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      Reply to the reviewers

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

      Summary: This manuscript reports the identification of putative orthologues of mitochondrial contact site and cristae organizing system (MICOS) proteins in Plasmodium falciparum - an organism that unusually shows an acristate mitochondrion during the asexual part of its life cycle and then this develops cristae as it enters the sexual stage of its life cycle and beyond into the mosquito. The authors identify PfMIC60 and PfMIC19 as putative members and study these in detail. The authors at HA tags to both proteins and look for timing of expression during the parasite life cycle and attempt (unsuccessfully) to localise them within the parasite. They also genetically deleted both gene singly and in parallel and phenotyped the effect on parasite development. They show that both proteins are expressed in gametocytes and not asexuals, suggesting they are present at the same time as cristae development. They also show that the proteins are dispensible for the entire parasite life cycle investigated (asexuals through to sporozoites), however there is some reduction in mosquito transmission. Using EM techniques they show that the morphology of gametocyte mitochondria is abnormal in the knock out lines, although there is great variation.

      Major comments: The manuscript is interesting and is an intriguing use of a well studied organism of medical importance to answer fundamental biological questions. My main comments are that there should be greater detail in areas around methodology and statistical tests used. Also, the mosquito transmission assays (which are notoriously difficult to perform) show substantial variation between replicates and the statistical tests and data presentation are not clear enough to conclude the reduction in transmission that is claimed. Perhaps this could be improved with clearer text?

      We would like to thank the reviewer for taking the time to review our manuscript. We are happy to hear the reviewer thinks the manuscript is interesting and thank the reviewer for their constructive feedback.

      To clarify the statistical analyses used, we included a new supplementary dataset with all statistical analyses and p-values indicated per graph. Furthermore, figure legends now include the information on the exact statistical test used in each case.

      Regarding mosquito experiments, while we indeed reported a reduction in transmission and oocysts numbers we are aware that this effect might be due to the high variability in mosquito feeding assays. To highlight this point, we deleted the sentence "with the transmission reduction of [numbers]...." and we included the sentence "The high variability encountered in the standard membrane feeding assays, though, partially obstructs a clear conclusion on the biological relevance of the observed reduction in oocyst numbers"

      More specific comments to address: Line 101/Fig1E (and figure legend) - What is this heatmap showing. It would be helpful to have a sentence or two linking it to a specific methodology. I could not find details in the M+M section and "specialized, high molecular mass gels" does not adequately explain what experiments were performed. The reference to Supplementary Information 1 also did not provide information.

      We added the information "high molecular mass gels with lower acrylamide percentage" to clarify methodology in the text. Furthermore, we extended the figure legend to include all relevant information. Further experimental details can be found in the study cited in this context, where the dataset originates from (Evers et al., 2021).

      Line 115 and Supplementary Figure 2C + D - The main text says that the transgenic parasites contained a mitochondrially localized mScarlet for visualization and localization, but in the supplementary figure 2 it shows mitotracker labelling rather than mScarlet. This is very confusing. The figure legend also mentions both mScarlet and MitoTracker. I assume that mScarlet was used to view in regular IFAs (Fig S2C) and the MitoTracker was used for the expansion microscopy (Fig S2D)? Please clarify.

      We thank the reviewer for pointing this out - this was indeed incorrectly annotated. We used the endogenous mito-mScarlet signal in IFA and mitoTracker in U-ExM. The figure annotation has now been corrected.

      Figure 2C - what is the statistical test being used (the methods say "Mean oocysts per midgut and statistical significance were calculated using a generalized linear mixed effect model with a random experiment effect under a negative binomial distribution." but what test is this?)?

      The statistic test is now included in the material and method section with the sentence "The fitted model was used to obtain estimated means and contrasts and were evaluated using Wald Statistics". The test is now also mentioned in the figure legend.

      Also the choice of a log10 scale for oocyst intensity is an unusual choice - how are the mosquitoes with 0 oocysts being represented on this graph? It looks like they are being plotted at 10^-1 (which would be 0.1 oocysts in a mosquito which would be impossible).

      As the data spans three orders of magnitude with low values being biologically meaningful, we decided that a log scale would best facilitate readability of the graph. As the 0 values are also important to show, we went with a standard approach to handle 0s in log transformed data and substituted the 0s with a small value (0.001). We apologize for not mentioning this transformation in the manuscript. To make this transformation transparent, we added a break at the lower end of the log‑scaled y‑axis and relabelled the lowest tick as '0'. This ensures that mosquitoes with zero oocysts are shown along the x‑axis without being assigned an artificial value on the log scale. We would furthermore like to highlight that for statistics we used the true value 0 and not 0.001.

      Figure 2D - it is great that the data from all feeding replicates has been shared, however it is difficult to conclude any meaningful impact in transmission with the knock-out lines when there is so much variation and so few mosquitoes dissected for some datapoints (10 mosquitoes are very small sample sizes). For example, Exp1 shows a clear decrease in mic19- transmission, but then Exp2 does not really show as great effect. Similarly, why does the double knock out have better transmission than the single knockouts? Sure there would be a greater effect?

      We agree with the reviewer and with the new sentence added, as per major point, we hope we clarified the concept. Note that original Figure 2D has been moved to the supplementary information, as per minor comment of another reviewer.

      Figure 3 legend - Please add which statistical test was used and the number of replicates.

      Done

      Figure 4 legend - Please add which statistical test was used and the number of replicates.

      Done. Regarding replicates, note that while we measured over 100 cristae from over 30 mitochondria, these all stem from the same parasite culture.

      Figure 5C - the 3D reconstructions are very nice, but what does the red and yellow coloring show?

      Indeed, the information was missing. We added it to the figure legend.

      Line 352 - "Still, it is striking that, despite the pronounced morphological phenotype, and the possibly high mitochondrial stress levels, the parasites appeared mostly unaffected in life cycle propagation, raising questions about the functional relevance of mitochondria at these stages." How do the authors reconcile this statement with the proven fact that mitochondria-targeted antimalarials (such as atovaquone) are very potent inhibitors of parasite mosquito transmission?

      Our original sentence was reductive. What we wanted to state was related to the functional relevance of crista architecture and overall mitochondrial morphology rather than the general functional relevance of the mitochondria. We changed the sentence accordingly.

      Furthermore, even though we do not discuss this in the article, we are aware of mitochondria targeting drugs that are known to block mosquito transmission. We want to point out that it is difficult to discern the disruption of ETC and therefore an impact on energy conversion with the impact on the essential pathway of pyrimidine synthesis, highly relevant in microgamete formation. Still, a recent paper from Sparkes et al. 2024 showed the essentiality of mitochondrial ATP synthesis during gametogenesis so it is very likely that the mitochondrial energy conversion is highly relevant for transmission to the mosquito.

      Reviewer #1 (Significance (Required)):

      This manuscript is a novel approach to studying mitochondrial biology and does open a lot of unanswered questions for further research directions. Currently there are limitations in the use of statistical tests and detail of methodology, but these could be easily be addressed with a bit more analysis/better explanation in the text. This manuscript could be of interest to readers with a general interest in mitochondrial cell biology and those within the specific field of Plasmodium research. My expertise is in Plasmodium cell biology.

      We thank the reviewer for the praise.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Major comments: 1) In my opinion, the authors tend to sensationalize or overinterpret their results. The title of the manuscript is very misleading. While MICOS is certainly important for crista formation, it is not the only factor, as ATP synthase dimer rows make a highly significant contribution to crista morphology. Thus, one can argue with equal validity that ATP synthase should be considered the 'architect', as it's the conformation of the dimers and rows modulate positive curvature. Secondly, while cristae are still formed upon mic60/mic19 gene knockout (KO), they are severely deformed, and likely dysfunctional (see below). Thus, I do not agree with the title that MICOS is dispensable for crista formation, because the authors results show that it clearly is essential. So, the title should be changed.

      We thank the reviewer for taking the time to review our manuscript.

      Based on the reviewers' interpretation we conclude the title does not come across as intended. We have changed the title to: "The role of MICOS in organizing mitochondrial cristae in malaria parasites"

      The Discussion section starting from line 373 also suffers from overinterpretation as well as being repetitive and hard to understand. The authors infer that MICOS stability is compromised less in the single KOs (sKO) in compared to the mic60/mic19 double KO (dKO). MICOS stability was never directly addressed here and the composition of the MICOS complex is unaddressed, so it does not make sense to speculate by such tenuous connections. The data suggest to me that mic60 and mic19 are equally important for crista formation and crista junction (CJ) stabilization, and the dKO has a more severe phenotype than either KO, further demonstrating neither is epistatic.

      We do agree with the reviewer's notion that we did not address complex stability, and our wording did not make this sufficiently clear. We shortened and rephrased the paragraph in question.

      The following paragraphs (line 387 to 422) continues with such unnecessary overinterpretation to the point that it is confusing and contradictory. Line 387 mentions an 'almost complete loss of CJs' and then line 411 mentions an increase in CJ diameter, both upon Mic60 ablation. I do not think this discussion brings any added value to the manuscript and should be shortened. Yes, maybe there are other putative MICOS subunits that may linger in the KOS that are further destabilized in the dKO, or maybe Mic60 remains in the mic19 KO (and vice versa) to somehow salvage more CJs, which is not possible in the dKO. It is impossible to say with confidence how ATP synthase behaves in the KOs with the current data.

      We shortened this paragraph.

      2) While the authors went through impressive lengths to detect any effect on lifecycle progression, none was found except for a reduction in oocyte count. However, the authors did not address any direct effect on mitochondria, such as OXPHOS complex assembly, respiration, membrane potential. This seems like a missed opportunity, given the team's previous and very nice work mapping these complexes by complexome profiling. However, I think there are some experiments the authors can still do to address any mitochondrial defects using what they have and not resorting to complexome profiling (although this would be definitive if it is feasible):

      i) Quantification of MitoTracker Red staining in WT and KOs. The authors used this dye to visualize mitochondria to assay their gross morphology, but unfortunately not to assay membrane potential in the mutants. The authors can compare relative intensities of the different mitochondria types they categorized in Fig. 3A in 20-30 cells to determine if membrane potential is affected when the cristae are deformed in the mutants. One would predict they are affected.

      Interesting suggestion. As our staining and imaging conditions are suitable for such analysis (as demonstrated by Sarazin et al., 2025, https://www.biorxiv.org/content/10.1101/2025.11.27.690934v1), we performed the measurements on the same dataset which we collected for Figure 3. We did, however, not detect any difference in mitotracker intensity between the different lines. The result of this analysis is included in the new version of Supplementary figure S6.

      ii) Sporozoites are shown in Fig S5. The authors can use the same set up to track their motion, with the hypothesis that they will be slower in the mutants compared to WT due to less ATP. This assumes that sporozoite mitochondria are active as in gametocytes.

      While theoretically plausible and informative, we currently do not know the relevance of mitochondrial energy conversion for general sporozoite biology or specifically features of sporozoite movement. Given the required resources and time to set this experiment up and the uncertainty whether it is a relevant proxy for mitochondrial functioning, we argue it is out of scope for this manuscript.

      iii) Shotgun proteomics to compare protein levels in mutants compared to WT, with the hypothesis that OXPHOS complex subunits will be destabilized in the mutants with deformed cristae. This could be indirect evidence that OXPHOS assembly is affected, resulting in destabilized subunits that fail to incorporate into their respective complexes.

      While this experiment could potentially further our understanding of the interaction between MICOS and levels of OXPHOS complex subunits we argue that the indirect nature of the evidence does not justify the required investments.

      To expedite resubmission, the authors can restrict the cell lines to WT and the dKO, as the latter has a stronger phenotype that the individual KOs and conclusions from this cell line are valid for overall conclusions about Plasmodium MICOS.

      I will also conclude that complexome/shotgun proteomics may be a useful tool also for identifying other putative MICOS subunits by determining if proteins sharing the same complexome profile as PfMic60 and Mic19 are affected. This would address the overinterpretation problem of point 1.

      3) I am aware of the authors previous work in which they were not able to detect cristae in ABS, and thus have concluded that these are truly acristate. This can very well be true, or there can be immature cristae forms that evaded detection at the resolution they used in their volumetric EM acquisitions. The mitochondria and gametocyte cristae are pretty small anyway, so it not unreasonable to assume that putative rudimentary cristae in ABS may be even smaller still. Minute levels of sampled complex III and IV plus complex V dimers in ABS that were detected previously by the authors by complexome profiling would argue for the presence of miniscule and/or very few cristae.

      I think that authors should hedge their claim that ABS is acrisate by briefly stating that there still is a possibility that miniscule cristae may have been overlooked previously.

      We acknowledge that we cannot demonstrate the absolute absence of any membrane irregularities along the inner mitochondrial membrane. At the same time, if such structures were present, they would be extremely small and unlikely to contain the full set of proteins characteristic of mature cristae. For this reason, we consider it appropriate to classify ABS mitochondria as acristate. To reflect the reviewer's point while maintaining clarity for readers, we have slightly adjusted our wording in the manuscript, changing 'fully acristate' to 'acristate'.

      This brings me to the claim that Mic19 and Mic60 proteins are not expressed in ABS. This is based on the lack of signal from the epitope tag; a weak signal is detected in gametocytes. Thus, one can counter that Mic19 and Mic60 are also expressed, but below the expression limits of the assay, as the protein exhibits low expression levels when mitochondrial activity is upregulated.

      We agree with the reviewer that the absence of a detectable epitope‑tag signal does not definitively exclude low‑level expression, and we have therefore replaced the term 'absent' with 'undetectable' throughout the manuscript. In context with previous findings of low-level transcripts of the proteins in a study by Lopez-Berragan et al. and Otto et al., we also added the sentence "The apparent absence could indicate that transcripts are not translated in ABS or that the proteins' expression was below detection limits of western blot analysis." to the discussion. _At the same time, we would like to clarify that transcript levels for both genes fall within the

      To address this point, the authors should determine of mature mic60 and mic19 mRNAs are detected in ABS in comparison to the dKO, which will lack either transcript. RT-qPCR using polyT primers can be employed to detect these transcripts. If the level of these mRNAs are equivalent to dKO in WT ABS, the authors can make a pretty strong case for the absence of cristae in ABS.

      We appreciate the reviewer's suggestion. As noted in the Discussion, existing transcriptomic datasets already show detectable MIC19 and MIC60 mRNAs in ABS. For this reason, we expect RT-qPCR to reveal low (but not absent) levels of both transcripts, unlike the true loss expected to be observed in the dKO. Because such residual signals have been reported previously and their biological relevance remains uncertain, we do not believe transcript levels alone can serve as a definitive indicator of cristae absence in ABS.

      They should highlight the twin CX9C motifs that are a hallmark of Mic19 and other proteins that undergo oxidative folding via the MIA pathway. Interestingly, the Mia40 oxidoreductase that is central to MIA in yeast and animals, is absent in apicomplexans (DOI: 10.1080/19420889.2015.1094593).

      Searching for the CX9C motifs is a valuable suggestion. In response to the reviewer´s suggestion we analysed the conservation of the motif in PfMIC19 and included this in a new figure panel (Figure 1 F).

      Did the authors try to align Plasmodium Mic19 orthologs with conventional Mic19s? This may reveal some conserved residues within and outside of the CHCH domain.

      In response to this comment we made Figure 1 F, where we show conserved residues within the CHCH domains of a broad range of MIC19 annotated sequences across the opisthokonts, and show that the Cx9C motifs are conserved also in PfMIC19. Outside the CHCH domain, we did not find any meaningful conservation, as PfMIC19 heavily diverges from opisthokont MIC19.

      5) Statistcal significance. Sometimes my eyes see population differences that are considered insignificant by the statistical methods employed by the authors, eg Fig. 4E, mutants compared to WT, especially the dKO. Have the authors considered using other methods such as student t-test for pairwise comparisons?

      The graphs in figures 3, 4 and 5 got a makeover, such that they now are in linear scale and violin plots (also following a suggestion from further down in the reviewer's comments). We believe that this improves interpretability. ANOVA was kept as statistical testing to assure the correction for multiple comparisons that cannot be performed with standard t-test. A full overview of statistics and exact p-values can also be found in the newly added supplementary information 2.

      Minor comments: Line 33. Anaerobes (eg Giardia) have mitochondria that do produce ATP, unlike aerobic mitochondria

      We acknowledge that producing ATP via OXPHOS is not a characteristic of all mitochondria-like organelles (e.g. mitosomes), which is why these are typically classified separately from canonical mitochondria. When not considering mitochondria-like organelles, energy conversion is the function that the mitochondrion is most well-known for and the one associated with cristae.

      Line 56: Unclear what authors mean by "canonical model of mitochondria"

      To clarify we changed this to "yeast or human" model of mitochondria.

      Lines 75-76: This applies to Mic10 only

      We removed the "high degree of conservation in other cristate eukaryotes" statement.

      Line 80: Cite DOI: 10.1016/j.cub.2020.02.053

      Done

      Fig 2D: I find this table difficult to read. If authors keep table format, at least get rid of 'mean' column' as this data is better depicted in 2C. I suggest depicted this data either like in 3B depicting portion of infected vs unaffected flies in all experiments, then move modified Table to supplement. Important to point out experiment 5 appears to be an outlier with reduced infectivity across all cell lines, including WT.

      To clarify: the mean reported in the table indicates the mean per replicate while the mean reported in figure 2C is the overall mean for a given genotype that corrects for variability within experiments. We agree that moving the table to the supplementary data is a good idea. We decided to not include a graph for infected and non-infected mosquitoes as this information would be partially misleading, highlighting a phenotype we argue to be influenced by the strong variability.

      Fig. 3C-G: I feel like these data repeatedly lead to same conclusions. These are all different ways of showing what is depicted in Fig 2B: mitochondria gross morphology is affected upon ablation of MICOS. I suggest that these graphs be moved to supplement and replaced by the beautiful images.

      Thank you for the nice comment on our images. We have now moved part of the graphs to supplementary figure 6 and only kept the Relative Frequency, Sphericity and total mitochondria volume per cell in the main figure.

      Line 180: Be more specific with which tubulin isoform is used as a male marker and state why this marker was used in supplemental Fig S6.

      We have now specified the exact tubulin isoform used as the male gametocyte marker, both in the main text and in Supplementary Fig. S6. This is a commercial antibody previously known to work as an effective male marker, which is why we selected it for this experiment. This is now clearly stated in the manuscript.

      Line 196 and Fig 3C: the word 'intensities' in this context is very ambiguous. Please choose a different term (puncta, elements, parts?). This is related to major point 2i above.

      To clarify the biological effect that we can conclude form the measurement, we added an explanation about it in the respective section of the results, and we decided to replace the raw results of the plug-in readout with the deduced relative dispersion.

      Line 222: Report male/female crista measurements

      We added Supplementary information 2, which contains exact statistical test and outcomes on all presented quantifications as well as a per-sex statistical analysis of the data from figure 4. Correspondingly, we extended supplementary information 2 by a per-sex colour code for the thin section TEM data.

      Fig. 4B-E: depict data as violin plots or scatter plots like Fig. 2C to get a better grasp of how the crista coverage is distributed. It seems like the data spread is wider in the double KO. This would also solve the problem with the standard deviation extending beyond 0%.

      We changed this accordingly.

      Lines 331-333: Please clarify that this applies for some, but not all MICOS subunits. Please also see major point 1 above. Also, the authors should point out that despite their structural divergence, trypanosomal cryptic mitofilins Mic34 and Mic40 are essential for parasite growth, in contrast to their findings with PfMic60 (DOI: https://doi.org/10.1101/2025.01.31.635831).

      This has been changed accordingly.

      Line 320: incorrect citation. Related to point 1above.

      Correct citation is now included in the text.

      Lines 333-335. This is related to the above. Again, some subunits appear to affect cell growth under lab conditions, and some do not. This and the previous sentence should be rewritten to reflect this.

      This has been changed accordingly.

      Line 343-345: The sentence and citation 45 are strange. Regarding the former, it is about CHCHD10, whose status as a bona fide MICOS subunit is very tenuous, so I would omit this. About the phenomenon observed, I think it makes more sense to write that Mic60 ablation results in partially fragmented mitochondria in yeast (Rabl et al., 2009 J Cell Biol. 185: 1047-63). A fragmented mitochondria is often a physiological response to stress. I would just rewrite as not to imply that mitochondrial fission (or fusion) is impaired in these KOs, or at least this could be one of several possibilities.

      The sentence has been substituted following the indication of the reviewer. Though we still include the data of the human cells as this has also been shown in Stephens et al. 2020.

      Line 373: 'This indicates' is too strong. I would say 'may suggest' as you have no proof that any of the KOs disrupts MICOS. This hypothesis can be tested by other means, but not by penetrance of a phenotype.

      Done

      Line 376-377; 'deplete functionality' does not make sense, especially in the context of talking about MICOS subunit stability. In my opinion, this paragraph overinterprets the KO effects on MICOS stability. None of the experiments address this phenomenon, and thus the authors should not try to interpret their results in this context. See major point 1. Other suggestions for added value

      We removed the sentence. Also, the entire paragraph has been shortened, restructured and wording was changed to address major point 1.

      1) Does Plasmodium Sam50 co-fractionate with Mic60 and Mic19 in BN PAGE (Fig. 1E)

      While we did identify SAMM50 in our BN PAGE, the protein does not co-migrate with the MICOS components but instead comigrates with other components of a putative sorting and assembly machinery (SAM) complex. As SAMM50, the SAM complex and the overarching putative mitochondrial membrane space bridging (MIB) complex are not mentioned in the manuscript, we decided to not include the information in the figure.

      Reviewer #2 (Significance (Required)):

      The manuscript by Tassan-Lugrezin is predicated on the idea that Plasmodium represents the only system in which de novo crista formation can be studied. They leverage this system to ask the question whether MICOS is essential for this process. They conclude based on their data that the answer is no, which the authors consider unprecedented. But even if their claim is true that ABS is acristate, this supposed advantage does not really bring any meaningful insight into how MICOS works in Plasmodium.

      First the positives of this manuscript. As has been the case with this research team, the manuscript is very sophisticated in the experimental approaches that are made. The highlights are the beautiful and often conclusive microscopy performed by the authors. Only the localization of Mic60 and Mic19 was inconclusive due to their very low expression unfortunately.

      The examination of the MICOS mutants during in vitro life cycle of Plasmodium falciparum is extremely impressive and yields convincing results. Mitochondrial deformation is tolerated by life cycle stage differentiation, with a modest but significant reduction of oocyte production, being observed.

      However, despite the herculean efforts of the authors, the manuscript as it currently stands represents only a minor advance in our understanding of the evolution of MICOS, which from the title and focus of the manuscript, is the main goal of the authors. In its current form, the manuscript reports some potentially important findings:

      1) Mic60 is verified to play a role in crista formation, as is predicted by its orthology to other characterized Mic60 orthologs.

      2) The discovery of a novel Mic19 analog (since the authors maintain there is no significant sequence homology), which exhibits a similar (or the same?) complexome profile with Mic60. This protein was upregulated in gametocytes like Mic60 and phenocopies Mic60 KO.

      3) Both of these MICOS subunits are essential (not dispensable) for proper crista formation

      4) Surprisingly, neither MICOS subunit is essential for in vitro growth or differentiation from ABS to sexual stages, and from the latter to sporozoites. This says more about the biology of plasmodium itself than anything about the essentiality of Mic60, ie plasmodium life cycle progression tolerates defects to mitochondrial morphology. But yes, I agree with the authors that Mic60's apparent insignificance for cell growth in examined conditions does differ with its essentiality in other eukaryotes. But fitness costs were not assayed (eg by competition between mutants and WT in infection of mosquitoes)

      5) Decreased fitness of the mutants is implied by a reduction of oocyte formation.

      While interesting in their own way, collectively they do not represent a major advance in our understanding of MICOS evolution. Furthermore, the findings bifurcate into categories informing MICOS or Plasmodium biology. Both aspects are somewhat underdeveloped in their current form.

      This is unfortunate because there seem to be many missed opportunities in the manuscript that could, with additional experiments, lead to a manuscript with much wider impact. For me, what is remarkable about Plasmodium MICOS that sets it apart from other iterations is the apparent absence of the Mic10 subunit. Purification of plasmodium MICOS via the epitope tagged Mic60 and Mic19 could have verified that MICOS is assembled without this core subunit. Perhaps Mic60 and Mic19 are the vestiges of the complex, and thus operate alone in shaping cristae. Such a reduction may also suggest the declining importance of mitochondria in plasmodium.

      Another missed opportunity was to assay the impact of MICOS-depletion of OXPHOS in plasmodium. This is a salient issue as maybe crista morphology is decoupled from OXPHOS capacity in Plasmodium, which links to the apparent tolerance of mitochondrial morphology in cell growth and differentiation. I suggested in section A experiments to address this deficit.

      Finally, the authors could assay fitness costs of MICOS-ablation and associated phenotypes by assaying whether mosquito infectivity is reduced in the mutants when they are directly competing with WT plasmodium. Like the authors, I am also surprised that MICOS mutants can pass population bottlenecks represented by differentiation events. Perhaps the apparent robustness of differentiation may contribute plasmodium's remarkable ability to adapt.

      I realize that the authors put a lot of efforts into their study and again, I am very impressed by the sophistication of the methods employed. Nevertheless, I think there is still better ways to increase the impact of the study aside from overinterpreting the conclusions from the data. But this would require more experiments along the lines I suggest in Section A and here.

      We thank the reviewer for their extensive analysis of the significance of our findings, including the compliments on our microscopy images and the sophisticated experimental approaches. We hope we have convincingly argued why we could or could not include some of the additional analyses suggested by the reviewer in section 1 above.

      With regard to the significance statement, we want to point out that our finding that PfMICOS is not needed for initial formation of cristae (as opposed to organization thereof), is a confirmation of something that has been assumed by the field, without being the actual focus of studies. We argue that the distinction between formation and organization of cristae is important and deserves some attention within the manuscript. The result of MICOS not being involved in the initial formation of cristae, we argue to be relevant in Plasmodium biology and beyond. As for the insights into how MICOS works in Plasmodium we have confirmed that the previously annotated PfMIC60 is indeed involved in the organization of cristae. Furthermore, we have identified and characterized PfMIC19. These findings, we argue, are indeed meaningful insights into PfMICOS.

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

      Summary:

      MICOS is a conserved mitochondrial protein complex responsible for organising the mitochondrial inner membrane and the maintenance of cristae junctions. This study sheds first light on the role of two MICOS subunits (Mic60 and the newly annotated Mic19) in the malaria parasite Plasmodium falciparum, which forms cristae de novo during sexual development, as demonstrated by EM of thin section and electron tomography. By generating knockout lines (including a double knockout), the authors demonstrate that knockout of both MICOS subunits leads to defects in cristae morphology and a partial loss of cristae junctions. With a formidable set of parasitological assays, the authors show that despite the metabolically important role of mitochondria for gametocytes, the knockout lines can progress through the life stages and form sporozoites, albeit with diminished infection efficiency.

      We thank the reviewer for their time and compliment.

      Major comments:

      1) The authors should improve to present their findings in the right context, in particular by:

      (i) giving a clearer description in the introduction of what is already known about the role of MICOS. This starts in the introduction, where one main finding is missing: loss of MICOS leads to loss of cristae junctions and the detachment of cristae membranes, which are nevertheless formed, but become membrane vesicles. This needs to be clearly stated in the introduction to allow the reader to understand the consistency of the authors' findings in P. falciparum with previous reports in the literature.

      We extended the introduction to include this information.

      (ii) at the end to the introduction, the motivating hypothesis is formulated ad hoc "conclusive evidence about its involvement in the initial formation of cristae is still lacking" (line 83). If there is evidence in the literature that MICOS is strictly required for cristae formation in any organism, then this should be explained, because the bona fide role of MICOS is maintenance of cristae junctions (the hypothesis is still plausible and its testing important).

      To clarify we rephrased the sentence to: "Although MICOS has been described as an organizer of crista junctions, its role during the initial formation of nascent cristae has not been investigated."

      2) Line 96-97: "Interestingly, PfMIC60 is much larger than the human MICOS counterpart, with a large, poorly predicted N-terminal extension." This statement is lacking a reference and presumably refers to annotated ORFs. The authors should clarify if the true N-terminus is definitely known - a 120kDa size is shown for the P. falciparum but this is not compared to the expected length or the size in S. cerevisiae.

      To solve the reference issue, we added the uniprot IDs we compared to see that the annotated ORF is bigger in Plasmodium. We also changed the comparison to yeast instead of human, because we realized it is confusing to compare to yeast all throughout the figure, but then talk about human in this specific sentence.

      Regarding whether the true N-terminus is known. Short answer: No, not exactly.

      However, we do know that the Pf version is about double the size of the yeast protein.

      As the reviewer correctly states, we show the size of 120kDa for the tagged protein in Figure 1G. Considering that we tagged the protein C-terminally, and observed a 120kDa product on western blot, it is safe to conclude that the true N-terminus does not deviate massively from the annotated ORF, and hence, that there is a considerable extension of the protein beyond a 60kDa protein. We do not directly compare to yeast MIC60 on our western blots, however, that comparison can be drawn from literature: Tarasenko et al., 2017 showed that purified MIC60 running at ~60kDa on SDS-PAGE actively bends membranes, suggesting that in its active form, the monomer of yeast MIC60 is indeed 60kDa in size.

      To clarify, we now emphasize that we ran the Alphafold prediction on the annotated open reading frame (annotated and sequenced by Bohme et al. and Chapell et al. now cited in the manuscript), and revised the wording to make clear what we are comparing in which sentence.

      3) lines 244-245: "Furthermore, our data indicates the effect size increases with simultaneous ablation of both proteins?". The authors should explain which data they are referring to, as some of the data in Fig 3 and 4 look similar and all significance tests relate to the wild type, not between the different mutants, so it is not clear if any overserved differences are significant. The authors repeat this claim in the discussion in lines 368-369 without referring to a specific significance test. This needs to be clarified.

      As a reply to this and other comments from the reviewers we added the multiple testing within all samples. In addition, to clarify statistics used we included a supplementary dataset with all p-values and statistical tests used.

      4) lines 304-306: "Though well established as the cristae organizing system, the role of MICOS in initial formation of cristae remains hidden in model organisms that constitutively display cristae.". This sentence is misleading since even in organisms that display numerous cristae throughout their life cycle, new cristae are being formed as the cells proliferate. Thus, failure to produce cristae in MICOS knockout lines would have been observable but has apparently not been reported in the literature. Thus, the concerted process in P. falciparum makes it a great model organism, but not fundamentally different to what has been studied before in other organisms.

      We deleted this statement.

      5) lines 373-378. "where ablation of just MIC60 is sufficient to deplete functionality of the entire MICOS (11, 15),". The authors' claim appears to be contrary to what is actually stated in ref 15, which they cite:

      "MICOS subunits have non-redundant functions as the absence of both MICOS subcomplexes results in more severe morphological and respiratory growth defects than deletion of single MICOS subunits or subcomplexes."

      This seems in line with what the authors show, rather than "different".

      This sentence has been removed.

      6) lines 380-385: "... thus suggesting that membrane invaginations still arise, but are not properly arranged in these knockout lines. This suggests that MICOS either isn't fully depleted,...". These conclusions are incompatible with findings from ref. 15, which the authors cite. In that study, the authors generated a ∆MICOS line which still forms membrane invaginations, showing that MICOS is not required at all for this process in yeast. Hence the authors' implication that MICOS needs to be fully depleted before membrane invaginations cease to occur is not supported by the literature.

      This sentence has been deleted in the revised version of the manuscript.

      Minor comments:

      7) The authors should consider if the first part of their title could be seen as misleading: It suggests that MICOS is "the architect" in cristae formation, but this is not consistent with the literature nor their own findings.

      Title is changed accordingly

      Minor comments:

      • Line 43, of the three seminal papers describing the discovery of MICOS in 2011, the authors only cite two (refs 6 and 7), but miss the third paper, Hoppins et al, PMID: 21987634, which should probably be corrected.

      Done, the paper is now cited

      • Page 2, line 58: for a more complete picture the authors should also cite the work of others here which shows that although at very low levels, e.g. complex III (a drug target) and ATP synthase do assemble (Nina et al, 2011, JBC).

      Done

      • Page 3, line 80: "Irrespective of the shape of an organism's cristae, the crista junctions have been described as tubular channels that connect the cristae membrane to the inner boundary membrane (22, 24)." This omits the slit-shaped cristae junctions found in yeast (Davies et al, 2011, PNAS), which the authors should include.

      The paper and concept have been added to the manuscript, though the sentence has been moved up in the introduction, when crista junctions are first introduced.

      • Line 97: "poorly predicted N-terminal extension", as there is no experimental structure, we don't know if the prediction is poor. Presumably the authors mean either poorly ordered or the absence of secondary structure elements, or the poor confidence score for that region in the prediction? This should be clarified or corrected.

      We were referring to the poor confidence score. To address this comment as well as major point 2, we rewrote the respective paragraph. It now clearly states that confidence of the prediction is low, and we mention the tool that was used to identify conserved domains (Topology-based Evolutionary Domains).

      • Line 98: "an antiparallel array of ten β-sheets". They are actually two parallel beta-sheets stacked together. The authors could find out the name of this fold, but the confidence of the prediction is marked a low/very low. So, its existence is unknown, not just its "function".

      We adapted the domain description to "a stack of two parallel beta-sheets" and replaced the statement on unknown function by the statement "Because this domain is predicted solely from computational analysis, both its actual existence in the native protein and its biological function remain unknown."

      Fig 1B: The authors show two alphafold predictions of S. cerevisiae and P. falciparum Mic60 structures. There is however an experimental Mic60/19 (fragment) structure from the former organism (PMID: 36044574), which should be included if possible

      We appreciate the reviewer's suggestion and note that the available structural data indeed provides valuable insight into how MIC60 and MIC19 interact. However, these structures represent fusion constructs of limited protein fragments and therefore capture only a small portion of each protein, specifically the interaction interface. Because our aim in Fig. 1B is to compare the overall domain architecture of the full‑length proteins, we believe that including fragment‑based structures would be less informative in this context.

      Line: 318-321: "The same trend was observed for PfMIC19 and PfMIC60. Although transcriptomic data suggested that low-level transcripts of PfMIC19 and PfMIC60 are present in ABS (38), we did not detect either of the proteins in ABS by western blot analysis. While this statement is true, the authors should comment on the sensitivity of the respective methods - how well was the antibody working in their hands and how do they interpret the absence of a WB band compared to transcriptomics data?

      The HA antibody used in our experiments is a standard commercial reagent that performs reliably in both WB and IFA, although it shows a low background signal in gametocytes. We agree that the sensitivity of the method and the interpretation of weak or absent bands should be addressed explicitly. Transcript levels for both PfMIC19 and PfMIC60 in asexual blood stages fall within the

      • Lines 322-323: would the authors not typically have expected an IFA signal given the strength of the band in Western blot? If possible, the authors should comment if the negative fluorescence outcome can indeed be explained with the low abundance or if technical challenges are an equally good explanation.

      Considering the nature of the investigated proteins (embedded in the IMM and spread throughout the mitochondria) difficulties in achieving a clear signal in IFA or U-ExM are not very surprizing. While epitopes may remain buried in IFA, U-ExM usually increases accessibility for the antibodies. However, U-ExM comes at the cost of being prone to dotty background signals, therefore potentially hiding low abundance, naturally dotty signals such as the signal of MICOS proteins that localize to distinct foci (at the CJ) along the mitochondrion. Current literature suggests that, in both human and yeast, STED is the preferred method for accurate spatial resolution of MICOS proteins (https://www.ncbi.nlm.nih.gov/pubmed/32567732,https://www.ncbi.nlm.nih.gov/pubmed/32067344). Unfortunately, we do not have experience with, nor access to, this particular technique/method.

      Lines 357-365: the authors describe limitations of the applied methods adequately. Perhaps it would be helpful to make a similar statement about the analysis of 3D objects like mitochondria and cristae from 2D sections. E.g. the apparent cristae length depends on whether cristae are straight (e.g. coiled structures do not display long cross sections despite their true length in 3D).

      The limitations of other methods are described in the respective results section.

      We added a clarifying sentence in the results section of Figure 4:

      "Note that such measurements do not indicate the true total length or width of cristae, as the data is two-dimensional. The recorded values are to be considered indicative of possible trends, rather than absolute dimensions of cristae."

      This statement refers to the length/width measurements of cristae.

      In the context of Figure 4 D we mention the following (see preprint lines 229 - 230): "We expect this effect to translate into the third dimension and thus conclude that the mean crista volume increases with the loss of either PfMIC19,PfMIC60, or both."

      For Figure 5, we included a clarifying statement in the results section of the preprint (lines 269 - 273): "Note that these mitochondrial volumes are not full mitochondria, but large segments thereof. As a result of the incompleteness of the mitochondria within the section, and the tomography specific artefact of the missing wedge, we were unable to confirm whether cristae were in fact fully detached from the boundary membrane, or just too long to fit within the observable z-range. "

      Line 404: perhaps undetected or similar would be a better description than "hidden"?

      The sentence does not exist in the revised manuscript

      Reviewer #3 (Significance (Required)):

      The main strength of the study is that it provides the first characterisation of the MICOS complex in P. falciparum, a human parasite in which the mitochondrion has been shown to be a drug target. Mic60 and the newly annotated Mic19 are confirmed to be essential for proper cristae formation and morphology, as well as overall mitochondrial morphology. Furthermore, the mutant lines are characterised for their ability to complete the parasite life cycle and defects in infection effectivity are observed. This work is an important first step for deciphering the role of MICOS in the malaria parasite and the composition and function of this complex in this organism. The limitation of the study stems from what is already known about MICOS and its subunits in

      great detail in yeast and humans with similar findings regarding loss of cristae and cristae defects. The findings of this study do not provide dramatic new insight on MICOS function or go substantially beyond the vast existing literature in terms of the extent of the study, which focuses on parasitological assays and morphological analysis. Exploring the role of MICOS in an early-divergent organism and human parasite is however important given the divergence found in mitochondrial biology and P. falciparum is a uniquely suited model system. One aspect that would increase the impact of the paper would be if the authors could mechanistically link the observed morphological defects to the decreased infection efficiency, e.g. by probing effects on mitochondrial function. This will likely be challenging as the morphological defects are diverse and the fitness defects appear moderate/mild.

      As suggested by Reviewer 2, we examined mitochondrial membrane potential in gametocytes using MitoTracker staining and did not observe any obvious differences associated with the morphological defects. At present, additional assays to probe mitochondrial function in P. falciparum gametocytes are not sufficiently established, and developing and validating such methods would require substantial work before they could be applied to our mutant lines. For these reasons, a more detailed mechanistic link between the observed morphological changes and the reduced infection efficiency is currently beyond reach.

      The advance presented in this study is to pioneer the study of MICOS in P. falciparum, thus widening our understanding of the role of this complex to different model organism. This study will likely be mainly of interest for specialised audiences such as basic research parasitologists and mitochondrial biologists. My own field of expertise is mitochondrial biology and structural 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

      Summary:

      MICOS is a conserved mitochondrial protein complex responsible for organising the mitochondrial inner membrane and the maintenance of cristae junctions. This study sheds first light on the role of two MICOS subunits (Mic60 and the newly annotated Mic19) in the malaria parasite Plasmodium falciparum, which forms cristae de novo during sexual development, as demonstrated by EM of thin section and electron tomography. By generating knockout lines (including a double knockout), the authors demonstrate that knockout of both MICOS subunits leads to defects in cristae morphology and a partial loss of cristae junctions. With a formidable set of parasitological assays, the authors show that despite the metabolically important role of mitochondria for gametocytes, the knockout lines can progress through the life stages and form sporozoites, albeit with diminished infection efficiency.

      Major comments:

      1) The authors should improve to present their findings in the right context, in particular by:

      (i) giving a clearer description in the introduction of what is already known about the role of MICOS. This starts in the introduction, where one main finding is missing: loss of MICOS leads to loss of cristae junctions and the detachment of cristae membranes, which are nevertheless formed, but become membrane vesicles. This needs to be clearly stated in the introduction to allow the reader to understand the consistency of the authors' findings in P. falciparum with previous reports in the literature.

      (ii) at the end to the introduction, the motivating hypothesis is formulated ad hoc "conclusive evidence about its involvement in the initial formation of cristae is still lacking" (line 83). If there is evidence in the literature that MICOS is strictly required for cristae formation in any organism, then this should be explained, because the bona fide role of MICOS is maintenance of cristae junctions (the hypothesis is still plausible and its testing important).

      2) Line 96-97: "Interestingly, PfMIC60 is much larger than the human MICOS counterpart, with a large, poorly predicted N-terminal extension." This statement is lacking a reference and presumably refers to annotated ORFs. The authors should clarify if the true N-terminus is definitely known - a 120kDa size is shown for the P. falciparum but this is not compared to the expected length or the size in S. cerevisiae.

      3) lines 244-245: "Furthermore, our data indicates the effect size increases with simultaneous ablation of both proteins?". The authors should explain which data they are referring to, as some of the data in Fig 3 and 4 look similar and all significance tests relate to the wild type, not between the different mutants, so it is not clear if any overserved differences are significant. The authors repeat this claim in the discussion in lines 368-369 without referring to a specific significance test. This needs to be clarified.

      4) lines 304-306: "Though well established as the cristae organizing system, the role of MICOS in initial formation of cristae remains hidden in model organisms that constitutively display cristae.". This sentence is misleading since even in organisms that display numerous cristae throughout their life cycle, new cristae are being formed as the cells proliferate. Thus, failure to produce cristae in MICOS knockout lines would have been observable but has apparently not been reported in the literature. Thus, the concerted process in P. falciparum makes it a great model organism, but not fundamentally different to what has been studied before in other organisms.

      5) lines 373-378. "where ablation of just MIC60 is sufficient to deplete functionality of the entire MICOS (11, 15),". The authors' claim appears to be contrary to what is actually stated in ref 15, which they cite:

      "MICOS subunits have non-redundant functions as the absence of both MICOS subcomplexes results in more severe morphological and respiratory growth defects than deletion of single MICOS subunits or subcomplexes."

      This seems in line with what the authors show, rather than "different".

      6) lines 380-385: "... thus suggesting that membrane invaginations still arise, but are not properly arranged in these knockout lines. This suggests that MICOS either isn't fully depleted,...". These conclusions are incompatible with findings from ref. 15, which the authors cite. In that study, the authors generated a ∆MICOS line which still forms membrane invaginations, showing that MICOS is not required at all for this process in yeast. Hence the authors' implication that MICOS needs to be fully depleted before membrane invaginations cease to occur is not supported by the literature.

      7) The authors should consider if the first part of their title could be seen as misleading: It suggests that MICOS is "the architect" in cristae formation, but this is not consistent with the literature nor their own findings.

      Minor comments:

      • Line 43, of the three seminal papers describing the discovery of MICOS in 2011, the authors only cite two (refs 6 and 7), but miss the third paper, Hoppins et al, PMID: 21987634, which should probably be corrected.
      • Page 2, line 58: for a more complete picture the authors should also cite the work of others here which shows that although at very low levels, e.g. complex III (a drug target) and ATP synthase do assemble (Nina et al, 2011, JBC).
      • Page 3, line 80: "Irrespective of the shape of an organism's cristae, the crista junctions have been described as tubular channels that connect the cristae membrane to the inner boundary membrane (22, 24)." This omits the slit-shaped cristae junctions found in yeast (Davies et al, 2011, PNAS), which the authors should include.
      • Line 97: "poorly predicted N-terminal extension", as there is no experimental structure, we don't know if the prediction is poor. Presumably the authors mean either poorly ordered or the absence of secondary structure elements, or the poor confidence score for that region in the prediction? This should be clarified or corrected.
      • Line 98: "an antiparallel array of ten β-sheets". They are actually two parallel beta-sheets stacked together. The authors could find out the name of this fold, but the confidence of the prediction is marked a low/very low. So, its existence is unknown, not just its "function".
      • Fig 1B: The authors show two alphafold predictions of S. cerevisiae and P. falciparum Mic60 structures. There is however an experimental Mic60/19 (fragment) structure from the former organism (PMID: 36044574), which should be included if possible
      • Line: 318-321: "The same trend was observed for PfMIC19 and PfMIC60. Although transcriptomic data suggested that low-level transcripts of PfMIC19 and PfMIC60 are present in ABS (38), we did not detect either of the proteins in ABS by western blot analysis. While this statement is true, the authors should comment on the sensitivity of the respective methods - how well was the antibody working in their hands and how do they interpret the absence of a WB band compared to transcriptomics data?
      • Lines 322-323: would the authors not typically have expected an IFA signal given the strength of the band in Western blot? If possible, the authors should comment if the negative fluorescence outcome can indeed be explained with the low abundance or if technical challenges are an equally good explanation.
      • Lines 357-365: the authors describe limitations of the applied methods adequately. Perhaps it would be helpful to make a similar statement about the analysis of 3D objects like mitochondria and cristae from 2D sections. E.g. the apparent cristae length depends on whether cristae are straight (e.g. coiled structures do not display long cross sections despite their true length in 3D).
      • Line 404: perhaps undetected or similar would be a better description than "hidden"?

