chemical and light-induced heterodimerization of microtubule-based motors to rapidly disrupt the distribution of the vimentin cytoskeletal network. Rapid clustering of vimentin did not significantly affect the microtubule or actin networks, cell spreading or focal adhesions. Other organelles were repositioned together with vimentin. Interestingly, in some cell lines, keratin networks were displaced along with vimentin while in other cells they were not.

      Major comments:

      The conclusions are well supported by the data presented and appropriate controls are included.

      Optional comments:

        • The authors should expand on why they think the plus end directed KIF5A gives such a strong localization of vimentin to the perinuclear area.* Response:

      We think that two factors can contribute to this counterintuitive effect. First, vimentin is strongly concentrated and entangled in the perinuclear region, and displacement of some vimentin filaments to the cell periphery can cause the collapse of the rest to the cell center, with kinesins being unable to pull the perinuclear network apart. Second, kinesin-1 KIF5A is a motor that strongly prefers stable, post-translationally modified microtubules, and our previous study has shown that a significant proportion of such microtubules are located with their minus ends facing towards the cell periphery (Chen et al., Elife 2016). This could contribute to the accumulation of vimentin in the cell center upon KIF5A recruitment. These considerations were added to the revised text, lines 344-347.

      • Consideration should be given to the idea that the pulling of ER and mitochondria along with the vimentin could be due to trapping of these organelles within the vimentin matrix and not necessarily due to direct interactions. Such reasoning could explain the transient localization of lysosomes with the center aggregate since lysosomes are generally not thought to significantly bind to vimentin networks.*

      Response:

      This is an excellent point, and we have included it in the revised article, lines 333-335 and 405.

      Reviewer #3 (Significance):

      This study describes some valuable tools that should be useful to cell biologists interested in determining the role of the cytoskeleton and possibly other organelles in a variety of cellular contexts. It overcomes some of the existing shortcomings of the pharmacological reagents currently available for studying intermediate filament biology and will provide a useful adjunct to other more long-term manipulations of the cytoskeleton. While much of the data presented confirm results obtained by other methods, this is a significant technical advance as it provides a short time scale, and in one instance, reversible manipulation of the cytoskeleton.

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

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This is an excellent paper describing the use of chemical and light-induced heterodimerization of microtubule-based motors to rapidly disrupt the distribution of the vimentin cytoskeletal network. Rapid clustering of vimentin did not significantly affect the microtubule or actin networks, cell spreading or focal adhesions. Other organelles were repositioned together with vimentin. Interestingly, in some cell lines, keratin networks were displaced along with vimentin while in other cells they were not.

      Major comments:

      The conclusions are well supported by the data presented and appropriate controls are included.

      Optional comments:

      1. The authors should expand on why they think the plus end directed KIF5A gives such a strong localization of vimentin to the perinuclear area.
      2. Consideration should be given to the idea that the pulling of ER and mitochondria along with the vimentin could be due to trapping of these organelles within the vimentin matrix and not necessarily due to direct interactions. Such reasoning could explain the transient localization of lysosomes with the center aggregate since lysosomes are generally not thought to significantly bind to vimentin networks.

      Significance

      This study describes some valuable tools that should be useful to cell biologists interested in determining the role of the cytoskeleton and possibly other organelles in a variety of cellular contexts. It overcomes some of the existing shortcomings of the pharmacological reagents currently available for studying intermediate filament biology and will provide a useful adjunct to other more long-term manipulations of the cytoskeleton. While much of the data presented confirm results obtained by other methods, this is a significant technical advance as it provides a short time scale, and in one instance, reversible manipulation of the cytoskeleton.

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

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: The manuscript presents a novel methodology for acute manipulation of vimentin intermediate filaments (IFs) using chemical genetic and optogenetic tools. By recruiting microtubule-based motors to vimentin via inducible dimerization systems, the authors achieve precise temporal and spatial control over vimentin distribution. Apart from the significant advancement in terms of methods development, key findings include:

      • Vimentin's role in organelle positioning: Mitochondria and ER are repositioned with vimentin, while lysosomes are less dependent on its organization.
      • Cytoskeletal interactions: Vimentin clustering minimally impacts actin and microtubule networks in the short term.
      • Cell stiffness: Vimentin repositioning reduces cell stiffness, indicating its significant role in cellular mechanics.
      • Cell-type-specific keratin interactions: The study highlights diverse interactions between vimentin and keratin-8 across cell lines.

      The study demonstrates methodological advancements enabling rapid vimentin manipulation and provides insights into vimentin's interactions with cellular structures. A major shortcoming is the unclear narrative, what do the authors want to present? This aspect requires significant attention.

      General Comments and Overall Assessment

      The manuscript represents an interesting contribution to the cytoskeletal field, addressing limitations of long-term perturbation methods. The tools developed are innovative, allowing controlled and reversible vimentin reorganization with minimal off-target effects. The findings are robust and provide important insights into the role of vimentin in cellular mechanics and organelle positioning.

      Strengths:

      Methodological novelty with broad applicability - this is the most exciting aspect. Comprehensive validation of the tools in multiple cell lines. Clear differentiation between vimentin's short- and long-term roles. Addressing gaps in understanding vimentin-organelle interactions.

      Limitations:

      • The manuscript is a little bit all over the place. While the method development is clear, the manuscript makes claims way beyond the method development. The message and narrative needs to be improved, and in the respect the whole structure needs an overhaul.
      • Unclear how much the differences in expression levels impact results and reproducibility.
      • Would be good to discuss some findings that are specific to a given experimental cell line. How generalizable are these results?

      Major Comments

      Evidence and Claims:

      • While the methodological aspect is very strong the balance between presenting a novel method and presenting specific cell biological findings needs to be improved. Now it is quite unclear what the manuscript wants to present.
      • The abstract needs a complete overhaul. From reading the abstract, it is not clear what the manuscript wants to present.

      Regarding the research findings there are a number of things for the authors to consider. Since the methods aspect is, in the eyes of this reviewer, in focus, I have not stringently assessed the experimental findings. Hence, the comments below are things to be considered in order to make the findings related to IF research stronger:

      • Cell-specific keratin interactions: The manuscript could benefit from some further validation of the physical interactions between vimentin and keratin-8 across different cell types.
      • Impact on microtubules: The disorganization of stable microtubules in cells expressing KIF5A was attributed to overexpression effects. It would be helpful to include additional controls, such as expressing KIF5A without vimentin constructs, to confirm this claim.
      • ER-vimentin linkages: The observation that ER-vimentin interactions persist in RNF26 knockout cells is intriguing. The manuscript would benefit from a discussion on possible candidates for alternative linkers.
      • Construct variability: Do the authors have some data on how much Expression level differences significantly affect the outcomes (e.g., incomplete recovery)?

      Significance

      General Assessment: The study represents a significant technical advance in the study of cytoskeletal dynamics. The tools developed address critical limitations of traditional vimentin perturbation methods, allowing for spatiotemporally precise manipulation without long-term effects on gene expression or signaling pathways.

      Novelty:

      This is, to my knowledge, the first demonstration of reversible and acute vimentin repositioning using optogenetics. The study extends understanding of vimentin's short-term mechanical and organizational roles, distinguishing them from compensatory effects observed in knockdown models.

      Audience and Impact: The manuscript will appeal to researchers in cytoskeletal dynamics, cell mechanics, and organelle biology. The tools have broader applicability in studying other cytoskeletal systems and could inspire translational applications, such as investigating the role of vimentin in cancer or fibrosis.

      The reference list provide a relatively representative selection of articles relevant for the article. However, the authors may consider whether there could be relevant information in the relatively recent special edition of Current Opinion in Cell Biology, which focused on IFs, specially featuring vimentin https://www.sciencedirect.com/special-issue/10TFHK2QCKW

      Field of Expertise

      I specialize in cell biology, intermediate filaments, post-translational modifications, cytoskeletal dynamics, and advanced microscopy techniques.

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

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      Referee #1

      Evidence, reproducibility and clarity

      Summary The manuscript is well written, with excellent explanation and documentation of experimental approaches. All conclusions are well supported by the data. The discussion is balanced and appropriate. The data, including images and movies, are of high quality and beautifully presented. The experimental design and analysis, including quantification of parameters in the images, is rigorous. Additional rigor is provided by comparing different cell types. The rapalog and iLID dimerization strategies have been described previously, as has their use to recruit kinesin motors to membranous organelles. However, this is the first application of these strategies to recruit motors to intermediate filaments. The evidence that vimentin filaments can be redistributed locally is clear and convincing and offers appealing potential for future experimentation. The redistribution was not fully reversible in all cells, but this is not surprising given the entanglement that must result from the action of motors along the length of these long flexible polymers.

      In terms of the biology of intermediate filaments, the authors show that vimentin redistribution had negligible effect on microtubule or F-actin organization, cell area, or the number of focal adhesions. Depletion of vimentin filaments locally reduced cell stiffness. Both ER and mitochondria segregated with vimentin filaments, but not lysosomes. These findings are consistent with published reports (e.g. comparing vimentin null and wildtype cell lines), but the acute and reversible nature of the motor recruitment strategy is a more elegant experimental approach, and the selectivity of the observed effects is evidence of its specificity. It is interesting that the ER network segregated with vimentin even in the absence of RNF26. While this is not explored further, it points to the potential power of this motor recruitment strategy for future studies on intermediate filament interactions.

      The following are some major and minor issues, which should all be easy for the authors to address.

      Major Comments:

      • Fig. S1 shows that the Vim-mCherry-FKBP construct coassembles with endogenous vimentin, but similar data for the iLID constructs appears to be lacking. I would like to see data demonstrating the incorporation of the Vim-mCherry-SspB constructs into the vimentin filaments. This should include high magnification images of single filaments in the cytoplasm of the cells.
      • The authors do not discuss the density of motor recruitment along the filaments. To address this, I'd like to see images showing the extent of recruitment of motors to the filaments using the rapalog and LID strategies. This should include high magnification images of single filaments in the cytoplasm of the cells.
      • For the experiments on vimentin and keratin organization, the authors do not explain that these proteins form distinct networks and do not coassemble. The authors should show this in the cell types examined. This should also be explained explicitly in the body of the manuscript, though the data could be placed in the supplementary data. This is important because many intermediate filaments can coassemble freely, and coassembled proteins would be expected to segregate together.

      Minor Comments:

      • The authors refer to selecting cells within an "optimized expression range" for their transiently expressed recombinant proteins. They should state the proportion of the cells that met this criterion in their transient transfection experiments as this is important information for other researchers that might wish to use this approach in their own studies.
      • In Fig. 1F there should be a statistical comparison between cells transfected with the Kin14 construct and control (untransfected) cells in the absence of rapalog
      • In Fig. 1G there should be a statistical comparison between cells expressing Kin14 and KIF5A in the absence of rapalog
      • The depletion of the ER network in the cell periphery is not evident in Fig. 7B, though the perinuclear accumulation is evident. Perhaps the authors could select another example or explain to the reader what exactly to look for in these images.
      • In Fig. 7C, the intensity of the mCherry declines markedly over time. This is presumably due to photobleaching but should be explained in the legend.

      Referees cross-commenting

      This session contains comments of Reviewer 1 and Reviewer 2

      Reviewer 1:

      I don't understand what Reviewer 2 means by "A major shortcoming is the unclear narrative, what do the authors want to present? This aspect requires significant attention." I found the narrative, purpose and conclusions of this study very clear to me. I also do not understand Reviewer 2's concern with the abstract. I re-read it and it still seems very clear and appropriate to me. For example, the authors state "Here, we present tools that allow rapid manipulation of vimentin IFs in the whole cytoplasm or within specific subcellular regions by inducibly coupling them to microtubule motors, either pharmacologically or using light". This seems clear and correct to me. It would be helpful if Reviewer 2 could point to specific language and explain why it is problematic.

      Reviewer 2:

      The strength of this paper is clearly the strong methods development and I find this aspect very intriguing and attractive. There is an imbalance in the narrative presenting on one hand the method and on the other hand presenting concrete research results. In my view, although interesting, the different experimental results serve more as proof-of-concept and they should not be presented as bona fide evidence of an existing or lacking bilateral interrealtionship.

      Indeed, the cited sentence makes sense: "Here, we present tools that allow rapid manipulation of vimentin IFs in the whole cytoplasm or within specific subcellular regions by inducibly coupling them to microtubule motors, either pharmacologically or using light." as it features the methods aspect of the paper. However, the following sentences: "Perinuclear clustering of vimentin had no strong effect on the actin or microtubule organization, cell spreading, and focal adhesions, but reduced cell stiffness. Mitochondria and endoplasmic reticulum sheets were repositioned together with vimentin, whereas lysosomes were only briefly repositioned and rapidly regained their normal distribution. Keratin was displaced along with vimentin in some cell lines but remained intact in others. " embraces everything from actin to microtubules to cell spreading to focal ahdesions to cell stiffness to mitochondrial function to lysosomes to interactions with other IF family members etc. This gives the impression that the authors want to make claims on how vimentin affects or does not affect these cellular functions and structures and once just cannot make such sweeping claims with so little evidence. With the experimental setting included, non of these claims can be really made without rigidly examining each and every interaction (which has been done separately for many of these bilateral interactions during the past 20 years or so).

      Hence, it should be made clear that these observations are used and mentioned as proof of concept that the tool is working, not as evidence that this or that interaction takes place or does not take place. As I indicated in my review, such claims on any of these bilateral interactions would require a lot more evidence to be properly substantiated.