      Significance

      The main strength of the study is that it provides the first characterisation of the MICOS complex in P. falciparum, a human parasite in which the mitochondrion has been shown to be a drug target. Mic60 and the newly annotated Mic19 are confirmed to be essential for proper cristae formation and morphology, as well as overall mitochondrial morphology. Furthermore, the mutant lines are characterised for their ability to complete the parasite life cycle and defects in infection effectivity are observed. This work is an important first step for deciphering the role of MICOS in the malaria parasite and the composition and function of this complex in this organism.

      The limitation of the study stems from what is already known about MICOS and its subunits in other organism. MICOS subunit knockouts have been characterised in great detail in yeast and humans with similar findings regarding loss of cristae and cristae defects. The findings of this study do not provide dramatic new insight on MICOS function or go substantially beyond the vast existing literature in terms of the extent of the study, which focuses on parasitological assays and morphological analysis.

      Exploring the role of MICOS in an early-divergent organism and human parasite is however important given the divergence found in mitochondrial biology and P. falciparum is a uniquely suited model system. One aspect that would increase the impact of the paper would be if the authors could mechanistically link the observed morphological defects to the decreased infection efficiency, e.g. by probing effects on mitochondrial function. This will likely be challenging as the morphological defects are diverse and the fitness defects appear moderate/mild.

      The advance presented in this study is to pioneer the study of MICOS in P. falciparum, thus widening our understanding of the role of this complex to different model organism. This study will likely be mainly of interest for specialised audiences such as basic research parasitologists and mitochondrial biologists. My own field of expertise is mitochondrial biology and structural biology.

    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

      Major comments:

      1) In my opinion, the authors tend to sensationalize or overinterpret their results. The title of the manuscript is very misleading. While MICOS is certainly important for crista formation, it is not the only factor, as ATP synthase dimer rows make a highly significant contribution to crista morphology. Thus, one can argue with equal validity that ATP synthase should be considered the 'architect', as it's the conformation of the dimers and rows modulate positive curvature. Secondly, while cristae are still formed upon mic60/mic19 gene knockout (KO), they are severely deformed, and likely dysfunctional (see below). Thus, I do not agree with the title that MICOS is dispensable for crista formation, because the authors results show that it clearly is essential. So, the title should be changed.

      The Discussion section starting from line 373 also suffers from overinterpretation as well as being repetitive and hard to understand. The authors infer that MICOS stability is compromised less in the single KOs (sKO) in compared to the mic60/mic19 double KO (dKO). MICOS stability was never directly addressed here and the composition of the MICOS complex is unaddressed, so it does not make sense to speculate by such tenuous connections. The data suggest to me that mic60 and mic19 are equally important for crista formation and crista junction (CJ) stabilization, and the dKO has a more severe phenotype than either KO, further demonstrating neither is epistatic.

      The following paragraphs (line 387 to 422) continues with such unnecessary overinterpretation to the point that it is confusing and contradictory. Line 387 mentions an 'almost complete loss of CJs' and then line 411 mentions an increase in CJ diameter, both upon Mic60 ablation. I do not think this discussion brings any added value to the manuscript and should be shortened. Yes, maybe there are other putative MICOS subunits that may linger in the KOS that are further destabilized in the dKO, or maybe Mic60 remains in the mic19 KO (and vice versa) to somehow salvage more CJs, which is not possible in the dKO. It is impossible to say with confidence how ATP synthase behaves in the KOs with the current data.

      2) While the authors went through impressive lengths to detect any effect on lifecycle progression, none was found except for a reduction in oocyte count. However, the authors did not address any direct effect on mitochondria, such as OXPHOS complex assembly, respiration, membrane potential. This seems like a missed opportunity, given the team's previous and very nice work mapping these complexes by complexome profiling. However, I think there are some experiments the authors can still do to address any mitochondrial defects using what they have and not resorting to complexome profiling (although this would be definitive if it is feasible):

      i) Quantification of MitoTracker Red staining in WT and KOs. The authors used this dye to visualize mitochondria to assay their gross morphology, but unfortunately not to assay membrane potential in the mutants. The authors can compare relative intensities of the different mitochondria types they categorized in Fig. 3A in 20-30 cells to determine if membrane potential is affected when the cristae are deformed in the mutants. One would predict they are affected.

      ii) Sporozoites are shown in Fig S5. The authors can use the same set up to track their motion, with the hypothesis that they will be slower in the mutants compared to WT due to less ATP. This assumes that sporozoite mitochondria are active as in gametocytes.

      iii) Shotgun proteomics to compare protein levels in mutants compared to WT, with the hypothesis that OXPHOS complex subunits will be destabilized in the mutants with deformed cristae. This could be indirect evidence that OXPHOS assembly is affected, resulting in destabilized subunits that fail to incorporate into their respective complexes.

      To expedite resubmission, the authors can restrict the cell lines to WT and the dKO, as the latter has a stronger phenotype that the individual KOs and conclusions from this cell line are valid for overall conclusions about Plasmodium MICOS.

      I will also conclude that complexome/shotgun proteomics may be a useful tool also for identifying other putative MICOS subunits by determining if proteins sharing the same complexome profile as PfMic60 and Mic19 are affected. This would address the overinterpretation problem of point 1.

      3) I am aware of the authors previous work in which they were not able to detect cristae in ABS, and thus have concluded that these are truly acristate. This can very well be true, or there can be immature cristae forms that evaded detection at the resolution they used in their volumetric EM acquisitions. The mitochondria and gametocyte cristae are pretty small anyway, so it not unreasonable to assume that putative rudimentary cristae in ABS may be even smaller still. Minute levels of sampled complex III and IV plus complex V dimers in ABS that were detected previously by the authors by complexome profiling would argue for the presence of miniscule and/or very few cristae.

      I think that authors should hedge their claim that ABS is acrisate by briefly stating that there still is a possibility that miniscule cristae may have been overlooked previously.

      This brings me to the claim that Mic19 and Mic60 proteins are not expressed in ABS. This is based on the lack of signal from the epitope tag; a weak signal is detected in gametocytes. Thus, one can counter that Mic19 and Mic60 are also expressed, but below the expression limits of the assay, as the protein exhibits low expression levels when mitochondrial activity is upregulated.

      To address this point, the authors should determine of mature mic60 and mic19 mRNAs are detected in ABS in comparison to the dKO, which will lack either transcript. RT-qPCR using polyT primers can be employed to detect these transcripts. If the level of these mRNAs are equivalent to dKO in WT ABS, the authors can make a pretty strong case for the absence of cristae in ABS.

      4) The major finding of the manuscript is of a Mic19 analog in plasmodium should be highlighted. As far as I know, this manuscript could represent the first instance of Mic19 outside of opisthokonts that was not found by sensitive profile HMM searches and certainly the first time such a Mic19 was functionally analyzed.

      They should highlight the twin CX9C motifs that are a hallmark of Mic19 and other proteins that undergo oxidative folding via the MIA pathway. Interestingly, the Mia40 oxidoreductase that is central to MIA in yeast and animals, is absent in apicomplexans (DOI: 10.1080/19420889.2015.1094593).

      Did the authors try to align Plasmodium Mic19 orthologs with conventional Mic19s? This may reveal some conserved residues within and outside of the CHCH domain.

      5) Statistcal significance. Sometimes my eyes see population differences that are considered insignificant by the statistical methods employed by the authors, eg Fig. 4E, mutants compared to WT, especially the dKO. Have the authors considered using other methods such as student t-test for pairwise comparisons?

      Minor comments:

      Line 33. Anaerobes (eg Giardia) have mitochondria that do produce ATP, unlike aerobic mitochondria

      Line 56: Unclear what authors mean by "canonical model of mitochondria"

      Lines 75-76: This applies to Mic10 only

      Line 80: Cite DOI: 10.1016/j.cub.2020.02.053

      Fig 2D: I find this table difficult to read. If authors keep table format, at least get rid of 'mean' column' as this data is better depicted in 2C. I suggest depicted this data either like in 3B depicting portion of infected vs unaffected flies in all experiments, then move modified Table to supplement. Important to point out experiment 5 appears to be an outlier with reduced infectivity across all cell lines, including WT.

      Fig. 3C-G: I feel like these data repeatedly lead to same conclusions. These are all different ways of showing what is depicted in Fig 2B: mitochondria gross morphology is affected upon ablation of MICOS. I suggest that these graphs be moved to supplement and replaced by the beautiful images

      Line 180: Be more specific with which tubulin isoform is used as a male marker and state why this marker was used in supplemental Fig S6.

      Line 196 and Fig 3C: the word 'intensities' in this context is very ambiguous. Please choose a different term (puncta, elements, parts?). This is related to major point 2i above.

      Line 222: Report male/female crista measurements

      Fig. 4B-E: depict data as violin plots or scatter plots like Fig. 2C to get a better grasp of how the crista coverage is distributed. It seems like the data spread is wider in the double KO. This would also solve the problem with the standard deviation extending beyond 0%.

      Lines 331-333: Please clarify that this applies for some, but not all MICOS subunits. Please also see major point 1 above. Also, the authors should point out that despite their structural divergence, trypanosomal cryptic mitofilins Mic34 and Mic40 are essential for parasite growth, in contrast to their findings with PfMic60 (DOI: https://doi.org/10.1101/2025.01.31.635831).

      Line 320: incorrect citation. Related to point 1above.

      Lines 333-335. This is related to the above. Again, some subunits appear to affect cell growth under lab conditions, and some do not. This and the previous sentence should be rewritten to reflect this.

      Line 343-345: The sentence and citation 45 are strange. Regarding the former, it is about CHCHD10, whose status as a bona fide MICOS subunit is very tenuous, so I would omit this. About the phenomenon observed, I think it makes more sense to write that Mic60 ablation results in partially fragmented mitochondria in yeast (Rabl et al., 2009 J Cell Biol. 185: 1047-63). A fragmented mitochondria is often a physiological response to stress. I would just rewrite as not to imply that mitochondrial fission (or fusion) is impaired in these KOs, or at least this could be one of several possibilities.

      Line 373: 'This indicates' is too strong. I would say 'may suggest' as you have no proof that any of the KOs disrupts MICOS. This hypothesis can be tested by other means, but not by penetrance of a phenotype.

      Line 376-377; 'deplete functionality' does not make sense, especially in the context of talking about MICOS subunit stability. In my opinion, this paragraph overinterprets the KO effects on MICOS stability. None of the experiments address this phenomenon, and thus the authors should not try to interpret their results in this context. See major point 1.

      Other suggestions for added value

      1) Does Plasmodium Sam50 co-fractionate with Mic60 and Mic19 in BN PAGE (Fig. 1E)

      2) Can Alphafold3 predict a heterotetramer of PfMic60? What about the four Mic19 and Mic60 subunits together. Is this tetramer consistent with the Bock-Bierbaum model. Is this model consistent with the CJ diameter measured in plasmodium, which is perhaps better evidence than that in lines 419-422.

      Significance

      The manuscript by Tassan-Lugrezin is predicated on the idea that Plasmodium represents the only system in which de novo crista formation can be studied. They leverage this system to ask the question whether MICOS is essential for this process. They conclude based on their data that the answer is no, which the authors consider unprecedented. But even if their claim is true that ABS is acristate, this supposed advantage does not really bring any meaningful insight into how MICOS works in Plasmodium.

      First the positives of this manuscript. As has been the case with this research team, the manuscript is very sophisticated in the experimental approaches that are made. The highlights are the beautiful and often conclusive microscopy performed by the authors. Only the localization of Mic60 and Mic19 was inconclusive due to their very low expression unfortunately.

      The examination of the MICOS mutants during in vitro life cycle of Plasmodium falciparum is extremely impressive and yields convincing results. Mitochondrial deformation is tolerated by life cycle stage differentiation, with a modest but significant reduction of oocyte production, being observed.

      The manuscript by Tassan-Lugrezin is predicated on the idea that Plasmodium represents the only system in which de novo crista formation can be studied. They leverage this system to ask the question whether MICOS is essential for this process. They conclude based on their data that the answer is no, which the authors consider unprecedented. But even if their claim is true that ABS is acristate, this supposed advantage does not really bring any meaningful insight into how MICOS works in Plasmodium.

      First the positives of this manuscript. As has been the case with this research team, the manuscript is very sophisticated in the experimental approaches that are made. The highlights are the beautiful and often conclusive microscopy performed by the authors. Only the localization of Mic60 and Mic19 was inconclusive due to their very low expression unfortunately.

      The examination of the MICOS mutants during in vitro life cycle of Plasmodium falciparum is extremely impressive and yields convincing results. Mitochondrial deformation is tolerated by life cycle stage differentiation, with a modest but significant reduction of oocyte production, being observed.

      However, despite the herculean efforts of the authors, the manuscript as it currently stands represents only a minor advance in our understanding of the evolution of MICOS, which from the title and focus of the manuscript, is the main goal of the authors.

      In its current form, the manuscript reports some potentially important findings:

      1) Mic60 is verified to play a role in crista formation, as is predicted by its orthology to other characterized Mic60 orthologs.

      2) The discovery of a novel Mic19 analog (since the authors maintain there is no significant sequence homology), which exhibits a similar (or the same?) complexome profile with Mic60. This protein was upregulated in gametocytes like Mic60 and phenocopies Mic60 KO.

      3) Both of these MICOS subunits are essential (not dispensable) for proper crista formation

      4) Surprisingly, neither MICOS subunit is essential for in vitro growth or differentiation from ABS to sexual stages, and from the latter to sporozoites. This says more about the biology of plasmodium itself than anything about the essentiality of Mic60, ie plasmodium life cycle progression tolerates defects to mitochondrial morphology. But yes, I agree with the authors that Mic60's apparent insignificance for cell growth in examined conditions does differ with its essentiality in other eukaryotes. But fitness costs were not assayed (eg by competition between mutants and WT in infection of mosquitoes)

      5) Decreased fitness of the mutants is implied by a reduction of oocyte formation.

      While interesting in their own way, collectively they do not represent a major advance in our understanding of MICOS evolution. Furthermore, the findings bifurcate into categories informing MICOS or Plasmodium biology. Both aspects are somewhat underdeveloped in their current form.

      This is unfortunate because there seem to be many missed opportunities in the manuscript that could, with additional experiments, lead to a manuscript with much wider impact.

      For me, what is remarkable about Plasmodium MICOS that sets it apart from other iterations is the apparent absence of the Mic10 subunit. Purification of plasmodium MICOS via the epitope tagged Mic60 and Mic19 could have verified that MICOS is assembled without this core subunit. Perhaps Mic60 and Mic19 are the vestiges of the complex, and thus operate alone in shaping cristae. Such a reduction may also suggest the declining importance of mitochondria in plasmodium.

      Another missed opportunity was to assay the impact of MICOS-depletion of OXPHOS in plasmodium. This is a salient issue as maybe crista morphology is decoupled from OXPHOS capacity in Plasmodium, which links to the apparent tolerance of mitochondrial morphology in cell growth and differentiation. I suggested in section A experiments to address this deficit.

      Finally, the authors could assay fitness costs of MICOS-ablation and associated phenotypes by assaying whether mosquito infectivity is reduced in the mutants when they are directly competing with WT plasmodium. Like the authors, I am also surprised that MICOS mutants can pass population bottlenecks represented by differentiation events. Perhaps the apparent robustness of differentiation may contribute plasmodium's remarkable ability to adapt.

      I realize that the authors put a lot of efforts into their study and again, I am very impressed by the sophistication of the methods employed. Nevertheless, I think there is still better ways to increase the impact of the study aside from overinterpreting the conclusions from the data. But this would require more experiments along the lines I suggest in Section A and here.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This manuscript reports the identification of putative orthologues of mitochondrial contact site and cristae organizing system (MICOS) proteins in Plasmodium falciparum - an organism that unusually shows an acristate mitochondrion during the asexual part of its life cycle and then this develops cristae as it enters the sexual stage of its life cycle and beyond into the mosquito. The authors identify PfMIC60 and PfMIC19 as putative members and study these in detail. The authors at HA tags to both proteins and look for timing of expression during the parasite life cycle and attempt (unsuccessfully) to localise them within the parasite. They also genetically deleted both gene singly and in parallel and phenotyped the effect on parasite development. They show that both proteins are expressed in gametocytes and not asexuals, suggesting they are present at the same time as cristae development. They also show that the proteins are dispensible for the entire parasite life cycle investigated (asexuals through to sporozoites), however there is some reduction in mosquito transmission. Using EM techniques they show that the morphology of gametocyte mitochondria is abnormal in the knock out lines, although there is great variation.

      Major comments: The manuscript is interesting and is an intriguing use of a well studied organism of medical importance to answer fundamental biological questions. My main comments are that there should be greater detail in areas around methodology and statistical tests used. Also, the mosquito transmission assays (which are notoriously difficult to perform) show substantial variation between replicates and the statistical tests and data presentation are not clear enough to conclude the reduction in transmission that is claimed. Perhaps this could be improved with clearer text?

      More specific comments to address:

      Line 101/Fig1E (and figure legend) - What is this heatmap showing. It would be helpful to have a sentence or two linking it to a specific methodology. I could not find details in the M+M section and "specialized, high molecular mass gels" does not adequately explain what experiments were performed. The reference to Supplementary Information 1 also did not provide information. Line 115 and Supplementary Figure 2C + D - The main text says that the transgenic parasites contained a mitochondrially localized mScarlet for visualization and localization, but in the supplementary figure 2 it shows mitotracker labelling rather than mScarlet. This is very confusing. The figure legend also mentions both mScarlet and MitoTracker. I assume that mScarlet was used to view in regular IFAs (Fig S2C) and the MitoTracker was used for the expansion microscopy (Fig S2D)? Please clarify. Figure 2C - what is the statistical test being used (the methods say "Mean oocysts per midgut and statistical significance were calculated using a generalized linear mixed effect model with a random experiment effect under a negative binomial distribution." but what test is this?)? Also the choice of a log10 scale for oocyst intensity is an unusual choice - how are the mosquitoes with 0 oocysts being represented on this graph? It looks like they are being plotted at 10^-1 (which would be 0.1 oocysts in a mosquito which would be impossible). Figure 2D - it is great that the data from all feeding replicates has been shared, however it is difficult to conclude any meaningful impact in transmission with the knock-out lines when there is so much variation and so few mosquitoes dissected for some datapoints (10 mosquitoes are very small sample sizes). For example, Exp1 shows a clear decrease in mic19- transmission, but then Exp2 does not really show as great effect. Similarly, why does the double knock out have better transmission than the single knockouts? Sure there would be a greater effect? Figure 3 legend - Please add which statistical test was used and the number of replicates. Figure 4 legend - Please add which statistical test was used and the number of replicates. Figure 5C - the 3D reconstructions are very nice, but what does the red and yellow coloring show? Line 352 - "Still, it is striking that, despite the pronounced morphological phenotype, and the possibly high mitochondrial stress levels, the parasites appeared mostly unaffected in life cycle propagation, raising questions about the functional relevance of mitochondria at these stages." How do the authors reconcile this statement with the proven fact that mitochondria-targeted antimalarials (such as atovaquone) are very potent inhibitors of parasite mosquito transmission?

      Significance

      This manuscript is a novel approach to studying mitochondrial biology and does open a lot of unanswered questions for further research directions. Currently there are limitations in the use of statistical tests and detail of methodology, but these could be easily be addressed with a bit more analysis/better explanation in the text. This manuscript could be of interest to readers with a general interest in mitochondrial cell biology and those within the specific field of Plasmodium research.

      My expertise is in Plasmodium cell biology.

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      Referee #3

      Evidence, reproducibility and clarity

      This study builds upon previous work in schizophrenia and other disorders using fibroblasts derived from patients, assessing mitochondrial phenotypes and then using these to identify compounds which reverse these phenotypes. The study is one of the largest of its kind performed to date with 168 patients included. The authors undertake mitochondrial phenotyping and machine learning of the outputted images to be segregate the patients based on clinical features and the associated cellular phenotype. The authors then go on to screening virtually publicly available datasets of cancer cells treated with compounds and also genetic modulations. In doing so, they can identify compounds which modulate the phenotypes and therefore might be of value to test in the patient derived lines. The study has strengths in the large number of samples, the advanced machine learning and the virtual screening. Furthermore, the authors highlight and discuss the limitations of the study well. There are some weaknesses which the authors can address. Firstly in the introduction, although it is comprehensive in some areas, in other areas for example outlining the fibroblast mitochondrial phenotype and indeed the use of patient fibroblasts to identify compounds, there is significant literature missing, particularly in Parkinson's Disease where screening in fibroblasts has resulted in compounds entering Phase 3 clinical trials. In addition to the studies using 100 or more PD patient fibroblast lines for phenotyping and patient stratification have not been included. It would be useful if the authors could comment on the robustness of the phenotypes identified in the fibroblasts over multiple passages. This is important when considering the biological and disease relevance of the phenotypes and it is not something the authors show or comment on. In discussing the genetic manipulations it would be useful to comment on the genes identified in more detail particularly those which are not known to be associated with changes in mitochondrial phenotypes.

      Significance

      This study builds on work from multiple labs investigating the utility of fibroblasts to identify phenotypes and find potential novel therapeutics. The size of the cohort and the advanced machine learning methods are a particular strength and this advances the field in this area. The availability of the data and code is a strength to allow others to replicate the findings. The lack of experimental validation of any of the compounds or genes identified by the virtual screening is a weakness which could be addressed.

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      Referee #2

      Evidence, reproducibility and clarity

      In their study, Haghighi et al. seek to build upon prior literature linking alterations in mitochondrial network distribution with various kinds of psychosis. Correlations between subcellular mitochondrial localization and different psychological states is an interesting and potentially fruitful frontier and should be explored; however, despite their ambitious strategy to screen 168 skin fibroblasts from patients experiencing psychosis, and examine various online image databases, there is a concerning number of issues related to the image-analysis approach. The foremost of these is a lack of direct measures of mitochondrial distribution, which might serve to validate their proposed MITO-SLOPE protocol. There is also a worrisome lack of robust controls, which are critical in light of how admittedly subtle some of the distribution phenotypes may be. Overall, the aim to screen differences in mitochondrial distribution is a laudable goal and, in the context of psychological disorders, could be helpful in identifying new therapeutic targets; but the methodology employed in this study does not seem to be sufficiently rigorous to be able to leverage this approach for screening purposes.

      I have extensive experience investigating mitochondria with advanced imaging technologies, including super-resolution microscopy as well as high-throughput and 4D imaging modalities. I am also familiar with standard as well as machine-learning approaches for quantifying mitochondrial morphology as well as distribution or trafficking. In my opinion, this study requires substantial revision, both in terms of the indirect and often opaque image-analysis pipeline as well as the inclusion of orthogonal experiments, which could serve to lessen concerns regarding purported differences in mitochondrial distribution, which are so difficult to discern as to be imperceptible. It is worth noting, too, that this study appears to be predicated, in many ways, upon a 2010 study (Cataldo et al.) of mitochondria in patients with bipolar disorder, which appears to reflect its own lack of critical controls for cell size.

      Major comments:

      The authors state, in the first paragraph of the results section: "By eye, we observed that samples from patients in the control and MDD categories show a more fine-grained, dispersed mitochondrial network extending to the edges of the cell, whereas patients in the categories experiencing psychosis tend to show an agglomerated, thicker network more concentrated around the nucleus. The pattern is subtle and heterogeneous across a cell population." The pattern is indeed subtle. I am concerned that it is so subtle as to be imperceptible. Firstly, it is important to note that the mitochondrial reticulum in BP, SZ, and SZA is more difficult to differentiate, by eye, because the signal appears to be saturated in places, such that the boundaries of individual mitochondria are indistinguishable due to differences in contrast or possibly from the fluorescence intensity itself. Although the authors indicate in the legend that the intensity of the mitochondrial fluorescence was adjusted "for visual clarity," it appears that the contrast needs to be decreased in the BP, SZ, and SZA conditions. It is also important to note that MitoTrackers load into mitochondria in a membrane-potential-dependent fashion. Did the authors detect differences in membrane potential between these groups? While imaging, was the same laser power and gain utilized from condition to condition? With this being said, it is not clear that mitochondria in control and MDD categories have different morphologies from the other conditions. It is also not clear what "fine-grained" means in this context. Is this a comment on aspect ratio? If so, it would be better to use standard terminology. (Why are there large red circular structures in the nucleus? These are likely not mitochondria, so why are they showing up in the channel with MitoTracker?) It is also not evident that one condition has more dispersed mitochondria than another. Given that the authors appear to be making this a central claim of their manuscript, it would seem appropriate to highlight specifically the regions of the different cells that they believe exhibit meaningful differences. If I attempt to look at the merged image, which is important because it is really the only way that one can gauge the relative distance of the mitochondrial network from the edge of the cell, there would seem to be no obvious differences between the conditions. Another key point that I think important to mention, given that it is frequently referenced in this manuscript, Cataldo et al., 2010 indicate that mitochondria in patient fibroblasts with bipolar disorder (BD) are more perinuclear than those in control. However, a cursory inspection of the images from this study (e.g., Figure 2A-B; Figure 4A-D; and Figure 6A-H) unambiguously demonstrate that the BD cells are smaller than the control cells. Of course, if the cells are smaller, the distance from the nucleus will tend to be shorter. In Cataldo et al., 2010, the authors state, "We also measured cell area, cell length, cell width, and cell perimeter of the fibroblasts used in this analysis to verify that the observed mitochondrial distributional differences were not simply a result of BD cells being smaller, shorter, or fatter. No significant differences in any of these measurements were seen based on diagnosis after two sample t tests." Notably, the data is not shown, so it is difficult to appreciate what the variance of the population of cells from control and BD would look like, but it must be said, nevertheless, that the representative images in this paper all point to the BD cells being smaller. In light of this, it would be helpful if Haghighi et al. could add scale bars to all the images (e.g., in Figure 2), so readers can ascertain whether all the cells are portrayed at the same scale and are of similar areas.

      As the authors indicate, interpretable measures of mitochondrial morphology include values like size and shape. It is concerning, therefore, that Figure 3 purports to identify a number of significantly different mitochondrial "features" in the patient groups experiencing psychosis, but they do not appear to make an effort to clarify how any of these features might reflect ground truths of mitochondrial architecture, which can be understood directly by values such as aspect ratio, circularity, area, number organelles, number of nodes or branching points in a network, etc. Unless the authors can specifically tie their machine-learning classifications to standard mitochondrial shape descriptors, their classifications will remain opaque and therefore of limited credibility or value. One way to improve the validation of their machine-learning classification methods would be to use empirically sound methods for manipulating a mitochondrial morphology and distribution, which could serve as positive or negative controls. For example, treatment of cells with the uncoupler FCCP would induce mitochondrial fragmentation, treatment with cycloheximide results in stress-induced mitochondrial hyperfusion (SIMH), or treatment with Nocodazole would block mitochondrial trafficking. Treating control cells with these chemicals would help to establish baseline measurements for how far the patient cells are deviating from untreated controls, in one direction or another. Such considerations, I think, are especially important when the mitochondrial phenotypes are so subtle. I agree with the authors' argument that, for the purposes of screening, it is best to focus on a single metric. Based on their apparent discernment of the subtle differences in mitochondrial distribution in patients experiencing psychosis, they opted to examine possible differences in network density. To this end, they developed "MITO-SLOPE." Out of multiple categories of features, they highlight the following as the most powerful for establishing differences in mitochondrial network density:

      "(a) A subset of texture measures in the nuclei and cytoplasm area of the mito channel. (b) A subset of features measuring the intensity of the mitochondria area across the cell."

      Within the concentric bins around the cell nuclei, they measure:

      • FracAtD: Fraction of total stain in an object at a given radius.
      • MeanFrac: Mean fractional intensity at a given radius, calculated as the fraction of total intensity normalized by the fraction of pixels at a given radius.
      • RadialCV: Coefficient of variation of intensity within a ring, calculated across 8 slices."

      While the authors have recommended the use of a single metric for purposes of screening, MITO-SLOPE appears to represent a bundle of metrics, which, in the end, do not amount to a clear readout of what is being measured. From my point of view, if one were interested in measuring mitochondrial distribution, then, in an ideal situation, one would measure the average distance of all the mitochondria from the center of the nucleus. And, since the size of the cell is critical for establishing relative distances to the boundaries or periphery of the cell, one would normalize this metric by cellular area. Thus, the readout would be: [average mitochondrial distance from the nuclear center (µm)]/[cellular area (µm2)]. An even simpler metric could be: [average mitochondrial distance from nuclear center (µm)]/[average cytoplasmic radius (µm)]. When talking about mitochondrial distribution, we typically think in terms of where is the mitochondrial network, on average, in relation to the nucleus (perinuclear) or to the edge of the cell (peripheral). By quantifying the actual mean distance of the mitochondrial network in relation to both the nucleus and the bona fide cell extremities, via the metrics I described above, one can obtain direct measurements of the truly meaningful values related to mitochondrial distribution. It seems deviating from these approaches introduces more and more opportunities for confounding variables.

      However, the MITO-SLOPE analysis does not seem to consider this metric. Is this, or a similar variation, not the most direct way to establish differences in the mitochondrial network distribution? I would, of course, at least want to see a discussion of why the authors have not chosen to use the most direct form of quantification for this purely spatial value. Why opt for a multifaceted measurement of a relatively straightforward quantity, when a simpler form of quantification would not only suffice but arguably be more likely to capture the ground truth? With this being said, it is not clear to me why, within MITO-SLOPE there seems to be a reliance on measuring the "intensity" of the mitochondria. (And what intensity is it? Mean intensity per ROI?) Of course, particularly if MitoTrackers were used for staining mitochondria, there will be heterogeneity in fluorescence intensity from organelle to organelle, which introduces potential confounders into the workflow. Furthermore, as indicated above, to know if the subcellular distribution of mitochondria is truly altered, it is essential to know if the cell size has likewise changed. Therefore, any unbiased measure of mitochondrial distribution must take into consideration the size of the cell; however, based on the information provided about MITO-SLOPE, it does not appear that the authors are accounting for possible variations in cell size that might account for alterations in mitochondrial network distribution - i.e., a smaller cell will have a more constrained area in which mitochondria will be able to disperse - thus, not accounting for cell size (area) will yield ambiguous results. For example, how can we know if mitochondrial motility is impaired or if the cell is simply smaller and there is less space in which to move? Another complexity, here, is if the cell boundaries were not accounted for via staining of actin, etc., then establishing a true cell boundary will be very challenging. How many bins are sufficient to capture the whole cell? Just 12? Furthermore, human fibroblasts have a tendency to be quite large (sometimes several hundred microns from end to end); how can the authors account for the whole cell, particularly in cases where part of the cell is beyond the field of view or cells are growing on top of each other, as is often the case?

      In Figure 6, there is no control image that could be used as a frame of reference. I have extensive experience imaging A549 cells. The mitochondria in these images appear to be highly fragmented. The staining patterns, particularly of the cells treated with divalproex-sodium, are quite dim, indicating mitochondrial depolarization. Of course, depolarization affects the fluorescence intensity of mitochondria stained with vital dyes, such as MitoTrackers, which will, in turn, presumably affect the values obtained from MITO-SLOPE, which appear to rely on intensity gradients, rather than more concrete spatial coordinates. Also, as indicated above, it is unclear how the authors are establishing the edges of cells without a marker of the plasma membrane or cytoskeleton.

      The authors note that "Divalproex-sodium is a benzodiazepine receptor agonist and HDAC inhibitor (Rahman et al. 2025) used to manage a variety of seizure disorders (Willmore 2003) and bipolar disorder(Bond et al. 2010; Cipriani et al. 2013); it shows a positive MITO-SLOPE which is the direction expected to normalize the centralized mitochondrial localization associated with psychosis." Insofar as this recommends the drug for use in "normalizing" perinuclear mitochondria within neurons, it would seem only prudent to mention that this drug also appears to induce mitochondrial depolarization and fragmentation, which are both associated with a range of severe human pathologies. I would caution the authors to not highlight one potential benefit while omitting an obvious side effect involving what appears to be significant perturbation of mitochondrial structure and function. What is the point of normalizing mitochondrial distribution if the mitochondria being redistributed are dysfunctional?

      The authors note, in Figure 7, that their MITO-SLOPE analysis was unable to discern a statistically significant difference in cells with specific knockouts of genes associated with mitochondrial trafficking. If the MITO-SLOPE cannot discern a difference in the context of a substantial abrogation of mitochondrial transport capacity, how is it that it could detect meaningful differences where there is only a "subtle" change in distribution? This result would seem to militate strongly against the efficacy of this analysis pipeline and would not recommend its use for unbiased screening and discovery.

      Minor comments:

      For Figure 6 b and c, "µm" should be "µM."

      The introduction and discussion could be more concise.

      Significance

      This study attempts to fill an important gap in knowledge relating to mitochondrial distribution and psychological disorders. It aims to perform an initial screen to try to validate a novel analysis pipeline called MITO-SLOPE, however, the study appears to lack analytical rigor, both in terms of the underlying cell biology together with the approach for quantification, itself. Conceptually, this study has great promise, but the authors will need to improve their pipeline prior to publication, which will likely require fundamental revisions, including an array of orthogonal measures (largely lacking here) as well as detailed demonstrations of how the segmentation actually works and ultimately yields data reflecting demonstrable mitochondrial trafficking/distribution defects.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Haghighi and McPhie et al. builds upon their previous findings by exploring the mitochondrial localization as a disease-associated phenotype in mental disorders, particularly in psychotic disorders. They recruited a cohort of patients diagnosed with schizophrenia, schizoaffective disorder, bipolar disorder and MDD. By taking advantage of skin biopsies, they screened patient-derived fibroblasts for aberrant mitochondrial localization and morphology using common staining techniques. Then, they use a machine learning approach to classify patients into their respective groups, which was effective for BP, SZA and pooled psychotic patients. Authors then develop a single feature for phenotyping, Mito-SLOPE, a metric of mitochondria density distribution across a cell by radial areas. With this metric, psychotic patients tend to have more nuclear-localized than edge-localized mitochondria; whereas MDD patients show a trend for higher edge-to-nucleus distribution. To find candidate drugs, authors screen publicly available datasets of cells treated with small compounds using mito-SLOPE. Furthermore, authors then apply mitoSLOPE on a CRISPR screen dataset, showcasing the role of mitochondrial dynamics genes and three genes of interest because of their association with psychosis. Finally, they identified the top genes whose KO or overexpression may explain (or reverse) the mitoSLOPE phenotype.

      Overall, the manuscript is well-written, the conclusions are supported within their limitations and this work represents an advancement in the field. I recommend it for publication provided these concerns are addressed:

      Major comments:

      1. The mitoSLOPE measure is very interesting and most likely reflects a subtle changes in mitochondrial transport. How does the microtubule network look like in the patient fibroblasts, are there obvious alterations in e.g. their posttranslational modifications? Is there a difference in mito transport speed or pausing frequency?
      2. I concur with the exclusion of compounds that obviously alter cell shape, as the authors mention for the cancer therapeutics. Some cancer therapeutics actually affect microtubule dynamics (see 1st point), which may underlie their effect on both cell shape and mitoSLOPE. To undertand the mechanism of action, the top hits should also be tested for the integrity of the microtubular network and mitochondrial transport parameters.
      3. While I agree with the authors' reasoning that the observed phenotype could be a result of the disease or the result of a compensatory mechanism, their hypothesis could be experimentally tested by addition of any of the top hits in order to reverse mitoSLOPE in their patient cell lines. It may not have worked for Lithium in their last manuscript, but the mechanism of action of the novel compounds could be cell intrinsic.
      4. Does recreation of the CRISPR cell line in their hands produce the same phenotype?
      5. Additionally, the observed phenotypes could also be a product of the medication taken by the patients. Deeper patient data from the cohort may be relevant to put the findings in context. How were patients diagnosed? Which medications were the patients taking? Was substance abuse present? In Mertens et al, Lithium responders and Lithium non-responders showed a differential mitochondrial response, how does this affect their dataset?
      6. While MDD itself is not a psychotic disorder, it can still present with psychotic features. Was this evaluated during the recruitment? Also important, were they on antipsychotic medication in addition to antidepressant therapy?
      7. The fact that CACNA1C is excluded from the "unbiased" hit discovery (Fig 8) undermines the power of the filtering criteria selected by the authors. Authors should include some discussion around this.

      Minor comments:

      1. Colored images should be made colorblind-accessible. This applies to microscopy images and graphs.
      2. Fig 3: Exact p-values should be reported in the graphs
      3. Fig. 5 and Fig 7a-b: It is not immediately clear what the lines in these graphs represent. Is it the individual drug/gene hits in a pre-ranked manner?
      4. Fig 6 b-c: should the "m" be capitalized for Molarity?
      5. The annotation of divalproex/valproic acid as a "benzodiazepine receptor agonist" is incorrect. While it is known to enhance GABAergic neurotransmission, the mechanism is supported to be through GABA synthesis rather than being a GABA-A receptor agonist (see eg. PMID: 23407051).
      6. Supplementary Fig 3 and 4 could be swapped to match the main text order.
      7. One reference was inaccessible: Anon, Phenomics-Enabled Discovery and Optimization of Small Molecule RBM39 Degraders as Alternative to CDK12 Targeting in High-Grade Serious Ovarian Cancer (HGSOC).

      Significance

      Recently, mitochondria have emerged as mediators of anxious behavior and are increasingly studied in the context of neuropsychiatric disorders. However, the molecular mechanisms that connect altered mitochondrial performance to specific neuropathological conditions are unknown. This study extends our knowledge in this realm. While it is in principle an extension of earlier work from the authors (Cataldo, A.M. et al. Am. J. Pathol. 2010), it has added value due to the application of their automated analysis to publicly available datasets, providing a clear technical advance. This identified known as well as novel compounds that could revert the mitochondrial phenotype and makes this study specifically interesting to an audience interested in translational research. The strength of the manuscript certainly lies in the large number of examples studied and their well-rounded discussion of their findings. It is limited by the fact that the phenotype of neuropsychiatric conditions is studied in peripheral cells, and thus may not be a simple cell-autonomous response but a compensatory, systemic response that is not easy to replicate in a fibroblast in isolation. No mechanistic insight is gained on the underlying cell biology in the current format.

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      Reply to the reviewers

      General statements.

      We thank the reviewers for their positive response and useful suggestions on our manuscript. They recognize the ‘proof of concept’ nature of the work and the importance of extending the number of human mutation-specific DMD mouse models from one to five for preclinical research. We feel that the quality of the manuscript has been improved upon implementation of the reviewer’s suggestions.

      Reviewer 1.

      OPTIONAL - From the point of view of the reviewer, it seems plausible to use CRISP/Cas9 to "clean up" the original hDMDmdx mouse line by selectively removing one of the YACs forming the tail-to-tail tandem in the mouse genome. Once such single copy mouse line is generated (and proven viable?) any subsequent rearrangement of the hDMD transgene would prove much less challenging. Such mouse line would also better represent human model where only one DMD copy is carried on the X chromosome.

      The reviewer gives the optional suggestion that the generation of these models could have been combined with the removal of one of the copies of the YAC to extend the use of the new models to CRISPR-based therapies. This is correct, but we note that when the data on the removal of a copy of the YAC were published, our new models were already generated and in different stages of QC, colony building and analysis. The procedure described by Chey et al could be used on our new models, but this would require additional time and funding and is therefore outside the scope of this manuscript.

      The labels in figure 2B and 3A would benefit from showing the PCR fragment lengths as well as the sizes of obtained hDMD exon deletions. On could also include an additional figure panel demonstrating the principle of ASO-induced exon skipping

      Reviewer #1 also has a minor comment regarding the exact deletions in figure 2B and 3A. For fig. 2B he/she suggests to include the sizes of the PCR fragments next to the gel. Especially for the gel regarding PCR1, which detects the deleted YAC copy, this will not be very informative as this can be (and is) different for different clones depending on the NHEJ-mediated repair in the specific clone. Adding sizes is only interesting for each specific clone, and adding them all will make a very messy figure. The important message from this gel is the presence of any fragment, as the undeleted copy is not amplified under the conditions used. For the gel of PCR2 the opposite is the case, here the PCR fragment shown is simply the undeleted YAC copy, and here we are only interested in the absence of the PCR fragment.

      We thank the reviewer for the suggestion of adding the deletion sizes to fig 3A. This made us realize that an additional table with the details of the mutant alleles in all models had been omitted, and we apologize for this error. With the revised version we include details on the size of the deletions and their genomic coordinates (in the human genome as it is in the human YAC) of each of the new models (revised Sup. Table 1). We trust that adding these details will clarify this reviewer’s minor comment.

      The reviewer requests to include an additional figure panel demonstrating the principle of ASO-induced exon skipping. We have now added this to the revised version of the manuscript (new fig. 5).

      The study is fairly limited in scope and will be of primary interest to those working in the DMD field.

      We are aware of 9 clinical trials for exon 51 and 53 studies that are ongoing or were recently stopped. For four of these compounds companies have a license to our hDMDdel52/mdx mouse model, and one of these studies has been published. An additional 7 clinical trials are planned or ongoing for exon 44, 45 and 50 skipping for which the newly developed models are being or can be used for preclinical studies.

      Reviewer 2.

      To further strengthen the rigor of the study, it would be valuable to include an analysis of potential off-target effects of CRISPR editing, particularly given that double targeting of two YAC copies was required. This is especially important for germline edits, as off-target mutations could introduce confounding phenotypes in the resulting mice. Demonstrating minimal or absent off-target activity would increase confidence in the specificity and safety of the generated models.

      There has indeed been one major study suggesting a large number of CRISPR-induced off-target mutations in mouse models. However, this publication was rapidly questioned by multiple groups for having used the wrong control animals and the original publication was retracted (https://doi.org/10.1038/nmeth0518-394a). Another study at that time, using the correct controls, did not find mutations that could be attributed to CRISPR-induced off-target mutations. A more recent study analysed founder animals from transgenic projects using 163 different guide RNAs and concluded ‘In total, only 4.9% (8/163) of guides tested have detectable off-target activity, at a rate of 0.2 Cas9 off-target mutations per founder analysed. In comparison, we observe __~1,100 unique variants in each mouse regardless of genome exposure to Cas9 __indicating off-target variants comprise a small fraction of genetic heterogeneity in Cas9-edited mice.’ In short, the background mutation rate in mice is much higher than the Cas9 off-target mutation rate. In addition to this, we only used guide RNAs that did not have any predicted off-target sites (according to the CRISPOR tool; https://crispor.gi.ucsc.edu/crispor.py) on the same chromosome or in protein coding sequences, so that any undetected off-target mutation will rapidly be lost in the subsequent breeding. We also would like to refer the reviewer to the ‘referee cross-commenting remark’ from reviewer #3 on this topic.

      The validation of the dystrophic phenotype is generally convincing. However, the authors should clarify how "human dystrophin" is detected in the deletion models. Since only part of the dystrophin gene in these mice is humanized (the remainder is murine), it is important to specify, also in the results, which antibody was used and which epitope/exon it recognizes. If the antibody targets a deleted exon in a given model, this could lead to misinterpretation of the dystrophin signal. Providing this clarification would ensure the conclusions regarding dystrophin expression are fully supported.

      This question is based on the incorrect assumption that only part of the DMD gene in these models is humanized. As described in the original publication on the YAC transgenics the complete human gene is in the YAC. Here, we deleted a particular exon from this complete human DMD gene. In combination with the mdx allele, these mice lack the full-length mouse and human dystrophin isoforms expressed in muscle. As mentioned in the materials section, the human dystrophin protein was detected with the Mandys 106 antibody (recognizing exon 43; amino acids 2063-2078), which only has reactivity with human dystrophin according to the product specification of Sigma Aldrich. We confirmed this for wild type mouse tissue, showing no dystrophin for this antibody. In fig 4 we confirm lack of human dystrophin in the deletion models using this antibody. The mouse and human dystrophin protein was detected with the AB154168 antibody of Abcam (recognizing the last 100 amino acids of the C-terminal part of the protein), which has reactivity with both mouse and human. So neither antibody did target a deleted exon. For the exon skipping validation, solely the Abcam antibody was used, as none of the deleted or skipped exons was recognized by this antibody. Information regarding the targeted protein region has now been added to the materials section.

      Additionally, to further strengthen the characterization of the muscular dystrophy phenotype, the authors could quantify muscle fibre size and the percentage of centrally nucleated fibres, both of which are widely accepted quantitative markers of ongoing degeneration/regeneration in DMD models.

      and

      The validation of exon skipping in the new hDMD deletion models is convincing at the molecular level. However, since the ASOs were injected into both gastrocnemius and triceps muscles, it would be helpful to include at least a brief characterization of the triceps, even in supplementary data, as different muscles can show slightly different pathology and responses. Additionally, while the molecular readouts (RT-PCR and Western blot) demonstrate restoration of dystrophin expression, including simple histological analysis, such as H&E staining, could further support functional improvement and reinforce the physiological relevance of exon skipping in these models.

      The proof-of-principle nature of the current manuscript is focused on restoration of dystrophin expression shortly after ASO treatment, and the current sample sizes (n=3 mice per strain) are too limited for actual quantification of histopathological improvements. Furthermore, the timespan between the intramuscular injection and tissue collection (2 weeks) does not allow sufficient time for histopathological improvements to develop. Notably, a large natural history analysis of all these new models is currently ongoing, which includes a large variety of in vivo functional outcome measures and provides a full description of the histopathological aspects of these mice. The proposed characterization of the triceps is now included as supplementary data of the manuscript (Sup. Fig 1).

      Reviewer 3.

      This reviewer starts with pointing out some typos, or requested rephrasing to sentences for clarification. We appreciate this and have addressed this in the revised version of the manuscript.

      Generation of the models: it is not clear why the authors generated line 44 in ES cells, then switched to direct gene editing in zygotes. Was this due to advent of electroporation of zygotes at the time? This may need clarification beyond the sentence "Encouraged by the specificity of our new prescreen workflow and the efficiency of correct targeting of human exon 44 in ES cells, we generated additional models ... directly in mouse zygotes".

      The simple answer to this is that we were (pleasantly) surprised ourselves by the efficiency we got in the ES cells (which was based on the previous experience generating the del52 model). For animal welfare reason we prefer to generate models via ES cells if we expect a long and cumbersome quality control process and / or very low efficiency, as ES cells allow us to do this QC before the actual animals are generated, thus reducing the number of animals generated during the model generation phase. Expecting very low efficiency, we originally picked 10 x 96 well plates of clones for this del44 targeting, but after pre-screening the first two plates (192 clones), we realized this was an enormous overkill in clones, and the additional 8 plates were not analysed. With this much higher than expected efficiency, and the power of the two-step pre-screen described in the manuscript, we decided to try the next model (the del45) directly in zygotes. This was found efficient enough to also do the last two models directly in zygotes. We can only speculate on the much higher efficiency than observed for the del52 targeting. Clearly the fact that we knew of the double integration this time allowed us to develop the successful 2-step pre-screen. Another difference is that the del52 model was generated using TALENs as genome editors, whereas now we could use CRISPR/Cas9.

      Antisense oligonucleotide treatment: there is no description of the design of the ASOs beyond their sequence in suppl. Table 4. How were they designed? Moreover, they have been injected at two different doses (i.e., 50ul for Exon 51 & 53; 100ul for Exon 44 & 45). What is the rational for this? There is no justification in the manuscript.

      The requested additional details on ASO design and dosing have been added to the materials section of the revised manuscript. The reviewer also pointed out that fig 4 includes both a protein sample diluted to 10% of protein of both a C57BL/6J and hDMD/mdx control mouse, and requested a justification for this. We included samples of both wildtype strains to confirm species reactivity of the dystrophin antibodies used, with the AB145168 antibody being specific for both mouse and human protein (showing a dystrophin band in both wildtype samples), and the Mandys106 antibody being specific to only human protein (showing a dystrophin band in the hDMD/mdx control only).

      Phenotypic validation of the new models: a description of the mdx line with C57BL/6J mice is mentioned. Is this why Fig.4 includes "10% Bl6" and "10% hDMD/mdx"? If so, this should be clarified in the text (or deleted from the figure). The authors mentioned "As expected, the gastrocnemius of healthy hDMD/mdx mice expressed dystrophin of human origin at wildtype levels". Why would this be expected? If 2 copies of the gene, including the human promoter, are integrated, why would one expect a wildtype level of expression? In fact, in the original paper describing the hDMD/mdx model ('t Hoen et al. 2008), the human transcripts are expressed at 2 to 4-fold higher than their endogenous counterparts (which is in line with the integration of 2 copies).

      It is true, as he/she points out, that qRT-PCR data in the original YAC transgenic publication showed double expression of the human transcript, consistent with the double integration. However, fig. 3b in the same paper shows that at the protein level the expression of human DMD is comparable to the mouse protein. We don’t know the reason for the discrepancy between transcript and protein levels in this model, but in the current manuscript we are referring to this protein expression.

      A quantification of the expression levels on Figure 4 should be done (normalized to actinin) to resolve this. The size of the Marker should also be added on Figure 4.

      We feel that proper quantification can only be done with the utilization of a standard curve. As we expected no, or trace levels of dystrophin in the deletion models, we only included wildtype samples diluted to 10% of wildtype protein. This prevents us from accurate quantification of the trace dystrophin levels observed in the del45 and del51 models. However, as can be appreciated from fig 4, expression is very minimal. We added information on the marker in the materials section, and indicated the size (85 kDa) in the figure legend.

      Finally, the authors observed histological hallmarks of the disease in the new models (i.e., muscle degeneration and fibrosis). Although obvious on the images, it may be useful to add indications (e.g., arrows) on the images for readers non familiar with DMD.

      We added information on the marker in the materials section, and indicated the size (85 kDa) in the figure legend. Lastly, we also added the requested arrows to the pictures of fig. 4B to allow distinction between different histopathological hallmarks, and refer to these in the figure legend.

      Prescreen PCR of hDMD/mdx ES cells (Fig. 2): the authors mentioned that "The PCR conditions were chosen for not being able to amplify the undeleted allele." What does this mean? Was the elongation time reduced? As per the text, the theoretical size of a WT band is around 1.6kb. Yet, on the gel, bands higher than 1kb are visible for some clones.

      This is indeed based on the extension time of the PCR reaction shown in PCR 1 from fig 2B, amplified with primers upstream and downstream of the deleted region (see fig 1 and 2A). However, the approx. 1.6 kb fragment the reviewer refers to is the undeleted-specific amplification shown in Fig 2B PCR 2, which is the result of a primer outside and a primer inside the deleted region (fig 1and 2A). Amplification of the undeleted copy with the primers used in PCR 1 would give a fragment of 3902 nt. The deletion of exon 44 in the final model is 3584 nt, which details will be shown in the excel file that was erroneously omitted (see our response to reviewer #1), with the PCR 1 product of the deleted copy in the clone used for the mouse model being 318 nt. It is straight-forward to select an extension time that would be insufficient for a 3.9 kb fragment, but which can amplify fragments that are shorter due to the deletion. Even in a clone with a single copy of exon 44 deleted, one would not expect to see the 3902 nt fragment due to preferential amplification of the much shorter mutant band. This has now been clarified in the legend of figure 2 of the revised version of the manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by van Putten et al. describes the generation and initial characterization of four new mouse models of DMD, based on the previously generated hDMD/mdx murine model, which expressed human dystrophin from a yeast artificial chromosome (YAC) in a DMD null (mdx) background. The four new models are based on the deletion of four Exons (44, 45, 51 & 53), which accounts for most human deletions (hotspot) in DMD.

      The description of the generation of these models using CRISPR/Cas9 gene editing is thorough, and the quality control is adequate. Moreover, preliminary testing of exon skipping therapy using ASO showed it is possible to restore the production of dystrophin protein (albeit truncated) in these models, which increase their translational value. Although the study is valuable and methodologically sound, there are minor points that need to be addressed:

      • Few typos need to be corrected:
        • "Therapeutic approaches aiming to restore dystrophin for DMD are based on the discrepancy between DMD and BMD mutations." This needs to be rephrased to clarify the meaning for readers not familiar with DMD.
        • "Western blot and immune fluorescence analysis on gastrocnemius muscles..." replace" immune fluorescence" with immunofluorescence.
        • "Two weeks after the last injection muscles were isolated, and RNA and protein was isolated from muscle..." protein WERE isolated.
        • "However, gene editing-based therapies could run into the same unpredictable outcome reduced efficiency of a therapy ..." This sentence is confusing, consider rephrasing.
      • Generation of the models: it is not clear why the authors generated line 44 in ES cells, then switched to direct gene editing in zygotes. Was this due to advent of electroporation of zygotes at the time? This may need clarification beyond the sentence "Encouraged by the specificity of our new prescreen workflow and the efficiency of correct targeting of human exon 44 in ES cells, we generated additional models ... directly in mouse zygotes".
      • Antisense oligonucleotide treatment: there is no description of the design of the ASOs beyond their sequence in suppl. Table 4. How were they designed? Moreover, they have been injected at two different doses (i.e., 50ul for Exon 51 & 53; 100ul for Exon 44 & 45). What is the rational for this? There is no justification in the manuscript.
      • Phenotypic validation of the new models: a description of the mdx line with C57BL/6J mice is mentioned. Is this why Fig.4 includes "10% Bl6" and "10% hDMD/mdx"? If so, this should be clarified in the text (or deleted from the figure). The authors mentioned "As expected, the gastrocnemius of healthy hDMD/mdx mice expressed dystrophin of human origin at wildtype levels". Why would this be expected? If 2 copies of the gene, including the human promoter, are integrated, why would one expect a wildtype level of expression? In fact, in the original paper describing the hDMD/mdx model ('t Hoen et al. 2008), the human transcripts are expressed at 2 to 4-fold higher than their endogenous counterparts (which is in line with the integration of 2 copies). A quantification of the expression levels on Figure 4 should be done (normalized to actinin) to resolve this. The size of the Marker should also be added on Figure 4. Finally, the authors observed histological hallmarks of the disease in the new models (i.e., muscle degeneration and fibrosis). Although obvious on the images, it may be useful to add indications (e.g., arrows) on the images for readers non familiar with DMD.
      • Prescreen PCR of hDMD/mdx ES cells (Fig. 2): the authors mentioned that "The PCR conditions were chosen for not being able to amplify the undeleted allele." What does this mean? Was the elongation time reduced? As per the text, the theoretical size of a WT band is around 1.6kb. Yet, on the gel, bands higher than 1kb are visible for some clones.

      Referee cross-commenting

      The comments from the other reviewers seem fair, reasonable, and should be easily addressed by the authors. The off-target analysis might however be a bit of a stretch, given that (as per published data) the off-target rate is low (i.e., no higher than genetic drift) in mouse zygotes when using CRISPR RNPs, and any potential off-target mutation could easily be segregated out by means of backcrossing.

      Significance

      The four new mouse models generated in this study will advance the field both at the preclinical and the clinical levels, because they more closely recapitulate the human mutations linked to DMD than previous models, while presenting with a translational potential (the authors showed a proof of concept of exon-skipping therapy in these mice).

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors generated four novel humanized DMD mouse models carrying deletions of exons 44, 45, 51, or 53 in the human DMD gene on an mdx C57BL/6J background. They developed an optimized CRISPR-Cas9 pre-screening workflow for embryonic stem cells and zygotes, allowing efficient and precise targeting of the human DMD YAC, which carries a complex double tail-to-tail integration. The models display absent or trace dystrophin and classical DMD muscle pathology, including fibrosis. ASO-mediated exon skipping of flanking exons successfully restores dystrophin expression, validating their use for preclinical testing of mutation-specific therapies. These models address a key limitation of the standard mdx mouse, which carries a mutation only in exon 23, and provide a more clinically relevant platform for evaluating human sequence-specific therapeutic strategies for the most frequently mutated DMD exons.

      Minor comments:

      1. The pre-screen workflow and model generation are impressive and well-optimized. To further strengthen the rigor of the study, it would be valuable to include an analysis of potential off-target effects of CRISPR editing, particularly given that double targeting of two YAC copies was required. This is especially important for germline edits, as off-target mutations could introduce confounding phenotypes in the resulting mice. Demonstrating minimal or absent off-target activity would increase confidence in the specificity and safety of the generated models.
      2. The validation of the dystrophic phenotype is generally convincing. However, the authors should clarify how "human dystrophin" is detected in the deletion models. Since only part of the dystrophin gene in these mice is humanized (the remainder is murine), it is important to specify, also in the results, which antibody was used and which epitope/exon it recognizes. If the antibody targets a deleted exon in a given model, this could lead to misinterpretation of the dystrophin signal. Providing this clarification would ensure the conclusions regarding dystrophin expression are fully supported. Additionally, to further strengthen the characterization of the muscular dystrophy phenotype, the authors could quantify muscle fibre size and the percentage of centrally nucleated fibres, both of which are widely accepted quantitative markers of ongoing degeneration/regeneration in DMD models.
      3. The validation of exon skipping in the new hDMD deletion models is convincing at the molecular level. However, since the ASOs were injected into both gastrocnemius and triceps muscles, it would be helpful to include at least a brief characterization of the triceps, even in supplementary data, as different muscles can show slightly different pathology and responses. Additionally, while the molecular readouts (RT-PCR and Western blot) demonstrate restoration of dystrophin expression, including simple histological analysis, such as H&E staining, could further support functional improvement and reinforce the physiological relevance of exon skipping in these models.

      Significance

      This study presents a clear and technically robust advance in the field of Duchenne muscular dystrophy (DMD) preclinical research. The strongest aspects are the generation of four novel humanized DMD mouse models carrying clinically relevant exon deletions (44, 45, 51, 53) and the development of an optimized CRISPR-Cas9 pre-screening workflow that efficiently and precisely targets the human DMD YAC, despite its complex double tail-to-tail integration. These models display relevant dystrophic phenotypes and are validated for ASO-mediated exon skipping, demonstrating their applicability for preclinical testing of mutation-specific therapies.

      Compared to existing models, such as the standard mdx mouse or previously generated hDMDdel52/mdx line, these new models address the critical limitation that most human DMD mutations cluster outside exon 23, providing a more clinically relevant system. The study extends knowledge both technically, by demonstrating an efficient pre-screening workflow for complex humanized YAC edits, and functionally, by creating models that allow preclinical evaluation of human sequence-specific therapeutic strategies for the most frequent DMD mutations. The audience for this work includes basic and translational researchers in the muscular dystrophy, gene therapy, and genome editing fields, as well as clinicians interested in the development and preclinical testing of exon skipping and gene-editing therapies. These models will likely be widely used to optimize therapy design, dosage, and delivery, enhancing translatability to clinical applications.

      Field of expertise: Duchenne muscular dystrophy, preclinical models, genome editing, exon skipping therapies, regenerative medicine.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The study describes the creation and preliminary validation of four humanized mouse DMD models. The authors utilized the pre-existing hDMDmdx mouse line as a platform to generate clinically relevant models that carry the deletion of exons 44, 45, 51 and 52 of the hDMD transgene using CRISPR/Cas9 technology. This proved somewhat challenging as the hDMD YAC transgene present in the original hDMDmdx line is inserted as a tail-to-tail tandem in the mouse genome. The initial mouse line carrying deletion of exon 44 was performed using a combination of CRISPR/Cas9 and ES cell technologies whereas the remaining mouse lines were generated by applying CRISPR technology directly of hDMCmdx zygotes. In order to identify and select ES cell lines and animals carrying the desired deletion patterns, the authors devised a two steps PCR-based selection strategy followed by a copy number PCR for individual hDMD exons. Once the desired mouse lines were obtained, the authors performed Western blots and histological staining to prove the loss of the hDMD protein expression and the appearance of associated DMD muscle phenotypes. Finally, in vivo experiment was carried out where intramuscular injection of exon-specific ASOs lead to exon skipping and partial restoration of the expression of the truncated but potentially functional hDMD protein variants. The experiment was carried out solely as a proof-of-concept and was terminated before any therapeutic effect of the ASOs could be potentially observed. Nevertheless, the authors argue (correctly) that such models can prove useful in future development of treatment strategies for DMD.

      Major comments:

      The study has clear aims and is well described. The performed experiments support the final conclusions presented in the paper. OPTIONAL - From the point of view of the reviewer, it seems plausible to use CRISP/Cas9 to "clean up" the original hDMDmdx mouse line by selectively removing one of the YACs forming the tail-to-tail tandem in the mouse genome. Once such single copy mouse line is generated (and proven viable?) any subsequent rearrangement of the hDMD transgene would prove much less challenging. Such mouse line would also better represent human model where only one DMD copy is carried on the X chromosome.

      Minor comments:

      The labels in figure 2B and 3A would benefit from showing the PCR fragment lengths as well as the sizes of obtained hDMD exon deletions. On could also include an additional figure panel demonstrating the principle of ASO-induced exon skipping

      Significance

      General Assessment:

      The study is fairly limited in scope and will be of primary interest to those working in the DMD field. The new patient-derived hDMD exon deletions will allow testing and validation of human therapeutic moieties in mouse models but as such the study does not advance our knowledge about DMD or transgenic mouse model generation.

      Advance:

      Perhaps the only novelty is a very diligent genotyping approach aimed at identifying lines where both exons in the tail-to-tail hDMD tandem have been deleted. Given the extensive work put into this approach, the author may have missed an opportunity to reengineer the original hDMDmdx mouse line (see OPTIONAL) to generate a mouse line where any future modifications of the hDMD allele would be much more accessible to both CRISPR-mediate NHEJ and HDR approaches.

      Audience:

      The study is fairly limited in scope and will be of primary interest to those working in the DMD field.

      Reviewer's expertise focuses on CRISPR/Cas9 technologies and transgenic mouse model generation.

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      Reply to the reviewers

      COMBINED REVIEW REPORTS

      __1.1. The biochemical and biophysical experiments performed in this study were well designed, data were clear and the conclusions were well supported by the results. One potential improvement is to check whether NLS could affect the normal activation targets of ΔNp63α, such as KRT14 and other epithelial genes. This could complement the experiments testing the inhibition effect of ΔNp63α on p53-mediated gene activation. This will be interesting, as ΔNp63α is a master regulator in epithelial cells via regulation of diverse epithelial genes. __

      We thank the Review for such useful comment. In order to further investigate the relationship between p63 nuclear import and function, and the importance of the oligomerization driven tolerance to point mutations in the latter, we have now performed a number of novel experiments. First of all, we have included both DNp63a NLSn and NLSc mutants in DNA binding/p53 -inhibition assays shown in original Figure 7. The new data is shown in Figure 4E and Supplementary Figure__ S5__. As expected, such mutants had a much smaller effect on DNA binding/p53-inhibition as compared to the NLSbip mutant, further establishing a functional link between p63 nuclear levels and transcriptional activity, and proving the functional relevance of the compensatory mechanism evolved by p63 to tolerate the effect of mutations inactivating either NLSn or NLSc.

      In addition, and as specifically suggested by the Reviewer, we have measured the effect of NLS impairing mutations on the ability of DNap63 to transactivate the K14 and the Bax promoters, which. Our results, shown in revised Figure 4F and 4G, as well as in Supplementary Figure S6 clearly show that both DNp63a NLSn and NLSc mutants transactivate the promoters at undistinguishable levels compared to the wild-type, consistent with their minimal effect on DNA binding and nuclear transport, while the NLSbip mutation, which prevents nuclear localization and DNA binding, also prevents transcriptional transactivation.

      __1.2. A minor suggestion: authors could consider use p63 rather than ΔNp63α in the manuscript. The heterogenous sequences of NLS regions are relevant for the delta isoform of p63. In addition, all experiments performed in the study are not necessarily specific for the biology of the ΔNp63α isoform, but they are probably informative for all p63 isoforms. __

      We thank the Reviewer for this suggestion. We have modified the text in the discussion to introduce this concept. Indeed, we expect the bipartite NLS to mediate nuclear transport of most p63 isoforms, whereas the p63 delta isoform, which lacks NLSn, would be transported into the nucleus by NLSc. We have modified the text in the Discussion section to make this point clearer and more explicit "the bipartite NLS identified here is responsible for nuclear localization of most p63 isoforms, while p63 delta is transported into the nucleus by NLSc: SIKKRRSPD)." To further corroborate this statement, we have also included new data obtained with the TAp63a and gNp63a isoforms. Our data clearly show that nuclear import of both isoforms depends on the NLSbip identified here and is mediated by the IMPa/b1 heterodimer, so that the findings obtained for the ΔNp63α isoform can be generalized to others. The new data is shown in Figure 3 and in Supplementary Figure S3.

      __1.3. Another minor suggestion: As p63 forms a tetramer when binding to DNA sequence for gene regulation, it would be good for authors to speculate the role of NLS and its variations in tetramerization. __

      We thank the Reviewer for such comment. Since the NLS is located outside of the tetramerization domain, it is not expected to play a direct role in tetramerization. We have addressed this issue by generating computational models of ΔNp63α and DNp63α;mNLS dimers and tetramers to allow a direct comparison. The new data is shown in Figure 5A-D and Supplementary Figure S11A-D. The data suggests that mutation of the NLS residues, which lies outside of the oligomerizaiton domain, does not affect ΔNp63α oligomerization abilities supporting the experimental evidences from Figure 5E (BRET experiments).

      __

      2.1. In immunofluorescence images it is sometime difficult to see nuclear accumulation. Single channels of the GFP signal may help to make the point. __

      We thank the Reviewer for pointing out this issue. We have provided single channels for every microscopic image in Supplemental Figures.

      __ 2.2. The binding assays in Fig. 3 would profit from using the most efficient imp a variant together with imp beta to show potential cooperative binding.__

      We thank the Reviewer for such comment, which helped enhancing the physiological relevance of our binding data. We have now introduced the requested data in Supplementary Figure S2A. In the revised Figure panel, we compared binding of FITC-labelled p63-NLS peptide to either full length IMPa1 alone, IMPa1DIBB and pre-heterodimerized IMPa1/IMPb1 complex. The data are consistent with a classical binding mode whereby interaction with IMPb1 releases full length IMPa1 binding minor and major binding sites by engaging with the autoinhibitory IBB domain. To corroborate our results even further and demonstrate the bipartite nature of p63 NLS identified here, we have also performed FP experiments between p63-NLS and LTA SV40 NLS (a well characterized monopartite NLS) in the presence of either wt IMPa1DIBB or its minor and major site mutants. As expected from a bipartite NLS, either mutation impaired binding significantly, whereas the mutation of the minor site had a much smaller effect on binding of SV40 LTA NLS. The new data, shown in Supplementary Figure S2BC and Supplementary Table S3 confirm our hypothesis by highlighting a very strong binding affinity reduction of p63 NLS peptide for IMPa1 major site mutant (

      __2.3. please mention that NTR can also recognize 3D structures of structural RNAs, e.g. tRNAs or miRNAs __

      We thank the Reviewer for this very useful suggestion. We have now introduced this concept in the Introduction and added two references to support our statement. The paragraph is as follows: "Additionally, Exportin 5 and Exportin-T evolved to recognize specific RNA structures within pre-miRNAs and t-RNAs, respectively (5, 6)."

      2.4. longer TA isoforms

      We have added corrected the typo and we thank the Reviewer for noticing it.

      __ 2.5. homologues or orthologues? __

      We thank the reviewer for pointing out this issue. We have corrected the text, so now IMPas and members of the p53 family are referred to as paralogs and not as orthologs

      __3.1. The major function of DNp63a seems to be that of a bookmarking factor that ensures the establishment of an epithelial transcriptional program. It is found to bind more to enhancer than to promoter regions. While it might also act for a few genes as a classical transcription factor (K14). this bookmarking and interaction with other transcriptional regulators seems to be its major task. This should be included in the introduction. __

      We thank the Reviewer for this suggestion. The Introduction has been modified as requested to incorporate this important concept "Additionally, p63 has been shown to act as a pioneer factor, shaping the chromatin and enhancer landscape, thus regulating accessibility to activating and repressing transcription factors (18-20)."

      __ 3.2. "DNp63a can be imported into the nucleus as a dimer" What is the evidence that DNp63a is imported as a dimer and not as a tetramer? Although functional not really relevant, because all conclusions drawn for a dimer are true for a tetramer (such as the mutation compensation), this statement (and others in the text) should either be substantiated or modified. __

      The Reviewer is correct in pointing out that, while p63 isoforms bind DNA as tetramers (7), the precise oligomeric state at which nuclear import occurs is not firmly established. Indeed, little is known about the regulation of the p63 oligomerization process during nucleocytoplasmic trafficking. While TA isoforms are generally maintained in an inactive, closed, and dimeric conformation-requiring external stimuli such as phosphorylation to undergo activation and tetramerization-ΔNp63α has been reported to form tetramers even in the absence of such stimuli (4, 8). In light of this, we have modified the text to explicitly acknowledge the possibility that ΔNp63α may be transported into the nucleus either as a dimer or as a tetramer, rather than implying a single obligatory oligomeric state.

      Importantly, to directly address the Reviewer's concern, we have broadened the scope of the manuscript to include additional p63 isoforms, particularly TAp63α, which is predominantly present as a dimer under basal conditions. Our new data (Figure 3) demonstrate that TAp63α is efficiently translocated into the nucleus via the IMPα/β1 heterodimer in an NLSbip-dependent manner. Notably, despite its inability to form tetramers, TAp63α displays a similar tolerance to mutations that inactivate individual basic clusters within the bipartite NLS, analogous to what is observed for ΔNp63α (Supplementary Figure S11).

      Together, these results formally demonstrate that dimerization is sufficient to support efficient nuclear import in the presence of NLS-inactivating mutations, and that higher-order oligomerization (i.e., tetramerization) is not required for this property. We have therefore revised the manuscript accordingly to avoid over-interpretation and to more accurately reflect the experimental evidence.

      __ 3.3. The explanation for the difference in the sensitivity of mutations in the bipartite NLS in the isolated peptide experiments and experiments with the full length DNp63a is intriguing. Unfortunately, it is not based on direct experimental evidence. To proof their model (which is the central claim of this manuscript) they should fuse the bipartite NLS to any dimerization module (e.g. a leucine zipper sequence) and show that by dimerization of the bipartite NLS the same results towards mutations are obtained as for full length DNp63a. This would strongly support their model. __

      We agree that the model for nuclear transport is a central claim of our work, and deserves additional experimental validation. In order to support our hypothesis, in the revised manuscript we have generated a number of additional DNp63a mutants uncapable of self-interaction, based on deletion of residues 301-347(p63-DOD).

      We have now:

      (i) Validated the inability of the DOD mutant to self-interact by means of BRET assays in living cells, whereby a strong decrease in BRET ratio is observed compared to wild-type DNp63a (New Figure 6E and New Supplementary Figure S8).

      (ii) Shown that, in such context, substitution of either the N-terminal or C-terminal basic stretch of amino acids in the NLS is sufficient to impact p63 nuclear import, whereas in the context of the full-length protein, they are not (New Figure 6F-H, and New Supplementary Figure S9).

      (iii) Shown that while FLAG-p63 wt could relocalize to the nucleus YFP-p63mNLSbip but not YFP-p63;DOD;mNLSbip (New Supplementary Figure S10).

      We believe that these new data further demonstrate the impact of p63 self-association on subcellular localization and strongly support our hypothesis. We greatly thank the Reviewer for their inspiring comment, which led to a significant improvement of our manuscript.

      References

      Lotz R, Osterburg C, Chaikuad A, Weber S, Akutsu M, Machel AC, et al. Alternative splicing in the DBD linker region of p63 modulates binding to DNA and iASPP in vitro. Cell Death Dis. 2025;16(1):4. Ciribilli Y, Monti P, Bisio A, Nguyen HT, Ethayathulla AS, Ramos A, et al. Transactivation specificity is conserved among p53 family proteins and depends on a response element sequence code. Nucleic Acids Res. 2013;41(18):8637-53. Monti P, Ciribilli Y, Bisio A, Foggetti G, Raimondi I, Campomenosi P, et al. ∆N-P63alpha and TA-P63alpha exhibit intrinsic differences in transactivation specificities that depend on distinct features of DNA target sites. Oncotarget. 2014;5(8):2116-30. Pitzius S, Osterburg C, Gebel J, Tascher G, Schafer B, Zhou H, et al. TA*p63 and GTAp63 achieve tighter transcriptional regulation in quality control by converting an inhibitory element into an additional transactivation domain. Cell Death Dis. 2019;10(10):686. Okada C, Yamashita E, Lee SJ, Shibata S, Katahira J, Nakagawa A, et al. A high-resolution structure of the pre-microRNA nuclear export machinery. Science. 2009;326(5957):1275-9. Kutay U, Lipowsky G, Izaurralde E, Bischoff FR, Schwarzmaier P, Hartmann E, et al. Identification of a tRNA-specific nuclear export receptor. Mol Cell. 1998;1(3):359-69. Enthart A, Klein C, Dehner A, Coles M, Gemmecker G, Kessler H, et al. Solution structure and binding specificity of the p63 DNA binding domain. Scientific reports. 2016;6:26707. Deutsch GB, Zielonka EM, Coutandin D, Weber TA, Schafer B, Hannewald J, et al. DNA damage in oocytes induces a switch of the quality control factor TAp63alpha from dimer to tetramer. Cell. 2011;144(4):566-76.

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      Referee #3

      Evidence, reproducibility and clarity

      Demarinis et al describe a detailed analysis of different stretches of basic amino acids located between the DBD and the OD of DNp63a to act as nuclear localization signals. They convincingly show that two stretches exist that form a bipartite NLS. They combine both functional import data with structure determination of the NLS sequence with IMP⍺ showing that both parts interact with the major and the minor site. The data are presented well provide a very good model of how nuclear important is regulated for DNp63a.

      This is a nice study of the bipartite NLS of DNp63a. Most interestingly, the authors show that nuclear import experiments using either the isolated peptide fused to GFP or DNp63a have a different outcome when the individual sequences are mutated. While in the case of the isolated peptide experiments a mutation in either of the two sequences has a measurable effect, this is not the case in the full length DNp63a context. The authors explain this with the oligomeriic state of DNp63a, which provides additional sequences from the other monomers within the tetramer, even when one of the NLS sequences is mutated. They provide alphaFold models to support this explanation. This in trans substitution effect explains why the NLS is not a mutation hotspot for inactivating DNp63a. These results are new and interesting in the context of how DNp63a regulates the development of epithelial tissues.

      Criticism:

      1. The major function of DNp63a seems to be that of a bookmarking factor that ensures the establishment of an epithelial transcriptional program. It is found to bind more to enhancer than to promoter regions. While it might also act for a few genes as a classical transcription factor (K14). this bookmarking and interaction with other transcriptional regulators seems to be its major task. This should be included in the introduction.
      2. "DNp63a can be imported into the nucleus as a dimer" What is the evidence that DNp63a is imported as a dimer and not as a tetramer? Although functional not really relevant, because all conclusions drawn for a dimer are true for a tetramer (such as the mutation compensation), this statement (and others in the text) should either be substantiated or modified.
      3. The explanation for the difference in the sensitivity of mutations in the bipartite NLS in the isolated peptide experiments and experiments with the full length DNp63a is intriguing. Unfortunately it is not based on direct exerimental evidence. To proof their model (which is the central claim of this manuscript) they should fuse the bipartite NLS to any dimerization module (e.g. a leucine zipper sequence) and show that by dimerization of the bipartite NLS the same results towards mutations are obtained as for full length DNp63a. This would strongly support their model.

      Significance

      Demarinis et al describe a detailed analysis of different stretches of basic amino acids located between the DBD and the OD of DNp63a to act as nuclear localization signals. They convincingly show that two stretches exist that form a bipartite NLS. They combine both functional import data with structure determination of the NLS sequence with IMP⍺ showing that both parts interact with the major and the minor site. The data are presented well provide a very good model of how nuclear important is regulated for DNp63a.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      General assessment:

      The authors show a number of generally very solid experiments that consistently support what is stated in the headline and further developed. They use wt and recombinant deltaN63alpha (N63) to sort out a previously published NLS whose inactivation did not lead to preventing nuclear localization of N63. The authors convincingly show that import is governed by a bipartite NLS. The interesting observation is that - when the bipartite stretch is transferred to GFP to drive the import, each motif is required - but the full-length protein tolerates alterations in either motif. The puzzle is solved by further structural analysis of binding of the NLS to importin alpha that shows the bipartite signal to work as expected. However, additional binding studies using BRET demonstrate dimerization that brings two copies of N63 and thus two bipartite signals together that compensate for mutations in one or the other part. Transcriptional activity of p53 can be modulated consistently with nuclear import, i.e. functional NLS motifs.

      The manuscript is overall in a very mature state, and I foresee publication essentially in its present form. A few suggestions may be considered prior to publication:

      1. In immunofluorescence images it is sometime difficult to see nuclear accumulation. Single channels of the GFP signal may help to make the point.
      2. The binding assays in Fig. 3 would profit from using the most efficient imp a variant together with imp beta to show potential cooperative binding.
      3. wording:

      please mention that NTR can also recognize 3D structures of structural RNAs, e.g. tRNAs or miRNAs

      longer TA isoforms

      homologues or orthologues?

      Significance

      General assessment:

      see above: this is a very consistent and mature study that can be pubslihed essentially in its present form.

      Advance:

      Even though the described mechanisms are not novel, they clarify how N63 is imported into human cell nuclei. We understand that in molecular mechanism and can deduce that the amounts of nuclear N63 are directly linked to its transcriptional response on p53.

      Audience:

      I see that this is interesting to experts in the nucleo-cytoplasmic transport field since it adds a novel aspect how robustness of import via dimerization can be reached. Beyond, the work brings news in translational research for physiology and pathology of epithelial tissue differentiation and homeostasis.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, authors performed a solid biochemical and biophysical study to identify the nuclear localization signal (NLS) of the transcription factor p63 and its regulatory mechanism. By deletion and mutagenesis experiments, the two partially overlapping NLS were identified. They were shown to have relatively minor consequences for nuclear localization when disrupted individually but the nuclear localization was abolished when both were affected. The nuclear localization was important for transactivation activity but not for dimerization. In addition, authors also performed bioinformatics analysis and showed that sequences of part of these NLS were diverse in p63 in different species. This led to the conclusion that NLS of p63 is quite robust for nuclear localization which not easily affected by sequence divergence. This is important information for the p63 field.

      Major comments

      The biochemical and biophysical experiments performed in this study were well designed, data were clear and the conclusions were well supported by the results. One potential improvement is to check whether NLS could affect the normal activation targets of ΔNp63α, such as KRT14 and other epithelial genes. This could complement the experiments testing the inhibition effect of ΔNp63α on p53-mediated gene activation. This will be interesting, as ΔNp63α is a master regulator in epithelial cells via regulation of diverse epithelial genes.

      Major comments

      The biochemical and biophysical experiments performed in this study were well designed, data were clear and the conclusions were well supported by the results. One potential improvement is to check whether NLS could affect the normal activation targets of ΔNp63α, such as KRT14 and other epithelial genes. This could complement the experiments testing the inhibition effect of ΔNp63α on p53-mediated gene activation. This will be interesting, as ΔNp63α is a master regulator in epithelial cells via regulation of diverse epithelial genes.

      Minor comments

      A minor suggestion: authors could consider use p63 rather than ΔNp63α in the manuscript. The heterogenous sequences of NLS regions are relevant for the delta isoform of p63. In addition, all experiments performed in the study are not necessarily specific for the biology of the ΔNp63α isoform, but they are probably informative for all p63 isoforms. Another minor suggestion: As p63 forms a tetramer when binding to DNA sequence for gene regulation, it would be good for authors to speculate the role of NLS and its variations in tetramerization.

      Significance

      In this manuscript, authors performed a biochemical and biophysical study on nuclear localization signal (NLS) of the transcription factor ΔNp63α, a topic that is not yet fully understood. Previous study did not yet provide sufficiently convincing evidence for NLS that is essential for ΔNp63α nuclear localization. Authors also investigated the robustness of the NLS and its function, which provides important information for the field of p63, a key factor in epithelial development and in cancer.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      Authors should be commended for the availability of data/code and detailed methods. Clarity is good. Authors have clearly spent a lot of time thinking about the challenges of metabolomics data analysis.

      Significance

      Schmidt et al. present MetaProViz, a comprehensive and modular platform for metabolomics data analysis. The tool provides a full suite of processing capabilities spanning metabolite annotation, quality control, normalization, differential analysis, integration of prior knowledge, functional enrichment, and visualization. The authors also include example datasets, primarily from renal cancer studies, to demonstrate the functionality of the pipeline. The MetaProViz framework addresses several long-standing challenges in metabolomics data analysis, particularly issues of reproducibility, ambiguous metabolite annotation, and the integration of metabolite features with pathway knowledge. The platform is likely to be a valuable addition for the community, but the reviewer has some comments that need to be addressed prior to publication.

      We thank the reviewer for this positive feedback.

      Comments:

      (1) (Planned)

      The section "Improving the connection between prior knowledge and metabolomics features" could benefit from additional clarification. It is not entirely clear to the reader what specific steps were taken beyond using RaMP-DB to translate metabolite identifiers. For example, how exactly were ambiguous mappings ("different scenarios") handled in practice, and to what extent does this process "fix" or merely flag inconsistencies? A more explicit description or example of how MetaProViz resolves these cases would help readers better understand the improvements claimed.

      We thank the reviewer for pointing this out and we agree that this section requires extension to ensure clarity. Beyond using RaMP-DB, we are characterising the mapping ambiguity (one-to-none, one-to-many, many-to-one, many-to-many) within and across metabolite-sets (i.e. pathways) and return this information to the user together with the translated identifiers. This is important to understand potential inflation/deflation of metabolite-sets that occur due to the translation. Moreover, we also offer the manually curated amino-acid collection to ensure L-, D- and zwitterion without chirality IDs are assigned for aminoacids (Fig. 2b). Ambiguous mappings are handled based on the measured data (Fig. 2e). Indeed, many translation cases that deflate (many-to-one mapping) or inflate (one-to-many mapping) the metabolite-sets are resolved when merging the prior knowledge with actual measured data (i.e. Fig. 2e, one-to-many in scenario 1, which becomes obsolete as only one/none of the many potential metabolite IDs is detected). By sorting each mapping into one of those scenarios, we only flag those cases. The reason for this decision has been that in many cases multiple decisions are valid (i.e. Fig. 2e, Scenario 5: Here the values of the two detected metabolites could be summed or the metabolite value with the larger Log2FC could be kept) and it should really be up to the user to make those dependent on their knowledge of the biological system and the analytical LC-MS method used.

      Since these points have not been clear enough, we will add a more explicit description to the results section by showcasing more details on how we exactly tackled this problem in the ccRCC example data. This has also been suggested by Reviewer 3 (Minor Comment 7 and 8), so feel free to also see the responses below.

      (2) (Planned)

      The introduction of MetSigDB is intriguing, but its construction and added value are not sufficiently described. It would be helpful to clarify what specific advantages MetSigDB provides over directly using existing pathway resources such as KEGG, Reactome, or WikiPathways. For example, how many features, interactions, or metabolite-set relationships are included, and in what way are these pathways improved or extended compared to those already available in public databases?

      We thank the reviewer for this valuable comment and we apologise that this was not described sufficiently. One of the major advantages is that all the resources are available in one place following the same table format without the need to visit the different original resources and perform data wrangling prior to enrichment analysis. In addition, where applicable, we have removed metabolites that are not detectable by LC-MS (i.e. ions, H2O, CO2) to circumvent pathway inflation with features that are never within the data and hence impacting the statistical testing in enrichment analysis workflows.

      During the revision, we will compile an Extended Data Table listing all the resources present in MetSigDB, their number of features and interactions. We will also extend the methods section "Prior Knowledge access" about MetSigDB and how we removed metabolites.

      (3)

      Figure 1D/1E: The reviewer appreciates the inclusion of the visualizations illustrating the different mapping scenarios, as these effectively convey the complexity of metabolite ID translation. However, it took some time to interpret what each scenario represented. It would be helpful to include brief annotations or explanatory text directly on the figures to clarify what each scenario depicts and how it relates to the underlying issue being addressed.

      *We think the reviewer refers to Fig. 2D/E and we acknowledge that this is a complex problem we try to convey. We received a similar comment from Reviewer 2 (Minor Comment 1), who asked to extend the figure legend description of what the different scenarios display. *

      We have extended the figure legend and specifically explained each displayed case and its meaning (Line 222-242):

      "d-e) Schematics of possible mapping cases between metabolite IDs (= each circle corresponds to one ID) of a pathway-metabolite set (e.g. KEGG) to metabolites IDs of a different database (e.g. HMDB) with (d) showing many-to-many mappings that can occur within and across pathway-metabolite sets and (e) additionally showing the mapping to metabolite IDs that were assigned to the detected peaks within and across pathway-metabolite sets. (d) __Translating the metabolite IDs of a pathway-metabolite set can lead to special cases such as many-to-one mappings (Pathway 1), where for example the original resource used the ID for L-Alanine (Pathway 1, green) and D-Alanine (Pathway 1, yellow) in the amino-acid pathway, whilst the translated resources only has an entry for Alanine zwitterion (Pathway 1, blue). Additionally, many-to-one mappings can also occur across pathways (Pathway 2-4), where this mapping is only detected when mappings are analysed taking all pathways into account. Both of these cases deflate the pathways, which can also happen for one-to-none mappings (Pathway 1, white). There are also cases that inflate the pathway such as one-to-many mappings (e.g. Pathway 2-4, orange mapping to pink and violet). (e)__ Showcasing the different scenarios when merging measured data (detected) based on the translated metabolites within pathways (scenario 1-5) and across pathways (scenario 6-8) highlighting problematic scenarios (4-7) that require further actions. Unproblematic scenarios (1-3 and 8) can include special cases between original and translated (i.e. one-to-many in scenario 1), which become obsolete as only one/none of the many potential metabolite IDs is detected. Yet, if multiple metabolites are detected action is required (scenario 5), which can include building the sum of the multiple detected features or only keeping the one with the highest Log2FC between two conditions. Other special cases between original and translated (i.e. many-to-one in scenario 4 and 6) also depend on what has been mapped to the measured features. If features have been measured in those scenarios, pathway deflation (i.e. only one original entry remains) or measured feature duplication (the same measurement is mapped to many features in the prior knowledge) are the possible results within and across pathways. Those scenarios should be addressed on a case-by-case basis as they also require biological information to be taken into account."

      We have also rearranged the Scenarios in Fig. 2e. We hope that together with the extended figure legend this is now clear.

      (4) (Planned)

      "By assigning other potential metabolite IDs and by translating between the present ID types, we not only increase the number of features within all ID types but also increase the feature space with HMDB and KEGG IDs (Fig. 2a, right, SFig. 2 and Supplementary Table 1)". The reviewer would appreciate additional clarification on how this was done. It is not clear what specific steps or criteria were used to assign additional metabolite IDs or to translate between identifier types. The reviewer also appreciates the inclusion of the UpSet plots. However, simply having the plots side-by-side makes it difficult to determine the specific differences. An alternative visualization, such as stacked bar plots, scatter plots summarizing the changes in feature counts, or other representation that more clearly highlights the deltas, might make these results easier to interpret.