      My comment is to be regarded as a positive one. If I would judge the paper based on how one could interpret the abstract and the text regarding, for example, that vimentin does not affect focal adhesions but changes cellular stiffness, my review would be significantly more stringent. However, I would really like to see this paper being published, but the claims on revealing new vimentin functions or disproving earlier observations based on these very limited data are just not sufficiently substantiated to be acceptable. Hence, I urge the authors to adjust the narrative to be clear on the methods development, which is also the focus of the title. I believe this is a justified recommendation and also, overall, a fair shake of the study and a constructive approach on how to publish this manuscript without extensive experiments.

      Reviewer 1:

      I thank Reviewer 2 for this explanation. I do understand their point. However, while not the end of the story, I do feel the authors' data are a bit more than just a proof of principle and do offer important insights into the biology which the field will need to grapple with. Each graph includes measurements on dozens of cells from multiple experiments and there is clearly selectivity to what segregates with the vimentin filaments and what does not. I would just ask the authors to be a bit more nuanced in their interpretation and conclusions about the biology to address Reviewer 2's concerns. Reviewer 2:

      That sounds like a fair assessment. Main thing is that this data is presented in a balanced way, with emphasis on the model development. Some of the presented data are in contradiction with quite established concepts by several researchers and the data presented here does not substantiate a paradigm shift. Regardless of this, some pieces of the data are intriguing, for example, the live cell imaging.

      Significance

      Summary: The authors show that chemical-induced and light-induced dimerization strategies can be used to recruit microtubule motors to vimentin filaments, allowing rapid and reversible experimental manipulation of vimentin filament organization either locally or globally in cells. These strategies provide an experimental approach for investigating the physical interaction of intermediate filaments with organelles and other cytoskeletal component, as well as a method for probing the role of intermediate filaments in cell mechanics, cytoskeletal dynamics, etc. This is a technical improvement over previous experimental strategies, which have relied largely on chronic manipulation such as global disassembly or genetic deletion of intermediate filaments, e.g. comparison of vimentin null and wild type cells.

      The principal weakness of this study is that it offers limited insight into intermediate filament biology. As such, it might be most appropriate for a tools or techniques section of a journal. The dimerization strategies have been reported previously, so that is not new, but the application to intermediate filaments is novel.

      Audience: This paper will be of interest to cell biologists who study cytoskeletal interactions, particularly the interaction of intermediate filaments with other cellular organelles or cytoskeletal polymers, or the role of intermediate filaments in cellular mechanics.

      Reviewer Expertise This reviewer has expertise on the cytoskeleton, cytoskeletal dynamics, and intracellular transport including intermediate filament biology.

  4. Mar 2025
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      Reply to the reviewers

      __Reviewer #1 __

      (Evidence, reproducibility and clarity (Required)):

      The manuscript identified a novel role of Intraflagellar Transport Protein 20 (IFT20) in the function and homeostasis of lymphatic endothelial junctions. The authors showed that IFT20 regulates VE-cadherin localization at adherens junctions in lymphatic endothelial cells. The authors performed impressive in vivo work that shows the requirement for IFT20 for the homeostasis of intercellular junctions, lymphangiogenesis, and drainage function of lymphatic vessels. In contrast, the cell biology part of the paper was underwhelming and will need significant revisions to support the proposed model. In the result section, several conclusions have to be toned down to match the actual results. The study employs in vivo mouse models, immunofluorescence, biochemical assays, and loss-of-function experiments to support their conclusions.

      Major comments - The authors present disrupted localization of VE-cadherin. Is this a mislocalization and/or protein stability issue in IFT20 KD cells? A western blot can help assess protein levels, and a phase-chase endocytosis assay of VE-cadherin can strengthen evidence. The authors did not confirm the permeability phenotype seen in vivo.

      We thank the reviewer for this helpful suggestion.

      Planned 1: Western blot to assess total VE-cadherin protein levels in IFT20 WT and KD cells.

      Planned 2: Immunofluorescence staining for cell-surface VE-cadherin using permeabilized and non-permeabilized IFT20 WT and KD cells during VEGF-C stimulation and washout.

      Together, these two experiments will assess VE-cadherin stability and more directly test the hypothesis that VE-cadherin does not recycle effectively back to the cell surface in the absence of IFT20.

      • While the authors focused on IFT20 and rab5, we do not have a clear idea about the vesicular dynamics as well as the status of early, late, and recycling endosomes in IFT20 KD cells. Is IFT 20 localized to non-rab5+ endosomes, and if yes, what are the species? A more general endosomal profiling would help strengthen the authors' message. For example, in Fig. 4-5, the authors will have to stain for other early endosomal markers as well as late, and recycling endosomal markers in control and IFT20 KD cells.

      Thank you for this helpful suggestion.

      Planned 3: Immunofluorescence staining for EEA1 (early endosome), RAB7 (late endosome), RAB4 (fast recycling), RAB11 (recycling endosome) along with IFT20 to determine its localization pattern.

      This experiment will determine the localization of IFT20 relative to various endosomal compartments.

      • In fig. 6C, a majority of VE-cadherin is not associated with Rab5. Staining with additional endosomal markers might help identify other endosomal species colocalizing with VE-cadherin. It will be critical to add to Fig. 6c the intensity profiles depicting colocalizations. The authors can also live image a fluorescently (f)-tagged VE-cadherin (maybe with another f-tagged rab5) and assess their association dynamics in IFT20 KD cells (similar to fig6C).

      Thank you for this helpful suggestion.

      Planned 4: Immunofluorescence staining for EEA1 (early endosome), RAB7 (late endosome), RAB4 (fast recycling), RAB11 (recycling endosome) along with VE-cadherin in IFT20 WT and KD cells to determine its localization pattern.

      Planned 5: Additional colocalization analysis such as adding intensity profiles and possibly proximity ligation assay.

      Beyond the scope of this manuscript 1: While we agree that imaging the dynamics of FP-tagged VE-cadherin in live cells would provide more detail about its localization, we feel that this is beyond the scope of the current manuscript.

      These experiments will determine the localization of VE-cadherin across various endosomal compartments and strengthen the current colocalization data.

      • Primary cilia do seem to regulate vascular plexus in the mouse retina as well as endothelial permeability through mediating subcellular localization of junction proteins. The authors do not clearly exclude the ciliary function of IFT20 in mediating lymphatic endothelial cell-cell junctions. A rescue experiment can help settle this question by targeting IFT20 exclusively to cilia (or not) and assessing, for example, VE-cadherin localization. The following is optional: It is also unclear whether the described regulation is specific to IFT20 or can be phenocopied by the ablation of another IFT subunit and/or cilia ablation through the depletion of a non-IFT cilia assembly regulator.

      Thank you for this helpful suggestion. We propose an alternative strategy.

      Planned 6: To determine the role of ciliary vs. nonciliary functions, we will knockdown IFT74, an IFT protein in the same IFT complex B as IFT20 that is required for cilia assembly and function but is not known to participate in vesicular trafficking. We will assess VE-cadherin localization in IFT74 WT and KD cells by immunofluorescence.

      Beyond the scope of the manuscript 2: We have not optimized reagents for targeting IFT20 to the cilium (e.g. ciliary targeting sequence) and believe that assessing the effects of a protein from the same IFT complex (IFT74) without known nonciliary functions will alleviate the reviewer’s concern.

      • Figs. 7A and B do not seem very convincing. The control vs. IFT20 KD western blot levels look mostly similar between the two conditions. The result section does not translate the actual data in Fig. 7A and B. Additionally, there are no statistical comparisons between control and KD conditions in the graphs. Except for a potential pVEGFR-3 increase at 30 min VEGF-C in IFT20 KD cells, but after washout the level is similar to control. This figure does not support well the model presented in fig. 8. The conclusion in lines 456-459 has to be toned down.

      Thank you for this helpful suggestion.

      Planned 7: We will remove these western blot data with the exception of pVEGFR-3 and add phospho-tyrosine immunofluorescence. We will use immunofluorescence to quantify phosphorylated tyrosine levels and repeat western blots for pVEGFR-3 at different concentrations and time points of VEGF-C stimulation in IFT20 WT and KD cells. We will remove the other western blot data and revise the text accordingly. We will also attempt to pull down total VEGFR-3 and then blot for pVEGFR-3 to improve sensitivity of this assay.

      These experiments will focus our analysis on the activation of VEGFR-3.

      • The authors were not able to stain for pVEGFR3. It would still be helpful to see a colocalization between total VEGFR3, IFT20, and VE-cadherin in control cells and IFT20 KD cells (VEGFR3 and VE-cadherin).

      Thank you for this helpful suggestion.

      Planned 8: We will perform immunofluorescence for VEGFR-3, IFT20, and VECAD and assess their localization.

      Minor comments - The control used in Figures 1 and 2 does not seem ideal. The proper control would be IFT20fl/fl cre neg. Is there a reason why the authors excluded a lox allele in control? Also, the authors have to provide the mice age used in these figures and when the Cre kicks in in the result section.

      Thank you for this helpful suggestion.

      Planned 9: We will clarify the use of control genotypes, and add mouse ages and Cre details to results/methods. This is a constitutive LYVE-1 Cre.

      • Please describe the overall mouse phenotype(s) of the LYVE1 CRE-IFT20 flox.

      Thank you for pointing out this oversight.

      Planned 10: We will include a description of the overall phenotypes of LYVE1 Cre IFT20 KOs in the text. One notable phenotype is abdominal ascites.

      • Line 109:'By expression, the authors probably mean immunostained.

      Thank you for pointing out this oversight.

      Planned 11: We will change to “immunostaining for”.

      • Many graphs exhibit undefined Y-axis labels and units. Please clarify these as well as the way they were quantified. Include such information in figure legends and/or in the materials and methods section. The figures in question are fig1C, E and F, fig2E, fig3E, fig4b and D, fig6b and D, fig7B and C.

      Thank you for this helpful suggestion.

      Planned 12: We will clarify the quantification strategies and units in the text and figure legends and make sure the axes are clearly labeled.

      • Line 295:'homeostasis"-the authors probably mean in a serum-rich condition.

      Thank you for this helpful suggestion.

      Planned 13: That is indeed what we meant. We will merge this sentence and the next sentence to be clearer.

      • Fig4C specifically the lower two images on the right side: the images do not seem to represent the corresponding graphs.

      Thank you for this helpful suggestion.

      Planned 14: We will double check these images and adjust if necessary.

      • Please add the statistical tests used to evaluate significance in all figure legends.

      Thank you for pointing out this oversight.

      Planned 15: We will be sure statistical tests are named in all figure legends.

      Reviewer #1 (Significance (Required)):

      This study provides novel insights into IFT20's role in VE-cadherin trafficking and endothelial junction stability, with its strongest aspect being the in vivo data in Figures 1 and 2, demonstrating lymphatic defects upon IFT20 loss. This represents a conceptual advance by extending IFT protein function beyond cilia (if one of the major comments is addressed) to vascular integrity. However, mechanistic depth is lacking, and ciliary role was not tested-additional rescue and colocalization experiments are needed to confirm the model. The study will interest vascular and lymphatic biologists, as well as cell biologists studying intracellular trafficking and cilia.

      Expertise: cilia and mouse genetics

      __Reviewer #2 __

      (Evidence, reproducibility and clarity (Required)):

      Paulson et al. use an in vivo model of IFT20 deletion (Lyve1-Cre) and primary lymphatic endothelial cell (LEC) cultures to investigate the role of IFT20 in controlling LEC-LEC junction dynamics. The key findings/suggestions include: i) Authors show alterations in the VE-cadherin (or ZO-1) staining at the LEC junctions upon IFT20 deletion or silencing. ii) They also show evidence of the IFT20 localization to RAB5 endosomes and alteration of RAB5 endosome dynamics upon IFT20 silencing.

      In the current manuscript, some of the key data are not convincing. Further experimentation and analysis (also of the existing data) are needed to solidify the authors' statements as detailed below. I expect that the suggested experiments can be executed in 3-to-6 months and require, at least, antibodies, which have not been used in the current manuscript.

      Major comments

      1. The data information, presented in the figure legends, is difficult to understand. The authors should always indicate how many biological replicates and independent experiments the data is derived from. This holds also for the representative images. Now, it seems that some of the quantified data are derived from only 1 experiment (see, for example, rows 423-425: "Graphs show one representative biological replicate of two, each comprising two technical replicates with 100+ cells per condition"). The quantifications should be based on data from at least three independent experiments.

      Often data points represent the field of views from a single sample, thus, biasing the statistical testing. The data points should represent biological replicates or independent experiments to allow the reader to make conclusions, about whether the findings are statistically significant and can be repeated.

      Thank you for this helpful critique.

      Planned 16: We will be sure to indicate biological and technical replicates and ensure that quantifications are representative of at least three independent experiments. We will also ensure that quantifications are statistically robust.

      The Lyve1-Cre is not specific for lymphatic vasculature (for example https://www.jax.org/strain/012601# and Lee LK et al. 2020, Cell Reports), as also stated by the authors (row 112). However, this is not shown in the data and complicates the interpretation of the data. Here, authors can stain the IFT20 with their existing mouse IFT20-specific antibody to show the loss in the lymphatic and/or blood vasculature. If IFT20 is lost in both vasculature types, it is not possible to say "lymphatic specific" (for example, row 143) and draw conclusions that the observed phenotypes would be primary to IFT20 loss in the lymphatic vasculature.

      Thank you for this helpful suggestion.

      Planned 17: We will assess IFT20 KO in blood vasculature and tone down lymphatic-specific language in the text.

      The authors write (rows 164-168) "Lymphatic vessels in the IFT20 KO or VE-cadherin KO embryonic dorsal skin exhibited increased and variable lumen size and excessive branching, suggesting that impaired lymphatic organization and function contributed to the fluid homeostasis defect. Here, immunofluorescence staining for LYVE-1 in the ear skin revealed similar patterning defects in adult IFT20 KO lymphatic vessels (Figure 2A), that have also been described in VE-cadherin KO mice (Hägerling et al., 2018)." However, based on Figure 2A, it is not obvious that there would be excessive lymphatic vessel branching, impaired organization or similarities to VE-cadherin deleted lymphatic vessels. To justify their statement, the authors should provide quantification of the branching (at least 3 mice/genotype).

      Thank you for this helpful critique.

      Planned 18: Based on the suggestion from Reviewer 3, we will remove these morphological and skin drainage data.

      IFT20 deletion or silencing causes alterations in the cell junction pattern/VE-cadherin intensity. The authors' interpretation that IFT20 deletion/silencing would cause discontinuous or "button-like" junctions is not supported by the provided images (Figures 1E, 3F, 6A, 6C). Rather, it seems that the levels of VE-cadherin in vivo are decreased, whereas the "continuity" of the junction is not altered. In cell culture, IFT20 silencing seems to cause wider and, to some extent, overlapping VE-cadherin junctions and not "discontinuous". These junctions may represent a more immature state. The authors should change the nomenclature accordingly or provide additional data. Using the existing cell culture experiment images, it would be more appropriate to analyze the width of the VE-cadherin junctions, instead of the "granularity".

      Thank you for this helpful suggestion.

      To assess VE-cadherin levels in vitro, we will perform western blots as described in Planned 1 above.

      Planned 19: We will measure widths of junctions from IFT20 KD and WT images and adjust the language in the text.

      Paulson et al. show images of IFT20 and RAB5 double-stained samples. The co-localization seems to happen mostly at the weakly IFT20 positive puncta (Figure 3A-B). Authors should show the disappearance of the signal in the siIFT20 treated samples (in comparison to siControl samples) to highlight the specificity of the weak signal.

      Thank you for this helpful suggestion.

      Planned 20: We will add data showing the IFT20 KD more clearly at high magnification.

      1. The Authors analyze the co-localization of VE-cadherin and RAB5 as co-localization area (Figure 6C-D). The images show that the co-localization is stated to happen at LEC periphery/junctions. LEC periphery is notoriously thin and microscope Z-resolution does not allow distinction of truly co-localizing or "on top of each other" signal. Based on row 607 co-localization would be expected to happen at least in EEA1+ vesicles, which are located perinuclearly (not at the junctions) in LECs (Korhonen et al. 2022, JCI). Authors could use EEA1, RAB5, and VE-cadherin triple staining for the quantification.

      Thank you for this helpful suggestion.

      Please see Planned 3 and Planned 4 above where we propose experiments to address this concern.

      In the current experiments, authors cannot conclude whether the VE-cadherin signal is at the cell junction (non-internalized), in endosomes (internalized during the experiment), or newly produced VE-cadherin on its way to the plasma membrane. To allow conclusions about the internalized VE-cadherin, and its localization in RAB5 vesicles, authors should conduct, for example, a classical endocytosis assay: incubation of live cells with non-blocking anti-VE-cadherin antibody, followed by acid wash to remove the non-internalized antibody, fixation and staining for RAB5. Also, shorter VEGF-C treatment would allow conclusions about the VE-cadherin dynamics.

      Thank you for this helpful suggestion.

      In Planned 2 above, we will perform immunofluorescence staining for cell-surface VE-cadherin using permeabilized and non-permeabilized IFT20 WT and KD cells during VEGF-C stimulation at various timepoints and washout to address this concern.

      siRNAs can have off-target effects and, thus, the use of at least two independent methods/oligos for silencing is needed. Paulson et al. use a pool of 4 oligos for silencing. They should rather test the efficacy of the single oligos and then use the two best oligos (1/sample) to show and quantify the same phenotype. This is needed at least for the key experiments shown in Figures 4C-D, Figure 6A-B (see also comment #3), Figure 7A-B

      Thank you for this helpful suggestion. We chose these reagents based on pooled siRNAs at low concentration minimizing off-target effects while still achieving strong KD vs. single siRNAs at higher concentration. Please see this technical note for further information about minimizing off-target effects by the use of pooled siRNAs vs. single siRNAs: https://horizondiscovery.com/-/media/Files/Horizon/resources/Application-notes/off-target-tech-review-technote.pdf?sc_lang=en

      1. “SMARTpool siRNA reagents pool four highly functional SMARTselection designed siRNAs targeting the same gene. Studies show that strong on-target gene knockdown can be achieved with minimal off-target effects if a pool consisting of highly functional multiple siRNA is subsituted for individual duplexes. This finding is in contrast to speculation that mixtrues of siRNAs can compound off-target effects. … [Their data show that] while individual duplexes delivered at 100 nM can induce varying numbers of off-targeted genes, transfection of the corresponding SMARTpool siRNA (100 nM total concentration) induces only a fraction of the total off-target profile.”
      2. “Our scientists have identified a unique combination of [chemical] modifications that eliminate as much as 80% of off-target effects.”
      3. “The ON-TARGETplus product line is comprised of four individual siRNAs, and SMARTpool reagents which are chemically modified and rationally designed to minimize off-target effects.”

        OPTIONAL: Paulson et al stated in the first article (2021, Front. Cell Dev. Biol.) that IFT20 deletion/silencing causes lymphatic endothelial phenotypes due to its role in primary cilia, whereas here the authors conclude that IFT20 controls VE-cadherin dynamics at the RAB5 vesicles. However, the current experiments cannot dissect the role of IFT20 in these two distinct locations. For this, authors could delete/silence another gene required for primary cilia or RAB5 endosomes and then analyze, which IFT20 phenotypes are recapitulated.

      Thank you for this helpful suggestion. Please see Planned 6 above where we propose to determine the role of ciliary vs. nonciliary IFT functions by knocking down IFT74, an IFT protein in the same IFT complex B as IFT20 that is required for cilia assembly and function but is not known to participate in vesicular trafficking. We will assess VE-cadherin localization in IFT74 WT and KD cells by immunofluorescence.

      The data shown in Figure 2 B-E (Lymphatic drainage) is not necessary for the current manuscript ("IFT20 regulates VE-cadherin traffic in LECs") and can be removed. As the authors state in the manuscript, the drainage phenotype may be due to lymphatic vessel valve defects (rows 584-585) rather than primary for LEC-LEC junction defects. The data does not justify the abstract sentence "and lymph transport is impaired by intracellular sequestration of VE-cadherin" (row 42).

      Thank you for this helpful suggestion. Please see Planned 18 above, where we propose to remove these data.

      Minor comments

      1. For some of the images, the signal should be enhanced to allow visual inspection also in the paper version (Figures 5A-B and 6C, magenta).

      Thank you for this helpful suggestion.

      Planned 21: We will enhance the signal in the indicated figures.

      Authors show representative Western Blots and quantification of several biological replicates/sample types to investigate signaling responses upon VEGF-C treatment of control and siIFT20 cells. The authors state that the P-levels of VEGFR3, ERK, VE-cadherin, and AKT have different dynamics in control and IFT20-silenced cells. To justify this conclusion, authors should test the statistical significance between the siControl and siIFT20 samples at each time point. The current quantification (Figure 7B) shows that there is, at least, a trend of increased p-VEGFR3, p-VE-cadherin, p-ERK, and p-AKT in IFT20 silenced cells. However, the representative Western Blot image does not display a clear difference (Figure 7A). Authors should include the original western blots, used for quantification, as supplements.

      Thank you for this helpful suggestion. Please see Planned 7 above where we propose to remove these data with the exception of pVEGFR-3 and add corresponding immunofluorescence data. We will ensure blots are included as supplemental figures.

      The authors use western blot quantification to show that the altered LEC junctions affect VEGFR3 signaling. They further hypothesize that the increased VEGFR3 signaling may be a consequence of VEGFR3 localization in endosomes. The authors did not detect any signal using the phospho-specific VEGFR3 antibody (rows 441-442). To analyze the location of VEGFR3 upon VEGF-C treatment in siControl and siIFT20 LECs, the authors should use anti-VEGFR3 (total) antibodies that have been shown to detect VEGFR3 in similar assays.

      Thank you for this helpful suggestion.

      Please see Planned 8 above where we will perform immunofluorescence for VEGFR-3, IFT20, and VECAD and assess their localization.

      The normality of the data should be tested before the selection of the statistical test. If this has been done, please, indicate it in the materials and methods or re-run the statistical analysis, if some of the data is not normally distributed.

      Thank you for this helpful suggestion.

      Planned 22: We will double check the statistics and normality for all quantifications.

      The authors should use arrows, arrowheads, etc. to highlight examples of relevant features in the images. For example, in Figure 3C, the increased stress fiber formation is not obvious to the reader.

      Thank you for this helpful suggestion.

      Planned 23: We will add arrows etc. where appropriate.

      Reviewer #2 (Significance (Required)):

      Lymphatics are essential for fluid, leukocyte, and lipid trafficking to lymph nodes and/or systemic circulation. Recent findings have promoted lymphatics as a potential target to control the level of adaptive immunity in inflammation-associated diseases, including tumorigenesis (for example Song et al 2020, Nature). Early work on lymphatic endothelium in vivo, highlighted the dynamics of lymphatic endothelial junction, which, reversibly, can alter between continuous and discontinuous ("button-like") states (Baluk 2007, Am. Jour. Pathol.; Yao 2012, Am J. Pathol.). These changes may have an effect on fluid drainage capacity, lymphatic vessel growth, and prevention of pathogen dissemination to the systemic circulation. Recently, lymphatic junctions have been shown to present hubs of VEGFR3 signaling, VEGFR3 and VE-cadherin dynamics, and leukocyte transmigration (Sung et al. 2022, Nat. Cardiovasc. Res.; Hagerling et al. 2018, EMBO J.; Liaqat et al. 2024, EMBO J.). Thus, the manuscript by Paulson et al. investigates a topical subject.

      The authors suggest a role for IFT20 in the control of VE-cadherin dynamics. Based on my expertise in lymphatic endothelial biology, I envision that the manuscript can potentially increase knowledge on the regulators of the lymphatic endothelial junctions, which might have physiological, and in the long term, translational significance. However, in the current manuscript, the exact mechanisms of how IFT20 controls lymphatic endothelial junctions are left open. In addition to the lymphatic research field, the study is, potentially of interest to researchers working on blood vasculature or, even, epithelium, i.e. tissues where junctional dynamics play a major role in health and disease.

      Furter controls, analysis, and experimentation are needed to warrant the authors' statements. In their future work, the authors should also consider means to rigorously dissect the IFT20 functions in primary cilia and endosomes.

      __Reviewer #3 __

      (Evidence, reproducibility and clarity (Required)):

      In this manuscript, the group of Fink and coworkers investigates mechanistic aspects of the intraflagellar transport protein 20 (IFT20) function in lymphatic endothelial cells (LECs). In a previous study, this group had demonstrated the presence of primary cilia on LECs and shown that loss of IFT20 during development resulted in edema, lymphatic vessel dilation and altered branching. Lymphatic-specific deletion of IFT20 cell-autonomously exacerbated acute lymphangiogenesis after corneal suture. In this manuscript, Paulson et al. recapitulate the suture-induced hyper-lymphangiogenesis after lymphatic-specific IFT20 KO using a LYVE1-Cre delete strain and demonstrate a reduced, more discontinuous VE-Cadherin (VECad) staining in newly formed lymphatic vessels (LVs). Prompted by distended and hyperbranching dermal vessels, the performed functional tracer injection experiments and demonstrate increased lymphatic backflow and leakage into the interstitium. To gain further mechanistic insights the authors turned to reductionist cell culture models, starting with a mouse LEC line, in which IFT20 had been deleted using CRISPR/Cas9 resulting in loss of primary cilia, increased stress fibre formation and impaired junctional integrity. More importantly, similar effects were detected in human dermal (HD)LECs after IFT20 KD. Further IFT20 KD HDLECs showed accumulation of RAB5+ vesicles indicating defective endosome maturation. Indistinguishable formation of RAB5+ endosomes after VEGF-C stimulation in HDLECs and IFT20KD HDLECs indicated that endocytosis and formation of early endosomes occur independent if IFT20. Through starvation, stimulation and wash-out experiments the authors provide colocalization data suggesting that after VEGF-C stimulation IFT20 is recruited to endosomes where it contributes to VECad recycling. Finally, the authors addressed if the increase in RAB5+ endosomes following VEGF-C stimulation resulted in prolonged retention of signaling-active VEGFR-3 in endosomes. Western blotting for phosphorylation of VEGFR-3 and its downstream signaling components after activation of starved HDLECs or IFT20KD HDLECs and subsequent factor wash out provided evidence towards this model.

      Subsequently open question and potential suggestions for improvement are listed: The authors describe a slight leakiness of the LYVE1-Cre deleter strain to result in massive hemangiogenesis (line112). How extensive is the resulting deletion in blood endothelial cells? What are the consequences for VECad distribution in BEC junctions i.e. for blood vessels and vascular permeability? Are the defects described specific for LECs or are the manifestation of generic defects in LECs?

      Thank you for these helpful suggestions.

      Please see Planned 17 above where we will assess IFT20 KO in blood vasculature and tone down lymphatic-specific language in the text.

      Fig. 1 E, what is the distribution of LYVE1 in IFT20 KO LECs at higher magnification, is LYVE1 excluded from the VECad expression domain?

      Thank you for this helpful suggestion.

      Planned 24: We will review our corneal confocal data to address this question.

      Fig.1 F, what does VECad-positive LV (%) area (line 154 - 155) refer to, given that all LECs are VECad+ but the junctional distribution of the protein is distinctly different?