      The main Fig. 2a shows the original (left) metabolite ID availability per detected metabolite feature in the ccRCC data and the adapted (right) metabolite IDs. The individual steps taken to extend the metabolite ID coverage of the measured features and obtain Fig 2a (right), are shown in SFig. 2 for HMDB (SFig. 2a) and KEGG (SFig. 2b). We did not include the plots for the pubchem IDs as they follow the same principle. The individual steps we are showcasing with SFig. 2 are (I) How many of the detected features (577) have a HMDB ID (341, red bar + grey bar), (II) How this distribution changed after equivalent amino-acid IDs are added, which does not change the number of features with an HMDB ID, but the number of features with a single HMDB ID, and (III) How this distribution changed after translating from the other available ID types (KEGG and PubChem) to HMDB IDs using RaMP-DBs knowledge, which leads to 430 detected features with one or multiple HMDB IDs. The exact numbers can be extracted from Supplementary Table 1, Sheet "Feature metadata", where for example N-methylglutamate had no HMDB ID assigned in the original publication (see column HMDB_Original), yet by translating HMDB from KEGG (hmdb_from_kegg) and PubChem (see column hmdb_from_pubchem) we obtain in both cases the same HMDB ID "HMDB0062660". In order to clarify this in the manuscript, we have extended the figure legend of SFig. 2: "a-b) Bargraphs showing the frequency at which a certain number of metabolite IDs per integrated peak are available as per ccRCC patients feature metadata provided in the original publication (left), after potential equivalent IDs for amino-acid and amnio-acid-related features were assigned (middle), which increases the number of features with multiple (middle: grey bars) and after IDs were translated from the other available ID types (right). for a) Of 577 detected features, 341 had at least one HMDB IDs assigned (left graph, red + grey bar) according to the original publication (left). Translating from KEGG-to-HMDB and from PubChem-to-HMDB increased the number of features with an HMDB ID from 341 to 430 (left). and __b) __Of 577 detected features, 306 had at least one KEGG IDs assigned (left graph, red + grey bar) according to the original publication (left). Translating from HMDB-to-KEGG and from PubChem-to-KEGG did not increase the total number of features with an KEGG ID (left)."

      We like the suggestion of the reviewer to provide representations of the deltas and will add additional plots to SFig. 2 as part of our planned revision.

      (5) (Planned)

      MetaboAnalyst is mentioned several times in the manuscript. The reviewer is familiar with some of the limitations and practical challenges associated with using MetaboAnalyst and its R package. Given that MetaboAnalyst already offers some overlapping functionality with MetaProViz (and offers it in the form of an interactive website and a sometimes functional R package), a more explicit comparison between the two tools would help readers fully understand the unique advantages and improvements provided by MetaProViz.

      This is a good point the reviewer raises. As part of the revisions, we plan to create a supplementary data table that includes both tools and their respective features. We will refer to this table within the manuscript text.

      (6)

      Page 11: The authors state that they used limma for statistical testing, including for the analysis of exometabolomics data, where the values appear to represent log2-transformed distances or ratios rather than normally distributed intensities. Since limma assumes approximately normal residuals, please provide evidence or justification that this assumption holds for these data types. If the distributions deviate substantially from normality, a non-parametric alternative might be more appropriate.

      For exometabolomics data we use data normalised to media blank and growth factor (formula (1)). Limma is performed on those data, not on the log2-transformed distances. The Log2(Distance) is calculated separately to the statistical results using the normalised exometabolomics data. In addition, we always perform the Shapiro-Wilk test as part of MetaProViz differential analysis function on each metabolite to understand the distribution. In this particular case we have the following distributions:

      Cell line

      Metabolites normal distribution [%]

      Metabolites not-normal distribution [%]

      HK2

      82.35

      17.65

      786-O

      95.71

      4.29

      786-M1A

      97.14

      2.86

      786-M2A

      88.57

      11.43

      OSRC2

      92.86

      7.14

      OSLM1B

      85.71

      14.29

      RFX631

      97.14

      2.86

      If a user would have distributions that deviate substantially from normality, non-parametric alternatives are also available in MetaProViz (see methods section for all options).

      7)

      Page 13: why were young and old defined this way? Authors should provide their reasoning and/or citations for this grouping.

      We thank the reviewer for pointing this out. The explanation of our choices of the age groups is purely based on the literature:

      First, ccRCC can be sporadic (>96%) or familial (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308682/pdf/nihms362390.pdf). This was also observed in other cohorts, where of 1233 patients only 93 were under 40 years of age (%, whilst 1140 (%) were older than 40 years (https://www.europeanurology.com/article/S0302-2838(06)01316-9/fulltext). Second, given the high frequency of sporadic cases it is unsurprising that ccRCC incidences were found to peak in patients aged 60 to 79 years with more male than female incidences (https://journals.lww.com/md-journal/Fulltext/2019/08020/Frequency,_incidence_and_survival_outcomes_of.49.aspx). Third, it was shown that sex impacts on the renal cancer-specific mortality and is modified by age, which is a proxy for hormonal status with premenopausal period below 42 years and postmenopausal period above 58 years (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4361860/pdf/srep09160.pdf). Putting all of this information together, we decided on our age groups of young (58years) following the hormonal period in order to account for sex impact. Additionally, our young age group is representative of the age of familial ccRCC, whilst our old age group summarises the age group where incidences were found to peak.

      To make this clear in the manuscript we have extended the method section of the manuscript (Line 547-548):

      "For the patient's ccRCC data, we compared tumour versus normal of two patient subset, "young" (58years)."

      (8)

      Figure 4e: It may help with interpretation to have these Sankey-like graph edges be proportional to the number of metabolites.

      We thank the reviewer for this suggestion, which we also pondered. When we tested this visualisation, the plot became convoluted, hard to interpret and not all potential flows exist in the data. This is why we have opted to create an overview graph of each potential flow, with each edge representing a potentially existing flow. The number of times a flow exists is shown in Fig. 4f.

      (9)

      Figure 4h: The values appear to be on an intensity scale (e.g., on the order of 3e10), yet some of them are negative, which would not be expected for raw or log-transformed mass spectrometry intensities. It is unclear whether these represent normalized abundance values, distances, or some other transformation. In addition, for the comparison of tumour versus normal tissue, it is not specified what statistical test was applied. Since mass spectrometry data are typically log2-transformed to approximate a log-normal distribution before performing t-tests or similar parametric methods, clarification is needed on how these data were processed.

      Thanks for pointing this out, it made us realize that we need to extend our figure legend for clarity for Fig. 4h (Line 343-345). In both cases we show normalized intensities following the workflow described in Fig. 3a. In case of the left graph labelled "CoRe", we are plotting an exometabolomics experiment, were additionally normalised using both media blanks (samples where no cells were cultured in) and growth factor (accounts for cell growth during experiment) as growth rate (accounts for variations in cell proliferation) has not been available (see also formula (1) in methods section). A result has a negative value if the metabolite has been consumed from the media, or a positive value if the metabolite has been released from the cell into the culture media.

      In addition, the reviewer refers to the comparison of tumour versus normal (Fig. 4a __and 4d__) and the missing description of the chosen statistical test. We have added the details to the figure legend (Lines 334 and 345).

      Adapted legend Fig. 4: "a) Differential metabolite analysis results for exometabolomics data comparing 786-O versus HK2 cells using Annova and false discovery rate (FDR) for p-value adjustment. b) __Heatmap of mean consumption-release of the measured metabolites across cell lines. c) Heatmap of normalised ccRCC cell line exometabolomics data for the selected metabolites of amino acid metabolism for a sample subset. __d) __Differential metabolite analysis results for intracellular data comparing 786-O versus HK2 cells using Annova and false discovery rate (FDR) for p-value adjustment. __e) __Schematics of bioRCM process to integrate exometabolomics with intracellular metabolomics and __f) __number of metabolites by their combined change patterns in intracellular- and exometabolomics in 786-M1A versus HK2. g)__ Heatmap of the metabolite abundances in the "Both_DOWN (Released/Comsumed)" cluster. __h) __Bar graphs of normalised methionine intensity for exometabolomics (CoRe: negative value, if the metabolite has been consumed from the media, or a positive value, if the metabolite has been released from the cell into the culture media) and intracellular metabolomics (Intra)."


      (10)

      Figure 5: "Tukey's p.adj We thank the reviewer for pointing this out. We have used the TukeyHSD (Tukey's Honestly Significant Difference) test in R on the Anova results. We have added more details into the figure legend (Line 384): "(Tukey's post-doc test after anova p.adj<br /> (11)

      The potential for multi-omics is mentioned. Please clarify how generalizable this framework is. Can it readily accommodate transcriptomics, proteomics, or fluxomics data, or does it require custom logic or formatting for each new data type?

      Thanks for raising this question. MetaProViz can readily accommodate transcriptomics and proteomics data for combined enrichment analysis using for example MetalinksDB metabolite-receptor pairs. Yet, MetaProViz does not support modelling fluxomics data into metabolic networks. We state in the discussion that this could be future development ("Beyond current capabilities, future developments could also incorporate mechanistic modeling to capture metabolic fluxes, subcellular compartmentalization, enzyme kinetics, regulatory feedback loops, and thermodynamic constraints to dissect metabolic response under perturbations."). To clarify on the availability of multi-omics integration for combined enrichment analysis, we have added some more details into the discussion section.

      Line 467-469: "In addition, providing knowledge of receptor-, transporter- and enzyme-metabolite pairs, MetaProViz can readily accommodate transcriptomics and proteomics data for combined enrichment analysis."

      (12)

      Please clarify if/how enrichment analyses account for varying set sizes and redundant metabolite memberships across pathways, which can bias over-representation analysis results.

      This is a very relevant point, which we have already been working on. Indeed, we agree that enrichment results from enrichment analyses can be biased due to varying set sizes and redundant metabolite memberships across pathways. MetaProViz explicitly accounts for varying set sizes when running over representation analysis (functions standard_ora()and cluster_ora()), which uses a model that computes the p-value under a hypergeometric distribution. Thereby, larger pathways are penalized unless the overlap is proportionally large, while smaller pathways can be significant with fewer overlaps. Hence, the test quantifies whether the observed overlap between the query set and a pathway is larger than would be expected under random sampling. In addition, we explicitly filter by gene‑set size using min_gssize/max_gssize, which further controls for extreme small or large sets. So both the statistical test itself and the size filters incorporate gene‑set size variation.

      Regarding the redundant metabolite-set (i.e. pathways) memberships, we have now implemented a new function (cluster_pk()) to cluster metabolite-sets like pathways based on overlapping metabolites. Thereby we allow investigation of enrichment results in regard to redundancy and similarity. For given metabolite-sets, the function calculates pathway similarities via either overlap- or correlation-based metrics. After optional thresholding to remove weak similarities, we implemented three clustering algorithms (connected-components clustering, Louvain community detection and hierarchical clustering) to group similar pathways. We then visualize the clustering results as a network graph using the new function viz_graph based on igraph. We have added all information into our methods section "Metabolite-set clustering" (Lines 656-671). In addition, we have also added the results of the clustering into Fig. 5f.

      New Fig. 5f:"f) *Network graph of top enriched pathways (p.adjusted

      Reviewer #2

      Evidence, reproducibility and clarity

      Schmidt et al report the development of MetaProViz, an integrated R package to process, analyze and visualize metabolomics data, including integration with prior knowledge. The authors then go on to demonstrate utility by analyzing several metabolomes of cell lines, media and patient samples from kidney cancer. The manuscript provides a concise description of key challenges in metabolomics that the authors identify and address in their software. The examples are helpful and illustrative, although I should point out that I lack the expertise to evaluate the R package itself. I only have a few very minor comments.

      Significance

      This is a very significant advance from one of the leading groups in the field that is likely to enhance metabolomics data analysis in the wider community.

      We thank the reviewer for this positive feedback on our package. We appreciate that there are no major comments from the reviewer.

      Minor comments:

      (1)

      Figure 2D, E: While the schematics are fairly intuitive, a brief figure legend description of what the different scenarios etc. represent would make this easier to grasp.

      We thank the reviewer for pointing this out and we acknowledge that this is a complex problem we try to convey. We received a similar comment from Reviewer 1 (Comment 3), so please see the extensive response there. In brief, we have extended the figure legend and specifically explained each displayed case and its meaning (Line 222-242) and extended the Figure itself by adding additional categories to Fig. 2e.

      Extended legend Fig.2 d-e: "d-e) Schematics of possible mapping cases between metabolite IDs (= each circle corresponds to one ID) of a pathway-metabolite set (e.g. KEGG) to metabolites IDs of a different database (e.g. HMDB) with (d) showing many-to-many mappings that can occur within and across pathway-metabolite sets and (e) additionally showing the mapping to metabolite IDs that were assigned to the detected peaks within and across pathway-metabolite sets. (d) __Translating the metabolite IDs of a pathway-metabolite set can lead to special cases such as many-to-one mappings (Pathway 1), where for example the original resource used the ID for L-Alanine (Pathway 1, green) and D-Alanine (Pathway 1, yellow) in the amino-acid pathway, whilst the translated resources only has an entry for Alanine zwitterion (Pathway 1, blue). Additionally, many-to-one mappings can also occur across pathways (Pathway 2-4), where this mapping is only detected when mappings are analysed taking all pathways into account. Both of these cases deflate the pathways, which can also happen for one-to-none mappings (Pathway 1, white). There are also cases that inflate the pathway such as one-to-many mappings (e.g. Pathway 2-4, orange mapping to pink and violet). (e)__ Showcasing the different scenarios when merging measured data (detected) based on the translated metabolites within pathways (scenario 1-5) and across pathways (scenario 6-8) highlighting problematic scenarios (4-7) that require further actions. Unproblematic scenarios (1-3 and 8) can include special cases between original and translated (i.e. one-to-many in scenario 1), which become obsolete as only one/none of the many potential metabolite IDs is detected. Yet, if multiple metabolites are detected action is required (scenario 5), which can include building the sum of the multiple detected features or only keeping the one with the highest Log2FC between two conditions. Other special cases between original and translated (i.e. many-to-one in scenario 4 and 6) also depend on what has been mapped to the measured features. If features have been measured in those scenarios, pathway deflation (i.e. only one original entry remains) or measured feature duplication (the same measurement is mapped to many features in the prior knowledge) are the possible results within and across pathways. Those scenarios should be addressed on a case-by-case basis as they also require biological information to be taken into account."

      (2) Fig. 4: The authors briefly state that they integrate prior knowledge to identify the changes in methionine metabolism in kidney cancer, but it is not clear how exactly they contribute to this conclusion. It could be helpful to expand a bit on this to better illustrate how MetaProViz can be used to integrate prior knowledge into the analysis workflow.

      We think the reviewer refers to this section in the text (Line 363-370):

      "Next, we focused on the cluster "Both_DOWN (Released-Consumed)" and found that several amino acids are consumed by the ccRCC cell line 786-M1A but released by healthy HK2 cells. At the same time, intracellular levels are significantly lower than in HK2 (Log2FC = -0.9, p.adj = 4.4e-5) (Fig. 4g). To explore the role of these metabolites in signaling, we queried the prior knowledge resource MetalinksDB, which includes metabolite-receptor, metabolite-transporter and metabolite-enzyme relationships, for their known upstream and downstream protein interactors for the measured metabolites (Supplementary Table 5). This approach is especially valuable for exometabolomics, as it allows us to generate hypotheses about cell-cell communication. Notably, we identified links involving methionine (Fig. 4h), enzymes such as BHMT, and transporters such as SLC43A2 that were previously shown to be important in ccRCC25,42 (Supplementary Table 5)."

      We have now extended this part to clearly state that here MetalinkDB is the prior knowledge resource we used to identify the links for methionine (Line 363-364). In addition we have extended our summary statement to ensure clarity for the reader that we combine the biological clustering, which revealed the amino acid changes, with prior knowledge for the mechanistic insight (Line 380-381):

      "In summary, calculating consumption-release and combining it with intracellular metabolomics via biological regulated clustering reveals metabolites of interest. Further combining these results with prior knowledge using the MetaproViz toolkit facilitates biological interpretation of the data."

      (3)

      Given the functional diversity among metabolites -central to diverse pathways, are key signaling molecules, restricted functions, co-variation within a pathway - I wonder how informative approaches such as PCA or enrichment analyses are for identifying metabolic drivers of a (patho)physiological state. To some extent, this can be addressed by integrating prior knowledge, and it would be helpful if the authors could comment on (and if applicable explain) whether/how this is integrated into MetaProViz.

      The reviewer is correct in stating the functional diversity of metabolites, which is also why prior knowledge is needed to add mechanistic interpretation to the finding from the metadata analysis (as we showcased by focusing on the separation of age (Fig. 5c-d)). We think that approaches such as PCA or enrichment can be helpful, even if admittedly limited. For example, in the metadata analysis presented in Fig. 5b and the subsequent enrichment analysis presented in Fig. 5, we used PCA to extract the eigenvector and the loading, which act as weights indicating the contribution of each original metabolite to that specific principal components separation. Hence, the eigenvector of PCA shows the metabolite drivers of the separation. This does not necessarily mean that those metabolites are drivers of a (patho)physiological state - the (patho)physiological state can equally be the reason for those metabolites driving the separation on the Eigenvectors. Thus, the metadata analysis presented in Fig. 5b enables us to extract the metadata variables (patho)physiological states separated on a PC with the explained variance. This can also lead to co-variation, when multiple (patho)physiological states are separated on the same PC, as the reviewer correctly points out. Regarding the enrichment analysis, we provide different types of prior knowledge for classical mapping, but also the prior knowledge we used to create the biological regulated clustering, which together help to identify key metabolic groups as we can first cluster the metabolites and afterwards perform functional enrichment. Yet, this does not account for the technical issues of enrichment analysis. In this context multi-omics integration building metabolic-centric networks could further elucidate the diversity of metabolic pathways and connection to signalling and co-variation, yet this is not the scope of MetaProViz. To sum up, we are aware of the limitations of this analysis and the constraints on the downstream interpretation.

      To capture the functional diversity amongst metabolites, which leads to metabolites being present in multiple pathways of metabolite-pathways sets, we have implemented a new function to cluster metabolite-sets like pathways based on overlapping metabolites and visualize redundant metabolite-set (i.e. pathways) memberships (Fig.5f). For more details also see our response to Reviewer 1, Comment 12. We hope this will circumvent miss- and over-interpretation of the enrichment results.

      In addition, we have extended the text to include the analysis pitfalls explicitly (Line 416-419): "Another variable explaining the same amount of variance in PC1 is the tumour stage, which could point to adjacent normal tissue metabolic rewiring that happens in relation to stage and showcases that biological data harbour co-variations, which can not be disentangled by this method."

      Reviewer #3

      Evidence, reproducibility and clarity

      This manuscript introduces an R package MetaProViz for metabolomics data analysis (post anotation), aiming to solve a poor-analysis-choices problem and enable more people to do the analysis. MetaProViz not only guides people to select the best statistical method, but also enables to solve previously unsolved problems: e.g. multiple and variable metabolite names in different databases and their connections to prior knowledge. They also created exometabolomics analysis and the needed steps to visualise intra-cell / media processes. The authors demonstrated their new package via kidney cancer (clear-cell renal cell carcinoma dataset, steping one step closer to improve biological interpretability of omics data analysis.

      Significance

      This is a great tool and I can't wait to use it on many upcoming metabolomics projects! Authors tackle multiple ongoing issues within the field: from poor selection of statistical methods (they provide guidance or have default safer options) to the messiness of data annotation between databases and improving data interpretability. The field is still evolving quickly, and it's impossible to solve all problems with one package; thus some limitations within the package could be seen as a bit rigid. Nonetheless, this fully steps toward filling an existing methodological gap. All bioinformaticians doing metabolomic analysis, or those learning how to do it, will greatly benefit from this knowledge.

      I myself lead a team of 6 bioinformaticians, and we do analysis for researchers, clinicians, drug discovery, and various companies. We run internal metabolomics pipelines every day and fully sympathise with the problems addressed by the authors.

      Major comments affecting conclusions

      none.

      We thank the reviewer for this positive feedback on evidence, reproducibility and clarity as well as significance of our work given the reviewers experience with metabolomics data analysis mentioned. We appreciate that there are no major comments from the reviewer.

      Minor comments

      Minor comments, important issues that could be addressed and possibly improve the clarity or generally presentation of the tool. Please see all below.

      (1)

      1- You start with separating and talking about metabolomics and lipidomics, but lipidomics quickly dissapears (especially beyond abstract/intro) - no real need to discuss lipidomics.

      Thanks, that's a good note and we have removed it from the abstract and introduction.

      (2)

      2- You refer to the MetImp4 imputation web tool, but I cannot find an active website, manuscript, or R package for it, and the cited link does not load. This raises doubts about whether the tool is currently usable. Additionally, imputation choice should be guided by biological context and study design, not just by testing a few methods and selecting the one that performs best.

      We fully agree with the reviewer on imputation handling. The manuscript we cite from Wei et. al. (https://doi.org/10.1038/s41598-017-19120-0) compared a multitude of missing value imputation methods and made this comparison strategy available as a web-based tool not as any code-based package such as an R-package. Yet, the reviewer is right, the web-tool is no longer reachable. Hence, we have adapted the statement in our introduction (Line 61-62): "Moreover, there are tools that focus on specific steps of the pre-processing of feature intensities, which encompasses feature selection, missing value imputation (MVI)9 and data normalisation. For example, MetImp4 is a web-tool that includes and compares multiple MVI methods9. "

      (3)

      3- The authors address key metabolomics issues such as ambiguous metabolite names and isoforms, and their focus on resolving mapping ambiguities and translating between database identifiers is highly valuable. However, the larger challenge of de novo identification and the "dark matter" of unannotated metabolites remains unresolved (initiatives as MassIVE might help in the future https://massive.ucsd.edu/ProteoSAFe/ ), and readers may benefit from clearer acknowledgement that MetaProViz does not operate on raw spectral data. The introduction currently emphasizes annotation, but since MetaProViz requires already annotated metabolite tables (and then deals with all the messiness), this space might be better used to frame the interpretability and pathway-analysis challenges that the tool directly addresses.

      We appreciate the comment and have highlighted this in the abstract and introduction: "MetaProViz operates on annotated intensity values..." (Line 29 and 88).

      Given the newest advancements in metabolite identification using AI-based methods, MetaProViz toolkit with a focus on connecting metabolite IDs to prior knowledge becomes increasingly valuable. We added this to our discussion (Line 484-488): "Given the imminent shift in metabolite identification through AI-based approaches, including language model-guided48 methods and self-supervised learning49, the growing number of identified metabolites will make the MetaProViz toolkit increasingly valuable for the community to gain functional insights."

      In regards to the introduction, where we mention some tools for peak annotation: The reason why we have this paragraph where peak annotation are named is that we wanted to set the basis by (I) listing the different steps of metabolomics data analysis and (II) pointing to well-known tools of those steps. We also have a dedicated paragraph for pathway-analysis challenges.

      (4)

      4- I also really enjoyed you touching on the point of user-friendly but then inflexible and problem of reproducibility. We truly need well working packages for other bioinformaticians, rather than expecting wet-lab scientists to do all the analysis within the user interface.

      We thank the reviewer for this positive feedback.

      (5)

      5- It would be helpful to explain why the authors chose cancer/RCC samples for the demonstration. Was it because the dataset included both media and cell measurements? Does the tool perform best when multiple layers of information are available from the same experiment?

      We specifically chose the ccRCC cell line data as example since, for a multitude of cell lines, both media (exometabolomics) and intracellular metabolomics had been performed. The combination of both data types is only used in the biological regulated clustering (Fig. 5e-g), all other analyses do not require additional data modalities. We have not specifically tested how performance differs for this particular case as it would require multiple paired data (exometabolomics and intracellular metabolomics) taken at the same time and at different times.

      (6)

      6- Figure 2B: The upset plots effectively show increased overlap after adaptation, but it would be easier to compare changes if the order of the intersection bars in the "adapted" plot matched the original. For example, while total intersections increased (251→285), the PubChem+KEGG overlap decreased (24→5), likely due to reallocation to the full intersection.

      Thanks for raising this point. We initially had ordered the bars based on their intersection size, but we agree with the reviewers that for our point it makes sense to fix the order in the adapted plot to match the order of the original plot. We have done this (Fig 2a) and also extended the figure legend text of SFig. 2, which shows the individually performed adaptations summarized in Fig 2a.

      (7) (Planned)

      7- In your example of D-alanine and L-alanine - you mention how chirality is important biological feature, but up to this point it's not clear how do you do translation exactly and in which situations this would be treated just as "alanine" and when the more precise information would be retained? You mention RaMP-DB knowledge and one to X mappings as well as your general guidance in the "methods" part, but it would be useful to describe in this publication how you exactly tackled this problem in the ccRCC case.

      We thank the reviewer for this suggestion. Since this is a complex problem, we will add a more explicit description to the results section by showcasing more details on how we exactly tackled this problem in the ccRCC example data.

      In regards to D- and L-alanine, even though chirality is an important biological feature, in a standard experiment we can not distinguish if we detect the L- or D-aminoacid. This is why we try to assign all possible IDs to increase the overlap with the prior knowledge. In Fig. 2b we showcase that this can potentially lead to multiple mappings of the same measured feature to multiple pathways. For example, if we measure alanine and assign the pubchem ID for L-Alanine, D-Alanine and Alanine and try to map to metabolite-sets that include both L-Alanine and D-Alanine. In turn this could fall into Scenario 6 (Fig. 2e), where across pathways there is a D-Alanine specific one (Pathway 1) and a L-Alanine specific one (Pathway 2). Now we can decide, if we want to allow both mapping (many-to-one) or if we decide to exclude D-Alanine because we know our biological system is human and should primarily have L-Alanine.

      (8) (Planned)

      8- In one to many mappings, it would be interesting to see quantification how frequently it was happening within a pathway or across pathways. I.e. Would going into pathway analysis "solve" the issue of "lost in translation" or not really?

      We have quantified the frequency for the example of translating the KEGG metabolite-set into HMDB IDs (Fig. 2c, left panel). Yet, we are not showcasing the quantification across the KEGG metabolite-sets with this plot. During the revision we will add the full results available to the Extended Data Table 2, which currently only includes the results displayed in Fig.2c.

      (9)

      9- QC: the coefficient of variation (CV) helps identify features with high variability and thus low detection accuracy. Here it's important to acknowledge that if the feature is very variable between groups it can be extremely important, but if the feature is very variable within the group - only then one would have low trust in the accuracy.

      Yes, we totally agree with the reviewer on this. For this reason, we have applied CV only in instances where this is not leading to any condition-driven CV differences, but is truly feature-focused: (1) Function pool_estimation performs CV on the pool samples only, which are a homogeneous mixture of all samples, and hence can be used to assess feature variability. (2) Function processing performs CV on exometabolomics media samples (=blanks), which are also not impacted by different conditions.

      (10)

      10- Missing value imputation - while missing not at random is a great way to deal with missingness, it would be great to have options for others (not just MNAR), as missingness is of a complex nature. If a pretty strong decision has been made, it would be good to support this by some supplementary data (i.e. how results change while applying various combinations of missingness and why choosing MNAR seems to be the most robust).

      We have decided to only offer support for MNAR, since we would recommend MVI only if there is a biological basis for it.

      As mentioned in the response to your minor comment 2, Wei et. al. (https://doi.org/10.1038/s41598-017-19120-0) compared a multitude of missing value imputation methods. They compared six imputation methods (i.e., QRILC, Half-minimum, Zero, RF, kNN, SVD) for MNAR and systematically measured the performance of those imputation methods. They showed that QRILC and Half-Minimum produced much smaller SOR values, showing consistent good performances on data with different numbers of missing variables. This was the reason for us to only provide Half-minimum.

      (11) (Planned)

      11- In the pre-processing and imputation stages - it would be interesting to see a summary table of how many features are left after each stage.

      This is a good suggestion and refers to the steps described in Fig. 3a. We will create an overview table for this, add it into the Extended Data Table and refer to it in the results section.

      (12)

      12- Is there a reason not to do UMAP or PSL-DA graphs for outlier detection? Doing more than PCA would help to have more confidence in removing or retaining outliers in the cases where biological relevance is borderline.

      The reason we decided to use PCA was the standardly used combination with the Hotelling T2 outlier testing. Since PCA is a linear dimensionality reduction technique that preserves the overall variance in the data and has a clear mathematical foundation linked to the covariance structure, it specifically fits the required assumptions of the Hotelling T2 outlier testing. Indeed, Hotelling T2 relies on the properties of the covariance matrix and the assumption of a multivariate Gaussian distribution. UMAP is a non-linear dimensionality reduction technique, which prioritizes preserving local and global structures in a way that often results in good clustering visualization, but it distorts distances between clusters and does not have the same rigorous statistical underpinnings as PCA. In terms of PLS-DA, which focuses on maximizing the covariance between variables and the class labels, even though not commonly done, one could use the optimal latent variables for discrimination and apply Hotelling's T² to those latent variables. Yet, PLS-DA is supervised and actively tries to separate data points in the latent space, which can be misleading for outlier detection where methods like PCA that are unbiased, unsupervised and preserve global variance are advantageous.

      (13)

      13- Metadata vs metabolite features - can this be used beyond metabolomics (i.e. proteomics, transcriptomics, etc)? It can be always very useful when there are many metadata features and it's hard to pre-select beforehand which ones are the most biologically relevant.

      Yes, definitely. In fact, we have used the metadata analysis strategy also with proteomics data and it will work equally with any omics data type.

      (14)

      14- While authors discussed what KEGG pathways were significantly deregulated, it would be interesting to see all the pathways that were affected (e.g. aPEAR "bubble" graphs can show this (https://github.com/kerseviciute/aPEAR) , or something similar to NES scores). I appreciate the trickiness of it, but it would be quite interesting to see how authors e.g. Figure5e narrowed it down to the two pathways and how all the others looked like.

      We thank the reviewer for the suggestion of the aPEAR graphs. Following this suggestion, we have implemented a new function to enable clustering of the pathways based on overlapping metabolites (cluster_pk()). For more details regarding the method see also our response to Reviewer 1 (Comment 12) and our extended method section "Metabolite-set clustering" (Lines 656-671). We visualize the clustering results as a network graph, which we also included into Fig. 5f.

      The complete result of the KEGG enrichment can be found in Extended Data Table 1, Sheet 13 (Pathway enrichment analysis using KEGG on Young patient subset). The pathways are ranked by p.adjusted value and also include a score (FoldEnrichment) from the fishers exact test (similar to NES scores in GSEA). Here one can find a total of seven pathways with a p.adjusted value For Fig. 5e we narrowed down to these two pathways based on the previous findings of dysregulated dipeptides (Fig. 5d), as we searched for a potential explanation of this observation.

      (15)

      15- Could you comment on the runtime of the pipeline? In particular, do the additional translation steps and use of multiple databases substantially affect computational speed?

      Downloading and parsing databases takes significant time, especially large ones like RaMP or HMDB might take minutes on a standard laptop. Our local cache speeds up the process by eliminating the need for repeated downloads. In the future, database access will be even faster: according to our plans, all prior knowledge will be accessible in an already parsed format by our own API (omnipathdb.org). The ambiguity analysis, which is a complex data transformation pipeline, and plotting by ggplot2, another key component of MetaProViz, are the slowest parts, especially when performing analysis for the first time when no cache can be used. This means there are a few slow operations which complete in maximum a few dozens of seconds. However, the implementation and speed of these solutions doesn't fall behind what we commonly find in bioinformatics packages, and most importantly, the speed of MetaProViz doesn't pose an obstacle or difficulty regarding an efficient use of it in analysis pipelines.

      (16)

      16- I clap to the authors for automated checks if selected methods are appropriate!

      Thank you, this is something we think is important to ensure correct analysis and circumvent misinterpretation.

      (17)

      17- My suggestion would be to also look into power calculation or p-value histogram. In your example you saw some clear signal, but very frequently research studies are under-sampled and while effect can be clearly seen, there are just not enough samples to have statistically significant hits.

      We fully agree that power calculations are very important. Yet, this should ideally happen prior to the user's experiment. MetaProViz analysis starts at a later time-point and power calculations should have been done before. In regards to p-value histogram, we have implemented a similar measure, namely a density plot, which is plotted as a quality control measure within MetaProViz differential analysis function. The density plot is a smoothed version of a histogram that represents the distribution as a continuous probability density function and can be used to assess whether the p-values follow a uniform distribution.

      (18)

      18- Overall functional parts are novel and next step in helping with data interpretability, but I still found it hard to read into functionally clear insights (re to pathways / functional groupings of metabolites) - especially as you have e.g. enzyme-metabolite databases etc. I think clarity there could be improved and would help to get your message more widely across.

      Regarding the clarity to the pathway enrichment and their functional insights, we have extended the Figure legends of Fig. 4 and 5, clearly state that for the functional interpretation MetalinkDB is the prior knowledge resource we used to identify the links for methionine (Line 367-368), and we have extended our summary statement to highlight that we combine the biological clustering with prior knowledge for the mechanistic insight (Line 380-381).

    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 manuscript introduces an R package MetaProViz for metabolomics data analysis (post anotation), aiming to solve a poor-analysis-choices problem and enable more people to do the analysis. MetaProViz not only guides people to select the best statistical method, but also enables to solve previously unsolved problems: e.g. multiple and variable metabolite names in different databases and their connections to prior knowledge. They also created exometabolomics analysis and the needed steps to visualise intra-cell / media processes. The authors demonstrated their new package via kidney cancer (clear-cell renal cell carcinoma dataset, steping one step closer to improve biological interpretability of omics data analysis.

      Major comments affecting conclusions: none.

      Minor comments, important issues that could be addressed and possibly improve the clarity or generally presentation of the tool. Please see all below.

      1. You start with separating and talking about metabolomics and lipidomics, but lipidomics quickly dissapears (especially beyond abstract/intro) - no real need to discuss lipidomics.
      2. You refer to the MetImp4 imputation web tool, but I cannot find an active website, manuscript, or R package for it, and the cited link does not load. This raises doubts about whether the tool is currently usable. Additionally, imputation choice should be guided by biological context and study design, not just by testing a few methods and selecting the one that performs best.
      3. The authors address key metabolomics issues such as ambiguous metabolite names and isoforms, and their focus on resolving mapping ambiguities and translating between database identifiers is highly valuable. However, the larger challenge of de novo identification and the "dark matter" of unannotated metabolites remains unresolved (initiatives as MassIVE might help in the future https://massive.ucsd.edu/ProteoSAFe/ ), and readers may benefit from clearer acknowledgement that MetaProViz does not operate on raw spectral data. The introduction currently emphasizes annotation, but since MetaProViz requires already annotated metabolite tables (and then deals with all the messiness), this space might be better used to frame the interpretability and pathway-analysis challenges that the tool directly addresses.
      4. I also really enjoyed you touching on the point of user-friendly but then inflexible and problem of reproducibility. We truly need well working packages for other bioinformaticians, rather than expecting wet-lab scientists to do all the analysis within the user interface.
      5. It would be helpful to explain why the authors chose cancer/RCC samples for the demonstration. Was it because the dataset included both media and cell measurements? Does the tool perform best when multiple layers of information are available from the same experiment?
      6. Figure 2B: The upset plots effectively show increased overlap after adaptation, but it would be easier to compare changes if the order of the intersection bars in the "adapted" plot matched the original. For example, while total intersections increased (251→285), the PubChem+KEGG overlap decreased (24→5), likely due to reallocation to the full intersection.
      7. In your example of D-alanine and L-alanine - you mention how chirality is important biological feature, but up to this point it's not clear how do you do translation exactly and in which situations this would be treated just as "alanine" and when the more precise information would be retained? You mention RaMP-DB knowledge and one to X mappings as well as your general guidance in the "methods" part, but it would be useful to describe in this publication how you exactly tackled this problem in the ccRCC case.
      8. In one to many mappings, it would be interesting to see quantification how frequently it was happening within a pathway or across pathways. I.e. Would going into pathway analysis "solve" the issue of "lost in translation" or not really?
      9. QC: the coefficient of variation (CV) helps identify features with high variability and thus low detection accuracy. Here it's important to acknowledge that if the feature is very variable between groups it can be extremely important, but if the feature is very variable within the group - only then one would have low trust in the accuracy.
      10. Missing value imputation - while missing not at random is a great way to deal with missingness, it would be great to have options for others (not just MNAR), as missingness is of a complex nature. If a pretty strong decision has been made, it would be good to support this by some supplementary data (i.e. how results change while applying various combinations of missingness and why choosing MNAR seems to be the most robust).
      11. In the pre-processing and imputation stages - it would be interesting to see a summary table of how many features are left after each stage.
      12. Is there a reason not to do UMAP or PSL-DA graphs for outlier detection? Doing more than PCA would help to have more confidence in removing or retaining outliers in the cases where biological relevance is borderline.
      13. Metadata vs metabolite features - can this be used beyond metabolomics (i.e. proteomics, transcriptomics, etc)? It can be always very useful when there are many metadata features and it's hard to pre-select beforehand which ones are the most biologically relevant.
      14. While authors discussed what KEGG pathways were significantly deregulated, it would be interesting to see all the pathways that were affected (e.g. aPEAR "bubble" graphs can show this (https://github.com/kerseviciute/aPEAR) , or something similar to NES scores). I appreciate the trickiness of it, but it would be quite interesting to see how authors e.g. Figure5e narrowed it down to the two pathways and how all the others looked like.
      15. Could you comment on the runtime of the pipeline? In particular, do the additional translation steps and use of multiple databases substantially affect computational speed?
      16. I clap to the authors for automated checks if selected methods are appropriate!
      17. My suggestion would be to also look into power calculation or p-value histogram. In your example you saw some clear signal, but very frequently research studies are under-sampled and while effect can be clearly seen, there are just not enough samples to have statistically significant hits.
      18. Overall functional parts are novel and next step in helping with data interpretability, but I still found it hard to read into functionally clear insights (re to pathways / functional groupings of metabolites) - especially as you have e.g. enzyme-metabolite databases etc. I think clarity there could be improved and would help to get your message more widely across.

      Significance

      This is a great tool and I can't wait to use it on many upcoming metabolomics projects! Authors tackle multiple ongoing issues within the field: from poor selection of statistical methods (they provide guidance or have default safer options) to the messiness of data annotation between databases and improving data interpretability. The field is still evolving quickly, and it's impossible to solve all problems with one package; thus some limitations within the package could be seen as a bit rigid. Nonetheless, this fully steps toward filling an existing methodological gap. All bioinformaticians doing metabolomic analysis, or those learning how to do it, will greatly benefit from this knowledge.

      I myself lead a team of 6 bioinformaticians, and we do analysis for researchers, clinicians, drug discovery, and various companies. We run internal metabolomics pipelines every day and fully sympathise with the problems addressed by the authors.

    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

      Schmidt et al report the development of MetaProViz, an integrated R package to process, analyze and visualize metabolomics data, including integration with prior knowledge. The authors then go on to demonstrate utility by analyzing several metabolomes of cell lines, media and patient samples from kidney cancer. The manuscript provides a concise description of key challenges in metabolomics that the authors identify and address in their software. The examples are helpful and illustrative, although I should point out that I lack the expertise to evaluate the R package itself. I only have a few very minor comments.

      Minor comments:

      1. Figure 2D, E: While the schematics are fairly intuitive, a brief figure legend description of what the different scenarios etc. represent would make this easier to grasp.
      2. Fig. 4: The authors briefly state that they integrate prior knowledge to identify the changes in methionine metabolism in kidney cancer, but it is not clear how exactly they contribute to this conclusion. It could be helpful to expand a bit on this to better illustrate how MetaProViz can be used to integrate prior knowledge into the analysis workflow.
      3. Given the functional diversity among metabolites -central to diverse pathways, are key signaling molecules, restricted functions, co-variation within a pathway - I wonder how informative approaches such as PCA or enrichment analyses are for identifying metabolic drivers of a (patho)physiological state. To some extent, this can be addressed by integrating prior knowledge, and it would be helpful if the authors could comment on (and if applicable explain) whether/how this is integrated into MetaProViz.

      Significance

      This is a very significant advance from one of the leading groups in the field that is likely to enhance metabolomics data analysis in the wider community.

    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

      Schmidt et al. present MetaProViz, a comprehensive and modular platform for metabolomics data analysis. The tool provides a full suite of processing capabilities spanning metabolite annotation, quality control, normalization, differential analysis, integration of prior knowledge, functional enrichment, and visualization. The authors also include example datasets, primarily from renal cancer studies, to demonstrate the functionality of the pipeline. The MetaProViz framework addresses several long-standing challenges in metabolomics data analysis, particularly issues of reproducibility, ambiguous metabolite annotation, and the integration of metabolite features with pathway knowledge. The platform is likely to be a valuable addition for the community, but the reviewer has some comments that need to be addressed prior to publication.

      The section "Improving the connection between prior knowledge and metabolomics features" could benefit from additional clarification. It is not entirely clear to the reader what specific steps were taken beyond using RaMP-DB to translate metabolite identifiers. For example, how exactly were ambiguous mappings ("different scenarios") handled in practice, and to what extent does this process "fix" or merely flag inconsistencies? A more explicit description or example of how MetaProViz resolves these cases would help readers better understand the improvements claimed.

      The introduction of MetSigDB is intriguing, but its construction and added value are not sufficiently described. It would be helpful to clarify what specific advantages MetSigDB provides over directly using existing pathway resources such as KEGG, Reactome, or WikiPathways. For example, how many features, interactions, or metabolite-set relationships are included, and in what way are these pathways improved or extended compared to those already available in public databases?

      Figure 1D/1E: The reviewer appreciates the inclusion of the visualizations illustrating the different mapping scenarios, as these effectively convey the complexity of metabolite ID translation. However, it took some time to interpret what each scenario represented. It would be helpful to include brief annotations or explanatory text directly on the figures to clarify what each scenario depicts and how it relates to the underlying issue being addressed.

      "By assigning other potential metabolite IDs and by translating between the present ID types, we not only increase the number of features within all ID types but also increase the feature space with HMDB and KEGG IDs (Fig. 2a, right, SFig. 2 and Supplementary Table 1)". The reviewer would appreciate additional clarification on how this was done. It is not clear what specific steps or criteria were used to assign additional metabolite IDs or to translate between identifier types. The reviewer also appreciates the inclusion of the UpSet plots. However, simply having the plots side-by-side makes it difficult to determine the specific differences. An alternative visualization, such as stacked bar plots, scatter plots summarizing the changes in feature counts, or other representation that more clearly highlights the deltas, might make these results easier to interpret.

      MetaboAnalyst is mentioned several times in the manuscript. The reviewer is familiar with some of the limitations and practical challenges associated with using MetaboAnalyst and its R package. Given that MetaboAnalyst already offers some overlapping functionality with MetaProViz (and offers it in the form of an interactive website and a sometimes functional R package), a more explicit comparison between the two tools would help readers fully understand the unique advantages and improvements provided by MetaProViz.

      Page 11: The authors state that they used limma for statistical testing, including for the analysis of exometabolomics data, where the values appear to represent log2-transformed distances or ratios rather than normally distributed intensities. Since limma assumes approximately normal residuals, please provide evidence or justification that this assumption holds for these data types. If the distributions deviate substantially from normality, a non-parametric alternative might be more appropriate.

      Page 13: why were young and old defined this way? Authors should provide their reasoning and/or citations for this grouping.

      Figure 4e: It may help with interpretation to have these Sankey-like graph edges be proportional to the number of metabolites.

      Figure 4h: The values appear to be on an intensity scale (e.g., on the order of 3e10), yet some of them are negative, which would not be expected for raw or log-transformed mass spectrometry intensities. It is unclear whether these represent normalized abundance values, distances, or some other transformation. In addition, for the comparison of tumour versus normal tissue, it is not specified what statistical test was applied. Since mass spectrometry data are typically log2-transformed to approximate a log-normal distribution before performing t-tests or similar parametric methods, clarification is needed on how these data were processed.

      Figure 5: "Tukey's p.adj < 0.05" . Was this a Tukey's post-hoc test? This should be explicitly stated.

      The potential for multi-omics is mentioned. Please clarify how generalizable this framework is. Can it readily accommodate transcriptomics, proteomics, or fluxomics data, or does it require custom logic or formatting for each new data type?

      Please clarify if/how enrichment analyses account for varying set sizes and redundant metabolite memberships across pathways, which can bias over-representation analysis results.

      Significance

      The MetaProViz framework addresses several long-standing challenges in metabolomics data analysis, particularly issues of reproducibility, ambiguous metabolite annotation, and the integration of metabolite features with pathway knowledge. The platform is likely to be a valuable addition for the community, but the reviewer has some comments that need to be addressed prior to publication.

      Authors should be commended for the availability of data/code and detailed methods. Clarity is good. Authors have clearly spent a lot of time thinking about the challenges of metabolomics data analysis.

    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

      Reply to the reviewers

      We are grateful for the reviewers' constructive comments and suggestions, which contributed to improving our manuscript. We are pleased to see that our work was described as an "interesting manuscript in which a lot of work has been undertaken". We are also encouraged by the fact that the experiments were considered "on the whole well done, carefully documented, and support most of the conclusions drawn," and that our findings were viewed as providing "mechanistic insight into how HNRNPK modulates prion propagation" and potentially offering "new mechanical insight of hnRNPK function and its interaction with TFAP2C."

      We conducted several new experiments and revised specific sections of the manuscript, as detailed below in the point-by-point response in this letter.

      Referee #1

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

      The paper by Sellitto describes studies to determine the mechanism by which hnRNPK modulates the propagation of prion. The authors use cell models lacking HNRNPK, which is lethal, in a CRISPR screen to identify genes that suppress lethality. Based on this screen to 2 different cell lines, gene termed Tfap2C emerged as a candidate for interaction with HNRNPK. The show that Tfap2C counteracts the actions of HNRNPK with respect to prion propagation. Cells lacking HNRNPK show increased PrPSc levels. Overexpression of Tfap2C suppesses PrPSc levels. These effects on PrPSc are independent of PrPC levels. By RNAseq analysis, the authors hone in on metabolic pathways regulated by HNRPNK and Tfap2C, then follow the data to autophagy regulation by mTor. Ultimately, the authors show that short-term treatments of these cell models with mTor inhibitors causes increased accumulation of PrPSc. The authors conclude that the loss of HNRNPK leads to a reduced energy metabolism causing mTor inhibition, which is reduces translation by dephosphorylation of S6

      Major comments:

      1) Fig H and I, Fig 3L. The interaction between Tfap2C and HNRNPK is pretty weak. The interaction may not be consequential. The experiment seems to be well controlled, yielding limited interaction. The co-ip was done in PBS with no detergent. The authors indicate that the cells were mechanically disrupted. Since both of these are DNA binding proteins, is it possible that the observed interaction is due to the proximity on DNA that is linking the 2 proteins, including a DNAase treatment would clarify.

      Response: We agree that the observed co-IP between Tfap2c and hnRNP K is weak (previous Fig. 2H-I, Supp. Fig. 3L now shifted in Supp. Fig. 4C-E), and we have now highlighted this in the relevant section of the manuscript to reflect this observation better.

      Importantly, the co-IP was performed using endogenous proteins without overexpression or tagging, which can sometimes artificially enhance protein-protein interactions. However, we acknowledge that the use of a detergent-free lysis buffer and mechanical disruption alone may have limited nuclear protein extraction and solubilization, potentially contributing to the low co-IP signal.

      To address the reviewer's concerns and clarify whether the observed interaction could be DNA-mediated, we repeated the co-IP experiments under low-detergent conditions and included benzonase nuclease treatment to digest nucleic acids (Fig. 2H-I). DNA digestion was confirmed by agarose gel electrophoresis (Supp. Fig. 4F-G). Additionally, we performed the reciprocal IPs using both hnRNP K and Tfap2c antibodies (Fig. 2H-I). Although the level of co-immunoprecipitation remains modest, these updated experiments continue to demonstrate a specific co-immunoprecipitation between Tfap2c and hnRNP K, independent of DNA bridging. These additional controls and experimental refinements strengthen the validity of our findings. These results are also attached here for your convenience.

      2) Supplemental Fig 5B - The western blot images for pAMPK don't really look like a 2 fold increase in phosphorylation in HNRNPK deletion.

      Response: We thank the reviewer for raising this point. We re-examined the original pAMPK western blot (previously Supp. Fig. 5B; now presented as Supp. Fig. 6B) and confirmed the reported results. We note that the overall loading is not perfectly uniform across lanes (as suggested by the actin signal), which may affect the visual impression of band intensity. However, the phosphorylation change reported in the manuscript is based on the pAMPK/total AMPK ratio, which accounts for differences in AMPK expression and accurately reflects relative phosphorylation levels. To further address this concern, we performed three additional independent experiments. These new data reproduce the increase in pAMPK/AMPK upon HNRNPK deletion and are now included in the revised Supplementary Fig. 6B, together with the updated quantification. The new blot and the quantification are also attached here for your convenience.

      3) Fig. 5A - I don't think it is proper to do statistics on an of 2.

      Response: We believe the reviewer's comment refers to Fig. 5B, as Fig. 5A already has sufficient replication. We have now added two additional replicates, bringing the total to four. The updated statistical analysis corroborates our initial results. The new quantification is provided in the revised manuscript (Fig. 5B) along with the new blot (Supp. Fig. 6C). Both data are also attached here for your convenience.

      4) Fig 6D. The data look a bit more complicated than described in the text. At 7 days, compared to 2 days, it looks like there is a decrease in % cells positive for 6D11. Is there clearance of PrPSc or proliferation of un-infected cells?

      Response: We have now reworded our text in the results paragraph as follows:

      "These data show that TFAP2C overexpression and HNRNPK downregulation bidirectionally regulate prion levels in cell culture."

      We have now also included the following comments in the discussion section:

      "However, prion propagation relies on a combination of intracellular PrPSc seeding and amplification, as well as intercellular spread, which together contribute to the maintenance and expansion of infected cells within the cultured population. In this study, we were limited in our ability to dissect which specific steps of the prion life cycle are affected by TFAP2C. We also cannot fully exclude the possibility that TFAP2C overexpression influenced the relative proliferation of prion-infected versus uninfected cells in the PG127-infected HovL culture, thereby contributing to the observed reduction in the percentage of 6D11+ cells and overall 6D11+ fluorescence. However, we did not observe any signs of cell death, growth impairment, or increased proliferation under TFAP2C overexpression in PG127-infected HovL cells compared to NBH controls (data not shown). This suggests that a negative selective pressure on infected cells or a proliferative advantage of uninfected cells is unlikely in this context".

      5) The authors might consider a different order of presenting the data. Fig 6 could follow Fig. 2 before the mechanistic studies in Figs 3-5.

      Response: We believe that the current order of presenting the data is more appropriate. The first part of the manuscript focuses on the genetic and functional interactions between hnRNP K and its partners, particularly TFAP2C, which is a critical point for understanding the broader context before delving into the mechanistic studies involving prion-infected cells.

      6) The authors use SEM throughout the paper and while this is often used, there has been some interest in using StdDev to show the full scope of variability.

      Response: We chose to use SEM as it reflects the precision of the mean, which is central to our statistical comparisons. As the reviewer notes, this is a common and appropriate practice. To address variability, almost all graphs already include individual data points, which provide a direct visual representation of data spread. To further enhance clarity, we have now included StdDev in the Supplementary Source Data table of the revised manuscript.

      Discussion:

      The discrepancy between short-term and long-term treatments with mTor inhibitors is only briefly mentioned with a bit of a hand-waving explanation. The authors may need a better explanation.

      Response: We have now integrated a more detailed explanation in the discussion section of the revised manuscript as follows:

      "Previous studies showed that mTORC1/2 inhibition and autophagy activation generally reduce, rather than increase, PrPSc aggregation (79, 80). The reason for this discrepancy remains unclear and may be multifactorial. First, most prior studies were based on long-term mTOR inhibition, whereas our work examined acute inhibition, mimicking the time frame of HNRNPK and TFAP2C manipulation. Acute inhibition may trigger transient metabolic or signaling shifts that differ from adaptive changes associated with mTOR chronic inhibition, potentially overriding autophagy's effects on prion propagation. Additionally, while previous works were primarily conducted in murine in vivo models, our study focused on a human cell system propagating ovine prions. Differences in species background, model complexity (e.g., interactions between different cell types), and prion strain variability, as certain strains exhibit distinct responses to autophagy and mTOR modulation (https://doi.org/10.1371/journal.pone.0137958), likely contributed to the observed differences".

      Minor comments:

      Page 12 - no mention of chloroquine in the text or related data.

      Page 12 - Supp. Fig. E - should be 5E

      Response: We thank the reviewer for pointing this out. We have now better highlighted the use of chloroquine in Fig. 5B (see reviewer #1 - Point 3 - Major comments) and in the text as follows:

      "Furthermore, in the presence of chloroquine, LC3-II levels rose almost proportionally across all conditions (Fig. 5B), suggesting that the effects of HNRNPK and TFAP2C on autophagy occur at the level of autophagosome formation, rather than autophagosome-lysosome fusion and degradation."

      We have corrected the reference to Supp. Fig. 5E.

      Reviewer #1 (Significance (Required)):

      The study provides mechanistic insight into how HNRNPK modulates prion propagation. The paper is limited to cell models, and the authors note that long term treatment with mTor inhibitors reduced PrPSc levels in an in vivo model.

      The primary audience will be other prion researchers. There may be some broader interest in the mTor pathway and the role of HNRNPK in other neurodegenerative diseases.

      Referee #2

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript "Prion propagation is controlled by a hierarchical network involving the nuclear Tfap2c and hnRNP K factors and the cytosolic mTORC1 complex" by Sellitto et al aims to examine how heterogenous nuclear ribonucleoprotein K (hnRNPK), limits pion propagation. They perform a synthetic - viability CRISPR- ablation screen to identify epistatic interactors of HNRNPK. They found that deletion of Transcription factor AP-2g (TFAP2C) suppressed the death of hnRNP-K depleted LN-229 and U-251 MG cells whereas its overexpression hypersensitized them to hnRNP K loss. Moreover, HNRNPK ablation decreased cellular ATP, downregulated genes related to lipid and glucose metabolism and enhanced autophagy. Simultaneous deletion of TFAP2C reversed these effects, restored transcription and alleviated energy deficiency. They state that HNRNPK and TFAP2C are linked to mTOR signalling and observe that HNRNPK ablation inhibits mTORC1 activity through downregulation of mTOR and Rptor while TFAP2C overexpression enhances mTORC1 downstream functions. In prion infected cells, TFAP2C activation reduced prion levels and countered the increased prion propagation due to HNRNPK suppression. Pharmacological inhibition of mTOR also elevated prion levels and partially mimicked the effects of HNRNPK silencing. They state their study identifies TFAP2C as a genetic interactor of HNRNPK and implicates their roles in mTOR metabolic regulation and establishes a causative link between these activities and prion propagation.

      This is an interesting manuscript in which a lot of work has been undertaken. The experiments are on the whole well done, carefully documented and support most of the conclusions drawn. However, there are places where it was quite difficult to read as some of the important results are in the supplementary Figures and it was necessary to go back and forth between the Figs in the main body of the paper and the supplementary Figs. There are also Figures in the supplementary which should have been presented in the main body of the paper. These are indicated in our comments below.

      We have the following questions /points:

      Major comments:

      1) A plasmid harbouring four guide RNAs driven by four distinct constitutive promoters is used for targetting HNRNPK- is there a reason for using 4 guides- is it simply to obtain maximal editing - in their experience is this required for all genes or specific to HNRNPK?

      Response: The use of four guide RNAs driven by distinct promoters is chosen to maximize editing efficiency for HNRNPK. As previously demonstrated by J. A. Yin et al. (Ref. 32), this system provides better efficiency for gene knockout (or activation). For HNRNPK, achieving full knockout was crucial for observing a complete lethal phenotype, which made the four guide RNAs approach fundamental. However, other knockout systems, while potentially less efficient, have been shown to work well in other circumstances. We have now included this explanation in the revised manuscript as follows:

      "We employed a plasmid harboring quadruple non-overlapping single-guide RNAs (qgRNAs), driven by four distinct constitutive promoters, to target the human HNRNPK gene and maximize editing efficiency in polyclonal LN-229 and U-251 MG cells stably expressing Cas9 (32)."

      2) Is there a minimal amount of Cas9 required for editing?

      Response: We did not observe a correlation between Cas9 levels and activity, yet the C3 clone was the one with higher Cas9 expression and higher activity (Supp. Fig. 1A-B). We agree that comments about the amount of Cas9 expression may be misleading here. Thus, in the first result paragraph of the revised manuscript, we have now modified the text "we isolated by limiting dilutions LN-229 clones expressing high Cas9 levels" to "we isolated by limiting dilutions LN-229 single-cell clones expressing Cas9".

      3) It is stated that cell death is delayed in U251-MG cells compared to LN-229-C3 cells- why? Also, why use glioblastoma cells other than that they have high levels of HNRNPK? Would neuroblastoma cells be more appropriate if they are aiming to test for prion propagation?

      Response: As shown in Fig. 1A, U251-MG cells reached complete cell death at day 13, while LN-229 C3 reached it already at day 10. The percentage of viable U251-MG cells is higher (statistically significant) than LN-229 C3 cells at all time points before day 13, when both lines show complete death. The underlying reasons for this partial and relative resistance are probably multiple, but we clearly showed in Fig. 2 that TFAP2C differential expression is one modulator of cell sensitivity to HNRNPK ablation.

      We selected glioblastoma cells because their high expression of HNRNPK was essential for developing our synthetic lethality screen strategy, and we have now clarified it in the revised manuscript as follows:

      "As model systems, we chose the human glioblastoma-derived LN-229 and U-251 MG cell lines, which express high levels of HNRNPK (2, 3), a key factor for optimizing our synthetic lethality screen."

      While neuroblastoma cells might be more relevant in terms of prion neurotoxicity, glial cells, despite their resistance to prion toxicity, are fully capable of propagating prions. Prion propagation in glial cells has been shown to play crucial roles in mediating prion-dependent neuronal loss in a non-autonomous manner (see 10.1111/bpa.13056). This makes glioblastoma cells a valuable model for studying prion propagation (that is the focus of our study), despite the lack of direct toxicity (which is not the focus of our study). We have now added this explanation to the revised manuscript as follows:

      "Therefore, we continued our experiments using LN-229 cells, which provide a relevant model for studying prions, as glial cells can propagate prions and contribute to prion-induced neuronal loss through non-cell-autonomous mechanisms."

      4) Human CRISPR Brunello pooled library- does the Brunello library use constructs which have four independent guide RNAs as used for the silencing of HNRPNK?

      Response: No, the Human CRISPR Brunello pooled library does not use constructs with four independent guide RNAs (qgRNAs). Instead, each gene is targeted by 4 different single-guide RNAs (sgRNAs), each expressed on a separate plasmid. We have now clarified this in the main text of the revised manuscript as follows:

      "To identify functionally relevant epistatic interactors of HNRNPK, we conducted a whole-genome ablation screen in LN-229 C3 cells using the Human CRISPR Brunello pooled library (33), which targets 19,114 genes with an average of four distinct sgRNAs per gene, each expressed by a separate plasmid (total = 76,441 sgRNA plasmids)."

      5) To rank the 763 enriched genes, they multiply the -log10FDR with their effect size - is this a standard step that is normally undertaken?

      Response: The approach of ranking hits using the product of effect size and statistical significance is a well-established method in CRISPR screening studies. This strategy has been explicitly used in high-impact work by Martin Kampmann and others (see https://doi.org/10.1371/journal.pgen.1009103 and https://doi.org/10.1016/j.neuron.2019.07.014 as references). We have now added both references to the revised manuscript.

      6) The 32 genes selected- they were ablated individually using constructs with one guide RNA or four guide RNAs?

      Response: The 32 genes selected were ablated individually using constructs with quadruple-guide RNAs (qgRNAs), as this approach was intended to maximize editing efficiency for each gene. We have now clarified this in the main text of the revised manuscript as follows:

      "We ablated each gene individually using qgRNAs and then deleted HNRNPK."

      7) The identified targets were also tested in U251-MG cells and nine were confirmed but the percent viability was variable - is the variability simply a reflection of the different cell line?

      Response: The variability in percent viability observed in U251-MG cells likely reflects the inherent differences between cell lines, which can contribute to varying levels of susceptibility to gene ablation, even for the same targets. We have now highlighted these small differences in the main text of the revised manuscript as follows:

      "We confirmed a total of 9 hits (Fig. 1H), including the ELPs gene IKBAKP and the transcription factor TFAP2C, the two strongest hits identified in LN-229 C3 cells. However, in the U251-Cas9 the rescue effect did not always fall within the exact range observed in LN-229 C3 cells, likely due to intrinsic differences between the two cell lines."

      8) The two strongest hits were IKBAKP and TFAP2C. As TFAP2C is a transcription factor - is it known to modulate expression of any of the genes that were identified to be perturbed in the screen? Moreover, it is stated that it regulates expression of several lncRNAs- have the authors looked at expression of these lncRNAs- is the expression affected- can modulation of expression of these lncRNAs modulate the observed phenotypic effects and also some of the targets they have identified in the screen?

      Response: While TFAP2C is a transcription factor known to regulate the expression of several genes and lncRNAs, we did not identify any of its known target genes among the hits of our screen. However, our RNA-seq data and RT-qPCR (data not shown) indicate that the expression of lncRNA MALAT1 and NEAT1 (reported to interact with both HNRNPK and TFAP2C; ref 37, 41, 47) is strongly affected by HNRNPK ablation and to a lesser extent by TFAP2C deletion. However, the double deletion condition does not appear to change these lncRNA levels beyond what is observed with HNRNPK ablation alone. Therefore, we concluded that these changes do not play a primary role in the phenotypic effects observed in our study. Thus, although interesting, we believe that the description of such observations goes beyond the scope of this manuscript and the relevance of this work.

      9) As both HNRNPK and TFAP2C modulate glucose metabolism, the authors have chosen to explore the epistatic interaction. This is most reasonable.

      Response: We do not have further comments on this point.

      10) The orthogonal assay to confirm that deletion of TFAP2C supresses cell death upon removing HNRNPK- was this done using a single guide RNA or multiple guides - is there a level of suppression required to observe rescue? Interestingly ablation of HNRNPK increases TFAP2C expression in LN-229-C3 whereas in U251-Cas9 cells HNRNPK ablation has the opposite effect- both RNA and protein levels of TFAP2C are decreased - is this the cause of the smaller protective effect of TFAP2C deletion in this cell line?

      Response: TFAP2C deletion was performed using quadruple-guide RNAs (gqRNAs). We have clarified this point by addressing the reviewer #2's point 6 in "Major comments".

      We did not directly test the threshold of TFAP2C inhibition required to suppress HNRNPK ablation-induced cell death. We did not exclude that other effectors may take a role in the smaller protective effect of TFAP2C deletion in the U251-Cas9 cells, however, multiple lines of evidence from our study suggest that TFAP2C expression levels influence cellular sensitivity to HNRNPK loss:

      1) Both LN-229 C3 and U251-Cas9 cells are less sensitive to HNRNPK ablation upon TFAP2C deletion (Fig. 1G-H, Fig. 2A-B, Supp. Fig.3A-B).

      2) We observed a correlation between endogenous TFAP2C levels and HNRNPK ablation sensitivity. U251-Cas9 cells, where TFAP2C expression is reduced upon HNRNPK ablation (in contrast to LN-229 C3 cells, where HNRNPK ablation leads to an increase in TFAP2C expression) (Fig. 2C-F), are a) less sensitive to HNRNPK deletion than LN-229 C3 (Fig. 1A, 2A-B) and b) the protective effect of TFAP2C deletion is less pronounced than in LN-229 C3 (Fig. 1G-H, Fig. 2A-B, Supp. Fig.3A-B).

      3) TFAP2C overexpression experiments (Fig. 2G) establish a causal relationship to the former correlation: TFAP2C overexpression increased U251-Cas9 sensitivity to HNRNPK ablation.

      As clearly mentioned in the manuscript, we believe that, taken together, these findings strongly demonstrate a causal role for TFAP2C in modulating sensitivity to HNRNPK loss. Thus, despite the differences in the expression, the proposed viability interaction between TFAP2C and HNRNPK is conserved across cell lines.

      To further strengthen our conclusions, we have now added LN-229 C3 TFAP2C overexpression in Fig. 2G (also attached below for your convenience). As for the U251-Cas9, LN-229 C3 cells show increased sensitivity to HNRNPK ablation upon TFAP2C overexpression.

      11) Nuclear localisation studies indicate that the HNRNPK and TFAP2C proteins colocalise in the nucleus however the co-IP data is not convincing- although appropriate controls are present, the level of interaction is very low - the amount of HNRNPK pulled down by TFAP2C is really very low in the LN-229C3 cells and even lower in the U251-Cas9 cells. Have they undertaken the reciprocal co-IP expt?

      Response: We rephrased our text to better highlight this as also mentioned in our response to reviewer #1 (Point 1 - Major comments). However, as also noted by the reviewer, the experiments included all the relevant controls. Thus, the results are solid and confirm a degree of co-immunoprecipitation (although weak). As detailed in our response to reviewer #1 (Point 1 - Major comments), to strengthen our conclusion, we have now repeated the experiment in low-detergent conditions and used benzonase nuclease for DNA digestion. We also have performed the reciprocal experiment as suggested by the reviewer, confirming the initial results. In our opinion, these additional experiments support the conclusion that Tfap2c and hnRNP K co-immunoprecipitate through a weak, but direct, interaction.

      12) They state that LN-229 C3 ∆TFAP2C and U251-Cas9 ∆TFAP2C were only mildly resistant to the apoptotic action of staurosporin Fig 3E and F - I accept they have undertaken the stats which support their statement that at high concentrations of staurosporin the LN-229 C3 ∆TFAP2C cells are less sensitive but the U251-Cas9 ∆TFAP2C decreased sensitivity is hard to believe. Has this been replicated? I agree that HNRNPK deletion causes apoptosis in both LN-229 C3 and U251-Cas9 cells and this is blocked by Z-VAD-FMK - however the block is not complete- the max viability for HNRNPK deletion in LN-229 C3 cells is about 40% whereas for U251-Cas9 cells it is about 30% - does this suggest that cells are being lost by another pathway. Have they tested concentrations higher than 10nM?

      Response: The experiments in FIG. 3E-F have been replicated four times, as stated in the figure legend. We agree that TFAP2C plays a limited role in response to staurosporine-induced apoptosis, particularly in U251-Cas9 cells. To ensure clarity, we have now modified our previous sentence as follows:

      "LN-229 C3ΔTFAP2C cells were only mildly resistant to the apoptotic action of staurosporine, and U251-Cas9ΔTFAP2C showed even lower and minimal recovery (Fig. 3E-F). These results indicate that TFAP2C plays a limited role in apoptosis regulation and suggest that its suppressive effect on HNRNPK essentiality is not mediated through direct modulation of apoptosis but rather through upstream processes that eventually converge on it."

      The incomplete blockade of apoptosis by Z-VAD-FMK suggests that HNRNPK ablation may activate alternative, non-caspase-mediated cell death pathways. Regarding this point, we decided to not test Z-VAD-FMK above 10 nM as we noted that the rescue effect at the lowest concentration (2nM) was not proportionally increasing at higher concentrations, suggesting we already reached saturation. We have now added and clarified these observations in the revised manuscript as follows:

      "Z-VAD-FMK decreased cell death consistently and significantly in LN-229 C3 and U251-Cas9 cells transduced with HNRNPK ablation qgRNAs (Fig. 3C‑D), confirming that HNRNPK deletion promotes cell apoptosis. However, we observed that viability recovery plateaued already at the lowest concentration (2 nM) without further increase at higher doses, suggesting a saturation effect. This indicates that while caspase inhibition alleviates part of the cell death, HNRNPK loss triggers additional mechanisms beyond apoptosis".

      Following the suggestion of the reviewer, we have now also tested two higher concentrations of Z-VAD (20 and 50nM) in LN-229 cells. At these concentrations, we observed a slight decrease in cell viability in the NT condition, with a rescue effect in the HNRNPK-ablated cells comparable to what was observed at 2-10nM Z-VAD. For this reason, we did not include these data in the revised manuscript, and we attached them here for transparency.

      13) The RNA-seq comparisons- the authors use log2 FC Response: We used a log2 FC threshold of >0.5 and 0.25) is commonly used in RNA-seq studies to capture biologically relevant shifts (e.g.,https://doi.org/10.1371/journal.ppat.1012552; https://doi.org/10.1371/journal.ppat.1008653; https://doi.org/10.1016/j.neuron.2025.03.008; https://doi.org/10.15252/embj.2022112338). We complemented this analysis with Gene Set Enrichment Analysis (GSEA) to assess coordinated changes in biological/genetic pathways, ensuring that our conclusions are not based on isolated, minor expression changes nor on arbitrary thresholds. Finally, to enhance our result robustness, we applied False Discovery Rate (FDR) statistics, which is more stringent than a p-value cutoff. We hope this clarification strengthens the reviewer's confidence in the significance of the observed changes.

      14) It is stated" Accordingly, we observed increased AMPK phosphorylation (pAMPK) upon ablation of HNRNPK, which was consistently reduced in LN-229 C3ΔTFAP2C cells (Supp. Fig. 5B). LN-229 C3ΔTFAP2C; ΔHNRNPK cells also showed a partial reduction of pAMPK relative to LN-229 C3ΔHNRNPK cells (Supp. Fig. 5B). These results suggest that hnRNP K depletion causes an energy shortfall, leading to cell death.

      Response: I am not totally convinced by the data presented in this Fig. The authors have quantified the band intensity and present the ratio of pAMPK to AMPK. Please note that the actin levels are variable across the samples - did they normalise the data using the actin level before undertaking the comparisons? Also, if the authors think this is an important point which supports their conclusion, then it should be in the main body of the paper rather than the supplementary. If AMPK is being phosphorylated, this should lead to activation of the metabolic check point which involves p53 activation by phosphorylation. Activated p53 would turn on p21CIP1 which is a very sensitive indicator of p53 activation.

      We also refer the reviewer to our response to reviewer #1 (Point 2 - Major comments). We understand the point of the reviewer as pAMPK/Actin (absolute AMPK phosphorylation) may provide additional context regarding the downstream effects of AMPK activation, which, however, is not the primary scope of our experiment. We believe that in our specific case, a) the pAMPK/AMPK ratio is the most appropriate metric, as it reflects the energy status of the cell (ATP/AMP levels), which was our main point to assess in this experiment, and b) phospho-protein/total protein is the standard approach for quantifying phosphorylation ratio. For completeness, we have now included pAMPK/Actin quantifications in Supp. Fig. 6B of the revised manuscript (also attached below). pAMPK/Actin levels follow the same trend of pAMPK/AMPK in HNRNPK and TFAP2C single ablations. The pAMPK/AMPK partial rescue in HNRNPK;TFAP2C double ablation relative to HNRNPK single deletion is instead not observed at pAMPK/Actin level. We have now added the pAMPK/Actin quantification and this observation to the revised manuscript as follows:

      "Accordingly, we observed increased AMPK phosphorylation (pAMPK/AMPK ratio and pAMPK/Actin) upon ablation of HNRNPK, with a trend toward reduction in LN-229 C3ΔTFAP2C cells (Supp. Fig. 6B). LN-229 C3ΔTFAP2C;ΔHNRNPK cells also showed a reduction of pAMPK/AMPK ratio relative to LN-229 C3ΔHNRNPK cells, although absolute AMPK phosphorylation (pAMPK/Actin) remained high (Supp. Fig. 6B)."

      We prefer to keep the AMPK blots in Supplementary Fig. 6B, as we believe the main take-home message of the manuscript should remain centered on mTORC1 activity.

      15) We also do not understand why the mTOR Suppl. Fig. 5E is not in the main body of the paper. It's clear that RNA and protein levels of mTOR were downregulated in LN-229 C3ΔHNRNPK cells but were partially rebalanced by the ΔTFAP2C- however the ΔTFAP2C;ΔHNRNPK double deletion levels are only slightly higher than the ΔHNRNPK - they are not at the level NT or even ΔTFAP2C (Fig. 4C, Supp. Fig. 5E).

      Response: We moved the mTOR blot to Fig.5D of the revised manuscript. About the low rescue effect, this is in line with all the other observations where a full rescue of the effects of HNRNPK ablation is never achieved, but is only partial. As suggested by reviewer #3 (Figure 5 - Point 2), we have now added RT-qPCR in Fig.5C, which corroborates these data.

      16) The authors state: "Deletion of HNRNPK diminished the highly phosphorylated forms of 4EBP1, which instead were preserved in both LN-229 C3ΔTFAP2C and LN-229 C3ΔTFAP2C;ΔHNRNPK cells (Fig. 5C). Similarly, the S6 phosphorylation ratio was reduced in LN-229 C3ΔHNRNPK cells and was restored in the ΔTFAP2C;ΔHNRNPK double-ablated cells (Fig. 5C)."

      WE are not convinced that p4EBP1 is preserved in the LN-229 C3ΔTFAP2C cells - there is a very faint band which is at a lower level than the band in the LN-229 C3ΔHNRNPK cells. However, when both HNRNPK and TFAP2C were ablated, the p4EBP1 band is clear cut. I agree with the quantitation that deletion of HNRNPK and TFAP2C both reduce the level of 4EBP1 - the reduction is greater with TFAP2 but when both are deleted together the levels of 4EBP1 are higher and p4EBP1 is clearly present. In quantifying the S6 and pS6 levels, did the authors consider the actin levels- they present a ratio of the pS6 to S6. I may be lacking some understanding but why is the ratio of pS6/S6 being calculated. Is the level of pS6 not what is important - phosphorylation of S6 should lead it to being activated and thus it's the actual level of pS6 that is important, not the ratio to the non-phosphorylated protein.

      Response: In Fig. 5C, the three-band pattern of 4EBP1 is clearly visible in the NT+NT or WT condition, with the top band representing the highest phosphorylation state. Upon HNRNPK deletion, this top band almost completely disappears, mimicking the effect of our starvation control (Starv.). This top band remains clearly visible in both TFAP2C-ablated and double-ablated cells, supporting our conclusion. In our original text, we referred to the "highly phosphorylated forms" of 4EBP1, which might have caused some confusion, suggesting we were evaluating the two top bands. We are specifically referring only to the very top band (high p4EBP1), which represents the most highly phosphorylated form of 4EBP1. This is the relevant phosphorylated form to focus on, as it is the only one that disappears in the starvation control (Starv.) or upon mTORC1/2 inhibition with Torin-1 (Fig. 7B).

      To better clarify these points, we have now more clearly indicated the "high p4EBP1" band with an asterisk in Fig. 5E, added quantification of high p4EBP1/4EBP1, and rephrased the text as follows:

      "Deletion of HNRNPK diminished the highest phosphorylated form of 4EBP1 (high p4EBP1, marked with an asterisk), mimicking the effect observed in starved cells (Starv.). This high p4EBP1 band was preserved in both LN-229 C3ΔTFAP2C and LN-229 C3ΔTFAP2C;ΔHNRNPK cells (Fig. 5C).".

      Regarding pS6 quantification, we added pS6/Actin quantification in Supp. Fig. 6E and F of the revised manuscript, also attached here for your convenience.

      17) When determining ATP levels, do they control for cell number? HNRNPK depletion results in lower ATP levels, co-deletion of TFAP2C rescues this. But this could be because there is less cell-death? So, more cells express ATP. Have they controlled for relative numbers of cells.

      Response: As described in the Materials and Methods , we normalized ATP levels to total protein content, which is a standard approach for this type of quantification (see DOI:10.1038/nature19312).

      18) The construction of the HovL cell line that propagate ovine prions - very few details are provided of the susceptibility of the cell line to PG127 prions.

      Response: As with other prion-infected cell lines, HovL cells do not exhibit any specific growth defects, susceptibilities, or phenotypes beyond their ability to propagate prions. This is consistent with established observations in prion research, where immortalized cell lines (and in general in vitro cultures) normally do not show cytotoxicity upon prion infection and, therefore, are used as models for prion propagation rather than for prion toxicity (see https://doi.org/10.1111/jnc.14956 for reference).

      We now expanded the relevant section, including technical and conceptual details in the main text of the revised manuscript as follows:

      "As reported for other ovinized cell models (66), HovL cells were susceptible to infection by the PG127 strain of ovine prions and capable of sustaining chronic prion propagation, as shown by proteinase K (PK)-digested western blot and by detection of PrPSc using the anti-PrP antibody 6D11, which selectively stains prion-infected cells after fixation and guanidinium treatment (67) (Supp. Fig. 7C-E). Consistent with most prion-propagating cell lines (68), HovL cells did not exhibit specific growth defects, susceptibilities, or overt phenotypes beyond their ability to propagate prions."

      19) It is stated that HRNPK depletion from HovL cells increases PrpSC as determined by 6D11 fluorescence, but in the manuscript HRNPK depletion results in cell death. How does this come together?

      Response: As explicitly stated in the main text and shown in Fig.6-7, HNRNPK is downregulated (via siRNAs) in the prion experiments rather than fully deleted (via CRISPR) as in the first part of the manuscript. As shown in Supp. Fig. 8B, this downregulation does not affect cell viability within the experimental time window. Therefore, the observed increase in PrPSc levels upon HNRNPK downregulation, as determined by western blot and 6D11 staining, is independent of any potential cell death effects. Moreover, the same siRNA downregulation approach was used by M. Avar et al. (Ref. 26) in comparable experiments, yielding similar outcomes.

      20) They show that mTOR inhibition mimics the effect of HNRNPK deletion, why didn't they overexpress mTOR and see if that rescues this? This would indicate a causal relationship.

      Response: We appreciate the reviewer's suggestion. We agree that the proposed rescue strategy would be the best approach to indicate a causal relationship. However, we linked the activity of the mTORC1 complex (and not only that of mTOR) to prion propagation. Overexpression of only mTOR would not restore mTORC1 full function, as Rptor would still be downregulated in the context of HNRNPK siRNA silencing (Fig. 7A and Supp. Fig. 8E). Moreover, our RNA-seq data (Supp. Table 5) from HNRNPK ablation indicate the downregulation of other mTORC1 components (namely Pras40 (AKT1S1) and mLST8). Therefore, the rescue of the mTORC1 activity by an overexpression strategy would be a very challenging approach. Given these complexities, to infer causality, we used mTORC1 inhibition (via rapamycin and Torin1) to mimic the effects of HNRNPK downregulation in reducing mTORC1 activity (FIG. 7B).

      For clarification, we have now highlighted in Fig. 4C that HNRNPK ablation downregulates also AKT1S1 and mLST8, other than mTOR and Rptor (also attached below), and we have discussed this in the main text as well. We also have clarified in the revised manuscript (where we sometimes inadvertently referred to it as just mTOR inhibition) that the observed effects are due to mTORC1 inhibition, and not simply mTOR inhibition.

      21) Flow cytometric data: supplementary Fig of Fig6d. - when they are looking at fixed cells the gating strategy for cells results in the inclusion of a lot of debris. The gate needs to be moved and be more specific to ensure results are interpreted properly. Same with the singlet gating. It's not tight enough, they include doublets as well which will skew their data. The gating strategy needs to be regated.

      Response: We have reanalyzed the flow cytometry data in Fig. 6D with a more stringent gating approach to better exclude debris and ensure proper singlet selection. We confirm that there is no change in the final interpretation of the results after applying the updated gating strategy.

      Reviewer #2 (Significance (Required)):

      The manuscript "Prion propagation is controlled by a hierarchical network involving the nuclear Tfap2c and hnRNP K factors and the cytosolic mTORC1 complex" by Sellitto et al aims to examine how heterogenous nuclear ribonucleoprotein K (hnRNPK), limits pion propagation. They perform a synthetic - viability CRISPR- ablation screen to identify epistatic interactors of HNRNPK. They found that deletion of Transcription factor AP-2g (TFAP2C) suppressed the death of hnRNP-K depleted LN-229 and U-251 MG cells whereas its overexpression hypersensitized them to hnRNP K loss. Moreover, HNRNPK ablation decreased cellular ATP, downregulated genes related to lipid and glucose metabolism and enhanced autophagy. Simultaneous deletion of TFAP2C reversed these effects, restored transcription and alleviated energy deficiency.

      Referee #3

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

      Summary: Using a CRISPR-based high throughput abrasion assay, Sellitto et al. identified a list of genes that improve cell viability when deleted in hnRNP K knockout cells. Tfap2c, a transcription factor, was identified as a candidate with potential overlap with a hnRNP K function like modulating glucose metabolism. The deletion of Tfap2c in hnRNP K-deletion background prevented caspase-dependent apoptosis observed in hnRNP K single-deletion cells. Further analysis of bulk RNA-seq in hnRNP K/TFAP2C single- and double-deletion cells revealed the impairment in cellular ATP level. Accordingly, activation of AMPK led to perturbed autophagy in hnRNP K deleted cells. Moreover, the reduction and/or inactivation of the downstream mTOR protein resulted in the reduced phosphorylation of S6. Conversely, the phosphorylation of S6 and E4BP1 can be increased by TFAP2C overexpression. Finally, the pharmacological inhibition of the mTOR pathway increased the PrPSC level. This is an interesting paper potentially providing new mechanical insight of hnRNPK function and its interaction with TFAP2C. However, inconsistencies in TFAP2C expression across cell lines and conflicting mechanistic interpretations complicate conclusions. Co-IP experiments suggested hnRNP K and Tfap2c may interact, though further validation is needed. Several figures require additional clarification, statistical analysis, or experimental validation to strengthen conclusions.

      Major comments:

      1) Different responses of the TFAP2C expression level to deletion of hnRNPK in the two cell lines (LN-229 C3 and U251-Cas9) should be more adequately addressed. The manuscript focuses on the interaction between hnRNPK and TFAP2C, yet the hnRNPK deletion causes different changes in TFAP2C level in two different lines. Furthermore, in studies where the mechanistic link between hnRNPK and TFAP2C is being investigated, only results from the LN-229 line are presented (Figure 4-7). Thus, it is not clear whether these mechanisms also apply to another line, U251-Cas9, where hnRNPK deletion has the opposite effect on the TFAP1C level. Thus, key experiments should be performed in both lines.

      Response: The opposite effects of hnRNPK ablation on TFAP2C expression between LN-229 C3 and U251-Cas9 cells likely reflect intrinsic differences between the two cell lines. However, the viability interaction between hnRNPK and TFAP2C is conserved in both cell models (Fig. 1G-H, 2A-B, Supp. Fig. 3A-B), suggesting that shared molecular functions at the interface of this interaction exist across the lines. In fact, we believe that the opposite effect of hnRNPK ablation on TFAP2C expression in the two lines strengthens (rather than weakens) our model by highlighting how TFAP2C expression modulates cellular sensitivity to HNRNPK ablation, as detailed in our response to Reviewer #2 (Point 10 - Major comments).

      Regarding the mechanistic studies presented in FIG. 4-7, our initial goal in using two cell lines was to validate the functional viability interaction between HNRNPK and TFAP2C, as identified in our screening (performed in LN-229 C3 cells). After confirming this interaction, we chose to focus only on LN-229 C3 (beginning with RNA-seq analysis, which then led to subsequent mechanistic studies), as this provided the necessary foundation to investigate prion propagation in HovL cells (derived from LN-229). As a U251 model propagating prions does not exist, we are technically limited in performing prion experiments only in HovL and we do not believe that conducting additional experiments in U251 cells would add substantial value to our work or further our investigation.

      We hope this explanation clarifies our rationale and addresses the reviewer's concerns.

      2) Although a lot of data are presented, it is not clear how deletion of the TFAP2C reverses the toxicity caused by deletion of hnRNPK. Specifically, the first half of the paper seems to suggest an opposite mechanism than the second half of the paper. In Figure 2-4, the authors suggest a model that TFAP2C deletion has the opposite effect of hnRNPK deletion, thus rescuing toxicity. However, in Figure 5-6, it is suggested TFAP2C overexpression has the opposite effect of hnRNPK deletion. This two opposite effect of TFAP2C make it difficult to understand the models that the authors are proposing. Please also see below comment 2 for Figure 5.

      Response: We respectfully disagree with the notion that the first and second halves of the manuscript propose contradictory mechanisms.

      In Fig. 2-4, we describe the phenotypic rescue of cell viability upon TFAP2C deletion in hnRNPK-deficient cells. At this stage, we are not proposing a specific molecular mechanism but simply observing a rescue of viability and highlighting underlying transcriptional differences. There is no implication of an opposite molecular mechanism involving the individual activities of hnRNPK and TFAP2C; rather, we focused on the broader effect of TFAP2C deletion on the viability of HNRNPK-lacking cells. In Fig. 5, we isolated a partial mechanism underlying this interaction. We state that: "These data specify a role for TFAP2C in promoting mTORC1-mediated cell anabolism and suggest that its overexpression might hypersensitize cells to HNRNPK ablation by depleting the already limited ATP available, thus making its deletion advantageous". In the discussion, we now further reviewed our explanation: "HNRNPK deletion might cause a metabolic impairment leading to a nutritional crisis and a catabolic shift, whereas TFAP2C activation could promote mTORC1 anabolic functions. Thus, Tfap2c removal may rewire the bioenergetic needs of cells by modulating the mTORC1 signaling and augmenting their resilience to metabolic stress like the one induced by HNRNPK ablation". Therefore, we propose that TFAP2C expression might be particularly detrimental in hnRNPK-deficient cells, as it could push the cell into an anabolic biosynthetic state, further depleting energy stores that the cell is attempting to conserve in response to hnRNPK depletion. Removal of TFAP2C alleviates this metabolic strain. In our view, there is no contradiction between our observations.

      We hope this explanation clarifies our rationale and resolves any perceived inconsistency in our model. To further enhance the understanding of our interpretations, we have now also added (in substitution of Fig. 5E of the original manuscript) a graphical scheme (Fig. 5G of the revised manuscript) to visually explain and illustrate our model (attached below for your convenience).

      3) Similar to the point above, the first half of the paper focuses on hnRNPK deletion-induced toxicity (Fig. 1-5), while the second half of the paper focuses on hnRNPK deletion-induced PrPSC level (Fig. 6-7). The mechanistic link between these two downstream effects of hnRNPK deletion is not clear and thus, it is difficult to understand the reason that hnRNPK deletion-induced toxicity can be rescued by TFAP2C deletion, while hnRNPK deletion-induced PrPSC level increase can be rescued by TFAP2C overexpression.

      Response: Our study is not aimed at comparing viability and prion propagation as interconnected phenotypes but rather at identifying molecular processes regulated by the HNRNPK-TFAP2C interaction. Our study identifies mTORC1 activity as a molecular process at the interface of the HNRNPK-TFAP2C. HNRNPK knockout (or knockdown, which does not affect viability, and therefore is used in the prion section of the manuscript) tones mTORC1 activity down, while TFAP2C overexpression enhances it. This finding suggested an explanation for the viability interaction we observed (see reply to reviewer #3 - Point 2 -Major comments) and it provided a partial mechanism (mTORC1 activity) to explain the effect of HNRNPK knockdown and TFAP2C overexpression on prions.

      We hope this clarification addresses the reviewer's concern.

      Abstract:

      1) Please rephrase and clarify "We linked HNRNPK and TFAP2C interaction to mTOR signaling..." by distinguishing functional, genetic, and direct (molecule-to-molecule) interactions.

      Response: 1) We have now clarified it in the text of the revised manuscript as follows:

      "We linked HNRNPK and TFAP2C functional and genetic interaction to mTOR signaling, observing that HNRNPK ablation inhibited mTORC1 activity through downregulation of mTOR and Rptor, while TFAP2C overexpression enhanced mTORC1 downstream functions."

      2) A sentence reads, "...HNRNPK ablation inhibited mTORC1 activity through downregulation of mTOR and Rptor," although the downregulation of Rptor is observed only at the RNA level. The change in Rptor protein expression level is not reported in the manuscript. Please consider adding an experiment to address this or rephrase the sentence.

      Response: 2) We have now added the experiment in Supp. Fig. 9A of the revised manuscript. The blot shows that hnRNP K depletion reduces both mTOR and Rptor protein levels. "hnRNP K depletion inhibited mTORC1 activity through downregulation of mTOR and Rptor".

      Figure 2:

      1. H and I. Co-IP experiments were done using anti-TFAP2C antibody to the bead. Although the TFAP2C bands show robust signals on the blots, indicating successful enrichment of the protein, hnRNP K bands are very faint. Has the experiment been done by conjugating the hnRNP K antibody to the beads instead? Was the input lysate enriched in the nuclear fraction? Did the lysis buffer include nuclease (if so, please indicate in the figure legend and the methods section)? Addressing these would make the argument, "We also observed specific co-immunoprecipitation of hnRNP K and Tfap2c in LN-229 C3 and U251-Cas9 cells (Fig. 2H-I, Supp. Fig. 3L), suggesting that the two proteins form a complex inside the nucleus" stronger, providing information on potential direct binding.