      Thank you for pointing out our need to clarify. This quantification measures the overlap of VE-cadherin with LYVE-1 as a way to measure the area covered by adherens junctions between lymphatic endothelial cells. Where junctions are punctate, they have smaller area vs. long continuous junctions.

      Planned 25: We will update the text to clarify this measurement.

      In the discussion, the authors speculate that the development of valves could be potentially impaired in IFT20 LEC KO mice. Ear skin would be an excellent tissue to stain the valves and analyse their structure in collecting LVs. Of particular interest in this context are Int a9, VECad, FOXC2 and PROX1 expression. The later two are required for valve formation and upregulated in valve forming areas in response to oscillatory shear stress (Sabine A et al. (2012) Dev Cell 22 (2):430-445. doi:10.1016/j.devcel.2011.12.020).

      Thank you for this helpful suggestion. Based on the suggestion from Reviewer 2, we will remove the ear lymph drainage data and focus on the cell biology in this manuscript. Our current experiments focus more on lymphatic valve formation in this context and these data can be moved to a separate manuscript.

      Planned 26: We will revise the text to remove speculation about valve development in this model and address this in a later manuscript.

      Does IFT20 KO and loss of the primary cilium impair OSS sensing and result in a failure to express sufficient levels of PROX1 for valve formation (Fig. 7 C).

      Thank you for this helpful comment. We will address the role of cilia in OSS sensing and valve formation in a forthcoming manuscript.

      A larger area view including pre-collectors and collectors would be informative and reveal changes in the overall structure of the lymphatic vessel bed in absence of IFT20.

      Based on the suggestion from Reviewer 2, we will remove these data.

      Fig. 2 A, (line 187 - 190) please indicate the age of analysed animals.

      Planned 27: We will add the ages of mice used.

      With respect to Fig.1, LVs in the area are mainly capillaries, what is the distribution of VECad? Are the LVs comprised of oak-leave shaped LECs, higher magn. pictures would be required.

      Thank you for this helpful suggestion.

      Planned 28: We will include higher magnification images of capillaries.

      Fig. 2 (C - E) Line 201 - 203 the description of retrograde flow using a clock terminology is unusual and not clear to the reader. Is this meant relative to the point of injection with 12 being at the top or relative to the injection axis (i.e. forward / backward direction)? It would seem that indication of the angle in combination with a sketch of the analysis would help the reader to interpret these data.

      Thank you for this helpful critique. We will remove these data based on this suggestion and that of Reviewer 2.

      The application of cell culture models is appropriate, however, the value of the mLEC model is questionable given that VECad is not detectable in these cells and PROX1 and VEGFR-3 staining are not shown. Therefore, the HDLEC model bears significantly more relevance. In Fig. 3D, were mLECS mitotically arrested during the 24hrs transwell migration, to exclude division and crowding effects during the observation time?

      Thank you for this helpful critique.

      Planned 29: We will clarify the methods for this experiment in the text.

      Fig. 6 It is commendable that the authors report their lack of success to directly visualize VEGFR-3 endocytosis by IF and attempt a WB analysis instead. However given the spread of the results normalization to ß-actin as a loading control appears inappropriate. Phosphorylated forms of VEGFR-3 and VECad should be normalized to the expression of the total protein as measured with a non-phospospecific antibody, exactly the way done here for ERK1/2 and AKT. Generally, IP-WB experiments provide superior data in this type of setting.

      Thank you for this helpful suggestion. Based on suggestions from the other reviewers, we will remove these WB data with the exception of pVEGFR-3 and add corresponding immunofluorescence. We will include additional time points and include blots used for quantifications as supplements.

      Line 597 - 599: "VEGFR-3 signaling is required for the establishment of VE-cadherin button junctions as lymphatic collecting vessels mature but is not required for their maintenance (Jannaway et al., 2023)." Collecting LVs are characterized by zipper junctions, but not button junctions. Therefore, this sentence needs clarification.

      Thank you for this helpful suggestion.

      Planned 30: We will clarify this text.

      Reviewer #3 (Significance (Required)):

      The role of IFT20 in formation of the primary cilium and endocytic vesicle transport warrants its investigation in lymphatic endothelial cells. Therefore, this study addresses relevant questions and provides important first insights into the cell biological function of IFT20 in this cell type. IFT20 has so far not been implicated in endocytosis and recycling of VECad and VEGFR-3 and the model suggested by the authors is compelling and adds to the mechanistic understanding of previous studies on the role of VECad in LECs. In particular, it could be of relevance for the enigmatic formation of button junction in lymphatic capillaries and the mechano-response of LECS underlying valve formation. At this point, the picture obtained from the endocytosis assays is more conclusive compared to the analysis of the impact of IFT20 loss on button junction formation. Clearly the study is of interest for a general cell biological audience as well as vascular biologists.

      • *
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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the group of Fink and coworkers investigates mechanistic aspects of the intraflagellar transport protein 20 (IFT20) function in lymphatic endothelial cells (LECs). In a previous study, this group had demonstrated the presence of primary cilia on LECs and shown that loss of IFT20 during development resulted in edema, lymphatic vessel dilation and altered branching. Lymphatic-specific deletion of IFT20 cell-autonomously exacerbated acute lymphangiogenesis after corneal suture. In this manuscript, Paulson et al. recapitulate the suture-induced hyper-lymphangiogenesis after lymphatic-specific IFT20 KO using a LYVE1-Cre delete strain and demonstrate a reduced, more discontinuous VE-Cadherin (VECad) staining in newly formed lymphatic vessels (LVs). Prompted by distended and hyperbranching dermal vessels, the performed functional tracer injection experiments and demonstrate increased lymphatic backflow and leakage into the interstitium. To gain further mechanistic insights the authors turned to reductionist cell culture models, starting with a mouse LEC line, in which IFT20 had been deleted using CRISPR/Cas9 resulting in loss of primary cilia, increased stress fibre formation and impaired junctional integrity. More importantly, similar effects were detected in human dermal (HD)LECs after IFT20 KD. Further IFT20 KD HDLECs showed accumulation of RAB5+ vesicles indicating defective endosome maturation. Indistinguishable formation of RAB5+ endosomes after VEGF-C stimulation in HDLECs and IFT20KD HDLECs indicated that endocytosis and formation of early endosomes occur independent if IFT20. Through starvation, stimulation and wash-out experiments the authors provide colocalization data suggesting that after VEGF-C stimulation IFT20 is recruited to endosomes where it contributes to VECad recycling. Finally, the authors addressed if the increase in RAB5+ endosomes following VEGF-C stimulation resulted in prolonged retention of signaling-active VEGFR-3 in endosomes. Western blotting for phosphorylation of VEGFR-3 and its downstream signaling components after activation of starved HDLECs or IFT20KD HDLECs and subsequent factor wash out provided evidence towards this model.

      Subsequently open question and potential suggestions for improvement are listed: The authors describe a slight leakiness of the LYVE1-Cre deleter strain to result in massive hemangiogenesis (line112). How extensive is the resulting deletion in blood endothelial cells? What are the consequences for VECad distribution in BEC junctions i.e. for blood vessels and vascular permeability? Are the defects described specific for LECs or are the manifestation of generic defects in LECs? Fig. 1 E, what is the distribution of LYVE1 in IFT20 KO LECs at higher magnification, is LYVE1 excluded from the VECad expression domain? Fig.1 F, what does VECad-positive LV (%) area (line 154 - 155) refer to, given that all LECs are VECad+ but the junctional distribution of the protein is distinctly different?

      In the discussion, the authors speculate that the development of valves could be potentially impaired in IFT20 LEC KO mice. Ear skin would be an excellent tissue to stain the valves and analyse their structure in collecting LVs. Of particular interest in this context are Int a9, VECad, FOXC2 and PROX1 expression. The later two are required for valve formation and upregulated in valve forming areas in response to oscillatory shear stress (Sabine A et al. (2012) Dev Cell 22 (2):430-445. doi:10.1016/j.devcel.2011.12.020). Does IFT20 KO and loss of the primary cilium impair OSS sensing and result in a failure to express sufficient levels of PROX1 for valve formation (Fig. 7 C). A larger area view including pre-collectors and collectors would be informative and reveal changes in the overall structure of the lymphatic vessel bed in absence of IFT20. Fig. 2 A, (line 187 - 190) please indicate the age of analysed animals. With respect to Fig.1, LVs in the area are mainly capillaries, what is the distribution of VECad? Are the LVs comprised of oak-leave shaped LECs, higher magn. pictures would be required. Fig. 2 (C - E) Line 201 - 203 the description of retrograde flow using a clock terminology is unusual and not clear to the reader. Is this meant relative to the point of injection with 12 being at the top or relative to the injection axis (i.e. forward / backward direction)? It would seem that indication of the angle in combination with a sketch of the analysis would help the reader to interpret these data.

      The application of cell culture models is appropriate, however, the value of the mLEC model is questionable given that VECad is not detectable in these cells and PROX1 and VEGFR-3 staining are not shown. Therefore, the HDLEC model bears significantly more relevance. In Fig. 3D, were mLECS mitotically arrested during the 24hrs transwell migration, to exclude division and crowding effects during the observation time?

      Fig. 6 It is commendable that the authors report their lack of success to directly visualize VEGFR-3 endocytosis by IF and attempt a WB analysis instead. However given the spread of the results normalization to ß-actin as a loading control appears inappropriate. Phosphorylated forms of VEGFR-3 and VECad should be normalized to the expression of the total protein as measured with a non-phospospecific antibody, exactly the way done here for ERK1/2 and AKT. Generally, IP-WB experiments provide superior data in this type of setting. Line 597 - 599: "VEGFR-3 signaling is required for the establishment of VE-cadherin button junctions as lymphatic collecting vessels mature but is not required for their maintenance (Jannaway et al., 2023)." Collecting LVs are characterized by zipper junctions, but not button junctions. Therefore, this sentence needs clarification.

      Significance

      The role of IFT20 in formation of the primary cilium and endocytic vesicle transport warrants its investigation in lymphatic endothelial cells. Therefore, this study addresses relevant questions and provides important first insights into the cell biological function of IFT20 in this cell type. IFT20 has so far not been implicated in endocytosis and recycling of VECad and VEGFR-3 and the model suggested by the authors is compelling and adds to the mechanistic understanding of previous studies on the role of VECad in LECs. In particular, it could be of relevance for the enigmatic formation of button junction in lymphatic capillaries and the mechano-response of LECS underlying valve formation. At this point, the picture obtained from the endocytosis assays is more conclusive compared to the analysis of the impact of IFT20 loss on button junction formation. Clearly the study is of interest for a general cell biological audience as well as vascular biologists.

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      Referee #2

      Evidence, reproducibility and clarity

      Paulson et al. use an in vivo model of IFT20 deletion (Lyve1-Cre) and primary lymphatic endothelial cell (LEC) cultures to investigate the role of IFT20 in controlling LEC-LEC junction dynamics. The key findings/suggestions include: i) Authors show alterations in the VE-cadherin (or ZO-1) staining at the LEC junctions upon IFT20 deletion or silencing. ii) They also show evidence of the IFT20 localization to RAB5 endosomes and alteration of RAB5 endosome dynamics upon IFT20 silencing.

      In the current manuscript, some of the key data are not convincing. Further experimentation and analysis (also of the existing data) are needed to solidify the authors' statements as detailed below. I expect that the suggested experiments can be executed in 3-to-6 months and require, at least, antibodies, which have not been used in the current manuscript.

      Major comments

      1. The data information, presented in the figure legends, is difficult to understand. The authors should always indicate how many biological replicates and independent experiments the data is derived from. This holds also for the representative images. Now, it seems that some of the quantified data are derived from only 1 experiment (see, for example, rows 423-425: "Graphs show one representative biological replicate of two, each comprising two technical replicates with 100+ cells per condition"). The quantifications should be based on data from at least three independent experiments.

      Often data points represent the field of views from a single sample, thus, biasing the statistical testing. The data points should represent biological replicates or independent experiments to allow the reader to make conclusions, about whether the findings are statistically significant and can be repeated. 2. The Lyve1-Cre is not specific for lymphatic vasculature (for example https://www.jax.org/strain/012601# and Lee LK et al. 2020, Cell Reports), as also stated by the authors (row 112). However, this is not shown in the data and complicates the interpretation of the data. Here, authors can stain the IFT20 with their existing mouse IFT20-specific antibody to show the loss in the lymphatic and/or blood vasculature. If IFT20 is lost in both vasculature types, it is not possible to say "lymphatic specific" (for example, row 143) and draw conclusions that the observed phenotypes would be primary to IFT20 loss in the lymphatic vasculature. 3. The authors write (rows 164-168) "Lymphatic vessels in the IFT20 KO or VE-cadherin KO embryonic dorsal skin exhibited increased and variable lumen size and excessive branching, suggesting that impaired lymphatic organization and function contributed to the fluid homeostasis defect. Here, immunofluorescence staining for LYVE-1 in the ear skin revealed similar patterning defects in adult IFT20 KO lymphatic vessels (Figure 2A), that have also been described in VE-cadherin KO mice (Hägerling et al., 2018)." However, based on Figure 2A, it is not obvious that there would be excessive lymphatic vessel branching, impaired organization or similarities to VE-cadherin deleted lymphatic vessels. To justify their statement, the authors should provide quantification of the branching (at least 3 mice/genotype). 4. IFT20 deletion or silencing causes alterations in the cell junction pattern/VE-cadherin intensity. The authors' interpretation that IFT20 deletion/silencing would cause discontinuous or "button-like" junctions is not supported by the provided images (Figures 1E, 3F, 6A, 6C). Rather, it seems that the levels of VE-cadherin in vivo are decreased, whereas the "continuity" of the junction is not altered. In cell culture, IFT20 silencing seems to cause wider and, to some extent, overlapping VE-cadherin junctions and not "discontinuous". These junctions may represent a more immature state. The authors should change the nomenclature accordingly or provide additional data. Using the existing cell culture experiment images, it would be more appropriate to analyze the width of the VE-cadherin junctions, instead of the "granularity". 5. Paulson et al. show images of IFT20 and RAB5 double-stained samples. The co-localization seems to happen mostly at the weakly IFT20 positive puncta (Figure 3A-B). Authors should show the disappearance of the signal in the siIFT20 treated samples (in comparison to siControl samples) to highlight the specificity of the weak signal. 6. The Authors analyze the co-localization of VE-cadherin and RAB5 as co-localization area (Figure 6C-D). The images show that the co-localization is stated to happen at LEC periphery/junctions. LEC periphery is notoriously thin and microscope Z-resolution does not allow distinction of truly co-localizing or "on top of each other" signal. Based on row 607 co-localization would be expected to happen at least in EEA1+ vesicles, which are located perinuclearly (not at the junctions) in LECs (Korhonen et al. 2022, JCI). Authors could use EEA1, RAB5, and VE-cadherin triple staining for the quantification.