      Response: 1. We refer the reviewer to our response to reviewers #1 and #2 regarding the weak interaction, the nuclease treatment, and the HNRNPK IP (reviewer #1 Point 1 and reviewer #2 Point 11 - Major comments). As for the co-IP input, it was not enriched in the nuclear fraction, but as shown in Supp. Fig. 4A-B hnRNPK and Tfap2c are exclusively nuclear.

      Figure 3:

      1. C and D. Please add a sentence in the figure legend explaining which means the multiple comparisons were made between (DMSO vs each drug concentration?). Graphing individual data points instead of bars would also be helpful and more informative. Please discuss the lack of dose dependency.

      Response: 1. We have now added information about the comparison in the figure legend ("Multiple comparison was made between Z-VAD-FMK and DMSO treatments in ΔHNRNPK cells."), modified the graph to show the individual data points (attached below for your convenience), and expanded the discussion as detailed for reviewer #2 (Point 14 - Major comments). (For completeness, we have also modified Supp. FIG. 5F to show individual data points, and we have combined the graphs (the DMSO control was shared across treatments)).

      Supplemental Figure 4 (Now shifted in Supplemental Figure 5):

      1. A. Although the trend can be observed, the deletion of hnRNP K does not significantly reduce the GPX4 protein level in LN-229 C3. Therefore, the following statement requires more data points and additional statistical analysis to be accurate: "In LN-229 C3 and U251-Cas9 cells, the deletion of HNRNPK reduced the protein level of GPX4, whereas TFAP2C deletion increased it (Supp. Fig. 4A-B)."

      2. A and B. The results are confusing, considering the previous report cited (ref 49) shows an increase in GPX4 with TFAP2C. It may be possible that the deletion of TFAP2C upregulates the expression of proteins with similar functions (e.g., Sp1). If this is the case, the changes in GPX4 expression observed here are a consequence of TFAP2C deletion and may not "suggest a role for HNRNPK and TFAP2C in balancing the protein levels of GPX4."

      Response: 1. We agree with the reviewer that in LN-229 C3 cells the reduction of GPX4 protein levels upon HNRNPK deletion did not reach statistical significance in our initial Western blot analysis. To address this concern, we performed six additional independent experiments and repeated the statistical analysis. Although the trend toward reduced GPX4 protein levels remained consistent, statistical significance was still not achieved (p > 0.05). Importantly, this trend is supported by our RNA-seq dataset (Supplementary Table 5), which shows decreased GPX4 expression upon HNRNPK deletion. We have now revised the text to more accurately reflect the experimental observations and to avoid overstating the effect in LN-229 C3 cells as follows:

      "In LN-229 C3 and U251-Cas9 cells, deletion of HNRNPK was associated with reduced glutathione peroxidase 4 (GPX4) protein abundance (although not statistically significant in LN-229 C3; p ≈ 0.08), whereas deletion of TFAP2C increased it (Supp. Fig. 5A-B)."

      The six new experimental replicas have been added to the uncropped western blot section.

      __Response: __2. Concerning the potential role of TFAP2C deletion in upregulating proteins with similar functions, we recognize the reviewer's perspective. However, our primary focus is on the observed trends rather than a definitive mechanistic conclusion. We clarified our wording to acknowledge this possibility while maintaining the relevance of our findings within the broader context of hnRNPK and TFAP2C interactions.

      "This last result was interesting as a previous study reported that Tfap2c enhances GPX4 expression (51). Thus, the observed increase upon TFAP2C deletion suggests additional layers of regulation, potentially involving compensatory mechanisms."

      Supplemental Figure 5 (Now shifted in Supplemental Figure 6):

      1. B. To obtain statistical significance and strengthen the conclusion, more repeated Western blot experiments can be done to quantify the pAMPK/AMPK ratio.

      Response: We included three more experiments as detailed in our response to reviewer #1 (Point 2 - Major comments) and reviewer #2 (Point 14 - Major comments).

      Figure 5:

      1. B. I believe statistical analysis with two replicates or less is not recommended. Although the assay is robust, and the blot is convincing, please consider adding more replicates if the blot is to be quantified and statistically analyzed.

      2. "Interestingly, RNA and protein levels of mTOR were downregulated in LN-229 C3ΔHNRNPK cells but were partially rebalanced by the ΔTFAP2C;ΔHNRNPK double deletion (Fig. 4C, Supp. Fig. E)." The statement is based on a slight difference at the protein level between the single deletion and the double deletion, as well as the observation from the bulk RNA-seq data. mTOR (and Rptor) mRNA level can be assessed by RT-qPCR to validate and further support the existing data. It is also curious why deletion of TFAP2C alone, also induced decrease in mTOR, but double deletion rescued mTOR level slightly compared to deletion of HNRNPK alone.

      3. C. The main text refers to the changes in the level of phosphorylated E4BP1, stating, "Deletion of HNRNPK diminished the highly phosphorylated forms of 4EBP1, which instead were preserved in both LN-229 C3ΔTFAP2C and LN-229 C3ΔTFAP2C;ΔHNRNPK cells (Fig. 5C)." However, the quantification was done on the total E4BP1, which may be because separating pE4BP1 and E4BP1 bands on a blot is challenging. Please consider using phospho-E4BP1 specific antibody or rephrase the sentence mentioned above. The current data suggest the single- and double-deletion of hnRNP K/TFAP2C affect the overall stability of E4BP1, which may be a correlation and not due to the mTOR activity as claimed in "We conclude that HNRNPK and TFAP2C play an essential role in co-regulating cell metabolism homeostasis by influencing mTOR and AMPK activity and expression." How does the cap-dependent translation (or total protein level) change in TFAP2C deleted and overexpressing cells?

      Response: 1. We added two additional experiments as detailed in our response to reviewer #1 (Point 3 - Major comment).

      __Response: __2. Deletion of TFAP2C does not decrease mTOR levels as shown from the quantification in Fig. 5D. To further support our results, we have now included RT-qPCR in FIG. 5C as suggested by the reviewer. Data are also attached here for your convenience.

      __Response: __3. Regarding the assessment of phosphorylated 4EBP1, we think we achieved a clear separation of the differently phosphorylated forms of 4EBP1 in our blots, and we have now added the quantification for High p4EBP1/4EBP1 in Fig. 5E (see also our response to reviewer #2 Point 16 - Major comments). The quantification of total 4EBP1 represents an additional dataset, and we do not claim that 4EBP1 stability is affected by HNRNPK and TFAP2C directly through mTOR, which could be, in fact, correlative. We claim that HNRNPK and TFAP2C modulate mTORC1 and AMPK metabolic signaling as shown by the changed phosphorylation of 4EBP1, S6, AMPK, and ULK1 (Fig. 5C-E, Supp. FIG. 6B, D) and by the regulation of autophagy (Fig. 5B, Supp. Fig. 6C); we did not directly check cap-dependent translation.

      We have now rephrased our text to ensure clarity as follows:

      "We conclude that HNRNPK and TFAP2C play a role in co-regulating mTORC1 and AMPK expression, signaling, and activity."

      Figure 6:

      1. A. Did the sihnRNP K increase the TFAP2C level?

      2. A and C. Are the total PrP levels lower in TFAP2C overexpressing cells compared to mCherry cells when they are infected?

      3. D. Do the TFAP2C protein levels differ between 2-day+72-h and 7-day+96-h?

      __Response: __1. Yes, it does. We have now provided the quantification in Fig. 6A, C, and Supp. Fig. 8A (also attached below for your convenience).

      __Response: __2. We have now provided the quantification in Fig. 6A and Supp. Fig. 8A. The total PrP does not change in TFAP2C overexpressing cells. Total PrP consists of both PK-resistant PrP (PrPSc) and PK-sensitive PrP (PrPC plus potential other intermediate species), with PrPSc typically present at much lower levels. In our model, PrPC is exogenously expressed at high levels via a vector and remains constant across conditions (Fig. 6C and Supp. Fig. 8C). As a result, any changes in PrPSc may not necessarily reflect on total PrP levels.

      __Response: __3. No, there is no statistically significant change. We have now added a representative western blot and the quantification of 3 independent replicates in Supp. Fig. 8D. The other two western blots are only shown in the uncropped western blots section. This dataset is also attached here for your convenience.

      Figure 7:

      1. I agree with the latter half of the statement: "These findings suggest that HNRNPK influences prion propagation at least in part through mTORC1 signaling, although additional mechanisms may be involved." The first half requires careful rephrasing since (A) Independent of the background siRNA treatment, TFAP2C overexpression by itself can modulate PrPSC level as seen in Fig 6A and B, (B) Although the increase in TFAP2C level is observed with the hnRNP K deletion (Fig 1; LN-229 C3), sihnRNP K treatment may or may not influence the TFAP2C level (Fig 6; quantified data not provided), and (C) In the sihnRNP K-treated cells, E4BP1 level is increased compared to the siNT-treated cells, which was not observed hnRNP K-deleted cells. Discussions and additional experiments (e.g., mTOR knockdown) addressing these points would be helpful.

      __Response: __A, B) We respectfully disagree with the possibility that HNRNPK downregulation may increase prion propagation via TFAP2C upregulation. As shown in Fig. 6A-B, D and in Supp. Fig. 8A, TFAP2C overexpression reduces, rather than increases, prion levels. Therefore, it would be inconsistent to suggest that HNNRPK siRNA promotes prion propagation through TFAP2C upregulation (quantification is now provided, see reviewer #3 - Figure 6 - Point 1). C) Concerning 4EBP1 levels, we have quantified the total 4EBP1 (also attached below) and expanded the discussion on potential discrepancies between HNRNPK knockout and knockdown, as the former affects cell viability, while the latter does not. However, as explained also in the previous reply to reviewer #3 - Figure 5 - Point 3, our focus is on the highly phosphorylated band of 4EBP1 (High p4EBP1), which is the direct target of mTORC1 activity. In both the hnRNPK knockout LN-229 C3 (Fig. 5E) and knockdown HovL models (Fig. 7B), phosphorylation of 4EBP1, along with phosphorylation of S6, is clearly reduced (we have now included quantification for Fig. 7B), reinforcing our conclusion that mTORC1 activity is affected by hnRNPK depletion. As the reviewer noted, we do not claim that mTORC1 is the sole mediator of hnRNPK's effect on prion regulation. However, we think that our interpretation of a potential and partial role of mTORC1 inhibition in the effect of HNRNPK downregulation on prion propagation is in line with the data presented in Fig. 6-7 and Supp. Fig. 8-9. For further clarification, we expanded the text according to the new experiments and analysis, and we added mTOR and Raptor siRNA knockdown (Supp. Fig.9C) to further support our conclusions (also attached below for your convenience).

      Minor comments:

      1. Please clarify "independent cultures." Does this mean technical replicates on the same cell culture plate but different wells or replicated experiments on different days?

      __Response: __We have now clarified in each figure legend. "Individually treated wells" means different parental cultures grown and treated separately on the same day. n represents independent experiments on different days.

      1. Fig 2G. Please explain how the sigmoidal curves were fitted to the data points under the materials and methods section.

      2. Fig 3E and F. Please refer to the comment on Fig 2G above.

      __Response: __We have now added the explanation in Materials and Methods as follows:

      "Curve Fitting

      For sigmoidal curve fitting, we used GraphPad Prism (version X, GraphPad Software). Data in Figure 2G were fitted using nonlinear regression with a least squares regression model. For Figures 3E and 3F, data fitting was performed using an asymmetric sigmoidal model with five parameters (5PL) and log-transformed X-values (log[concentration])."

      3.Fig S3 F/H. Quantification of gel bands would be helpful when comparing protein expression changes after different treatments, as band intensities look different across.

      __Response: __We have now added the quantifications in Supp. FIG. 3D-H (attached below for your convenience). They confirm that there are no significant differences in the means of the normalized values.

      1. Supp Fig 5C and F. These panels can be combined with the corresponding panels in main Figure 5 if space allows so that the readers do not have to flip pages between the main text and Supplemental material.

      __Response: __We have now combined the panels. Previous Supp. FIG. 5C and F are now shown in FIG. 6C and E, respectively.

      Reviewer #3 (Significance (Required)):

      This is an interesting paper potentially providing new mechanical insight of hnRNPK function and its interaction with TFAP2C. It is also important to understand how hnRNPK deletion induces prion propagation and develop methods to mitigate its spread. However, inconsistencies in TFAP2C expression across cell lines and conflicting mechanistic interpretations complicate conclusions. I have expertise in RNA-binding protein, cell biology, and prion disease.

    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:

      Using a CRISPR-based high throughput abrasion assay, Sellitto et al. identified a list of genes that improve cell viability when deleted in hnRNP K knockout cells. Tfap2c, a transcription factor, was identified as a candidate with potential overlap with a hnRNP K function like modulating glucose metabolism. The deletion of Tfap2c in hnRNP K-deletion background prevented caspase-dependent apoptosis observed in hnRNP K single-deletion cells. Further analysis of bulk RNA-seq in hnRNP K/TFAP2C single- and double-deletion cells revealed the impairment in cellular ATP level. Accordingly, activation of AMPK led to perturbed autophagy in hnRNP K deleted cells. Moreover, the reduction and/or inactivation of the downstream mTOR protein resulted in the reduced phosphorylation of S6. Conversely, the phosphorylation of S6 and E4BP1 can be increased by TFAP2C overexpression. Finally, the pharmacological inhibition of the mTOR pathway increased the PrPSC level. This is an interesting paper potentially providing new mechanical insight of hnRNPK function and its interaction with TFAP2C. However, inconsistencies in TFAP2C expression across cell lines and conflicting mechanistic interpretations complicate conclusions. Co-IP experiments suggested hnRNP K and Tfap2c may interact, though further validation is needed. Several figures require additional clarification, statistical analysis, or experimental validation to strengthen conclusions.

      Major comments:

      1. Different responses of the TFAP2C expression level to deletion of hnRNPK in the two cell lines (LN-229 C3 and U251-Cas9) should be more adequately addressed. The manuscript focuses on the interaction between hnRNPK and TFAP2C, yet the hnRNPK deletion causes different changes in TFAP2C level in two different lines. Furthermore, in studies where the mechanistic link between hnRNPK and TFAP2C is being investigated, only results from the LN-229 line are presented (Figure 4-7). Thus, it is not clear whether these mechanisms also apply to another line, U251-Cas9, where hnRNPK deletion has the opposite effect on the TFAP1C level. Thus, key experiments should be performed in both lines.
      2. Although a lot of data are presented, it is not clear how deletion of the TFAP2C reverses the toxicity caused by deletion of hnRNPK. Specifically, the first half of the paper seems to suggest an opposite mechanism than the second half of the paper. In Figure 2-4, the authors suggest a model that TFAP2C deletion has the opposite effect of hnRNPK deletion, thus rescuing toxicity. However, in Figure 5-6, it is suggested TFAP2C overexpression has the opposite effect of hnRNPK deletion. This two opposite effect of TFAP2C make it difficult to understand the models that the authors are proposing. Please also see below comment 2 for Figure 5.
      3. Similar to the point above, the first half of the paper focuses on hnRNPK deletion-induced toxicity (Fig. 1-5), while the second half of the paper focuses on hnRNPK deletion-induced PrPSC level (Fig. 6-7). The mechanistic link between these two downstream effects of hnRNPK deletion is not clear and thus, it is difficult to understand the reason that hnRNPK deletion-induced toxicity can be rescued by TFAP2C deletion, while hnRNPK deletion-induced PrPSC level increase can be rescued by TFAP2C overexpression.

      Abstract.

      1. Please rephrase and clarify "We linked HNRNPK and TFAP2C interaction to mTOR signaling..." by distinguishing functional, genetic, and direct (molecule-to-molecule) interactions.
      2. A sentence reads, "...HNRNPK ablation inhibited mTORC1 activity through downregulation of mTOR and Rptor," although the downregulation of Rptor is observed only at the RNA level. The change in Rptor protein expression level is not reported in the manuscript. Please consider adding an experiment to address this or rephrase the sentence.

      Figure 2.

      1. H and I. Co-IP experiments were done using anti-TFAP2C antibody to the bead. Although the TFAP2C bands show robust signals on the blots, indicating successful enrichment of the protein, hnRNP K bands are very faint. Has the experiment been done by conjugating the hnRNP K antibody to the beads instead? Was the input lysate enriched in the nuclear fraction? Did the lysis buffer include nuclease (if so, please indicate in the figure legend and the methods section)? Addressing these would make the argument, "We also observed specific co-immunoprecipitation of hnRNP K and Tfap2c in LN-229 C3 and U251-Cas9 cells (Fig. 2H-I, Supp. Fig. 3L), suggesting that the two proteins form a complex inside the nucleus" stronger, providing information on potential direct binding.

      Figure 3.

      1. C and D. Please add a sentence in the figure legend explaining which means the multiple comparisons were made between (DMSO vs each drug concentration?). Graphing individual data points instead of bars would also be helpful and more informative. Please discuss the lack of dose dependency.

      Supplemental Figure 4.

      1. A. Although the trend can be observed, the deletion of hnRNP K does not significantly reduce the GPX4 protein level in LN-229 C3. Therefore, the following statement requires more data points and additional statistical analysis to be accurate: "In LN-229 C3 and U251-Cas9 cells, the deletion of HNRNPK reduced the protein level of GPX4, whereas TFAP2C deletion increased it (Supp. Fig. 4A-B)."
      2. A and B. The results are confusing, considering the previous report cited (ref 49) shows an increase in GPX4 with TFAP2C. It may be possible that the deletion of TFAP2C upregulates the expression of proteins with similar functions (e.g., Sp1). If this is the case, the changes in GPX4 expression observed here are a consequence of TFAP2C deletion and may not "suggest a role for HNRNPK and TFAP2C in balancing the protein levels of GPX4."

      Supplemental Figure 5.

      1. B. To obtain statistical significance and strengthen the conclusion, more repeated Western blot experiments can be done to quantify the pAMPK/AMPK ratio.

      Figure 5.

      1. B. I believe statistical analysis with two replicates or less is not recommended. Although the assay is robust, and the blot is convincing, please consider adding more replicates if the blot is to be quantified and statistically analyzed.
      2. "Interestingly, RNA and protein levels of mTOR were downregulated in LN-229 C3ΔHNRNPK cells but were partially rebalanced by the ΔTFAP2C;ΔHNRNPK double deletion (Fig. 4C, Supp. Fig. E)." The statement is based on a slight difference at the protein level between the single deletion and the double deletion, as well as the observation from the bulk RNA-seq data. mTOR (and Rptor) mRNA level can be assessed by RT-qPCR to validate and further support the existing data. It is also curious why deletion of TFAP2C alone, also induced decrease in mTOR, but double deletion rescued mTOR level slightly compared to deletion of HNRNPK alone.
      3. C. The main text refers to the changes in the level of phosphorylated E4BP1, stating, "Deletion of HNRNPK diminished the highly phosphorylated forms of 4EBP1, which instead were preserved in both LN-229 C3ΔTFAP2C and LN-229 C3ΔTFAP2C;ΔHNRNPK cells (Fig. 5C)." However, the quantification was done on the total E4BP1, which may be because separating pE4BP1 and E4BP1 bands on a blot is challenging. Please consider using phospho-E4BP1 specific antibody or rephrase the sentence mentioned above. The current data suggest the single- and double-deletion of hnRNP K/TFAP2C affect the overall stability of E4BP1, which may be a correlation and not due to the mTOR activity as claimed in "We conclude that HNRNPK and TFAP2C play an essential role in co-regulating cell metabolism homeostasis by influencing mTOR and AMPK activity and expression." How does the cap-dependent translation (or total protein level) change in TFAP2C deleted and overexpressing cells?

      Figure 6.

      1. A. Did the sihnRNP K increase the TFAP2C level?
      2. A and C. Are the total PrP levels lower in TFAP2C overexpressing cells compared to mCherry cells when they are infected?
      3. D. Do the TFAP2C protein levels differ between 2-day+72-h and 7-day+96-h?

      Figure 7.

      1. I agree with the latter half of the statement: "These findings suggest that HNRNPK influences prion propagation at least in part through mTORC1 signaling, although additional mechanisms may be involved." The first half requires careful rephrasing since (A) Independent of the background siRNA treatment, TFAP2C overexpression by itself can modulate PrPSC level as seen in Fig 6A and B, (B) Although the increase in TFAP2C level is observed with the hnRNP K deletion (Fig 1; LN-229 C3), sihnRNP K treatment may or may not influence the TFAP2C level (Fig 6; quantified data not provided), and (C) In the sihnRNP K-treated cells, E4BP1 level is increased compared to the siNT-treated cells, which was not observed hnRNP K-deleted cells. Discussions and additional experiments (e.g., mTOR knockdown) addressing these points would be helpful.

      Minor comments:

      1. Please clarify "independent cultures." Does this mean technical replicates on the same cell culture plate but different wells or replicated experiments on different days?
      2. Fig 2G. Please explain how the sigmoidal curves were fitted to the data points under the materials and methods section.
      3. Fig 3E and F. Please refer to the comment on Fig 2G above.
      4. Fig S3 F/H. Quantification of gel bands would be helpful when comparing protein expression changes after different treatments, as band intensities look different across.
      5. Supp Fig 5C and F. These panels can be combined with the corresponding panels in main Figure 5 if space allows so that the readers do not have to flip pages between the main text and Supplemental material.

      Significance

      This is an interesting paper potentially providing new mechanical insight of hnRNPK function and its interaction with TFAP2C. It is also important to understand how hnRNPK deletion induces prion propagation and develop methods to mitigate its spread. However, inconsistencies in TFAP2C expression across cell lines and conflicting mechanistic interpretations complicate conclusions. I have expertise in RNA-binding protein, cell biology, and prion disease.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript "Prion propagation is controlled by a hierarchical network involving the nuclear Tfap2c and hnRNP K factors and the cytosolic mTORC1 complex" by Sellitto et al aims to examine how heterogenous nuclear ribonucleoprotein K (hnRNPK), limits pion propagation. They perform a synthetic - viability CRISPR- ablation screen to identify epistatic interactors of HNRNPK. They found that deletion of Transcription factor AP-2 (TFAP2C) suppressed the death of hnRNP-K depleted LN-229 and U-251 MG cells whereas its overexpression hypersensitized them to hnRNP K loss. Moreover, HNRNPK ablation decreased cellular ATP, downregulated genes related to lipid and glucose metabolism and enhanced autophagy. Simultaneous deletion of TFAP2C reversed these effects, restored transcription and alleviated energy deficiency.

      They state that HNRNPK and TFAP2C are linked to mTOR signalling and observe that HNRNPK ablation inhibits mTORC1 activity through downregulation of mTOR and Rptor while TFAP2C overexpression enhances mTORC1 downstream functions. In prion infected cells, TFAP2C activation reduced prion levels and countered the increased prion propagation due to HNRNPK suppression. Pharmacological inhibition of mTOR also elevated prion levels and partially mimicked the effects of HNRNPK silencing. They state their study identifies TFAP2C as a genetic interactor of HNRNPK and implicates their roles in mTOR metabolic regulation and establishes a causative link between these activities and prion propagation.

      This is an interesting manuscript in which a lot of work has been undertaken. The experiments are on the whole well done, carefully documented and support most of the conclusions drawn. However, there are places where it was quite difficult to read as some of the important results are in the supplementary Figures and it was necessary to go back and forth between the Figs in the main body of the paper and the supplementary Figs. There are also Figures in the supplementary which should have been presented in the main body of the paper. These are indicated in our comments below.

      We have the following questions /points:

      1. A plasmid harbouring four guide RNAs driven by four distinct constitutive promoters is used for targetting HNRNPK- is there a reason for using 4 guides- is it simply to obtain maximal editing - in their experience is this required for all genes or specific to HNRNPK?
      2. Is there a minimal amount of Cas9 required for editing?
      3. It is stated that cell death is delayed in U251-MG cells compared to LN-229-C3 cells- why? Also, why use glioblastoma cells other than that they have high levels of HNRNPK? Would neuroblastoma cells be more appropriate if they are aiming to test for prion propagation?
      4. Human CRISPR Brunello pooled library- does the Brunello library use constructs which have four independent guide RNAs as used for the silencing of HNRPNK?
      5. To rank the 763 enriched genes, they multiply the -log10FDR with their effect size - is this a standard step that is normally undertaken?
      6. The 32 genes selected- they were ablated individually using constructs with one guide RNA or four guide RNAs?
      7. The identified targets were also tested in U251-MG cells and nine were confirmed but the percent viability was variable - is the variability simply a reflection of the different cell line?
      8. The two strongest hits were IKBAKP and TFAP2C. As TFAP2C is a transcription factor - is it known to modulate expression of any of the genes that were identified to be perturbed in the screen? Moreover, it is stated that it regulates expression of several lncRNAs- have the authors looked at expression of these lncRNAs- is the expression affected- can modulation of expression of these lncRNAs modulate the observed phenotypic effects and also some of the targets they have identified in the screen?
      9. As both HNRNPK and TFAP2C modulate glucose metabolism, the authors have chosen to explore the epistatic interaction. This is most reasonable.
      10. The orthogonal assay to confirm that deletion of TFAP2C supresses cell death upon removing HNRNPK- was this done using a single guide RNA or multiple guides - is there a level of suppression required to observe rescue? Interestingly ablation of HNRNPK increases TFAP2C expression in LN-229-C3 whereas in U251-Cas9 cells HNRNPK ablation has the opposite effect- both RNA and protein levels of TFAP2C are decreased - is this the cause of the smaller protective effect of TFAP2C deletion in this cell line?
      11. Nuclear localisation studies indicate that the HNRNPK and TFAP2C proteins colocalise in the nucleus however the co-IP data is not convincing- although appropriate controls are present, the level of interaction is very low - the amount of HNRNPK pulled down by TFAP2C is really very low in the LN-229C3 cells and even lower in the U251-Cas9 cells. Have they undertaken the reciprocal co-IP expt?
      12. They state that LN-229 C3 TFAP2C and U251-Cas9TFAP2C were only mildly resistant to the apoptotic action of staurosporin Fig 3E and F - I accept they have undertaken the stats which support their statement that at high concentrations of staurosporin the LN-229 C3 TFAP2C cells are less sensitive but the U251-Cas9TFAP2C decreased sensitivity is hard to believe. Has this been replicated? I agree that HNRNPK deletion causes apoptosis in both LN-229 C3 and U251-Cas9 cells and this is blocked by Z-VAD-FMK - however the block is not complete- the max viability for HNRNPK deletion in LN-229 C3 cells is about 40% whereas for U251-Cas9 cells it is about 30% - does this suggest that cells are being lost by another pathway. Have they tested concentrations higher than 10nM?
      13. The RNA-seq comparisons- the authors use log2 FC <0.5 upregulated or genes downregulated by a similar amount- this is a very low cut off and would include essentially minimal changes in expression - not convinced of the significance of such low-level changes.
      14. It is stated" Accordingly, we observed increased AMPK phosphorylation (pAMPK) upon ablation of HNRNPK, which was consistently reduced in LN-229 C3ΔTFAP2C cells (Supp. Fig. 5B). LN-229 C3ΔTFAP2C; ΔHNRNPK cells also showed a partial reduction of pAMPK relative to LN-229 C3ΔHNRNPK cells (Supp. Fig. 5B). These results suggest that hnRNP K depletion causes an energy shortfall, leading to cell death. I am not totally convinced by the data presented in this Fig. The authors have quantified the band intensity and present the ratio of pAMPK to AMPK. Please note that the actin levels are variable across the samples - did they normalise the data using the actin level before undertaking the comparisons? Also, if the authors think this is an important point which supports their conclusion, then it should be in the main body of the paper rather than the supplementary. If AMPK is being phosphorylated, this should lead to activation of the metabolic check point which involves p53 activation by phosphorylation. Activated p53 would turn on p21CIP1 which is a very sensitive indicator of p53 activation.
      15. We also do not understand why the mTOR Suppl. Fig. 5E is not in the main body of the paper. It's clear that RNA and protein levels of mTOR were downregulated in LN-229 C3ΔHNRNPK cells but were partially rebalanced by the ΔTFAP2C- however the ΔTFAP2C;ΔHNRNPK double deletion levels are only slightly higher than the ΔHNRNPK - they are not at the level NT or even ΔTFAP2C (Fig. 4C, Supp. Fig. 5E).
      16. The authors state: "Deletion of HNRNPK diminished the highly phosphorylated forms of 4EBP1, which instead were preserved in both LN-229 C3ΔTFAP2C and LN-229 C3ΔTFAP2C;ΔHNRNPK cells (Fig. 5C). Similarly, the S6 phosphorylation ratio was reduced in LN-229 C3ΔHNRNPK cells and was restored in the ΔTFAP2C;ΔHNRNPK double-ablated cells (Fig. 5C)."

      WE are not convinced that p4EBP1 is preserved in the LN-229 C3ΔTFAP2C cells - there is a very faint band which is at a lower level than the band in the LN-229 C3ΔHNRNPK cells. However, when both HNRNPK and TFAP2C were ablated, the p4EBP1 band is clear cut. I agree with the quantitation that deletion of HNRNPK and TFAP2C both reduce the level of 4EBP1 - the reduction is greater with TFAP2 but when both are deleted together the levels of 4EBP1 are higher and p4EBP1 is clearly present. In quantifying the S6 and pS6 levels, did the authors consider the actin levels- they present a ratio of the pS6 to S6. I may be lacking some understanding but why is the ratio of pS6/S6 being calculated. Is the level of pS6 not what is important - phosphorylation of S6 should lead it to being activated and thus it's the actual level of pS6 that is important, not the ratio to the non-phosphorylated protein. 17. When determining ATP levels, do they control for cell number? HNRNPK depletion results in lower ATP levels, co-deletion of TFAP2C rescues this. But this could be because there is less cell-death? So, more cells express ATP. Have they controlled for relative numbers of cells. 18. The construction of the HovL cell line that propagate ovine prions - very few details are provided of the susceptibility of the cell line to PG127 prions. 19. It is stated that HRNPK depletion from HovL cells increases PrpSC as determined by 6D11 fluorescence, but in the manuscript HRNPK depletion results in cell death. How does this come together? 20. They show that mTOR inhibition mimics the effect of HNRNPK deletion, why didn't they overexpress mTOR and see if that rescues this? This would indicate a causal relationship. 21. Flow cytometric data: supplementary Fig of Fig6d. - when they are looking at fixed cells the gating strategy for cells results in the inclusion of a lot of debris. The gate needs to be moved and be more specific to ensure results are interpreted properly. Same with the singlet gating. It's not tight enough, they include doublets as well which will skew their data. The gating strategy needs to be regated.

      Significance

      The manuscript "Prion propagation is controlled by a hierarchical network involving the nuclear Tfap2c and hnRNP K factors and the cytosolic mTORC1 complex" by Sellitto et al aims to examine how heterogenous nuclear ribonucleoprotein K (hnRNPK), limits pion propagation. They perform a synthetic - viability CRISPR- ablation screen to identify epistatic interactors of HNRNPK. They found that deletion of Transcription factor AP-2 (TFAP2C) suppressed the death of hnRNP-K depleted LN-229 and U-251 MG cells whereas its overexpression hypersensitized them to hnRNP K loss. Moreover, HNRNPK ablation decreased cellular ATP, downregulated genes related to lipid and glucose metabolism and enhanced autophagy. Simultaneous deletion of TFAP2C reversed these effects, restored transcription and alleviated energy deficiency.

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      Referee #1

      Evidence, reproducibility and clarity

      The paper by Sellitto describes studies to determine the mechanism by which hnRNPK modulates the propagation of prion. The authors use cell models lacking HNRNPK, which is lethal, in a CRISPR screen to identify genes that suppress lethality. Based on this screen to 2 different cell lines, gene termed Tfap2C emerged as a candidate for interaction with HNRNPK. The show that Tfap2C counteracts the actions of HNRNPK with respect to prion propagation. Cells lacking HNRNPK show increased PrPSc levels. Overexpression of Tfap2C suppesses PrPSc levels. These effects on PrPSc are independent of PrPC levels. By RNAseq analysis, the authors hone in on metabolic pathways regulated by HNRPNK and Tfap2C, then follow the data to autophagy regulation by mTor. Ultimately, the authors show that short-term treatments of these cell models with mTor inhibitors causes increased accumulation of PrPSc. The authors conclude that the loss of HNRNPK leads to a reduced energy metabolism causing mTor inhibition, which is reduces translation by dephosphorylation of S6.

      Major Comments

      Fig H and I, Fig 3L. The interaction between Tfap2C and HNRNPK is pretty weak. The interaction may not be consequential. The experiment seems to be well controlled, yielding limited interaction. The co-ip was done in PBS with no detergent. The authors indicate that the cells were mechanically disrupted. Since both of these are DNA binding proteins, is it possible that the observed interaction is due to proximity on DNA that is linking the 2 proteins, including a DNAase treatment would clarify.

      Supplemental Fig 5B - The western blot images for pAMPK don't really look like a 2 fold increase in phosphorylation in HNRNPK deletion.

      Fig. 5A - I don't think it is proper to do statistics on an of 2. Fig 6D. The data look a bit more complicated than described in the text. At 7 days, compared to 2 days, it looks like there is a decrease in % cells positive for 6D11. Is there clearance of PrPSc or proliferation of un-infected cells? The authors might consider a different order of presenting the data. Fig 6 could follow Fig. 2 before the mechanistic studies in Figs 3-5. The authors use SEM throughout the paper and while this is often used there has been some interest in using StdDev to show the full scope of variability.

      Discussion The discrepancy between short-term and long-term treatments with mTor inhibitors is only briefly mentioned with a bit of a hand-waving explanation. The authors may need a better explanation.

      Minor Comments

      Page 12 - no mention of chloroquine in the text or related data.

      Page 12 - Supp. Fig. E - should be 5E

      Significance

      The study provides mechanistic insight into how HNRNPK modulates prion propagation. The paper is limited to cell models, and the authors note that long term treatment with mTor inhibitors reduced PrPSc levels in an in vivo model.

      The primary audience will be other prion researchers. There may be some broader interest in the mTor pathway and the role of HNRNPK in other neurodegenerative diseases.

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      Reply to the reviewers

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

      Summary:

      The study provides a comprehensive overview of genome size variation in two related species of the genus Epidendrum, which appear to be homoploid, although their DNA content more closely corresponds to that of heteroploid species. While I have a few serious concerns regarding the data analysis, the study itself demonstrates a well-designed approach and offers a valuable comparison of different methods for genome size estimation. In particular, I would highlight the analysis of repetitive elements, which effectively explains the observed differences between the species. However, I encourage the authors to adopt a more critical perspective on the k-mer analysis and the potential pitfalls in data interpretation.

      Major comments:

      R1. p. 9: Genome size estimation via flow cytometry is an incorrect approach. The deviation is approximately 19% for E. anisatum and about 25% for E. marmoratum across three repeated measurements of the same tissue over three days? These values are far beyond the accepted standards of best practice for flow cytometry, which recommend a maximum deviation of 2-5% between repeated measurements of the same individual. Such variability indicates a systemic methodological issue or improper instrument calibration. Results with this level of inconsistency cannot be considered reliable estimates of genome size obtained by flow cytometry. If you provide the raw data, I can help identify the likely source of error, but as it stands, these results are not acceptable.

      __A: __Thanks a lot for pointing out this issue. We have identified the source of the wide interval after consulting with the staff of LabNalCit. We originally used human peripheral blood mononuclear cells (PBMCs) as a reference to estimate the genome size (GS) of P. sativum and used the resulting range to estimate the GS of Epidendrum. We calculated P. sativum's GS using a wide human GS range of 6-7 Gb, which resulted in a wide range of P. sativum GS and, consequently, in a wide range of GS for our samples. Therefore, the wide range reported is not an issue with the instruments, but about the specifics of the analysis.

      __We have done the following changes: __

      1. Reducing the range we calculated of P. sativum's GS using a narrower human genome size range (6.41-6.51; Piovesan et al. 2019; DOI: 10.1186/s13104-019-4137-z), and using these intervals to calculate our sample's GS.
      2. We have explained our procedure in the methods, changed our results as required, and included a supplementary table with cytometry data (Supplementary Data Table 1).
      3. Human peripheral blood mononuclear cells (PBMCs) from healthy individuals were used as a standard laboratory reference to calculate the P. sativum genome size. Pisum sativum and the Epidendrum samples were analyzed in a CytoFLEX S flow cytometer (Beckman-Coulter), individually and in combination with the internal references (PBMCs and P. sativum, respectively). Cytometry data analysis was performed using FlowJo® v. 10 (https://www.flowjo.com/). A genome size value for the Epidendrum samples was calculated as the average of the minimum and maximum 1C/2C values obtained from three replicates of the DNA content histograms of each tissue sample. Minimum and maximum values come from the interval of P. sativum estimations based on the human genome size range (human genome size range: 6.41-6.51; Piovesan et al. 2019).
      4. The 1C value in gigabases (Gb; calculated from mass in pg) of E. anisatum ranged from 2.55 to 2.62 Gb (mean 1C value = 2.59 Gb) and that of E. marmoratum from 1.11 to 1.18 Gb (mean 1C value = 1.13 Gb; Supplementary Data Table S1).
      5. We also eliminated from Figure 3 the range we had estimated previously.
      6. Finally, we changed the focus of the comparison and discussion of the evaluation of the bioinformatic estimations, highlighting this deviation rather than whether the GS bioinformatic estimations fall within the cytometric interval. We calculated the Mean Absolute Deviation (MAD) as the absolute difference between the genome size estimates using k-mers and flow cytometry. This meant changing the results in P. 11 and 12 and adding to Fig. 3 two boxplots depicting the MAD. We have also added Supplementary Data Fig. S3 depicting the absolute deviations for E. anisatum and E. marmoratum per tool using the estimates generated from a k-mer counting with a maximum k-mer coverage value of 10,000 using 16 different values of k; a Supplementary Data Figure S5 depicting the mean absolute deviations resulting from the different subsampled simulated depths of coverage of 5×, 10×, 20×, 30×, and 40×; and finally a Supplementary Data Fig. S6 depicting the MAD changes as a function of depth of coverage for E. anisatum and E. marmoratum.

      R1. p. 14 and some parts of Introduction: It may seem unusual, to say the least, to question genome size estimation in orchids using flow cytometry, given that this group is well known for extensive endoreplication. However, what effect does this phenomenon have on genome size analyses based on k-mers, or on the correct interpretation of peaks in k-mer histograms? How can such analyses be reliably interpreted when most nuclei used for DNA extraction and sequencing likely originate from endoreplicated cells? I would have expected a more detailed discussion of this issue in light of your results, particularly regarding the substantial variation in genome size estimates across different k-mer analysis settings. Could endoreplication be a contributing factor?

      A:

      We reworded the introduction p.3, 2nd paragraph to make our point on the effect of endoreplication on flow cytometry clearer. We eliminated the following sentence from discussion p. 15 : "Difficulties for cytometric estimation of genome size can thus be taxon-specific. Therefore, cross-validating flow cytometry and bioinformatics results can be the most effective method for estimating plant genome size, especially when only tissues suspected to show significant endoreplication, such as leaves, are available" We added the following, p. 18: Genome size estimation for non-model species is considered a highly standardized approach. However, tissue availability and intrinsic genome characteristics (large genomes, polyploidy, endoreplication, and the proportion of repetitive DNA) can still preclude genome size estimation (e.g. Kim et al. 2025) using cytometry and bioinformatic tools. Cross-validating flow cytometry and bioinformatics results might be particularly useful in those cases. For example, when only tissues suspected of showing significant conventional endoreplication, such as leaves, are available, bioinformatic tools can help to confirm that the first peak in cytometry histograms corresponds to 2C. Conversely, bioinformatic methods can be hindered by partial endoreplication, which only flow cytometry can detect.

           4. We included a paragraph discussing the effect of CE and PE on bioinformatic GS estimation P. 17:
      

      Besides ploidy level, heterozygosity, and the proportion of repetitive DNA, k-mer distribution can be modified by endoreplication. Since endoreplication of the whole genome (CE) produces genome copies (as in preparation for cell division, but nuclear and cell division do not occur ), we do not expect an effect on genome size estimates based on k-mer analyses. In contrast, PE alters coverage of a significant proportion of the genome, affecting k-mer distributions and genome size estimates (Piet et al., 2022). Species with PE might be challenging for k-mer-based methods of genome size estimation.

      R1. You repeatedly refer to the experiment on genome size estimation using analyses with maximum k-mer coverage of 10,000 and 2 million, under different k values. However, I would like to see a comparison - such as a correlation analysis - that supports this experiment. The results and discussion sections refer to it extensively, yet no corresponding figure or analysis is presented.

      A:

      We had previously included the results of the analyses using different k-mer coverage in the Supplementary Data Figure S2. We have added, to formally compare the results using analyses with maximum k-mer coverage of 10,000 and 2 million, a Wilcoxon paired signed-rank test, which showed a significant difference, p. 12: The estimated genome sizes using a maximum count value of 10,000 were generally lower for all tools in both species compared to using a maximum count value of 2 million (median of 2M experiment genome size - median of 10K experiment genome size= 0.24 Gb). The estimated genome size of the 2 million experiment also tended to be closer to the flow cytometry genome size estimation with significantly lower MAD than the 10K experiment (Wilcoxon paired signed-rank test p = 0.0009). In the 10K experiment (Supplementary Data Figure S2; S3), the tool with the lowest MAD for E. anisatum was findGSE-het (0.546 Gb) and for E. marmoratum it was findGSE-hom (0.116 Gb).

       2. We have added a boxplot in the Supplementary Data Figure S3 depicting the mean absolute deviations using maximum k-mer coverage of 10,000 and 2 million compared to flow cytometry.
      

      Minor comments:

      R1. p. 3: You stated: "Flow cytometry is the gold standard for genome size estimation, but whole-genome endoreplication (also known as conventional endoreplication; CE) and strict partial endoreplication (SPE) can confound this method." How did you mean this? Endopolyploidy is quite common in plants and flow cytometry is an excellent tool how to detect it and how to select the proper nuclei fraction for genome size estimation (if you are aware of possible misinterpretation caused by using inappropriate tissue for analysis). The same can be applied for partial endoreplication in orchids (see e.g. Travnicek et al 2015). Moreover, the term "strict partial endoreplication" is outdated and is only used by Brown et al. In more recent studies, the term partial endoreplication is used (e.g. Chumova et al. 2021- 10.1111/tpj.15306 or Piet et al. 2022 - 10.1016/j.xplc.2022.100330).

      A:

      We have reworded the paragraph where we stated "Flow cytometry is the gold standard for genome size estimation", as in the answer to Major comment 2. Additionally, we highlighted in the discussion how, while FC is the gold standard for GS estimation, studying multiple alternatives to it may be important for cases in which live tissue is not available or is available only to a limited extent (i.e. only certain tissues), p. 18 We have changed the term "strict partial endoreplication" to partial endoreplication (PE).

      R1. p. 5: "...both because of its outstanding taxic diversity..." There is no such thing as "taxic" diversity - perhaps you mean taxonomic diversity or species richness.

      __A: __We have changed "taxic diversity" to "species diversity".

      R1. p. 6: In description of flow cytometry you stated: "Young leaves of Pisum sativum (4.45

      pg/1C; Doležel et al. 1998) and peripheral blood mononuclear cells (PBMCs) from healthy

      individuals...". What does that mean? Did you really use blood cells? For what purpose?

      A: Please find the explanation and the modifications we've made in the answer to major comment 1.

      R1. p. 7: What do you mean by this statement "...reference of low-copy nuclear genes for each species..."? As far as I know, the Granados-Mendoza study used the Angiosperm v.1 probe set, so did you use that set of probes as reference?

      __A: __We rewrote: "To estimate the allele frequencies, the filtered sequences were mapped to a

      reference of low-copy nuclear genes for each species" to:

      To estimate the allele frequencies, the filtered sequences were mapped to the Angiosperm v.1 low-copy nuclear gene set of each species.

      R1. p. 7: Chromosome counts - there is a paragraph of methodology used for chromosome counting, but no results of this important part of the study.

      A: We are including a supplementary figure (Supplementary Data Figure 7) with micrographs of the chromosomes of E. anisatum and E. marmoratum.

      R1. p. 12: Depth of coverage used in repeatome analysis - why did you use different coverage for both species? Any explanation is needed.

      A: To make explicit the fact that the depth of coverage is determined automatically by the analysis with no consideration for the amount of input reads, but only of the graph density and the amount of RAM available (Box 3 in Novak et al. 2020), we rewrote:

      "To estimate the proportion of repetitive DNA, the individual protocol analyzed reads corresponding to depths of coverage of 0.06× for Epidendrum anisatum and 0.43× for E. marmoratum." to

      To estimate the proportion of repetitive DNA, the RepeatExplorer2 individual protocol determined a max number of analyzed reads (Nmax) corresponding to depths of coverage of 0.06x for Epidendrum anisatum and 0.43x for E. marmoratum.

      R1. p. 16: The variation in genome size of orchids is even higher, as the highest known DNA amount has been estimated in Liparis purpureoviridis - 56.11 pg (Travnicek et al 2019 - doi: 10.1111/nph.15996)

      A: We have updated it.

      R1. Fig. 1 - Where is the standard peak on Fig. 1? You mention it explicitly on page 9 where you are talking about FCM histograms.

      A: We reworded the results, eliminating the references to the standard internal reference.

      Reviewer #1 (Significance (Required)):

      Significance

      This study provides a valuable contribution to understanding genome size variation in two Epidendrum species by combining flow cytometry, k-mer analysis, and repetitive element characterization. Its strength lies in the integrative approach and in demonstrating how repetitive elements can explain interspecific differences in DNA content. The work is among the first to directly compare flow cytometric and k-mer-based genome size estimates in orchids, extending current knowledge of genome evolution in this complex plant group. However, the study would benefit from a more critical discussion of the limitations and interpretative pitfalls of k-mer analysis and from addressing methodological inconsistencies in the cytometric data. The research will interest a specialized audience in plant genomics, cytogenetics, and genome evolution, particularly those studying non-model or highly endoreplicated species.

      Field of expertise: plant cytogenetics, genome size evolution, orchid genomics.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      With this work, the authors provide genome profiling information on the Epidendrum genus. They performed low-coverage short read sequencing and analysis, as well as flow cytometry approaches to estimate genome size, and perform comparative analysis for these methods. They also used the WGS dataset to test different approaches and models for genome profiling, as well as repeat abundance estimation, empathising the importance of genome profiling to provide basic and comparative genomic information in our non-model study species. Results show that the two "closely-related" Epidendrum species analysed (E. marmoratum and E. anisatum) have different genome profiles, exhibiting a 2.3-fold genome size difference, mostly triggered by the expansion of repetitive elements in E. marmoratum, specially of Ty3-Gypsy LTR-retrotransposon and a 172 tandem repeat (satellite DNA).

      Major comments:

      Overall, the manuscript is well-written, the aim, results and methods are explained properly, and although I missed some information in the introduction, the paper structure is overall good, and it doesn't lack any important information. The quality of the analysis is also adequate and no further big experiments or analysis would be needed.

      However, from my point of view, two main issues would need to be addressed:

      __R2. __The methods section is properly detailed and well explained. However, the project data and scripts are not available at the figshare link provided, and the BioProject code provided is not found at SRA. This needs to be solved as soon as possible, as if they're not available for review reproducibility of the manuscript cannot be fully assessed.

      __A: __We have made public the .histo files for all depths of coverage and cluster table files necessary to reproduce the results. We will also make public a fraction of the sequencing sufficient to reproduce our genome size and repetitive DNA results as soon as the manuscript is formally published. Whole dataset availability will be pending on the publication of the whole genome draft.

      R2. The authors specify in the methods that 0.06x and 0.43x sequencing depths were used as inputs for the RE analysis of E. anisatum and E. marmoratum. I understand these are differences based on the data availability and genome size differences. However, they don't correspond to either of the recommendations from Novak et al (2020):

      In the context of individual analysis: "The number of analyzed reads should correspond to 0.1-0.5× genome coverage. In the case of repeat-poor species, coverage can be increased up to 1.0-1.5×." Therefore, using 0.06x for E. anisatum should be justified, or at least addressed in the discussion.

      Moreover, using such difference in coverage might affect any comparisons made using these results. Given that the amount of reads is not limiting in this case, why such specific coverages have been used should be discussed in detail.

      In the context of comparative analysis: "Because different genomes are being analyzed simultaneously, the user must decide how they will be represented in the analyzed reads, choosing one of the following options. First, the number of reads analyzed from each genome will be adjusted to represent the same genome coverage. This option provides the same sensitivity of repeat detection for all analyzed samples and is therefore generally recommended; however, it requires that genome sizes of all analyzed species are known and that they do not substantially differ. In the case of large differences in genome sizes, too few reads may be analyzed from smaller genomes, especially if many species are analyzed simultaneously. A second option is to analyze the same number of reads from all samples, which will provide different depth of analysis in species differing in their genome sizes, and this fact should be considered when interpreting analysis results. Because each of these analysis setups has its advantages and drawbacks, it is a good idea to run both and cross-check their results."

      Therefore, it should be confirmed how much it was used for this approach (as in the methods it is only specified how much it was used for the individual analysis), and why.

      __A: __In Box 3, Novak et al (2020) explain that the number of analyzed reads (Nmax) is determined automatically by RepeatExplorer2, based on the graph density and available RAM. Therefore, the reported depths of coverage are results, not the input of the analysis. We tried different amounts of reads as input and got consistently similar results, so we kept the analysis using the whole dataset.

      For the comparative analysis, we have added the resulting depth of coverage and explained that we used the same number of reads for both species.

      Added to methods:

      "For the comparative protocol, we used the same amount of reads for both species".

      Added to results:

      "To estimate the proportion of repetitive DNA, the RepeatExplorer2 individual protocol determined a maximum number of analyzed reads (Nmax) corresponding to depths of coverage of 0.06x for E. anisatum and 0.43x for E. marmoratum. "

      "The RepeatExplorer2 comparative protocol determined a maximum number of analyzed reads (Nmax) corresponding to depths of coverage of approximately 0.14x for E. marmoratum and 0.06x for E. anisatum"

      This is consistent with other works which utilize RepeatExplorer2, for example, Chumová et al (2021; https://doi.org/10.1111/tpj.15306), who wrote: "The final repeatome analysis for each species was done using a maximum number of reads representing between 0.049x and 1.389x of genome coverage."

      Minor comments:

      General comments:

      • The concept of genome endoreplication and the problem it represents for C-value estimations needs to be better contextualised. It would be nice to have some background information in the introduction on how this is an issue (specially in Orchid species). Results shown are valuable and interesting but require a little more context on how frequent this is in plants, especially in Orchids, and across different tissues.

      __A: __We have included information about the variation of conventional and partial endoreplication in plants.

      Differences in CE may also occur between individuals or even respond to environmental factors (Barow 2006). In contrast, PE results in cells that replicate only a fraction (P) of the genome (Brown et al. 2017) and it has only been reported in Orchidaceae (Brown et al. 2017). CE and PE can occur in one or several endoreplication rounds, and different plant tissues may have different proportions of 2C, 4C, 8C ... nC or 2C, 4E, 8E, ... nE nuclear populations, respectively. The 2C nuclear population sometimes constitutes only a small fraction in differentiated somatic tissues and can be overlooked by cytometry (Trávníček et al. 2015). Using plant tissues with a high proportion of the 2C population (such as orchid ovaries and pollinaria) can help overcome this difficulty (Trávníček et al. 2015; Brown et al. 2017).

      Comments and suggestions on the figures:

      __R2. __In fig 1, the flow cytometry histograms need to be more self-explanatory. What are the Y axis "counts" of? Also, please either place the label for both rows or for each, but don't make it redundant. The axis fonts need to be made a bit larger too. If possible, explain briefly in the figure legend (and not only in the text) what each peak means.

      __A: __We have modified the figure adding legends for Y and X axes, eliminated redundant labels, and changed the font size.

      __R2. __Fig 5. Horizontal axis labels are illegible. Please make these larger (maybe make the plot wider by moving the plot legend to the top/bottom of the figure? - just a suggestion).

      __A: __We consider the horizontal axis label to be superfluous and we removed it.

      Small text editing suggestions:

      R2. Methods, "Ploidy level estimation and chromosome counts" section. It would be easier for the reader if this paragraph were either divided into two methods sections, or into two paragraphs at least, since these are two very different approaches and provide slightly different data or information.

      A: We slightly modified: "Chromosome number was counted from developing root tips" to

      "Additionally, to confirm ploidy level, chromosome number was counted from developing root tips" and changed the subtitle to only "Ploidy level estimation".

      R2. Methods, "Genome size estimation by k-mer analysis" section. Please specify whether the coverage simulations (of 5x to 40x) were made based on 1c or 2c of the genome size? I assumed haploid genome size but best to clarify.

      A: We have added it to P7: "To assess the suitability of the whole dataset and estimate the minimum coverage required for genome size estimation, the depth of coverage of both datasets was calculated based on the flow cytometry 1C genome size values."

      R2. Results, "Genome size estimation by k-mer analysis and ploidy estimation" section. In the first two paragraphs, the results presented appear to conform to anticipated patterns based on known properties of these types of datasets. Although this information confirms expected patterns, it does not provide new or biologically significant insights into the genomes analysed. It may be beneficial to further summarize these paragraphs so that the focus of this section can shift toward the comparison of methods and the biological interpretation of the genome profiles of Epidendrum.

      __A: __We agree that those paragraphs deviate a little from the focus of our results. However, we believe they provide useful information both for pattern confirmation in a relatively understudied field and for readers which may not be very familiar with the methods utilized.

      __R2. __Discussion, "Genome size estimation using flow cytometry" section. In the second paragraph, it is discussed how potential endoduplication events can "trick" the flow cytometry measurements. This has probably previously been discussed on other C-value calculation studies and would benefit from context from literature. How does this endoduplication really affect C-value measurements across plant taxa? I understand it is a well-known issue, so maybe add some references?

      A: We have included in the Introduction information about CE and PE and their associated references. P. 3 and 4.

      __R2. __Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. In the second paragraph, when mentioning the relative abundance of Ty3-gypsy and Ty1-copia elements, it is also worth mentioning their differences in genomic distribution and the potential structural role of Ty3-gypsy elements.

      A: We added this paragraph in P.20:

      "Ty3-gypsy elements are frequently found in centromeric and pericentromeric regions, and may have an important structural role in heterochromatin (Jin et al. 2004; Neumann et al. 2011; Ma et al. 2023), particularly those with chromodomains in their structure (chromovirus, i.e. Tekay, CRM transposons; Neumann et al. 2011). Conversely, Ty1-copia elements tend to be more frequent in gene-rich regions (Wang et al. 2025A). However, Ty3-gypsy chromovirus elements can be found outside the heterochromatin regions (Neumann et al. 2011), and in Pennisetum purpureum (Poaceae) Ty1-copia elements are more common in pericentromeric regions (Yu et al. 2022)."

      R2. Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. In the third paragraph, it is mentioned that both species have 2n=40. I believe these are results from this work since there is a methods section for chromosome counting. This data should therefore go into results.

      __A: __We have added the chromosome count micrographs as Supplementary Data Fig. S7

      R2. Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. I'd recommend expanding a bit more on repetitive DNA differences based on the RepeatExplorer results. Providing references on whether this has been found in other taxa would be helpful too. For example, Ogre bursts have been previously described in other species (e.g. legumes, Wang et al., 2025). Moreover, I consider worth highlighting and discussing other interesting differences found, such as the differences in unknown repeats (could be due to one species having "older" elements- too degraded to give any database hits- compared to the other), or Class II TE differences between species (and how these account less for genome size difference because of their size), etc.

      A: We have rearranged and added discussion expanding on the role of repetitive DNA in E. anisatum and E. marmoratum and how it relates to the repetitive DNA in other species. This includes Ogre transposons, an expanded Ty1-copia vs. Ty3-gypsy discussion, and a section on unclassified repeats and can be found on P.19 to P.21.

      Reviewer #2 (Significance (Required)):

      Overall, this study provides a valuable contribution to our understanding of genome size diversity and repetitive DNA dynamics within Epidendrum, particularly through its combined use of low-coverage sequencing, flow cytometry, and comparative genome profiling. Its strongest aspects lie in the clear methodological framework and the integration of multiple complementary approaches, which together highlight substantial genome size divergence driven by repeat proliferation-an insight of clear relevance for orchid genomics and plant genome evolution more broadly.

      While the work would benefit from improved data availability, additional contextualization of the problem of endoreduplication in flow cytometry, and clarification of some figure elements and methodological details, the study nonetheless advances the field by presenting new comparative genomic information for two understudied species and by evaluating different strategies for genome profiling in non-model taxa.

      The primary audience will include researchers in non-model plant genomics, cytogenetics, and evolutionary biology, although the methodological comparisons may also be useful to a broader community working on genome characterization in diverse lineages. My expertise is in plant genomics, genome size evolution, and repetitive DNA biology; I am not a specialist in flow cytometry instrumentation or cytological methods, so my evaluation of those aspects is based on general familiarity rather than technical depth.

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

      A review on "Nuclear genome profiling of two Mexican orchids of the genus Epidendrum" by Alcalá-Gaxiola et al. submitted to ReviewCommons

      The present manuscript presented genomic data for two endemic Maxican orchids: Epidendrum anisatum and E. marmoratum. Authors aim to determine the genome size and ploidy using traditional (flow cytometry and chromosome counts) and genomic techniques (k-mer analysis, heterozygosity), along with the repetitive DNA composition characterization.

      Considering the genomic composition, the main difference observed in repeat composition between the two species was attributed to the presence of a 172 bp satDNA (AniS1) in E. anisatum, which represents about 11% of its genome but is virtually absent in E. marmoratum. The differences in the genomic proportion of AniS1 and Ty3-gypsy/Ogre lineage TEs between E. anisatum and E. marmoratum are suggested as potential drivers of the GS difference identified between the two species.

      Our main concern are about the GS estimation and chromosome number determination. Along with many issues related to GS estimations by flow cytometry, results related to chromosome number determination are missing on the manuscript. Improvements in both techiniques and results are crucial since authors aim to compare different methods to GS and ploidy determination.

      __R3. __Genome size: Following the abstract, it is no possible to understand that authors confirm the GS by flow cytometry - as clarified after on the manuscript. Please, since the approach used to obtain the results are crucial on this manuscript, make it clear on the abstract.

      A: We have highlighted the congruence of flow cytometry and bioinformatic approaches in the abstract:

      "Multiple depths of coverage, k values, and k-mer-based tools for genome size estimation were explored and contrasted with cytometry genome size estimations. Cytometry and k-mer analyses yielded a consistently higher genome size for E. anisatum (mean 1C genome size = 2.59 Gb) than * E. marmoratum* (mean 1C genome size = 1.13 Gb), which represents a 2.3-fold genome size difference."

      __R3.__Flow cytometry methodology: For a standard protocol, it is mandatory to use, at least, three individuals, each one analyzed on triplicate. Is is also important to check the variation among measurements obtained from the same individual and the values obtained from different individuals. Such variation should be bellow 3%. The result should be the avarege C-value following the standard deviation, what inform us the variation among individuals and measurements.

      __A: __We have done three technical replicates of each tissue of the individuals of E. anisatum and E. marmoratum. To show the variation from different replicates and tissues, we have included the Supplementary Data Table S1. Intraspecific variation on genome size is beyond the scope of this work.

      __R3. __Checking Fig. 1, we could not see the Pisum peack. If authors performed an analysis with external standart, it should be clarified on Methods. I suggest always use internal standard.

      Besides, comparing Fig. 1 for leave and pollinium, it seems to be necessary to set up the Flow Cytoemtry equipament. Note that the 2C peack change its position when comparing different graphs. The data could be placed more central on x-axis by setting the flow cytometry.

      Action Required: Considering that authors want to compare indirect genomic approaches to determine the GS, I suggest authors improve the GS determination by Flow Cytometry.

      Please, on Methodology section, keep both techniques focused on GS close one another. Follow the same order on Methodology, Results and Discussion sections.

      __A: __We have made several changes on the estimation and reporting of the flow cytometry genome size estimation. Among these:

      We have clarified the use of the P. sativum internal standard and PBMC's in methods (P.6). We have added the associated mean coefficient of variation for both the sample and the internal reference in Supplementary Data Table S1, in order to show that the variation is not the result of an instrument error. We have changed the order of the paragraphs in the methods section to follow the order in other sections.

      __R3. __Chromosome count: In Introduction section (page 5), the authors explicitly aim to provide "bioinformatics ploidy level estimation and chromosome counting." Furthermore, the Methods section (page 7, subsection "Ploidy level estimation and chromosome counts") details a specific protocol for chromosome counting involving root tip pretreatment, fixation, and staining. However, no results regarding chromosome counting are presented in the manuscript. There are no micrographs of metaphase plates, no tables with counts, and no mention of the actual counts in the Results section or Supplementary Material. Despite this absence of evidence, the Discussion (Page 18) states: "ploidy and chromosome counts of both E. anisatum and E. marmoratum are the same (2n=40)." The value of 2n=40 is presented as a finding of this study, however, there is no reference to this results.

      Action Required: The authors must resolve this discrepancy by either providing the missing empirical data (micrographs and counts). This detail needs to be reviewed with greater care and scientific integrity.

      __A: __We have added the chromosome count micrographs as Supplementary Data Fig. S7.

      Minor reviews (Suggestions):

      __R3. __Refining the Title (Optional): Although the current title is descriptive, we believe it undersells the value of the manuscript. Since this study provides the first genome profiling and repeatome characterization for the genus Epidendrum and offers important insights into the calibration of bioinformatics tools and flow cytometry for repetitive genomes, I suggest modifying the title to reflect these aspects. The comparative access of GS is also an importante feature. This would make the article more attractive to a broader audience interested in genomics of non-model organisms.

      __A: __We have changed the title to "Nuclear genome profiling of two species of Epidendrum (Orchidaceae): genome size, repeatome and ploidy"

      __R3. __Botanical Nomenclature (Optional): Although citing taxonomic authorities is not strictly required in all fields of plant sciences, most botanical journals expect the full author citation at the first mention of each species. Including this information would improve the nomenclatural rigor of the manuscript and align it with common practices in botanical publishing.

      A: We have added the citation of the taxonomic authorities:

      "This study aims to use two closely related endemic Mexican species, Epidendrum anisatum Lex and Epidendrum marmoratum A. Rich. & Galeotti, to provide the first genomic profiling for this genus..."

      __R3. __Abbreviation of Genus Names: I noticed inconsistencies in the abbreviation of scientific names throughout the manuscript. Standard scientific style dictates that the full genus name (Epidendrum) should be written out only at its first mention in the Abstract and again at the first mention in the main text. Thereafter, it should be abbreviated (e.g., E. anisatum, E. marmoratum), unless the name appears at the beginning of a sentence or if abbreviation would cause ambiguity with another genus. Please revise the text to apply this abbreviation consistently.

      A: We have made the changes requested as necessary.

      __R3. __Genome Size Notation: In the Abstract and throughout the text, genome size estimates are presented using the statistical symbol for the mean (x). While mathematically accurate, this notation is generic and does not immediately inform the reader about the biological nature of the DNA content (i.e., whether it refers to the gametic 1C or somatic 2C value). In plant cytometry literature, it is standard practice to explicitly label these values using C-value terminology to prevent ambiguity and eliminate the effect of the number of chromosome sets (Bennett & Leitch 2005; Greilhuber et al. 2005; Doležel et al. 2018). I strongly suggest replacing references to "x" with "1C" (e.g., changing "x = 2.58 Gb" to "mean 1C value = 2.58 Gb") to ensure immediate clarity and alignment with established conventions in the field.

      __A: __We have revised the text in every instance, for example, in the results section:

      "The 1C value in gigabases (Gb; calculated from mass in pg) of E. anisatum ranged from 2.55 to 2.62 Gb (mean 1C value = 2.59 Gb) and that of E. marmoratum from 1.11 to 1.18 Gb (mean 1C value = 1.13 Gb; Supplementary Data Table S1)."

      __R3. __Justification of the Sequencing Method: Although the sequencing strategy is clearly described, the manuscript would benefit from a bit more contextualization regarding the choice of low-pass genome skimming. In the Introduction, a short justification of why this approach is suitable for estimating genome size, heterozygosity, and repeat composition, particularly in plants with large, repeat-rich genomes, would help readers better understand the methodological rationale. Likewise, in the Methods section, briefly outlining why the selected sequencing depth is appropriate, and how it aligns with previous studies using similar coverage levels, would strengthen the clarity of the methodological framework. These additions would make the rationale behind the sequencing approach more transparent and accessible to readers who may be less familiar with low-coverage genomic strategies.

      __A: __We have added the following short sentence in P.7:

      "This sequencing method produces suitable data sets without systematic biases, allowing the estimation of genome size and the proportion of repetitive DNA. "

      __R3. __Wording Improvement Regarding RepeatExplorer2 Results: In the Results section, several sentences attribute biological outcomes to the RepeatExplorer2 "protocols" (e.g., "According to this protocol, both species have highly repetitive genomes..."; "The comparative protocol showed a 67% total repeat proportion, which falls between the estimated repeat proportions of the two species according to the results of the individual protocol"). Since the RepeatExplorer2 protocol itself only provides the analytical workflow and not species-specific results, this phrasing may be misleading.

      A: We have rephrased these sections to emphasize that these are "the results of" the protocols and not the protocols themselves.

      Reviewer #3 (Significance (Required)):

      Significance

      General assessment

      Strengths

      1.First Detailed Genomic Profile for the Genus Epidendrum: The study provides the first integrated dataset on genome size, ploidy, heterozygosity, and repeatome for species of the genus Epidendrum, a novel contribution for an extremely diverse and under-explored group in terms of cytogenomics.

      Cross-validation of in vitro and in silico analyses: Flow cytometry is considered the gold standard for genome size (GS) estimation because it physically measures DNA quantity (Doležel et al. 2007; Śliwińska 2018). However, it typically requires fresh tissue, which is not always available. Conversely, k-mer analysis is a rapid bioinformatics technique utilizing sequencing data that does not rely on a reference genome. Nevertheless, it is frequently viewed with skepticism or distrust due to discrepancies with laboratory GS estimates (Pflug et al. 2020; Hesse 2023). In this study, by comparing computational results with flow cytometry data, the authors were able to validate the reliability of computational estimates for the investigated species. Since the 'true' GS was already established via flow cytometry, the authors used this value as a benchmark to test various software tools (GenomeScope, findGSE, CovEst) and parameters. This approach allowed for the identification of which tools perform best for complex genomes. For instance, they found that tools failing to account for heterozygosity (such as findGSE-hom) drastically overestimated the genome size of E. anisatum, whereas GenomeScope and findGSE-het (which account for heterozygosity) yielded results closer to the flow cytometry values. Thus, they demonstrated that this cross-validation is an effective method for estimating plant genome sizes with greater precision. This integrative approach is essential not only for defining GS but also for demonstrating how bioinformatics methods must be calibrated (particularly regarding depth of coverage and maximum k-mer coverage) to provide accurate data for non-model organisms when flow cytometry is not feasible.

      Limitations

      1. Limited Taxonomic Sampling: The study analyzes only two species of Epidendrum, which restricts the ability to make broad inferences regarding genome evolution across the genus. Given the outstanding diversity of Epidendrum (>1,800 species), the current sampling is insufficient to propose generalized evolutionary patterns. As the authors state by the end of the Discussion (page 18) "Future work should investigate to what extent LTR transposons and satellite DNA have been responsible for shaping genome size variation in different lineages of Epidendrum, analyzing a greater portion of its taxic diversity in an evolutionary context.". 2.Lack of Cytogenetic Results and Mapping: One of the major finding of this study is the identification of the AniS1 satellite as a potential key driver of the genome size difference between the species, occupying ~11% of the E. anisatum genome and virtually absent in E. marmoratum. While the authors use bioinformatic metrics (C and P indices) to infer a dispersed organization in the Discussion (Page 18), the study lacks physical validation via Fluorescence in situ Hybridization (FISH) - and a basic validation of the chromosome number. Without cytogenetic mapping, it is impossible to confirm the actual chromosomal distribution of this massive repetitive array, for instance, whether it has accumulated in specific heterochromatic blocks (e.g., centromeric or subtelomeric regions) or if it is genuinely interspersed along the chromosome arms. I suggest acknowledging this as a limitation in the Discussion, as the physical organization of such abundant repeats has significant implications for understanding the structural evolution of the species' chromosomes.

      Advance

      To the best of our knowledge, this study represents the first comprehensive genome profiling and repeatome characterization for any species of the genus Epidendrum. By integrating flow cytometry, k-mer-based approaches, and low-pass sequencing, the authors provide the first insights into the genomic architecture of Epidendrum, including quantitative assessments of transposable elements, lineage-specific satellite DNA, and repeat-driven genome expansion. This constitutes both a technical and a conceptual advance: technically, the study demonstrates the feasibility and limitations of combining in vitro and in silico methods for genome characterization in large, repeat-rich plant genomes; conceptually, it offers new evolutionary perspectives on how repetitive elements shape genome size divergence within a highly diverse orchid lineage. These results broaden the genomic knowledge base for Neotropical orchids and establish a foundational reference for future comparative, cytogenomic, and phylogenomic studies within Epidendrum and related groups.

      Audience

      This study will primarily interest a broad audience, including researchers in plant genomics, evolutionary biology, cytogenomics, and bioinformatics, especially those working with non-model plants or groups with large, repetitive genomes. It also holds relevance for scientists engaged in genome size evolution, repetitive DNA biology, and comparative genomics. Other researchers are likely to use this work as a methodological reference for genome profiling in non-model taxa, especially regarding the integration of flow cytometry and k-mer-based estimations and the challenges posed by highly repetitive genomes. The detailed repeatome characterization, including identification of lineage-specific satellites and retrotransposon dynamics, will support comparative genomic analyses, repeat evolution studies, and future cytogenetic validation (e.g., FISH experiments). Additionally, this dataset establishes a genomic baseline that can inform phylogenomic studies, species delimitation, and evolutionary inference within Epidendrum and related orchid groups.

      Reviewer's Backgrounds

      The review was prepared by two reviewers. Our expertise lies in evolution and biological diversity, with a focus on cytogenomic and genome size evolution. Among the projects in development, the cytogenomics evolution of Neotropical orchids is one of the main studies (also focused on Epidendrum). These areas shape my perspective in evaluating the evolutionary, cytogenomic, and biological implications of the study. However, we have limited expertise in methodologies related to k-mer-based genome profiling and heterozygosity modeling. Therefore, our evaluation does not deeply assess the technical validity of these analytical pipelines.

    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

      A review on "Nuclear genome profiling of two Mexican orchids of the genus Epidendrum" by Alcalá-Gaxiola et al. submitted to ReviewCommons

      The present manuscript presented genomic data for two endemic Maxican orchids: Epidendrum anisatum and E. marmoratum. Authors aim to determine the genome size and ploidy using traditional (flow cytometry and chromosome counts) and genomic techniques (k-mer analysis, heterozygosity), along with the repetitive DNA composition characterization.

      Considering the genomic composition, the main difference observed in repeat composition between the two species was attributed to the presence of a 172 bp satDNA (AniS1) in E. anisatum, which represents about 11% of its genome but is virtually absent in E. marmoratum. The differences in the genomic proportion of AniS1 and Ty3-gypsy/Ogre lineage TEs between E. anisatum and E. marmoratum are suggested as potential drivers of the GS difference identified between the two species.

      Our main concern are about the GS estimation and chromosome number determination. Along with many issues related to GS estimations by flow cytometry, results related to chromosome number determination are missing on the manuscript. Improvements in both techiniques and results are crucial since authors aim to compare different methods to GS and ploidy determination.

      Genome size: Following the abstract, it is no possible to understand that authors confirm the GS by flow cytometry - as clarified after on the manuscript. Please, since the approach used to obtain the results are crucial on this manuscript, make it clear on the abstract. Flow cytometry methodology: For a standart protocol, it is mandatory to use, at least, three individuals, each one analyzed on triplicate. Is is also important to check the variation among measurements obtained from the same individual and the values obtained from different individuals. Such variation should be bellow 3%. The result should be the avarege C-value following the standard deviation, what inform us the variation among individuals and measurements. Checking Fig. 1, we could not see the Pisum peack. If authors performed an analysis with external standart, it should be clarified on Methods. I suggest always use internal standard. Besides, comparing Fig. 1 for leave and pollinium, it seems to be necessary to set up the Flow Cytoemtry equipament. Note that the 2C peack change its position when comparing different graphs. The data could be placed more central on x-axis by setting the flow cytometry. Action Required: Considering that authors want to compare indirect genomic approaches to determine the GS, I suggest authors improve the GS determination by Flow Cytometry. Please, on Methodology section, keep both techniques focused on GS close one another. Follow the same order on Methodology, Results and Discussion sections.

      Chromosome count: In Introduction section (page 5), the authors explicitly aim to provide "bioinformatics ploidy level estimation and chromosome counting." Furthermore, the Methods section (page 7, subsection "Ploidy level estimation and chromosome counts") details a specific protocol for chromosome counting involving root tip pretreatment, fixation, and staining. However, no results regarding chromosome counting are presented in the manuscript. There are no micrographs of metaphase plates, no tables with counts, and no mention of the actual counts in the Results section or Supplementary Material. Despite this absence of evidence, the Discussion (Page 18) states: "ploidy and chromosome counts of both E. anisatum and E. marmoratum are the same (2n=40)." The value of 2n=40 is presented as a finding of this study, however, there is no reference to this results. Action Required: The authors must resolve this discrepancy by either providing the missing empirical data (micrographs and counts). This detail needs to be reviewed with greater care and scientific integrity. Minor reviews (Sugestions): Refining the Title (Optional): Although the current title is descriptive, we believe it undersells the value of the manuscript. Since this study provides the first genome profiling and repeatome characterization for the genus Epidendrum and offers important insights into the calibration of bioinformatics tools and flow cytometry for repetitive genomes, I suggest modifying the title to reflect these aspects. The comparative access of GS is also an importante feature. This would make the article more attractive to a broader audience interested in genomics of non-model organisms. 

      Botanical Nomenclature (Optional): Although citing taxonomic authorities is not strictly required in all fields of plant sciences, most botanical journals expect the full author citation at the first mention of each species. Including this information would improve the nomenclatural rigor of the manuscript and align it with common practices in botanical publishing.

      Abbreviation of Genus Names: I noticed inconsistencies in the abbreviation of scientific names throughout the manuscript. Standard scientific style dictates that the full genus name (Epidendrum) should be written out only at its first mention in the Abstract and again at the first mention in the main text. Thereafter, it should be abbreviated (e.g., E. anisatum, E. marmoratum), unless the name appears at the beginning of a sentence or if abbreviation would cause ambiguity with another genus. Please revise the text to apply this abbreviation consistently.

      Genome Size Notation: In the Abstract and throughout the text, genome size estimates are presented using the statistical symbol for the mean (x). While mathematically accurate, this notation is generic and does not immediately inform the reader about the biological nature of the DNA content (i.e., whether it refers to the gametic 1C or somatic 2C value). In plant cytometry literature, it is standard practice to explicitly label these values using C-value terminology to prevent ambiguity and eliminate the effect of the number of chromosome sets (Bennett & Leitch 2005; Greilhuber et al. 2005; Doležel et al. 2018). I strongly suggest replacing references to "x" with "1C" (e.g., changing "x = 2.58 Gb" to "mean 1C value = 2.58 Gb") to ensure immediate clarity and alignment with established conventions in the field.

      Justification of the Sequencing Method: Although the sequencing strategy is clearly described, the manuscript would benefit from a bit more contextualization regarding the choice of low-pass genome skimming. In the Introduction, a short justification of why this approach is suitable for estimating genome size, heterozygosity, and repeat composition, particularly in plants with large, repeat-rich genomes, would help readers better understand the methodological rationale. Likewise, in the Methods section, briefly outlining why the selected sequencing depth is appropriate, and how it aligns with previous studies using similar coverage levels, would strengthen the clarity of the methodological framework. These additions would make the rationale behind the sequencing approach more transparent and accessible to readers who may be less familiar with low-coverage genomic strategies.

      Wording Improvement Regarding RepeatExplorer2 Results: In the Results section, several sentences attribute biological outcomes to the RepeatExplorer2 "protocols" (e.g., "According to this protocol, both species have highly repetitive genomes..."; "The comparative protocol showed a 67% total repeat proportion, which falls between the estimated repeat proportions of the two species according to the results of the individual protocol"). Since the RepeatExplorer2 protocol itself only provides the analytical workflow and not species-specific results, this phrasing may be misleading.

      Significance

      General assessment

      Strengths

      1. First Detailed Genomic Profile for the Genus Epidendrum: The study provides the first integrated dataset on genome size, ploidy, heterozygosity, and repeatome for species of the genus Epidendrum, a novel contribution for an extremely diverse and under-explored group in terms of cytogenomics.
      2. Cross-validation of in vitro and in silico analyses: Flow cytometry is considered the gold standard for genome size (GS) estimation because it physically measures DNA quantity (Doležel et al. 2007; Śliwińska 2018). However, it typically requires fresh tissue, which is not always available. Conversely, k-mer analysis is a rapid bioinformatics technique utilizing sequencing data that does not rely on a reference genome. Nevertheless, it is frequently viewed with skepticism or distrust due to discrepancies with laboratory GS estimates (Pflug et al. 2020; Hesse 2023). In this study, by comparing computational results with flow cytometry data, the authors were able to validate the reliability of computational estimates for the investigated species. Since the 'true' GS was already established via flow cytometry, the authors used this value as a benchmark to test various software tools (GenomeScope, findGSE, CovEst) and parameters. This approach allowed for the identification of which tools perform best for complex genomes. For instance, they found that tools failing to account for heterozygosity (such as findGSE-hom) drastically overestimated the genome size of E. anisatum, whereas GenomeScope and findGSE-het (which account for heterozygosity) yielded results closer to the flow cytometry values. Thus, they demonstrated that this cross-validation is an effective method for estimating plant genome sizes with greater precision. This integrative approach is essential not only for defining GS but also for demonstrating how bioinformatics methods must be calibrated (particularly regarding depth of coverage and maximum k-mer coverage) to provide accurate data for non-model organisms when flow cytometry is not feasible.

      Limitations

      1. Limited Taxonomic Sampling: The study analyzes only two species of Epidendrum, which restricts the ability to make broad inferences regarding genome evolution across the genus. Given the outstanding diversity of Epidendrum (>1,800 species), the current sampling is insufficient to propose generalized evolutionary patterns. As the authors state by the end of the Discussion (page 18) "Future work should investigate to what extent LTR transposons and satellite DNA have been responsible for shaping genome size variation in different lineages of Epidendrum, analyzing a greater portion of its taxic diversity in an evolutionary context.".
      2. Lack of Cytogenetic Results and Mapping: One of the major finding of this study is the identification of the AniS1 satellite as a potential key driver of the genome size difference between the species, occupying ~11% of the E. anisatum genome and virtually absent in E. marmoratum. While the authors use bioinformatic metrics (C and P indices) to infer a dispersed organization in the Discussion (Page 18), the study lacks physical validation via Fluorescence in situ Hybridization (FISH) - and a basic validation of the chromosome number. Without cytogenetic mapping, it is impossible to confirm the actual chromosomal distribution of this massive repetitive array, for instance, whether it has accumulated in specific heterochromatic blocks (e.g., centromeric or subtelomeric regions) or if it is genuinely interspersed along the chromosome arms. I suggest acknowledging this as a limitation in the Discussion, as the physical organization of such abundant repeats has significant implications for understanding the structural evolution of the species' chromosomes.

      Advance

      To the best of our knowledge, this study represents the first comprehensive genome profiling and repeatome characterization for any species of the genus Epidendrum. By integrating flow cytometry, k-mer-based approaches, and low-pass sequencing, the authors provide the first insights into the genomic architecture of Epidendrum, including quantitative assessments of transposable elements, lineage-specific satellite DNA, and repeat-driven genome expansion. This constitutes both a technical and a conceptual advance: technically, the study demonstrates the feasibility and limitations of combining in vitro and in silico methods for genome characterization in large, repeat-rich plant genomes; conceptually, it offers new evolutionary perspectives on how repetitive elements shape genome size divergence within a highly diverse orchid lineage. These results broaden the genomic knowledge base for Neotropical orchids and establish a foundational reference for future comparative, cytogenomic, and phylogenomic studies within Epidendrum and related groups.

      Audience

      This study will primarily interest a broad audience, including researchers in plant genomics, evolutionary biology, cytogenomics, and bioinformatics, especially those working with non-model plants or groups with large, repetitive genomes. It also holds relevance for scientists engaged in genome size evolution, repetitive DNA biology, and comparative genomics. Other researchers are likely to use this work as a methodological reference for genome profiling in non-model taxa, especially regarding the integration of flow cytometry and k-mer-based estimations and the challenges posed by highly repetitive genomes. The detailed repeatome characterization, including identification of lineage-specific satellites and retrotransposon dynamics, will support comparative genomic analyses, repeat evolution studies, and future cytogenetic validation (e.g., FISH experiments). Additionally, this dataset establishes a genomic baseline that can inform phylogenomic studies, species delimitation, and evolutionary inference within Epidendrum and related orchid groups.

      Reviewer's Backgrounds

      The review was prepared by two reviewers. Our expertise lies in evolution and biological diversity, with a focus on cytogenomic and genome size evolution. Among the projects in development, the cytogenomics evolution of Neotropical orchids is one of the main studies (also focused on Epidendrum). These areas shape my perspective in evaluating the evolutionary, cytogenomic, and biological implications of the study. However, we have limited expertise in methodologies related to k-mer-based genome profiling and heterozygosity modeling. Therefore, our evaluation does not deeply assess the technical validity of these analytical pipelines.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      With this work, the authors provide genome profiling information on the Epidendrum genus. They performed low-coverage short read sequencing and analysis, as well as flow cytometry approaches to estimate genome size, and perform comparative analysis for these methods. They also used the WGS dataset to test different approaches and models for genome profiling, as well as repeat abundance estimation, empathising the importance of genome profiling to provide basic and comparative genomic information in our non-model study species. Results show that the two "closely-related" Epidendrum species analysed (E. marmoratum and E. anisatum) have different genome profiles, exhibiting a 2.3-fold genome size difference, mostly triggered by the expansion of repetitive elements in E. marmoratum, specially of Ty3-Gypsy LTR-retrotransposon and a 172 tandem repeat (satellite DNA).

      Major comments:

      Overall, the manuscript is well-written, the aim, results and methods are explained properly, and although I missed some information in the introduction, the paper structure is overall good, and it doesn't lack any important information. The quality of the analysis is also adequate and no further big experiments or analysis would be needed. However, from my point of view, two main issues would need to be addressed:

      • The methods section is properly detailed and well explained. However, the project data and scripts are not available at the figshare link provided, and the BioProject code provided is not found at SRA. This needs to be solved as soon as possible, as if they're not available for review reproducibility of the manuscript cannot be fully assessed.
      • The authors specify in the methods that 0.06x and 0.43x sequencing depths were used as inputs for the RE analysis of E. anisatum and E. marmoratum. I understand these are differences based on the data availability and genome size differences. However, they don't correspond to either of the recommendations from Novak et al (2020):

      In the context of individual analysis: "The number of analyzed reads should correspond to 0.1-0.5× genome coverage. In the case of repeat-poor species, coverage can be increased up to 1.0-1.5×." Therefore, using 0.06x for E. anisatum should be justified, or at least addressed in the discussion. Moreover, using such difference in coverage might affect any comparisons made using these results. Given that the amount of reads is not limiting in this case, why such specific coverages have been used should be discussed in detail.

      In the context of comparative analysis: "Because different genomes are being analyzed simultaneously, the user must decide how they will be represented in the analyzed reads, choosing one of the following options. First, the number of reads analyzed from each genome will be adjusted to represent the same genome coverage. This option provides the same sensitivity of repeat detection for all analyzed samples and is therefore generally recommended; however, it requires that genome sizes of all analyzed species are known and that they do not substantially differ. In the case of large differences in genome sizes, too few reads may be analyzed from smaller genomes, especially if many species are analyzed simultaneously. A second option is to analyze the same number of reads from all samples, which will provide different depth of analysis in species differing in their genome sizes, and this fact should be considered when interpreting analysis results. Because each of these analysis setups has its advantages and drawbacks, it is a good idea to run both and cross-check their results." Therefore, it should be confirmed how much it was used for this approach (as in the methods it is only specified how much it was used for the individual analysis), and why.

      Minor comments:

      General comments:

      • The concept of genome endoreplication and the problem it represents for C-value estimations needs to be better contextualised. It would be nice to have some background information in the introduction on how this is an issue (specially in Orchid species). Results shown are valuable and interesting but require a little more context on how frequent this is in plants, especially in Orchids, and across different tissues.

      Comments and suggestions on the figures:

      • In fig 1, the flow cytometry histograms need to be more self-explanatory. What are the Y axis "counts" of? Also, please either place the label for both rows or for each, but don't make it redundant. The axis fonts need to be made a bit larger too. If possible, explain briefly in the figure legend (and not only in the text) what each peak means.
      • Fig 5. Horizontal axis labels are illegible. Please make these larger (maybe make the plot wider by moving the plot legend to the top/bottom of the figure? - just a suggestion).

      Small text editing suggestions:

      • Methods, "Ploidy level estimation and chromosome counts" section. It would be easier for the reader if this paragraph was either divided into two methods sections, or into two paragraphs at least, since these are two very different approaches and provide slightly different data or information.
      • Methods, "Genome size estimation by k-mer analysis" section. Please specify whether the coverage simulations (of 5x to 40x) were made based on 1c or 2c of the genome size? I assumed haploid genome size but best to clarify.
      • Results, "Genome size estimation by k-mer analysis and ploidy estimation" section. In the first two paragraphs, the results presented appear to conform to anticipated patterns based on known properties of these types of datasets. Although this information confirms expected patterns, it does not provide new or biologically significant insights into the genomes analysed. It may be beneficial to further summarize these paragraphs so that the focus of this section can shift toward the comparison of methods and the biological interpretation of the genome profiles of Epidendrum.
      • Discussion, "Genome size estimation using flow cytometry" section. In the second paragraph, it is discussed how potential endoduplication events can "trick" the flow cytometry measurements. This has probably previously been discussed on other C-value calculation studies and would benefit from context from literature. How does this endoduplication really affect C-value measurements across plant taxa? I understand it is a well-known issue, so maybe add some references?
      • Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. In the second paragraph, when mentioning the relative abundance of Ty3-gypsy and Ty1-copia elements, it is also worth mentioning their differences in genomic distribution and the potential structural role of Ty3-gypsy elements.
      • Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. In the third paragraph, it is mentioned that both species have 2n=40. I believe these are results from this work since there is a methods section for chromosome counting. This data should therefore go into results.
      • Discussion, "Repetitive DNA composition in Epidendrum anisatum and E. marmoratum" section. I'd recommend expanding a bit more on repetitive DNA differences based on the RepeatExplorer results. Providing references on whether this has been found in other taxa would be helpful too. For example, Ogre bursts have been previously described in other species (e.g. legumes, Wang et al., 2025). Moreover, I consider worth highlighting and discussing other interesting differences found, such as the differences in unknown repeats (could be due to one species having "older" elements- too degraded to give any database hits- compared to the other), or Class II TE differences between species (and how these account less for genome size difference because of their size), etc.

      Significance

      Overall, this study provides a valuable contribution to our understanding of genome size diversity and repetitive DNA dynamics within Epidendrum, particularly through its combined use of low-coverage sequencing, flow cytometry, and comparative genome profiling. Its strongest aspects lie in the clear methodological framework and the integration of multiple complementary approaches, which together highlight substantial genome size divergence driven by repeat proliferation-an insight of clear relevance for orchid genomics and plant genome evolution more broadly.

      While the work would benefit from improved data availability, additional contextualization of the problem of endoreduplication in flow cytometry, and clarification of some figure elements and methodological details, the study nonetheless advances the field by presenting new comparative genomic information for two understudied species and by evaluating different strategies for genome profiling in non-model taxa.