      In the current experiments, authors cannot conclude whether the VE-cadherin signal is at the cell junction (non-internalized), in endosomes (internalized during the experiment), or newly produced VE-cadherin on its way to the plasma membrane. To allow conclusions about the internalized VE-cadherin, and its localization in RAB5 vesicles, authors should conduct, for example, a classical endocytosis assay: incubation of live cells with non-blocking anti-VE-cadherin antibody, followed by acid wash to remove the non-internalized antibody, fixation and staining for RAB5. Also, shorter VEGF-C treatment would allow conclusions about the VE-cadherin dynamics. 7. siRNAs can have off-target effects and, thus, the use of at least two independent methods/oligos for silencing is needed. Paulson et al. use a pool of 4 oligos for silencing. They should rather test the efficacy of the single oligos and then use the two best oligos (1/sample) to show and quantify the same phenotype. This is needed at least for the key experiments shown in Figures 4C-D, Figure 6A-B (see also comment #3), Figure 7A-B 8. OPTIONAL: Paulson et al stated in the first article (2021, Front. Cell Dev. Biol.) that IFT20 deletion/silencing causes lymphatic endothelial phenotypes due to its role in primary cilia, whereas here the authors conclude that IFT20 controls VE-cadherin dynamics at the RAB5 vesicles. However, the current experiments cannot dissect the role of IFT20 in these two distinct locations. For this, authors could delete/silence another gene required for primary cilia or RAB5 endosomes and then analyze, which IFT20 phenotypes are recapitulated. 9. The data shown in Figure 2 B-E (Lymphatic drainage) is not necessary for the current manuscript ("IFT20 regulates VE-cadherin traffic in LECs") and can be removed. As the authors state in the manuscript, the drainage phenotype may be due to lymphatic vessel valve defects (rows 584-585) rather than primary for LEC-LEC junction defects. The data does not justify the abstract sentence "and lymph transport is impaired by intracellular sequestration of VE-cadherin" (row 42).

      Minor comments

      1. For some of the images, the signal should be enhanced to allow visual inspection also in the paper version (Figures 5A-B and 6C, magenta).
      2. Authors show representative Western Blots and quantification of several biological replicates/sample types to investigate signaling responses upon VEGF-C treatment of control and siIFT20 cells. The authors state that the P-levels of VEGFR3, ERK, VE-cadherin, and AKT have different dynamics in control and IFT20-silenced cells. To justify this conclusion, authors should test the statistical significance between the siControl and siIFT20 samples at each time point.

      The current quantification (Figure 7B) shows that there is, at least, a trend of increased p-VEGFR3, p-VE-cadherin, p-ERK, and p-AKT in IFT20 silenced cells. However, the representative Western Blot image does not display a clear difference (Figure 7A). Authors should include the original western blots, used for quantification, as supplements. 12. The authors use western blot quantification to show that the altered LEC junctions affect VEGFR3 signaling. They further hypothesize that the increased VEGFR3 signaling may be a consequence of VEGFR3 localization in endosomes. The authors did not detect any signal using the phospho-specific VEGFR3 antibody (rows 441-442). To analyze the location of VEGFR3 upon VEGF-C treatment in siControl and siIFT20 LECs, the authors should use anti-VEGFR3 (total) antibodies that have been shown to detect VEGFR3 in similar assays. 13. The normality of the data should be tested before the selection of the statistical test. If this has been done, please, indicate it in the materials and methods or re-run the statistical analysis, if some of the data is not normally distributed. 14. The authors should use arrows, arrowheads, etc. to highlight examples of relevant features in the images. For example, in Figure 3C, the increased stress fiber formation is not obvious to the reader.

      Significance

      Lymphatics are essential for fluid, leukocyte, and lipid trafficking to lymph nodes and/or systemic circulation. Recent findings have promoted lymphatics as a potential target to control the level of adaptive immunity in inflammation-associated diseases, including tumorigenesis (for example Song et al 2020, Nature). Early work on lymphatic endothelium in vivo, highlighted the dynamics of lymphatic endothelial junction, which, reversibly, can alter between continuous and discontinuous ("button-like") states (Baluk 2007, Am. Jour. Pathol.; Yao 2012, Am J. Pathol.). These changes may have an effect on fluid drainage capacity, lymphatic vessel growth, and prevention of pathogen dissemination to the systemic circulation. Recently, lymphatic junctions have been shown to present hubs of VEGFR3 signaling, VEGFR3 and VE-cadherin dynamics, and leukocyte transmigration (Sung et al. 2022, Nat. Cardiovasc. Res.; Hagerling et al. 2018, EMBO J.; Liaqat et al. 2024, EMBO J.). Thus, the manuscript by Paulson et al. investigates a topical subject.

      The authors suggest a role for IFT20 in the control of VE-cadherin dynamics. Based on my expertise in lymphatic endothelial biology, I envision that the manuscript can potentially increase knowledge on the regulators of the lymphatic endothelial junctions, which might have physiological, and in the long term, translational significance. However, in the current manuscript, the exact mechanisms of how IFT20 controls lymphatic endothelial junctions are left open. In addition to the lymphatic research field, the study is, potentially of interest to researchers working on blood vasculature or, even, epithelium, i.e. tissues where junctional dynamics play a major role in health and disease.

      Furter controls, analysis, and experimentation are needed to warrant the authors' statements. In their future work, the authors should also consider means to rigorously dissect the IFT20 functions in primary cilia and endosomes.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript identified a novel role of Intraflagellar Transport Protein 20 (IFT20) in the function and homeostasis of lymphatic endothelial junctions. The authors showed that IFT20 regulates VE-cadherin localization at adherens junctions in lymphatic endothelial cells. The authors performed impressive in vivo work that shows the requirement for IFT20 for the homeostasis of intercellular junctions, lymphangiogenesis, and drainage function of lymphatic vessels. In contrast, the cell biology part of the paper was underwhelming and will need significant revisions to support the proposed model. In the result section, several conclusions have to be toned down to match the actual results. The study employs in vivo mouse models, immunofluorescence, biochemical assays, and loss-of-function experiments to support their conclusions.

      Major comments

      • The authors present disrupted localization of VE-cadherin. Is this a mislocalization and/or protein stability issue in IFT20 KD cells? A western blot can help assess protein levels, and a phase-chase endocytosis assay of VE-cadherin can strengthen evidence. The authors did not confirm the permeability phenotype seen in vivo.
      • While the authors focused on IFT20 and rab5, we do not have a clear idea about the vesicular dynamics as well as the status of early, late, and recycling endosomes in IFT20 KD cells. Is IFT 20 localized to non-rab5+ endosomes, and if yes, what are the species? A more general endosomal profiling would help strengthen the authors' message. For example, in Fig. 4-5, the authors will have to stain for other early endosomal markers as well as late, and recycling endosomal markers in control and IFT20 KD cells.
      • In fig. 6C, a majority of VE-cadherin is not associated with Rab5. Staining with additional endosomal markers might help identify other endosomal species colocalizing with VE-cadherin. It will be critical to add to Fig. 6c the intensity profiles depicting colocalizations. The authors can also live image a fluorescently (f)-tagged VE-cadherin (maybe with another f-tagged rab5) and assess their association dynamics in IFT20 KD cells (similar to fig6C).
      • Primary cilia do seem to regulate vascular plexus in the mouse retina as well as endothelial permeability through mediating subcellular localization of junction proteins. The authors do not clearly exclude the ciliary function of IFT20 in mediating lymphatic endothelial cell-cell junctions. A rescue experiment can help settle this question by targeting IFT20 exclusively to cilia (or not) and assessing, for example, VE-cadherin localization. The following is optional: It is also unclear whether the described regulation is specific to IFT20 or can be phenocopied by the ablation of another IFT subunit and/or cilia ablation through the depletion of a non-IFT cilia assembly regulator.
      • Figs. 7A and B do not seem very convincing. The control vs. IFT20 KD western blot levels look mostly similar between the two conditions. The result section does not translate the actual data in Fig. 7A and B. Additionally, there are no statistical comparisons between control and KD conditions in the graphs. Except for a potential pVEGFR-3 increase at 30 min VEGF-C in IFT20 KD cells, but after washout the level is similar to control. This figure does not support well the model presented in fig. 8. The conclusion in lines 456-459 has to be toned down.
      • The authors were not able to stain for pVEGFR3. It would still be helpful to see a colocalization between total VEGFR3, IFT20, and VE-cadherin in control cells and IFT20 KD cells (VEGFR3 and VE-cadherin).

      Minor comments

      • The control used in Figures 1 and 2 does not seem ideal. The proper control would be IFT20fl/fl cre neg. Is there a reason why the authors excluded a lox allele in control? Also, the authors have to provide the mice age used in these figures and when the Cre kicks in in the result section.
      • Please describe the overall mouse phenotype(s) of the LYVE1 CRE-IFT20 flox.
      • Line 109:'By expression, the authors probably mean immunostained.
      • Many graphs exhibit undefined Y-axis labels and units. Please clarify these as well as the way they were quantified. Include such information in figure legends and/or in the materials and methods section. The figures in question are fig1C, E and F, fig2E, fig3E, fig4b and D, fig6b and D, fig7B and C.
      • Line 295:'homeostasis"-the authors probably mean in a serum-rich condition.
      • Fig4C specifically the lower two images on the right side: the images do not seem to represent the corresponding graphs.
      • Please add the statistical tests used to evaluate significance in all figure legends.

      Significance

      This study provides novel insights into IFT20's role in VE-cadherin trafficking and endothelial junction stability, with its strongest aspect being the in vivo data in Figures 1 and 2, demonstrating lymphatic defects upon IFT20 loss. This represents a conceptual advance by extending IFT protein function beyond cilia (if one of the major comments is addressed) to vascular integrity. However, mechanistic depth is lacking, and ciliary role was not tested-additional rescue and colocalization experiments are needed to confirm the model. The study will interest vascular and lymphatic biologists, as well as cell biologists studying intracellular trafficking and cilia.

      Expertise: cilia and mouse genetics

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      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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      Referee #2

      Evidence, reproducibility and clarity

      Kemp et al. aimed to explore the transcriptional cell cycle regulation of replication-dependent (RD) histone genes at histone locus body (HLB) in Drosophila. They evaluate the accumulation of RNA pol II and RD histone transcripts at HLB during the cell cycle using live and fixed imaging of Drosophila tissues at different stages of development. They find that RNA pol II is enriched at HLB, not only during S phase when RD histone genes are transcribed but throughout the cell cycle. Outside of S phase, they detect short but not full-length RD histone transcripts suggesting a mechanism of RNA pol II pausing. Full length RD transcripts are only produced upon cyclin E/Cdk2 activation when cells enter S phase, arguing that Cyclin E/Cdk2 can activate transcription elongation. They propose that the elongation release triggered by Cyclin E/Cdk2 is the critical step linking RD histone gene expression and cell cycle progression rather than the recruitment of RNA pol II to HLB. The data are interesting and robust, however, additional experiments could reinforce the findings and the proposed model.

      Specific comments/concerns are listed below.

      1. In Figure 3, quantifications of the fluorescence at HLBs for mCherry-RBP1 and MXC-mScarlet should be provided.
      2. In Figure 5C, both 5' and 3' transcripts are observed in G214 cells. However, their accumulation in the cytoplasm is not visible. How do the authors explain this result? What happens in S14 cells?
      3. In Figure 6, the authors observed RD histone 3' transcripts only in replicating cells (EdU positive) while they detected 5' transcripts in both replicating and non-replicating cells. They argue that the appearance of 3' transcripts is due to the release from transcriptional pausing. To further support particular states in the transcriptional arrest, data by immunofluorescence using specific antibodies recognizing either RNA pol II ser5P or ser2P would determine whether the presence of 3' transcripts is associated with the accumulation at HLB of RNA pol II ser2P (elongating polymerase). Moreover, is there a correlation between P-MXC and RNA pol II ser2P?
      4. In Figure 7 panels C and D, the 5' transcripts should be shown. Although RD histone 3' transcripts accumulate in CyE+ embryonic cells, unfortunately, their presence at HLBs (pointed by arrows) is not visible in the image of panel E. To firm up conclusions quantifications of the 3' and 5' transcripts should be provided for CycE+ and CycEnull cells. In Hur et al., 2020, the authors looked at RD histone transcripts in WT embryo and CycE+/-/Cdk2+/- mutant. They found that the amount of H3 transcripts using a probe corresponding to the coding sequence is not changed in the mutant as compared to the WT. In contrast, they found that there is an increase of transcripts that are not correctly processed using probes downstream the stem-loop region. This seems inconsistent with the results presented here where a decrease of 3' transcripts is observed. This needs an explanation/discussion. Are such incorrectly processed transcripts observed in CycEnull mutant?
      5. The authors suggest that active Cyclin E/Cdk2 triggers the release of RNA pol II promoter-proximal pausing and therefore induces transcriptional elongation at RD histone genes when cells enter S phase. To further support this hypothesis, determining whether there is an enrichment of the elongation factor p-TEFb at HLB when Cyclin E/Cdk2 is active would help.
      6. Instead of using cycling E mutants, to determine whether it is the phosphorylation of MXC which directly impacts the elongation of RD histone genes, it would be interesting to generate phospho-null or phospho-mimetic mutant of MXC.
      7. In Suzuki et al., 2022, the authors described 3' RNA pol II pausing at RD histone genes. Although this study used human cells, it would be interesting to discuss that in addition to a promoter-proximal pausing that regulates transcription elongation, a 3' pausing could also regulate the transcription termination and 3' processing.
      8. In the discussion, the authors should point out some limitations of their studies linked to the method and could propose for the future that a more precise and molecular view of the pausing mechanism could be carried out using sequencing methods such as ChIP-seq of various isoforms of the RNA pol II (total, ser2P, ser5P) and elongation regulators (p-TEFb.....) and PRO-seq.

      Minor points:

      1. In Figure 1, for panels B and D as well as for panels C and E, to falicitate comparison of the localization of the different proteins, it would help to show the same developmental stages and the same image scales.
      2. In Figures 3 and 7 (C-F), the developmental stages should be indicated on the images, as it is done in the other figures.
      3. In the legend of Figure 7, it is indicated (D) and (E) instead of (C) and (D) in the sentence: "Endocycling midgut cells in (D) contain cytoplasmic histone mRNA which is absent in (E) (boxed regions)."

      Significance

      Kemp et al. aimed to explore the transcriptional cell cycle regulation of replication-dependent (RD) histone genes at histone locus body (HLB) in Drosophila. They evaluate the accumulation of RNA pol II and RD histone transcripts at HLB during the cell cycle using live and fixed imaging of Drosophila tissues at different stages of development. They find that RNA pol II is enriched at HLB, not only during S phase when RD histone genes are transcribed but throughout the cell cycle. Outside of S phase, they detect short but not full-length RD histone transcripts suggesting a mechanism of RNA pol II pausing. Full length RD transcripts are only produced upon cyclin E/Cdk2 activation when cells enter S phase, arguing that Cyclin E/Cdk2 can activate transcription elongation. They propose that the elongation release triggered by Cyclin E/Cdk2 is the critical step linking RD histone gene expression and cell cycle progression rather than the recruitment of RNA pol II to HLB.

      The data are interesting and robust, however, additional experiments could reinforce the findings and the proposed model.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Kemp et al. seek to define the molecular interactions that limit replication-dependent histone gene transcription to S-phase of the cell cycle. They use the Drosophila model system and leverage live-imaging tools, such as tagged proteins and Jabba trap, and RNA FISH in several tissues to determine that RNA Pol II is enriched at the locus throughout the cell cycle and is paused outside of S-phase. Therefore, they conclude that it is not Pol II recruitment to the locus that couples histone transcription to S-phase, but release of Pol II pausing.

      Major comments:

      The data presented are clean and well-presented. The claims are supported by the data without exaggeration. It would be helpful to provide -omics support for this entirely image-based analysis (e.g. PRO- or GRO-seq data from synchronized, sorted Drosophila cells may already exist- OPTIONAL).

      A major requirement is that the authors make clear in Introduction and Discussion that the observation of Pol Ii pausing at RD histone genes is not novel. This requires, at minimum, a discussion of Liu (2024) and Suzuki (2022). This allows readers to focus on the advance novel to this work, which is specifically the cell cycle coupling of Pol II pausing.

      As the authors are claiming different dynamics between Spt6 and RPB1 in Figure 1, they should provide similarly-staged embryos for comparison. For example, the authors should show RPB1 in early/mid S of NC 14, as this is when they see Spt6 variability. In theory, this should be relatively easy as these are stills from the live videos.

      Minor comments:

      The use of Spt6 live imaging early on was slightly confusing. The authors should consider moving this data later in the results or providing more written justification for why they investigated Spt6 (further than "to further explore the regulation of RNA pol II dynamics... p6). Similarly, Spt6 is included in the model figure, which might be a stretch given the only Spt6 data involves the timing of Spt6 colocalization with Mxc during the cell cycle.

      Misleading language/missed citations:

      p3: "600 kB array" is misleading. The whole locus is ~ 600 kB.

      p3: Mxc may remain at the locus throughout the cell cycle, so the whole HLB does not disassemble (Terzo, 2015).

      p4: H1-specific factors include cramped (Gibert and Karch, 2011; Bodner et al. 2024 bioRxiv)

      p4: Hodkinson, 2023 is not the correct reference. The correct reference is Hodkinson, 2024, Genetics.

      p5: The Drosophila HLB is detectable at NC 10 (White, 2011; Terzo, 2015) not White, 2007

      p5: A typo: "imagining"

      p7: The section title "RNA pol II is necessary for HLB assembly" is incorrect, as Figure 3 shows that pol II is NOT necessary for Mxc recruitment, but for HLB growth. Mxc, however, is necessary for pol II recruitment.

      p9: The authors should clarify what "HisC" means as this is the first usage.

      Figures/experiments:

      Fig 2: The authors should show the gating in Figure I that led to the three categories in Figure J. The legend/colors in Figure J are not necessary.

      An "easy" experiment would be to use the FUCCI cell lines and 5'/3' RNA FISH in combination (assuming fluorophores allow) - OPTIONAL

      Discussion:

      p13: The reference to the work of Gugliemlmi, 2013 should first come up in the Introduction, as it provides rationale.

      p13: "without engaging in transcription" is misleading, as pol II is transcribing, but paused.

      p15: It makes sense for pol II to pause at histone genes in G1, as they are preparing for the rapid burst of histone transcription needed in S phase. But what might be the functional rationale for pol II pausing in G2, if the HLB disassembles in M?

      Methods:

      It should be made clear how embryos were staged for live imaging, as it is likely by timing after cell cycle events. What is this timing? It would be best if this detail is not just mentioned in the methods, but also in the main text. This is especially important for readers not familiar with Drosophila embryogenesis. Please cite/acknowledge DGRC for Fly-FUCCI line (if appropriate)

      Significance

      This study provides convincing evidence that pol II is enriched at the histone locus and paused outside of S-phase. What limits the significance is that several prior studies concluded that Pol II is paused at the histone locus:

      Lu et al. bioRxiv 2024, "Integrator-mediated clustering of poised RNA polymerase II synchronizes histone transcription"

      Suzuki et al. Nat Comm 2022, "The 3' Pol II pausing at replication-dependent histone genes is regulated by Mediator through Cajal bodies' association with histone locus bodies"

      Neither of these studies is discussed or even cited in the manuscript, which is disappointing. Therefore, the advance is limited to the cell-cycle coupling of pausing. This is still important, as a major knowledge gap as outlined by the authors is that it is not clear how histone transcription is coupled to S-phase and they rule out Pol II pausing as a possible mechanism, and point toward Pol II pausing release.

      Moreover, there is also evidence (from these authors) that Mxc phosphorylation is not always coupled to histone transcription in Drosophila ovaries. This work is also not discussed or cited:

      Potter-Birriel et al. J Cell Sci 2021, "A region of SLBP outside the mRNA-processing domain is essential for deposition of histone mRNA into the Drosophila egg"

      The current research may be of interest to the broad cell cycle field, but it may also be useful as a model for those conducting basic, foundational research who seek to describe how Pol II is released from pausing. The histone locus may be of interest as a novel, facile model for pausing.

      Reviewer expertise: Drosophila, chromatin, gene expression

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      Reply to the reviewers

      Reviewer #1

      __Evidence, reproducibility and clarity __

      This is a well-written manuscript that describes a thorough study of the functionality of individual residues of a central component of the ESX-3 type VII secretion system of Mycobacterium smegmatis, EccD3, in the essential role of this protein transport system in iron acquisition. Using the powerful and unbiased approach of deep mutational scanning (DMS), the authors assessed the impact of different mutations on a large number of residues of this component. This carefully executed research highlights the importance of hydrophobic residues at the center the ubiquitin-like domain, specific residues of the linker domain that connects this domain with the transmembrane domains and specific residues that connect EccD3 with the MycP3 component.

      Major comments

      Since the LOF effects in the iron-sufficient and iron-deficient condition differ less than expected, the differences of the DMS results between these two conditions should be better presented, explained and discussed: 1. The authors discuss: "Of the 270 LOF mutations seen in the iron-deficient condition, 37 (13.7%) were tolerant in the iron sufficient condition, and 39 (14.44%) had strong LOF effects but weak LOF effects in the iron sufficient condition." Do the authors mean that 39 (14.44%) had strong LOF effects in the iron-deficient condition, but weak LOF effects in the iron-sufficient condition. In turn, does this mean that the remaining mutants (71.9%) had similar LOF effects in the two conditions?

      We thank this reviewer for their comment and for highlighting a lack of clarity. We have updated the main text to more effectively communicate our point - that 270 mutants had LOF effects in the iron-deficient media. 37 of these 270 mutants were tolerant in the iron-sufficient media. 39 of these 270 mutants had strong LOF effects in iron-deficient media, but were weak LOF in iron-sufficient media. The remaining 124/270 mutants had weak LOF effects in both conditions. The larger point is that removing iron leads to stronger selection - tolerant mutants become LOF, weak LOF become strong LOF. Removing iron pushes mutants at the bounds over the limit.

      __ The diagonal shape of the scatter plot in Fig. 2C, which shows the correlation of the Enrich2 scores of all mutants in the two conditions, indicates that the growth of most mutants is affected similarly in these conditions, but in Fig. 2D lower graph, which shows only the Enrich2 scores of missense mutants, there are clear differences between the two conditions. How can this be explained?__

      We apologize for any confusion created by this presentation of our data. We hoped to highlight that while effects are largely similar across conditions, there are some differences. As communicated in our first response, 270 out of our ~2700 missense mutations had LOF effects in the iron-deficient condition. 37 of these 270 mutants were tolerant in the iron-sufficient media. 39 of these 270 mutants had strong LOF effects in iron-deficient media, but were weak LOF in iron-sufficient media. The remaining 124 mutations had weak LOF effects in both conditions.

      While Figure 2C shows this difference, it is hard to see by nature of using a scatter plot. We have added contours to highlight how our data is distributed. Our density plots in Figure 2D are meant to try to highlight these differences, where the top plot is showing the effects of all missense mutations. Negatively scored mutations represent LOF effects, mutations with scores around 0 are considered tolerant, and the extremely rare scores with positive scores have GOF effects. Our bottom plot specifically zooms into the negatively scored mutations, to show the 270 LOF mutants we discussed. Specifically, we were hoping to highlight the 39 mutations that have strong LOF effects in iron-deficient media (so the purple line scores are more negative), but weak LOF effects in iron-sufficient media (the green line scores are less negative).

      __ Regarding the authors' explanation for the observed LOF effects in the permissive condition, "This speaks to the sensitivity of next-generation sequencing compared to the strong differences observed between conditions in phenotypic growth curves." But this sensitivity does not explain the observed large LOF effects but no growth difference in the permissive condition, unless the analysis is less quantitative than expected? Could it be that there is local iron depletion in this mixed culture, causing selection pressure even in the iron-sufficient condition? Moreover, the severity of the growth defect at the time of sampling, i.e., after 24 hours of growth, is unclear. Indeed, the growth curve in Fig. 1 shows that the growth of the double mutant in iron-deficient conditions is significantly impaired at that timepoint. In the growth curve in Fig. 2B (and also slightly in Fig. 2F), however, the growth defect is less pronounced: the double mutant has a similar OD600 as the WT strain, although the error bar is larger. Is this variability between replicates also seen in the DMS analysis? In general, no statistics are shown for the DMS analysis and there is no information on the significance of the observed LOF effects. In addition, the legend should explain how many replicates the DMS data are based on.__

      We thank this reviewer for their comment and for highlighting a point of confusion. In addition to increased sensitivity in next generation sequencing compared to our growth curve experiments, our data analysis and variant scoring was performed by comparing growth rates of our mutant strains to our wild type strain. So, any effect on viability or growth rates seen by expression mutant variants will be more notable in our DMS scoring, as they are relative to wild type. In contrast, our growth curves are plotted as the raw OD600 values of each strain. We believe this difference underlies the difference seen in our heatmaps and growth rates.

      It is also a relevant and important point that our libraries are grown as mixed cultures, where there is competition over the limited iron in their growth media, as we highlight in our discussion.

      While the double mutant does show a stark growth defect at 24 hours in Figure 1 compared to the WT and complement, it grows just as well as those strains in Figure 2B. The growth defect becomes notable after 24 hours. Within this experiment, we observed variability in growth at the 24hr timepoint for the negative control strain, but also selection when compared to the positive control and library growth at later time points. We analyzed our DMS data in accordance with typical methods used in the field (see: https://doi.org/10.1186/s13059-017-1272-5). We include statistics for the DMS analysis as supplemental Figure 1. We apologize for any confusion regarding the figure caption, however in our manuscript we do point out that our library growth in Figure 2B was repeated in triplicate in the figure caption, and the samples collected during that experiment were the ones used to generate the DMS data.

      Minor comments

      1. Line and page numbering should be added to the manuscript to facilitate the reviewing process.

      We have updated our manuscript to include line and page numbering.

      __ "Knockout of the entire ESX-3 operon leads to inhibited M. smegmatis growth in a low-iron environment. When individual components of the ESX-3 system are deleted, growth is only available under impaired if the additional siderophore exochelin formyltransferase fxbA is also knocked out20." First, a reference should be added to the first sentence. Second, Siegrist et al. did not exactly show this. They showed that the fxbA/eccC3 double mutant grows slower that the fxbA single mutant. To my knowledge there is no publication showing that single esx-3 component mutants grow as WT in iron-deficient conditions. Do the authors have data demonstrating this? If true, it is surprising that mutating EccD3 has a milder phenotype compared the complete region deletion, as it is a crucial ESX-3 component.__

      We apologize for any confusion. We had the relevant reference two lines prior, and have since added it to that sentence as well.

      The reviewer is correct that Siegrest et al did not show the effects of just ESX-3 component single deletions. However, Siegrest et al. 2009 demonstrated that deleting the entire ESX-3 operon results in growth similar to the wild type strain in low-iron media. In contrast, the fxbA single knockout exhibits a notable growth defect, and the fxbA/ESX-3 double knockout has an even more severe growth defect. Following the logic that a double knockout is needed to observe a growth defect in low-iron media, Siegrest et al. 2014 demonstrated this also extends to single ESX-3 component knockouts, such as the fxbA/eccD3 double knockout strain. To ensure clarity and accuracy, I will edit the sentence to say "When individual components of the ESX-3 system are deleted, growth is significantly impaired when the additional siderophore exochelin formyltransferase fxbA is also knocked out."

      __ Reference to Table 1, should be a reference to Table S1.__

      We have updated our manuscript to correct this reference.

      __ "Our heatmaps surprisingly reveal residues where substitutions are deleterious specifically in the iron-sufficient condition" Refer here to Fig. S2.__

      We have updated our manuscript to include this reference.

      __ "In the iron-deficient condition, 6/551 (1.08%) missense mutations have a weak LOF effect, and 0 have strong effects." More clearly explain this refers to the residues of the transmembrane region.__

      We have updated our manuscript to provide more clarity.

      __ "The MycP transmembrane helix has been hypothesized to be required for ESX complex specificity, targeting MycP to associate with the correct ESX homologue." I miss a reference here. And I thought that the transmembrane domain of MycP was required for complex stability not for specificity?__

      We thank the reviewer for pointing out our missing citation, and asking us to clarify our point. I believe the literature suggests that both the protease and transmembrane domains of MycP are required for both complex stability and specificity. van Winden et al. 2016 https://doi.org/10.1128/mbio.01471-16 show that MycP5 needs to be present for secretion. The protease activity can be abolished and the ESX-5 complex can still secrete and be pulled down, as seen by BN-PAGE. van Winden et al. 2019 https://doi.org/10.1074/jbc.RA118.007090 show that truncated mutants missing either the protease domain or the transmembrane domain cannot rescue ESX-5 secretion or complex stability in a MycP knockout strain. More relevant, they attempted to rescue MycP1 and MycP5 mutants by creating chimeric proteins that either had the MycP1 protease domain and MycP5 transmembrane domain, or the MycP5 protease domain and MycP1 transmembrane domain. If the protease and transmembrane domains were required for complex stability and NOT specificity, we would see MycP5 rescue ESX-1 secretion in the MycP1 mutant strains and vice versa. We would also see the chimera proteins rescue both ESX-1 and ESX-5 secretion and complex stability. Instead, we see that neither chimera rescued ESX-1 nor ESX-5 secretion or complex stability, implying that both MycP domains are necessary.

      We will amend our paper text to reference MycP's role in complex stability instead of specificity, and soften the language: "The MycP transmembrane helix has been shown to be required for ESX complex stability, as MycP knockouts and truncated mutants abolish ESX secretion and pulldowns of the entire complex."

      __ "....role in ESX function relating to EccB3 and EccC3. In the transmembrane, ..... we" Insert "region" after "transmembrane"__

      We have updated our manuscript to include this update.

      Significance

      The study provides insight into individual residues of a central component of the ESX-3 type VII secretion system for functionality, which is useful for those studying the functioning of mycobacterial type VII secretion systems. Moreover, because this system is essential for the growth of the important pathogen M. tuberculosis, this knowledge can be used to design new anti-tuberculosis compounds that block the ESX-3 system. Although the results mainly confirm previous observations (highlighting specific residues important for the stability of ubiquitin and residues of other parts of EccD important for protein-protein interactions within the ESX-3/ESX-5 membrane complex), to my knowledge this is the first time DMS has been applied to mycobacteria. This study is therefore of interest to mycobacteriologists.


      Reviewer #2

      __Evidence, reproducibility and clarity __

      This work provides valuable insights into EccD3 function, a core component of the ESX-3 secretion system. The strength of this study lies in the development of a robust functional assay for the systematic mapping of functionally relevant amino acids in EccD3. The approach could potentially be expanded to analyze other ESX-3 components but remains limited to the ESX-3 secretion system. 1. The authors engineered an M. smegmatis knockout strain with deletions of fxbA and eccD3. Deletion of fxbA renders the exocholin iron uptake system non-functional, forcing the bacteria to rely on siderophore-mediated iron uptake under iron-limiting conditions. This process, in turn, depends on ESX-3 secretion activity, as PPE4, a known ESX-3 substrate, has been previously implicated in iron utilization in M. tuberculosis (Tufariello et al., 2016). This experimental setup links EccD3 function to a growth phenotype under iron-limiting conditions, as mutations impairing ESX-3 secretion disrupt iron utilization and mycobacterial growth. 2. By complementing the knockout strain with a library of EccD3 mutant variants, the authors systematically identify residues essential for protein-protein interactions within the ESX-3 core complex. Structural analysis corroborates the functional relevance of these residues, specifically those mediating interactions between EccD3 and other ESX-3 components, or those disrupting the hydrophobic core of the EccD3 ubiquitin-like (Ubl) domain. 3. Structural comparisons with the MycP5-bound ESX-5 complex allow the authors to predict residues within EccD3 that may interact with MycP3 during ESX-3 core complex assembly. Furthermore, comparisons with the ESX-5 hexamer suggest residues that may stabilize or drive oligomerization of the ESX-3 dimer into its putative hexameric state. These insights are significant and provide testable hypotheses for future studies. 4. The methodology is limited to ESX-3. The authors exploit the essentiality of ESX-3 for siderophore-dependent growth under iron-limiting conditions. However, this functional readout cannot be directly transferred to other ESX systems (ESX-1, ESX-2, ESX-4, ESX-5), which have distinct substrates, biological roles, and regulatory mechanisms.

      Significance

      This work provides valuable insights into EccD3 function, a core component of the ESX-3 secretion system. The strength of this study lies in the development of a robust functional assay for the systematic mapping of functionally relevant amino acids in EccD3. The approach could potentially be expanded to analyze other ESX-3 components but remains limited to the ESX-3 secretion system.

      Thank you for your thoughtful and supportive feedback. We appreciate your time and effort in reviewing our study.


      Reviewer #3

      __Evidence, reproducibility and clarity __

      The manuscript by Trinidad et al. provides a deep mutational scanning (DMS) analysis to investigate the functional roles of residues from the EccD3 subunit of the Type VII ESX-3 secretion apparatus from M. smegmatis. A previously published structure of ESX-3 from M. smegmatis by the Rosenberg group (Oren Rosenberg is also an author of this paper) is used as basis for structural interpretation of the DMS data presented in this contribution. A shortcoming of the previous structure, despite being very rich in terms of structural details, was in the lack of hexameric pore formation, which has been established more recently by structures of the related ESX-5 system.

      Technically, DMS is state-of-the art and a powerful approach to systematically scan residues of potential functional interest. Therefore, the data presented here, provide a remarkable repository for further interpretation in this contribution and by other future investigations. The experimental data have been deposited in Github enabling access by others in the future.

      Overall, the paper would benefit from an improved overall organisation. I found in part hard to extract some of the main points from the way the data are presented. In essence, two separate screens were performed, the first one focusing on the EccD3 Ubl domain and adjacent linker regions and a second one on the EccD3 TM region. I think the paper could be better structured accordingly. Tables of residues with strong effects in iron-deficient and iron-sufficient media, together with their structural annotation, would facilitate extracting main messages from this manuscript. Without going too much in detail, there is also scope for improvement of most of the structural figures. More consistency in terms of color coding with the previous paper by Powileit et al. (2019) would also help navigation.

      A potential weakness of the paper is in the limited scope of interpretation of the data in the context of the dimeric ESX-3 assembly, which is actually acknowledged by the authors. Computational AI-based methods should allow generating a complete pore model of ESX-3, which would allow interpretation of some of the data in a more functional relevant context. This would enhance the validity of the current interpretations presented.

      We acknowledge the lack of a hexameric ESX-3 structure, and would love to base our analysis on such a structure. Unfortunately, experimentally purifying and determining such a structure is beyond the scope of this manuscript. While AI-based methods are certainly exciting and helpful to make sense of mutational data, they are not able to computationally predict such large structures. The AlphaFold3 server website is commonly used for these purposes and allows predictions of up to 5000 tokens (or amino acids). An ESX-3 hexamer would be composed of 6x EccB proteins (519 AA each), 6x EccC proteins (1326 AA each), 12x EccD proteins (476 AA each), and 6x EccE proteins (310 AA each). Together, this complex would be made up of 18,642 amino acids.

      We tried using alphafold to predict an ESX-5 dimer complex, as well as reproduce the ESX-3 dimer complex, and were unable to produce these structures. Each ESX protomer is assembled correctly, as each protein within the complex makes appropriate contacts with each other. We see the EccD-dimers still form the membrane vestibule within each ESX complex. The issue is the ESX dimer complex has not assembled correctly: the EccC transmembrane helix 1 of a protomer should interact with the EccB transmembrane helix of the neighboring protomer; and, the N-terminus of EccB in one protomer should interact with the loop between the EccD transmembrane helices 10 and 11 in the neighboring protomer. Instead, Alphafold creates contacts along the EccD proteins from both complexes. We have included a "top-down" view of the ESX-5 dimer, where the periplasmic domains of EccB have been cleaved off for clarity.

      A side view:

      Here we have the ESX-3 dimer structure published by Poweleit et al. side-by-side with the ESX-3 dimer predicted by alphafold, visualized in Pmyol. The alphafold structure largely has each proteins' domains and folds properly predicted, including even the EccD3 dimer found in each ESX protomer. However, the protomers are not assembled into a dimer properly as compared to the purified ESX-3 dimer from PDB: 6umm. We included a "front" and "side view", as well as a "top down" view where the cytoplasmic domains have been hidden for visual clarity.

      The use of full names and acronyms needs to be more consistent. As an example, the terms "ubiquitin-like" and ubiquitin-like (Ubl) and UBl are used in parallel throughout the manuscript. The percentages given in various places of the paper could be reduced to integers, as they generally relate to relatively small data sets. Please express numbers with a precision, reasonable matching expected statistical significance.

      We apologize for the lack of consistency in how we referred to the ubiquitin-like domain. I originally wrote "ubiquitin-like (Ubl)" once per section (intro, results, discussion). I have edited these all to just "Ubl" after the introduction, except for figure and section titles. We have also reduced our percentages to integers.

      Some of the DMS experiments have been repeated three-fold, which should be a minimal number to allow extracting statistical significance, other experiments have only been repeated two-fold. Could this be clarified, please?

      We apologize for this oversight, and thank the reviewer for pointing this out. All experiments were done in triplicate, the exception being the site-directed mutant growth curves, which were performed in duplicate. We have repeated this experiment in triplicate in response to this point. As we repeated this experiment, mutant R134A dropped out due to technical reasons, and so we did not include it in the updated growth curves.

      Specific comments on text and figures:

      Figure 1: The EM densities shown considerably deviate from those that were shown in the original publication by Poweleit et al (2019). If there is an aim is to reinterpret the data this needs to be described in sufficient technical detail. There may be a case for this, in light of recent advances in computational AI-vased structural biology.

      We acknowledge this may be confusing and we apologize for that, as the EM density I have shown in this manuscript uses the same map we used to create the one seen in the original publication Poweleit et al 2019. There are existing crystal structures of EccB1 and the ATPase domains of EccC1 that we used to create homology models of EccB3 and EccC3 using the structure-prediction software RaptorX for the 2019 publication. These homology models were then combined with a low resolution EM density to create the model seen in the 2019 eLife paper. I did not include those homology models in this manuscript, as I did not believe those predictions were relevant to this study. I wanted to include the highest resolution and thus most accurate depiction of our ESX-3 structure.

      Introduction, statement "We made comparisons to a prior DMS on ubiquitin to increase signal-to-noise in our interpretation of the Ubl domain mutagenesis data." Could this be further explained please? I could not find anything in addition in the Methods section and elsewhere.

      __ __We apologize for the confusion!

      EccD3 Ubl domain and ubiquitin DMS dataset comparisons

      To compare the DMS data of EccD3 Ubl with that of ubiquitin, we first identified homologous residues in each structure. This was achieved by aligning the EccD3 Ubl domain with ubiquitin (PDB: 1ubq) using PyMOL and assessing the positional correspondence of side chains (e.g., ubiquitin residue I3 aligned with EccD3 residue V12). Next, we referenced missense mutation datasets to calculate the average DMS score for each residue position in both proteins. We then generated a scatter plot to compare the average missense scores for ubiquitin and EccD3 Ubl using ggplot2. Data points were color-coded according to the functional roles assigned to ubiquitin, with residues forming the hydrophobic patch and core highlighted, while all other residues were represented in grey.

      Description of "vestibule" as a core feature of the ESX-3 structure. As mentioned above, this is very much a result of the presented dimeric arrangement. In the context of a complete pore model, these features may change or even disappear.

      While we would certainly welcome an ESX-3 hexamer model to definitively determine whether this feature persists, such a model is not currently available. However, the highly homologous ESX-5 complex retains these EccD vestibules, and there is no reason to believe these features would change or disappear. Therefore, based on our interpretation of the ESX-3 dimer and ESX-5 hexamer we believe that the EccD membrane vestibule is not just an artifact of the ESX-3 dimer complex.

      It is possible that the reviewer misunderstood what we were referring to as the vestibule. We updated the language in the text to improve clarity. However the vestibule is not a consequence of ESX-3 complex dimer formation. It is an inherent feature of the ESX monomer complexes, where two EccD proteins dimerize to form said vestibule. Furthermore, there is no evidence to suggest that this feature would be lost in a hexameric state.

      Structurally, the ESX-3 dimer consists of two ESX-3 monomer complexes, each containing one EccB, one EccC, one EccE, and two EccD proteins. Therefore, each ESX-3 monomer inherently includes an EccD dimer. The presence of the EccD dimer is not exclusive to the ESX-3 dimer but is a fundamental component of each ESX-3 complex. Similarly, the ESX-5 hexamer retains the EccD dimer within each ESX-5 complex, further supporting the idea that this structural feature is conserved.

      Figure 2, panel B: Isn't right that "positive" and "negative" need to exchanged? Perhaps, there is something I misunderstood.

      We apologize for the confusion, and appreciate the reviewer pointing out this inconsistency. We have updated the manuscript to correct this.

      Figure 2, panel F: it is hard to extract the assignments from the overlaid curves.

      We apologize for a lack of clarity in how this growth curve was presented. We have included labels at the end point to show where each sample is.

      Figure 3, caption "from low (red) to white (tolerant)": for the sake of consistency, please either put the color in parentheses, or functional description. Does this statement relate to panel A or B? "All other residues are colored white". I can't see this.

      We apologize for the inconsistency, and have updated this label. We hope we have clarified the fact that the entire structure is white except for the residues we colored red.

      Results text "In contrast to ubiquitin, all hydrophobic core residues in the EccD3 Ubl domain are equally intolerant to charged residue swaps. Unsurprisingly, residues important for ubiquitin's specific degradation interactions are not sensitive to substitutions in the EccD3 Ubl domain." Does this mean that proper folding of Ubl is less critical for ESX_3 function? Please elaborate on this further.

      We apologize for any confusion. Our data shows that residues which side chains extend into the hydrophobic core of the Ubl domain are intolerant to swaps to charge residues. We hypothesize these missense mutations disrupt this hydrophobic core, and lead to destabilization of this domain. These intolerant missense mutations each have negative Enrich2 scores, implying a loss of ESX-3 function, and that proper folding of the Ubl is critical for ESX-3 function. We have updated our text to clarify this point:

      Unsurprisingly, residues important for ubiquitin function's specific interactions are not sensitive to substitutions in the EccD3 Ubl domain. There is no simple discernable preference within the Ubl domain to any side that maintains protein-protein interactions, implying that the scores are dominated by stability effects and that the Ubl domain must maintain a stable β-grasp fold for ESX-3 function.

      Figure 4, panel C: the surface does not provide residue-specific information, hence this panel is not very informative.

      We agree with the reviewer that Figure 4 panel C was not very informative, and so we have removed it from Figure 4 for the sake of brevity.

      Results text "T148 extends out from transmembrane helix 1 into a hydrophobic pocket between transmembrane helices 1, 2, and 3." Could this please be illustrated in one of the structural presentations?

      We have updated figure 5 to include a snapshot of this residue and the hydrophobic pocket it extends into, as panel E.

      Results text, last paragraph, Figure 5C-D: interpretation of the experimental ESX-3 data based on ESX-5 models is problematic, without showing proof of conservation of relevant sequence/structural features. As mentioned above, I would encourage the authors to establish a hexameric ESX-3 model and interpret the data starting from there. Extrapolation of the interpretation of data to other ESX systems, including ESX-5, would expand the scope by generalization, which however would open another chapter. The ESX-5 structure does not explain e.g. why W227 when mutated is less sensitive to iron depletion as opposed to iron being present.

      We do not believe we can use AI to predict a hexameric ESX-3 model. We will update our supplement to include a figure showing proof of conservation between the EccD3 and EccD5 sequences. We can superpose the ESX-3 dimer structure onto the ESX-5 hexamer structure, and see that this dimeric complex overlays quite well on top of an ESX-5 subcomplex. We can imagine this hexamer as a trimer of dimers, where three copies of this dimeric complex interact to form the hexamer. The superposition is not perfect and there are slight rearrangements to different helices to allow for hexamer formation, but these do not imply we cannot compare these two homologous structures.

      We have included a new structure snapshot in Figure 5, where panel D is the ESX-3 dimer (PDB: 6umm) shown as a side and top-down view. This allows for a comparison with panel C, the snapshot of the ESX-5 complex (PDB: 7np7) where in two protomers the EccB, EccC, and EccD proteins are colored the same way as ESX-3, and the other ESX-5 protomers are colored white. Note that in this hexamer, EccE is missing. We see the EccD membrane vestibule is conserved in both structures.

      Significance

      Strength and Limitations: already assessed under "Evidence, reproducibility and clarity".

      There is scope for further interpretation using experimental structural and modeling data. There is also scope for applying complementary assays for selected mutants, most likely within a lower throughput format.

      Advance: The contribution demonstrates well the power of DMS for systematic screening, in the context of Type VII secretion. The main advance is in the raw data generated and deposited.

      Audience: microbiology with a specific interest in secretion, structural biology

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by Trinidad et al. provides a deep mutational scanning (DMS) analysis to investigate the functional roles of residues from the EccD3 subunit of the Type VII ESX-3 secretion apparatus from M. smegmatis. A previously published structure of ESX-3 from M. smegmatis by the Rosenberg group (Oren Rosenberg is also an author of this paper) is used as basis for structural interpretation of the DMS data presented in this contribution. A shortcoming of the previous structure, despite being very rich in terms of structural details, was in the lack of hexameric pore formation, which has been established more recently by structures of the related ESX-5 system.

      Technically, DMS is state-of-the art and a powerful approach to systematically scan residues of potential functional interest. Therefore, the data presented here, provide a remarkable repository for further interpretation in this contribution and by other future investigations. The experimental data have been deposited in Github enabling access by others in the future.

      Overall, the paper would benefit from an improved overall organisation. I found in part hard to extract some of the main points from the way the data are presented. In essence, two separate screens were performed, the first one focusing on the EccD3 Ubl domain and adjacent linker regions and a second one on the EccD3 TM region. I think the paper could be better structured accordingly. Tables of residues with strong effects in iron-deficient and iron-sufficient media, together with their structural annotation, would facilitate extracting main messages from this manuscript. Without going too much in detail, there is also scope for improvement of most of the structural figures. More consistency in terms of color coding with the previous paper by Powileit et al. (2019) would also help navigation.

      A potential weakness of the paper is in the limited scope of interpretation of the data in the context of the dimeric ESX-3 assembly, which is actually acknowledged by the authors. Computational AI-based methods should allow generating a complete pore model of ESX-3, which would allow interpretation of some of the data in a more functional relevant context. This would enhance the validity of the current interpretations presented.

      The use of full names and acronyms needs to be more consistent. As an example, the terms "ubiquitin-like" and ubiquitin-like (Ubl) and UBl are used in parallel throughout the manuscript. The percentages given in various places of the paper could be reduced to integers, as they generally relate to relatively small data sets. Please express numbers with a precision, reasonable matching expected statistical significance.

      Some of the DMS experiments have been repeated three-fold, which should be a minimal number to allow extracting statistical significance, other experiments have only been repeated two-fold. Could this be clarified, please?

      Specific comments on text and figures:

      Figure 1: The EM densities shown considerably deviate from those that were shown in the original publication by Poweleit et al (2019). If there is an aim is to reinterpret the data this needs to be described in sufficient technical detail. There may be a case for this, in light of recent advances in computational AI-vased structural biology.

      Introduction, statement "We made comparisons to a prior DMS on ubiquitin to increase signal-to-noise in our interpretation of the Ubl domain mutagenesis data." Could this be further explained please? I could not find anything in addition in the Methods section and elsewhere.

      Description of "vestibule" as a core feature of the ESX-3 structure. As mentioned above, this is very much a result of the presented dimeric arrangement. In the context of a complete pore model, these features may change or even disappear.

      Figure 2, panel B: Isn't right that "positive" and "negative" need to exchanged? Perhaps, there is something I misunderstood.

      Figure 2, panel F: it is hard to extract the assignments from the overlaid curves.

      Figure 3, caption "from low (red) to white (tolerant)": for the sake of consistency, please either put the color in parentheses, or functional description. Does this statement relate to panel A or B? "All other residues are colored white". I can't see this.

      Results text "In contrast to ubiquitin, all hydrophobic core residues in the EccD3 Ubl domain are equally intolerant to charged residue swaps. Unsurprisingly, residues important for ubiquitin's specific degradation interactions are not sensitive to substitutions in the EccD3 Ubl domain." Does this mean that proper folding of Ubl is less critical for ESX_3 function? Please elaborate on this further.

      Figure 4, panel C: the surface does not provide residue-specific information, hence this panel is not very informative.

      Results text "T148 extends out from transmembrane helix 1 into a hydrophobic pocket between transmembrane helices 1, 2, and 3." Could this please be illustrated in one of the structural presentations?

      Results text, last paragraph, Figure 5C-D: interpretation of the experimental ESX-3 data based on ESX-5 models is problematic, without showing proof of conservation of relevant sequence/structural features. As mentioned above, I would encourage the authors to establish a hexameric ESX-3 model and interpret the data starting from there. Extrapolation of the interpretation of data to other ESX systems, including ESX-5, would expand the scope by generalization, which however would open another chapter. The ESX-5 structure does not explain e.g. why W227 when mutated is less sensitive to iron depletion as opposed to iron being present.

      Referee cross-commenting

      I especially second the comments of referee #1, major comments, point 3 (statistical significance of the data). Addressing this point is crucial for the paper. Referee #2, significance section "The approach could potentially be expanded to analyze other ESX-3 components but remains limited to the ESX-3 secretion system." I was considering making the same point but did not at the end. Of course, ultimately, it would be great if all components of ESX-3 could be analyzed they way it was done for the EccD3 component. However, I am afraid such exercise could become quite open ended. Already by now, there is some compromise on the depth of mechanistic interpretation in light of a large data set generated.

      Significance

      Strength and Limitations: already assessed under "Evidence, reproducibility and clarity".

      There is scope for further interpretation using experimental structural and modeling data. There is also scope for applying complementary assays for selected mutants, most likely within a lower throughput format.

      Advance: The contribution demonstrates well the power of DMS for systematic screening, in the context of Type VII secretion. The main advance is in the raw data generated and deposited.

      Audience: microbiology with a specific interest in secretion, structural biology

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      Referee #2

      Evidence, reproducibility and clarity

      This work provides valuable insights into EccD3 function, a core component of the ESX-3 secretion system. The strength of this study lies in the development of a robust functional assay for the systematic mapping of functionally relevant amino acids in EccD3. The approach could potentially be expanded to analyze other ESX-3 components but remains limited to the ESX-3 secretion system.

      1. The authors engineered an M. smegmatis knockout strain with deletions of fxbA and eccD3. Deletion of fxbA renders the exocholin iron uptake system non-functional, forcing the bacteria to rely on siderophore-mediated iron uptake under iron-limiting conditions. This process, in turn, depends on ESX-3 secretion activity, as PPE4, a known ESX-3 substrate, has been previously implicated in iron utilization in M. tuberculosis (Tufariello et al., 2016). This experimental setup links EccD3 function to a growth phenotype under iron-limiting conditions, as mutations impairing ESX-3 secretion disrupt iron utilization and mycobacterial growth.
      2. By complementing the knockout strain with a library of EccD3 mutant variants, the authors systematically identify residues essential for protein-protein interactions within the ESX-3 core complex. Structural analysis corroborates the functional relevance of these residues, specifically those mediating interactions between EccD3 and other ESX-3 components, or those disrupting the hydrophobic core of the EccD3 ubiquitin-like (Ubl) domain.
      3. Structural comparisons with the MycP5-bound ESX-5 complex allow the authors to predict residues within EccD3 that may interact with MycP3 during ESX-3 core complex assembly. Furthermore, comparisons with the ESX-5 hexamer suggest residues that may stabilize or drive oligomerization of the ESX-3 dimer into its putative hexameric state. These insights are significant and provide testable hypotheses for future studies.
      4. The methodology is limited to ESX-3. The authors exploit the essentiality of ESX-3 for siderophore-dependent growth under iron-limiting conditions. However, this functional readout cannot be directly transferred to other ESX systems (ESX-1, ESX-2, ESX-4, ESX-5), which have distinct substrates, biological roles, and regulatory mechanisms.

      Significance

      This work provides valuable insights into EccD3 function, a core component of the ESX-3 secretion system. The strength of this study lies in the development of a robust functional assay for the systematic mapping of functionally relevant amino acids in EccD3. The approach could potentially be expanded to analyze other ESX-3 components but remains limited to the ESX-3 secretion system.