      The primary audience will include researchers in non-model plant genomics, cytogenetics, and evolutionary biology, although the methodological comparisons may also be useful to a broader community working on genome characterization in diverse lineages. My expertise is in plant genomics, genome size evolution, and repetitive DNA biology; I am not a specialist in flow cytometry instrumentation or cytological methods, so my evaluation of those aspects is based on general familiarity rather than technical depth.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The study provides a comprehensive overview of genome size variation in two related species of the genus Epidendrum, which appear to be homoploid, although their DNA content more closely corresponds to that of heteroploid species. While I have a few serious concerns regarding the data analysis, the study itself demonstrates a well-designed approach and offers a valuable comparison of different methods for genome size estimation. In particular, I would highlight the analysis of repetitive elements, which effectively explains the observed differences between the species. However, I encourage the authors to adopt a more critical perspective on the k-mer analysis and the potential pitfalls in data interpretation.

      Major comments:

      p. 9: Genome size estimation via flow cytometry is an incorrect approach. The deviation is approximately 19% for E. anisatum and about 25% for E. marmoratum across three repeated measurements of the same tissue over three days? These values are far beyond the accepted standards of best practice for flow cytometry, which recommend a maximum deviation of 2-5% between repeated measurements of the same individual. Such variability indicates a systemic methodological issue or improper instrument calibration. Results with this level of inconsistency cannot be considered reliable estimates of genome size obtained by flow cytometry. If you provide the raw data, I can help identify the likely source of error, but as it stands, these results are not acceptable.

      p. 14 and some parts of Introduction: It may seem unusual, to say the least, to question genome size estimation in orchids using flow cytometry, given that this group is well known for extensive endoreplication. However, what effect does this phenomenon have on genome size analyses based on k-mers, or on the correct interpretation of peaks in k-mer histograms? How can such analyses be reliably interpreted when most nuclei used for DNA extraction and sequencing likely originate from endoreplicated cells? I would have expected a more detailed discussion of this issue in light of your results, particularly regarding the substantial variation in genome size estimates across different k-mer analysis settings. Could endoreplication be a contributing factor?

      You repeatedly refer to the experiment on genome size estimation using analyses with maximum k-mer coverage of 10,000 and 2 million, under different k values. However, I would like to see a comparison - such as a correlation analysis - that supports this experiment. The results and discussion sections refer to it extensively, yet no corresponding figure or analysis is presented.

      Minor comments:

      p. 3: You stated: "Flow cytometry is the gold standard for genome size estimation, but whole-genome endoreplication (also known as conventional endoreplication; CE) and strict partial endoreplication (SPE) can confound this method." How did you mean this? Endopolyploidy is quite common in plants and flow cytometry is an excellent tool how to detect it and how to select the proper nuclei fraction for genome size estimation (if you are aware of possible misinterpretation caused by using inappropriate tissue for analysis). The same can be applied for partial endoreplication in orchids (see e.g. Travnicek et al 2015). Moreover, the term "strict partial endoreplication" is outdated and is only used by Brwon et al. In more recent studies, the term partial endoreplication is used (e.g. Chumova et al. 2021- 10.1111/tpj.15306 or Piet et al. 2022 - 10.1016/j.xplc.2022.100330).

      p. 5: "...both because of its outstanding taxic diversity..." There is no such thing as "taxic" diversity - perhaps you mean taxonomic diversity or species richness.

      p. 6: In description of flow cytometry you stated: "Young leaves of Pisum sativum (4.45 pg/1C; Doležel et al. 1998) and peripheral blood mononuclear cells (PBMCs) from healthy individuals...". What does that mean? Did you really use blood cells? For what purpose?

      p. 7: What do you mean by this statement "...reference of low-copy nuclear genes for each species..."? As far as I know, the Granados-Mendoza study used the Angiosperm v.1 probe set, so did you use that set of probes as reference?

      p. 7: Chromosome counts - there is a paragraph of methodology used for chromosome counting, but no results of this important part of the study.

      p. 12: Depth of coverage used in repeatome analysis - why did you use different coverage for both species? Any explanation is needed.

      p. 16: The variation in genome size of orchids is even higher, as the highest known DNA amount has been estimated in Liparis purpureoviridis - 56.11 pg (Travnicek et al 2019 - doi: 10.1111/nph.15996)

      Fig. 1 - Where is the standard peak on Fig. 1? You mention it explicitly on page 9 where you are talking about FCM histograms.

      Significance

      This study provides a valuable contribution to understanding genome size variation in two Epidendrum species by combining flow cytometry, k-mer analysis, and repetitive element characterization. Its strength lies in the integrative approach and in demonstrating how repetitive elements can explain interspecific differences in DNA content. The work is among the first to directly compare flow cytometric and k-mer-based genome size estimates in orchids, extending current knowledge of genome evolution in this complex plant group. However, the study would benefit from a more critical discussion of the limitations and interpretative pitfalls of k-mer analysis and from addressing methodological inconsistencies in the cytometric data. The research will interest a specialized audience in plant genomics, cytogenetics, and genome evolution, particularly those studying non-model or highly endoreplicated species.

      Field of expertise: plant cytogenetics, genome size evolution, orchid genomics.

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      Reply to the reviewers

      Manuscript number: RC-2025-03131

      Corresponding author(s): Ginto George and Adriana Ordoñez

      1. General Statements

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

      2. Point-by-point description of the revisions

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

      Reviewer 1 & 2 (common points)

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

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

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

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

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

      __ Deletion of all NAT8 family members.__

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

      __ Generalisability beyond CHO-K1 cells.__

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

      __ Relationship between basal ATF6 and IRE1 signalling.__

      • Comment: Both reviewers argued that BiP::GFP does not appear to be active under basal conditions in parental cells, and therefore a failure to activate ATF6 would not be expected to affect the conditions of the cells basally. Thereby questioning how attenuated basal ATF6 activity in the SLC33a1 deleted cells could account for the derepression observed in the IRE1 pathway.
      • Response: The logic of the reviewer's critique is impeccable, and we thank them for the opportunity to clarify this important issue. Whilst the basal fluorescent signal arising from BiP::GFP (the ATF6α reporter) is indeed weak, it is not null. This is evident by comparing the BiP::GFP signal in wildtype and ATF6α -deleted cells (new Supplemental Fig. S1B) These experiments revealed a significant reduction in basal BiP::GFP fluorescence in ATF6αΔ cells compared with parental dual-reporter cells, indicating that the BiP::GFP reporter has basal activity that is dependent on ATF6α. These new data are consistent with previous published observations demonstrated that treatment with Ceapin, an ATF6α-specific inhibitor, lowered BiP::GFP fluorescence in tunicamycin-treated cells to levels below those observed in untreated controls (Tung et al., eLife 2024). Together these observations indicate that ATF6α is active basally in CHO-K1 cells. Given the established cross-pathway repression of IRE1 by ATF6α signalling, it renders plausible our suggestion that the basal activation of the XBP1::mCherry (IRE1-reporter) observed basally in the SLC33a1 deleted cells arises from the partial interruption of ATF6α Reviewer 1 (additional points)

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

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

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

      • Comment: Reviewer 1 suggested that we evaluate the effect of SLC33A1 loss on other ATF6-like transcription factors.
      • Response: We took this advice to heart, but our attempts to compare SREBP2 processing in wildtype and SLC33A1 knockout cells were frustrated by the low quality of the antibodies available to us. Reviewer 2 (additional points)

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

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

      __ Responsiveness of PERK signalling to ER stress.__

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

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

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

      Figure

      Figure Panels

      Amendment (if any)

      Fig 4

      4B (modified)

      Scale bar added.

      Fig 5

      5B (modified)

      Labelling correction according to the reviewer.

      Fig S1 (new)

      S1A-S1B

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

      Fig S2 (modified)

      S2D (new)

      New IncuCyte data added.

      Fig S3 (modified)

      S3B, S3C and S3E (new)

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

      Fig S4 (new)

      S4C (new)

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

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

      Fig S7 (new)

      S7A-S7C (new)

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

      Fig S8 (new)

      S8A-S8B (new)

      Casd1-deleted data added.

      Fig S9 (new)

      Unique panel

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

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

      Sincerely,

      Adriana Ordóñez

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

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

      Major comments.

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

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

      Minor comments.

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

      Referee cross-commenting

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

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

      Significance

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

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

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

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

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

      Major Comments:

      The following conclusions are well-supported:

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

      The following claims are less convincing:

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

      Minor Comments:

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

      Referee cross-commenting

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

      Significance

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

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      Reply to the reviewers

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

      Summary:

      Damaris et al. perform what is effectively an eQTL analysis on microbial pangenomes of E. coli and P. aeruginosa. Specifically, they leverage a large dataset of paired DNA/RNA-seq information for hundreds of strains of these microbes to establish correlations between genetic variants and changes in gene expression. Ultimately, their claim is that this approach identifies non-coding variants that affect expression of genes in a predictable manner and explain differences in phenotypes. They attempt to reinforce these claims through use of a widely regarded promoter calculator to quantify promoter effects, as well as some validation studies in living cells. Lastly, they show that these non-coding variations can explain some cases of antibiotic resistance in these microbes.

      Major comments

      Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

      The authors convincingly demonstrate that they can identify non-coding variation in pangenomes of bacteria and associate these with phenotypes of interest. What is unclear is the extent by which they account for covariation of genetic variation? Are the SNPs they implicate truly responsible for the changes in expression they observe? Or are they merely genetically linked to the true causal variants. This has been solved by other GWAS studies but isn't discussed as far as I can tell here.

      We thank the reviewer for their effective summary of our study. Regarding our ability to identify variants that are causal for gene expression changes versus those that only “tag” the causal ones, here we have to again offer our apologies for not spelling out the limitation of GWAS approaches, namely the difficulty in separating associated with causal variants. This inherent difficulty is the main reason why we added the in-silico and in-vitro validation experiments; while they each have their own limitations, we argue that they all point towards providing a causal link between some of our associations and measured gene expression changes. We have amended the discussion (e.g. at L548) section to spell our intention out better and provide better context for readers that are not familiar with the pitfalls of (bacterial) GWAS.

      They need to justify why they consider the 30bp downstream of the start codon as non-coding. While this region certainly has regulatory impact, it is also definitely coding. To what extent could this confound results and how many significant associations to expression are in this region vs upstream?

      We agree with the reviewer that defining this region as “non-coding” is formally not correct, as it includes the first 10 codons of the focal gene. We have amended the text to change the definition to “cis regulatory region” and avoided using the term “non-coding” throughout the manuscript. Regarding the relevance of this including the early coding region, we have looked at the distribution of associated hits in the cis regulatory regions we have defined; the results are shown in Supplementary Figure 3.

      We quantified the distribution of cis associated variants and compared them to a 2,000 permutations restricted to the -200bp and +30bp window in both E. coli * (panel A) and P. aeruginosa* (panel B). As it can be seen, the associated variants that we have identified are mostly present in the 200bp region and the +30bp region shows a mild depletion relative to the random expectation, which we derived through a variant position shuffling approach (2,000 replicates). Therefore, we believe that the inclusion of the early coding region results in an appreciable number of associations, and in our opinion justify its inclusion as a putative “cis regulatory region”.

      The claim that promoter variation correlates with changes in measured gene expression is not convincingly demonstrated (although, yes, very intuitive). Figure 3 is a convoluted way of demonstrating that predicted transcription rates correlate with measured gene expression. For each variant, can you do the basic analysis of just comparing differences in promoter calculator predictions and actual gene expression? I.e. correlation between (promoter activity variant X)-(promoter activity variant Y) vs (measured gene expression variant X)-(measured gene expression variant Y). You'll probably have to

      We realize that we may not have failed to properly explain how we carried out this analysis, which we did exactly in the way the reviewer suggests here. We had in fact provided four example scatterplots of the kind the reviewer was requesting as part of Figure 4. We have added a mention of their presence in the caption of Figure 3.

      Figure 7 it is unclear what this experiment was. How were they tested? Did you generate the data themselves? Did you do RNA-seq (which is what is described in the methods) or just test and compare known genomic data?

      We apologize for the lack of clarity here; we have amended the figure’s caption and the corresponding section of the results (i.e. L411 and L418) to better highlight how the underlying drug susceptibility data and genomes came from previously published studies.

      Are the data and the methods presented in such a way that they can be reproduced?

      No, this is the biggest flaw of the work. The RNA-Seq experiment to start this project is not described at all as well as other key experiments. Descriptions of methods in the text are far too vague to understand the approach or rationale at many points in the text. The scripts are available on github but there is no description of what they correspond to outside of the file names and none of the data files are found to replicate the plots.

      We have taken this critique to heart, and have given more details about the experimental setup for the generation of the RNA-seq data in the methods as well as the results sections. We have also thoroughly reviewed any description of the methods we have employed to make sure they are more clearly presented to the readers. We have also updated our code repository in order to provide more information about the meaning of each script provided, although we would like to point out that we have not made the code to be general purpose, but rather as an open documentation on how the data was analyzed.

      Figure 8B is intended to show that the WaaQ operon is connected to known Abx resistance genes but uses the STRING method. This requires a list of genes but how did they build this list? Why look at these known ABx genes in particular? STRING does not really show evidence, these need to be substantiated or at least need to justify why this analysis was performed.

      We have amended the Methods section (“Gene interaction analysis”, L799) to better clarify how the network shown in this panel was obtained. In short, we have filtered the STRING database to identify genes connected to members of the waa operon with an interaction score of at least 0.4 (“moderate confidence”), excluding the “text mining” field. Antimicrobial resistance genes were identified according to the CARD database. We believe these changes will help the readers to better understand how we derived this interaction.

      Are the experiments adequately replicated and statistical analysis adequate?

      An important claim on MIC of variants for supplementary table 8 has no raw data and no clear replicates available. Only figure 6, the in vitro testing of variant expression, mentions any replicates.

      We have expanded the relevant section in the Methods (“Antibiotic exposure and RNA extraction”, L778) to provide more information on the way these assays were carried out. In short, we carried out three biological replicates, the average MIC of two replicates in closest agreement was the representative MIC for the strain. We believe that we have followed standard practice in the field of microbiology, but we agree that more details were needed to be provided in order for readers to appreciate this.

      Minor comments

      Specific experimental issues that are easily addressable..

      Are prior studies referenced appropriately?

      There should be a discussion of eQTLs in this. Although these have mostly been in eukaryotes a. https://doi.org/10.1038/s41588-024-01769-9 ; https://doi.org/10.1038/nrg3891.

      We have added these two references, which provide a broader context to our study and methodology, in the introduction.

      Line 67. Missing important citation for Ireland et al. 2020 https://doi.org/10.7554/eLife.55308

      Line 69. Should mention Johns et al. 2018 (https://doi.org/10.1038/nmeth.4633) where they study promoter sequences outside of E. coli

      Line 90 - replace 'hypothesis-free' with unbiased

      We have implemented these changes.

      Line 102 - state % of DEGs relative to the entire pan-genome

      Given that the study is focused on identifying variants that were associated with changes in expression for reference genes (i.e. those present in the reference genome), we think that providing this percentage would give the false impression that our analysis include accessory genes that are not encoded by the reference isolate, which is not what we have done.

      Figure 1A is not discussed in the text

      We have added an explicit mention of the panels in the relevant section of the results.

      Line 111: it is unclear what enrichment was being compared between, FIgures 1C/D have 'Gene counts' but is of the total DEGs? How is the p-value derived? Comparing and what statistical test was performed? Comparing DEG enrichment vs the pangenome? K12 genome?

      We have amended the results and methods section, as well as Figure 1’s caption to provide more details on how this analysis was carried out.

      Line 122-123: State what letters correspond to these COG categories here

      We have implemented the clarifications and edits suggested above

      Line 155: Need to clarify how you use k-mers in this and how they are different than SNPs. are you looking at k-mer content of these regions? K-mers up to hexamers or what? How are these compared. You can't just say we used k-mers.

      We have amended that line in the results section to more explicitly refer to the actual encoding of the k-mer variants, which were presence/absence patterns for k-mers extracted from each target gene’s promoter region separately, using our own developed method, called panfeed. We note that more details were already given in the methods section, but we do recognize that it’s better to clarify things in the results section, so that more distracted readers get the proper information about this class of genetic variants.

      Line 172: It would be VERY helpful to have a supplementary figure describing these types of variants, perhaps a multiple-sequence alignment containing each example

      We thank the reviewer for this suggestion. We have now added Supplementary Figure 3, which shows the sequence alignments of the cis-regulatory regions underlying each class of the genetic marker for both E. coli and P. aeruginosa.

      Figure 4: THis figure is too small. Why are WaaQ and UlaE being used as examples here when you are supposed to be explicitly showing variants with strong positive correlations?

      We rearranged the figure’s layout to improve its readability. We agree that the correlation for waaQ and ulaE is weaker than for yfgJ and kgtP, but our intention was to not simply cherry-pick strong examples, but also those for which the link between predicted promoter strength and recorded gene expression was less obvious.

      Figure 4: Why is there variation between variants present and variant absent? Is this due to other changes in the variant? Should mention this in the text somewhere

      Variability in the predicted transcription rate for isolates encoding for the same variant is due to the presence of other (different) variants in the region surrounding the target variant. PromoterCalculator uses nucleotide regions of variable length (78 to 83bp) to make its predictions, while the variants we are focusing on are typically shorter (as shown in Figure 4). This results in other variants being included in the calculation and therefore slightly different predicted transcription rates for each strain. We have amended the caption of Figure 4 to provide a succinct explanation of these differences.

      Line 359: Need to talk about each supplementary figure 4 to 9 and how they demonstrate your point.

      We have expanded this section to more explicitly mention the contents of these supplementary figures and why they are relevant for the findings of this section (L425).

      Are the text and figures clear and accurate?

      Figure 4 too small

      We have fixed the figure, as described above

      Acronyms are defined multiple times in the manuscript, sometimes not the first time they are used (e.g. SNP, InDel)

      Figure 8A - Remove red box, increase label size

      Figure 8B - Low resolution, grey text is unreadable and should be darker and higher resolution

      Line 35 - be more specific about types of carbon metabolism and catabolite repression

      Line 67 - include citation for ireland et al. 2020 https://doi.org/10.7554/eLife.55308

      Line 74 - You talk about looking in cis but don't specify how mar away cis is

      Line 75 - we encoded genetic variants..... It is unclear what you mean here

      Line 104 - 'were apart of operons' should clarify you mean polycistronic or multi-gene operons. Single genes may be considered operonic units as well.

      We have addressed all the issues indicated above.

      Figure 2: THere is no axis for the percents and the percents don't make sense relative to the bars they represent??

      We realize that this visualization might not have been the most clear for readers, and have made the following improvement: we have added the number of genes with at least one association before the percentage. We note that the x-axis is in log scale, which may make it seem like the light-colored bars are off. With the addition of the actual number of associated genes we think that this confusion has been removed.

      Figure 2: Figure 2B legend should clarify that these are individual examples of Differential expression between variants

      Line 198-199: This sentence doesn't make sense, 'encoded using kmers' is not descriptive enough

      Line 205: Should be upfront about that you're using the Promoter Calculator that models biophysical properties of promoter sequences to predict activity.

      Line 251: 'Scanned the non-coding sequences of the DEGs'. This is far too vague of a description of an approach. Need to clarify how you did this and I didn't see in the method. Is this an HMM? Perfect sequence match to consensus sequence? Some type of alignment?

      Line 257-259: This sentence lacks clarity

      We have implemented all the suggested changes and clarified the points that the reviewer has highlighted above.

      Line346: How were the E. coli isolates tested? Was this an experiment you did? This is a massive undertaking (1600 isolates * 12 conditions) if so so should be clearly defined

      While we have indicated in the previous paragraph that the genomes and antimicrobial susceptibility data were obtained from previously published studies, we have now modified this paragraph (e.g. L411 and L418) slightly to make this point even clearer.

      Figure 6A: The tile plot on the right side is not clearly labeled and it is unclear what it is showing and how that relates to the bar plots.

      In the revised figure, we have clarified the labeling of the heatmap to now read “Log2(Fold Change) (measured expression)” to indicate that it represents each gene’s fold changes obtained from our initial transcriptomic analysis. We have also included this information in the caption of the figure, making the relationship between the measured gene expression (heatmap) and the reporter assay data (bar plots) clear to the reader.

      FIgure 6B: typo in legend 'Downreglation'

      We thank the review for pointing this out. The typo has been corrected to “Down regulation” in the revised figure.

      Line 398: Need to state rationale for why Waaq operon is being investigated here. WHy did you look into individual example?

      We thank the reviewer for asking for a clarification here. Our decision to investigate the waaQ gene was one of both biological relevance and empirical evidence. In our analysis associating non-coding variants with antimicrobial resistance using the Moradigaravand et al. dataset, we identified a T>C variant at position 3808241 that was associated with resistance to Tobramycin. We also observed this variant in our strain collection, where it was associated with expression changes of the gene, suggesting a possible functional impact. The waa operon is involved in LPS synthesis, a central determinant of the bacteria’s outer membrane integrity and a well established virulence factor. This provided a plausible biological mechanism through which variation could influence antimicrobial susceptibility. As its role in resistance has not been extensively characterized, this represents a good candidate for our experimental validation. We have now included this rationale in our revised manuscript (i.e. L476).

      Figure 8: Can get rid of red box

      We have now removed the red box from Figure 8 in the revised version.

      Line 463 - 'account for all kinds' is too informal

      Mix of font styles throughout document

      We have implemented all the suggestions and formatting changes indicated above.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In their manuscript "Cis non-coding genetic variation drives gene expression changes in the E. coli and P. aeruginosa pangenomes", Damaris and co-authors present an extensive meta-analysis, plus some useful follow up experiments, attempting to apply GWAS principles to identify the extent to which differences in gene expression between different strains within a given species can be directly assigned to cis-regulatory mutations. The overall principle, and the question raised by the study, is one of substantial interest, and the manuscript here represents a careful and fascinating effort at unravelling these important questions. I want to preface my review below (which may otherwise sound more harsh than I intend) with the acknowledgment that this is an EXTREMELY difficult and challenging problem that the authors are approaching, and they have clearly put in a substantial amount of high quality work in their efforts to address it. I applaud the work done here, I think it presents some very interesting findings, and I acknowledge fully that there is no one perfect approach to addressing these challenges, and while I will object to some of the decisions made by the authors below, I readily admit that others might challenge my own suggestions and approaches here. With that said, however, there is one fundamental decision that the authors made which I simply cannot agree with, and which in my view undermines much of the analysis and utility of the study: that decision is to treat both gene expression and the identification of cis-regulatory regions at the level of individual genes, rather than transcriptional units. Below I will expand on why I find this problematic, how it might be addressed, and what other areas for improvement I see in the manuscript:

      We thank the reviewer for their praise of our work. A careful set of replies to the major and minor critiques are reported below each point.

      In the entire discussion from lines roughly 100-130, the authors frequently dissect out apparently differentially expressed genes from non differentially expressed genes within the same operons... I honestly wonder whether this is a useful distinction. I understand that by the criteria set forth by the authors it is technically correct, and yet, I wonder if this is more due to thresholding artifacts (i.e., some genes passing the authors' reasonable-yet-arbitrary thresholds whereas others in the same operon do not), and in the process causing a distraction from an operon that is in fact largely moving in the same direction. The authors might wish to either aggregate data in some way across known transcriptional units for the purposes of their analysis, and/or consider a more lenient 'rescue' set of significance thresholds for genes that are in the same operons as differentially expressed genes. I would favor the former approach, performing virtually all of their analysis at the level of transcriptional units rather than individual genes, as much of their analysis in any case relies upon proper assignment of genes to promoters, and this way they could focus on the most important signals rather than get lots sometimes in the weeds of looking at every single gene when really what they seem to be looking at in this paper is a property OF THE PROMOTERS, not the genes. (of course there are phenomena, such as rho dependent termination specifically titrating expression of late genes in operons, but I think on the balance the operon-level analysis might provide more insights and a cleaner analysis and discussion).

      We agree with the reviewer that the peculiar nature of transcription in bacteria has to be taken into account in order to properly quantify the influence of cis variants in gene expression changes. We therefore added the exact analysis the reviewer suggested; that is, we ran associations between the variants in cis to the first gene of each operon and a phenotype that considered the fold-change of all genes in the operon, via a weighted average (see Methods for more details). As reported in the results section (L223), we found a similar trend as with the original analysis: we found the highest proportion of associations when encoding cis variants using k-mers (42% for E. coli and 45% for P. aeruginosa). More importantly, we found a high degree of overlap between this new “operon-level” association analysis and the original one (only including the first gene in each operon). We found a range of 90%-94% of associations overlapping for E. coli and between 75% and 91% for P. aeruginosa, depending on the variant type. We note that operon definitions are less precise for P. aeruginosa, which might explain the higher variability in the level of overlap. We have added the results of this analysis in the results section.

      This also leads to a more general point, however, which I think is potentially more deeply problematic. At the end of the day, all of the analysis being done here centers on the cis regulatory logic upstream of each individual open reading frame, even though in many cases (i.e., genes after the first one in multi-gene operons), this is not where the relevant promoter is. This problem, in turn, raises potentially misattributions of causality running in both directions, where the causal impact on a bona fide promoter mutation on many genes in an operon may only be associated with the first gene, or on the other side, where a mutation that co-occurs with, but is causally independent from, an actual promoter mutation may be flagged as the one driving an expression change. This becomes an especially serious issue in cases like ulaE, for genes that are not the first gene in an operon (at least according to standard annotations, the UlaE transcript should be part of a polycistronic mRNA beginning from the ulaA promoter, and the role played by cis-regulatory logic immediately upstream of ulaE is uncertain and certainly merits deeper consideration. I suspect that many other similar cases likewise lurk in the dataset used here (perhaps even moreso for the Pseudomonas data, where the operon definitions are likely less robust). Of course there are many possible explanations, such as a separate ulaE promoter only in some strains, but this should perhaps be carefully stated and explored, and seems likely to be the exception rather than the rule.

      While we again agree with the reviewer that some of our associations might not result in a direct causal link because the focal variant may not belong to an actual promoter element, we also want to point out how the ability to identify the composition of transcriptional units in bacteria is far from a solved problem (see references at the bottom of this comment, two in general terms, and one characterizing a specific example), even for a well-studied species such as E. coli. Therefore, even if carrying out associations at the operon level (e.g. by focusing exclusively on variants in cis for the first gene in the operon) might be theoretically correct, a number of the associations we find further down the putative operons might be the result of a true biological signal.

      1. Conway, T., Creecy, J. P., Maddox, S. M., Grissom, J. E., Conkle, T. L., Shadid, T. M., Teramoto, J., San Miguel, P., Shimada, T., Ishihama, A., Mori, H., & Wanner, B. L. (2014). Unprecedented High-Resolution View of Bacterial Operon Architecture Revealed by RNA Sequencing. mBio, 5(4), 10.1128/mbio.01442-14. https://doi.org/10.1128/mbio.01442-14

      2. Sáenz-Lahoya, S., Bitarte, N., García, B., Burgui, S., Vergara-Irigaray, M., Valle, J., Solano, C., Toledo-Arana, A., & Lasa, I. (2019). Noncontiguous operon is a genetic organization for coordinating bacterial gene expression. Proceedings of the National Academy of Sciences, 116(5), 1733–1738. https://doi.org/10.1073/pnas.1812746116

      3. Zehentner, B., Scherer, S., & Neuhaus, K. (2023). Non-canonical transcriptional start sites in E. coli O157:H7 EDL933 are regulated and appear in surprisingly high numbers. BMC Microbiology, 23(1), 243. https://doi.org/10.1186/s12866-023-02988-6

      Another issue with the current definition of regulatory regions, which should perhaps also be accounted for, is that it is likely that for many operons, the 'regulatory regions' of one gene might overlap the ORF of the previous gene, and in some cases actual coding mutations in an upstream gene may contaminate the set of potential regulatory mutations identified in this dataset.

      We agree that defining regulatory regions might be challenging, and that those regions might overlap with coding regions, either for the focal gene or the one immediately upstream. For these reasons we have defined a wide region to identify putative regulatory variants (-200 to +30 bp around the start codon of the focal gene). We believe this relatively wide region allows us to capture the most cis genetic variation.

      Taken together, I feel that all of the above concerns need to be addressed in some way. At the absolute barest minimum, the authors need to acknowledge the weaknesses that I have pointed out in the definition of cis-regulatory logic at a gene level. I think it would be far BETTER if they performed a re-analysis at the level of transcriptional units, which I think might substantially strengthen the work as a whole, but I recognize that this would also constitute a substantial amount of additional effort.

      As indicated above, we have added a section in the results section to report on the analysis carried out at the level of operons as individual units, with more details provided in the methods section. We believe these results, which largely overlap with the original analysis, are a good way to recognize the limitation of our approach and to acknowledge the importance of gaining a better knowledge on the number and composition of transcriptional units in bacteria, for which, as the reference above indicates, we still have an incomplete understanding.

      Having reached the end of the paper, and considering the evidence and arguments of the authors in their totality, I find myself wondering how much local x background interactions - that is, the effects of cis regulatory mutations (like those being considered here, with or without the modified definitions that I proposed above) IN THE CONTEXT OF A PARTICULAR STRAIN BACKGROUND, might matter more than the effects of the cis regulatory mutations per se. This is a particularly tricky problem to address because it would require a moderate number of targeted experiments with a moderate number of promoters in a moderate number of strains (which of course makes it maximally annoying since one can't simply scale up hugely on either axis individually and really expect to tease things out). I think that trying to address this question experimentally is FAR beyond the scope of the current paper, but I think perhaps the authors could at least begin to address it by acknowledging it as a challenge in their discussion section, and possibly even identify candidate promoters that might show the largest divergence of activities across strains when there IS no detectable cis regulatory mutation (which might be indicative of local x background interactions), or those with the largest divergences of effect for a given mutation across strains. A differential expression model incorporating shrinkage is essential in such analysis to avoid putting too much weight on low expression genes with a lot of Poisson noise.

      We again thank the reviewer for their thoughtful comments on the limitations of correlative studies in general, and microbial GWAS in particular. In regards to microbial GWAS we feel we may have failed to properly explain how the implementation we have used allows to, at least partially, correct for population structure effects. That is, the linear mixed model we have used relies on population structure to remove the part of the association signal that is due to the genetic background and thus focus the analysis on the specific loci. Obviously examples in which strong epistatic interactions are present would not be accounted for, but those would be extremely challenging to measure or predict at scale, as the reviewer rightfully suggests. We have added a brief recap of the ability of microbial GWAS to account for population structure in the results section (“A large fraction of gene expression changes can be attributed to genetic variations in cis regulatory regions”, e.g. L195).

      I also have some more minor concerns and suggestions, which I outline below:

      It seems that the differential expression analysis treats the lab reference strains as the 'centerpoint' against which everything else is compared, and yet I wonder if this is the best approach... it might be interesting to see how the results differ if the authors instead take a more 'average' strain (either chosen based on genetics or transcriptomics) as a reference and compared everything else to that.

      While we don’t necessarily disagree with the reviewer that a “wild” strain would be better to compare against, we think that our choice to go for the reference isolates is still justified on two grounds. First, while it is true that comparing against a reference introduces biases in the analysis, this concern would not be removed had we chosen another strain as reference; which strain would then be best as a reference to compare against? We think that the second point provides an answer to this question; the “traditional” reference isolates have a rich ecosystem of annotations, experimental data, and computational predictions. These can in turn be used for validation and hypothesis generation, which we have done extensively in the manuscript. Had we chosen a different reference isolate we would have had to still map associations to the traditional reference, resulting in a probable reduction in precision. An example that will likely resonate with this reviewer is that we have used experimentally-validated and high quality computational operon predictions to look into likely associations between cis-variants and “operon DEGs”. This analysis would have likely been of worse quality had we used another strain as reference, for which operon definitions would have had to come from lower-quality predictions or be “lifted” from the traditional reference.

      Line 104 - the statement about the differentially expressed genes being "part of operons with diverse biological functions" seems unclear - it is not apparent whether the authors are referring to diversity of function within each operon, or between the different operons, and in any case one should consider whether the observation reflects any useful information or is just an apparently random collection of operons.

      We agree that this formulation could create confusion and we have elected to remove the expression “with diverse biological functions”, given that we discuss those functions immediately after that sentence.

      Line 292 - I find the argument here somewhat unconvincing, for two reasons. First, the fact that only half of the observed changes went in the same direction as the GWAS results would indicate, which is trivially a result that would be expected by random chance, does not lend much confidence to the overall premise of the study that there are meaningful cis regulatory changes being detected (in fact, it seems to argue that the background in which a variant occurs may matter a great deal, at least as much as the cis regulatory logic itself). Second, in order to even assess whether the GWAS is useful to "find the genetic determinants of gene expression changes" as the authors indicate, it would be necessary to compare to a reasonable, non-straw-man, null approach simply identifying common sequence variants that are predicted to cause major changes in sigma 70 binding at known promoters; such a test would be especially important given the lack of directional accuracy observed here. Along these same lines, it is perhaps worth noting, in the discussion beginning on line 329, that the comparison is perhaps biased in favor of the GWAS study, since the validation targets here were prioritized based on (presumably strong) GWAS data.

      We thank the reviewer for prompting us into reasoning about the results of the in-vitro validation experiments. We agree that the agreement between the measured gene expression changes agree only partly with those measured with the reporter system, and that this discrepancy could likely be attributed to regulatory elements that are not in cis, and thus that were not present in the in-vitro reporter system. We have noted this possibility in the discussion. Additionally, we have amended the results section to note that even though the prediction in the direction of gene expression change was not as accurate as it could be expected, the prediction of whether a change would be present (thus ignoring directionality) was much higher.

      I don't find the Venn diagrams in Fig 7C-D useful or clear given the large number of zero-overlap regions, and would strongly advocate that the authors find another way to show these data.

      While we are aware that alternative ways to show overlap between sets, such as upset plots, we don’t actually find them that much easier to parse. We actually think that the simple and direct Venn diagrams we have drawn convey the clear message that overlaps only exist between certain drug classes in E. coli, and virtually none for P. aeruginosa. We have added a comment on the lack of overlap between all drug classes and the differences between the two species in the results section (i.e. L436 and L465).

      In the analysis of waa operon gene expression beginning on line 400, it is perhaps important to note that most of the waa operon doesn't do anything in laboratory K12 strains due to the lack of complete O-antigen... the same is not true, however, for many wild/clinical isolates. It would be interesting to see how those results compare, and also how the absolute TPMs (rather than just LFCs) of genes in this operon vary across the strains being investigated during TOB treatment.

      We thank the reviewer for this helpful suggestion. We examined the absolute expression (TPMs) of waa operon genes under the baseline (A) and following exposure to Tobramycin (B). The representative TPMs per strain were obtained by averaging across biological replicates. We observed a constitutive expression of the genes in the reference strain (MG1655) and the other isolates containing the variant of interest (MC4100, BW25113). In contrast, strains lacking the variants of interest (IAI76 and IAI78), showed lower expression of these operon genes under both conditions. Strain IAI77, on the other hand, displayed increased expression of a subset of waa genes post Tobramycin exposure, indicating strain-specific variation in transcriptional response. While the reference isolate might not have the O-antigen, it certainly expresses the waa operon, both constitutively and under TOB exposure.

      I don't think that the second conclusion on lines 479-480 is fully justified by the data, given both the disparity in available annotation information between the two species, AND the fact that only two species were considered.

      While we feel that the “Discussion” section of a research paper allows for speculative statements, we have to concede that we have perhaps overreached here. We have amended this sentence to be more cautious and not mislead readers.

      Line 118: "Double of DEGs"

      Line 288 - presumably these are LOG fold changes

      Fig 6b - legend contains typos

      Line 661 - please report the read count (more relevant for RNA-seq analysis) rather than Gb

      We thank the reviewer for pointing out the need to make these edits. We have implemented them all.

      Source code - I appreciate that the authors provide their source code on github, but it is very poorly documented - both a license and some top-level documentation about which code goes with each major operation/conclusion/figure should be provided. Also, ipython notebooks are in general a poor way in my view to distribute code, due to their encouragement of nonlinear development practices; while they are fine for software development, actual complete python programs along with accompanying source data would be preferrable.

      We agree with the reviewer that a software license and some documentation about what each notebook is about is warranted, and we have added them both. While we agree that for “consumer-grade” software jupyter notebooks are not the most ergonomic format, we believe that as a documentation of how one-time analyses were carried out they are actually one of the best formats we could think of. They in fact allow for code and outputs to be presented alongside each other, which greatly helped us to iterate on our research and to ensure that what was presented in the manuscript matched the analyses we reported in the code. This is of course up for debate and ultimately specific to someone’s taste, and so we will keep the reviewer’s critique in mind for our next manuscript. And, if we ever decide to package the analyses presented in the manuscript as a “consumer-grade” application for others to use, we would follow higher standards of documentation and design.

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

      In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance.

      A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

      We thank the reviewer for highlighting the strengths of our study. We hope that our replies to their comments and the other two reviewers will address some of the limitations.

      Major comments:

      1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

      2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

      In order to better guide readers through the different kinds of genetic variants we considered, we have added a brief explanation about what “promoter switches” are in the introduction (“meaning that the entire promoter region may differ between isolates due to recombination events”, L56). We believe this clarifies how they are very different in character from a large deletion. We have kept the reference to the original study (10.1073/pnas.1413272111) describing how widespread these switches are in E. coli as a way for readers to discover more about them.

      • I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

      We indeed agree with the reviewer in thinking that representing genetic variants as k-mers would encompass short variants (SNP/InDels) as well as larger variants and promoters presence/absence patterns. We believe that this assumption is validated by the fact that we identify the highest proportion of DEGs with a significant association when using this representation of variants (Figure 2A, 39% for both species). We have added a reference to a recent review on the advantages of k-mer methods for population genetics (10.1093/molbev/msaf047) in the introduction. Regarding the issue of multiple testing correction, we have employed a commonly recognized approach that, unlike a crude Bonferroni correction using the number of tested variants, allows for a realistic correction of association p-values. We used the number of unique presence/absence patterns, which can be shared between multiple genetic variants, and applied a Bonferroni correction using this number rather than the number of variants tested. We have expanded the corresponding section in the methods (e.g. L697) to better explain this point for readers not familiar with this approach.

      1. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?

      We appreciate the suggestion made by the reviewer to look into the distribution of effect sizes divided by variant type. We have now evaluated the distribution of the effect sizes and allele frequencies for the genetic markers (SNPs/InDels, IGRs, and k-mers) for both species (Supplementary Figure 2). In E. coli, IGR variants showed somewhat larger median effect sizes (|β| = 4.5) than SNPs (|β| = 3.8), whereas k-mers displayed the widest distribution (median |β| = 5.2). In P. aeruginosa, the trend differed with IGRs exhibiting smaller effects (median |β| = 3.2), compared to SNPs/InDels (median |β| =5.1) and k-mers (median |β| = 6.2). With respect to allele frequencies, SNPs/InDels generally occured at lower frequencies (median AF = 0.34 for E.coli, median AF = 0.33 for P. aeruginosa), whereas IGRs (median AF = 0.65 for E. coli and 0.75 for P. aeruginosa) and k-mers (median AF = 0.71 for E. coli and 0.65 for P. aeruginosa) were more often at the intermediate to higher frequencies respectively. We have added a visualization for the distribution of effect sizes (Supplementary Figure 2).

      1. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.

      We thanks the reviewer for their comment, which is similar in nature to that raised by reviewer #2 above. As noted in our reply above we have amended the results and discussion to indicate that although the direction of gene expression change was not highly accurate, focusing on the magnitude (or rather whether there would be a change in gene expression, regardless of the direction), resulted in a higher accuracy. We postulate that the cases in which the direction of the change was not correctly identified could be due to the influence of other genetic elements in trans with the gene of interest.

      1. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?

      We thank the reviewer for this comment. We agree that estimating the null expectation for the functional promoter would require testing promoter alleles with sequence variation that are not associated in the GWAS. Such experiments, which would directly address if the observed effects in our study exceeds background, would have required us to prepare multiple constructs, which was unfortunately not possible for us due to staff constraints. We therefore elected to clarify the scope of our GFP reporter assays instead. These experiments were designed as a paired comparison of the wild-type and the GWAS-associated variant alleles of the same promoter in an identical reporter background, with the aim of testing allele-specific functional effects for GWAS hits (Supplementary Figure 6). We also included a comparison in GFP fluorescence between the promoterless vector (pOT2) and promoter-containing constructs; we observed higher GFP signals in all but four (yfgJ, fimI, agaI, and yfdQ) variant-containing promoter constructs, which indicates that for most of the construct we cloned active promoter elements. We have revised the manuscript text accordingly to reflect this clarification and included the control in the supplementary information as Supplementary Figure 6.

      1. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.

      We thank the reviewer for pointing this out. We have reviewed Figure 6 to indicate significant differences between the test and control reporter constructs. We used the paired student’s t-test to match the matched plate/time point measurements. We also corrected for multiple testing using the Benhamini-Hochberg correction. As seen in the updated Figure 6A, 16 out of the 18 reporter constructs displayed significant differences (adjusted p-value

      1. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?

      We thank the reviewer for this helpful suggestion, which allows us to more closely look into the results of our in-vitro validation. We performed a direct comparison of RNAseq fold changes from the GWAS (x-axis) with the GFP reporter measurements (y-axis) as depicted in the figure above. The overall correlation between the two was weak (Pearson r = 0.17), reflecting the lack of thorough agreement between the associations and the reporter construct. We however note that the two metrics are not directly comparable in our opinion, since on the x-axis we are measuring changes in gene expression and on the y-axis changes in fluorescence expression, which is downstream from it. As mentioned above and in reply to a comment from reviewer 2, the agreement between measured gene expression and all other in-silico and in-vitro techniques increases when ignoring the direction of the change. Overall, we believe that these results partly validate our associations and predictions, while indicating that other factors in trans with the regulatory region contribute to changes in gene expression, which is to be expected. The scatter plot has been included as a new supplementary figure (Supplementary Figure 7).

      1. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?

      The T>C SNP upstream of waaQ did not show significant association with gene expression in our cis GWAS analysis. Instead, this variant was associated with resistance to tobramycin when referencing data from Danesh et al, and we observed the variant in our strain collection. We subsequently investigated whether this variant also influenced expression of the waa operon under sub-inhibitory tobramycin exposure. The differential expression results shown in the final section therefore represent a functional follow-up experiment, and not a direct replication of the GWAS presented in the first part of the manuscript.

      1. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?

      The awkwardness of that sentence is perhaps the consequence of our assumption that readers would be aware of the differences in population genetics differences between the two species. We however have realized that not much literature is available (if at all!) about these differences, which we have observed during the course of this and other studies we have carried out. As a result, we agree that we cannot assume that the reader is similarly familiar with these differences, and have changed that sentence (i.e. L548) to more directly address the differences between the two species, which will presumably result in a diverse population structure. We thank the reviewer for letting us be aware of a gap in the literature concerning the comparison of pangenome structures across relevant species.

      1. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.

      We have amended this sentence to be more adherent to what we can conclude from our analyses.

      1. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.

      We thank the reviewer for highlighting the need for clearer methodological detail. We have expanded this section (i.e. L608) to fully describe the generation and quality control of the E. coli RNA-seq data including RNA extraction and sequencing platform.

      1. How were the DEG p-values adjusted for multiple testing?

      As indicated in the methods section (“Differential gene expression and functional enrichment analysis”), we have used DEseq2 for E. coli, and LPEseq for P. aeruginosa. Both methods use the statistical framework of the False Discovery Rate (FDR) to compute an adjusted p-value for each gene. We have added a brief mention of us following the standard practice indicated by both software packages in the methods.

      1. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.

      In the context of providing more information about the transcriptomics experiments for E. coli, we have also more clearly indicated that we have two biological replicates for the E. coli dataset.

      1. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?

      In line with an earlier comment from this reviewer, we have expanded the section in the Methods (e.g. L689) that explains how this correction worked to include as many details as possible, in order to provide the readers with the full context under which our analyses were carried out.

      1. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.

      We thank both reviewer #1 and #3 for pointing out this lack of references to the extensive literature on the subject. We have added a number of references about the applications of eQTL studies, and specifically its application in microbial pangenomes, which we believe is more relevant to our study, in the introduction.

      Minor comments:

      1. I wasn't able to understand the top panels in Figure 4. For ulaE, most strains have the solid colors, and the corresponding bottom panel shows mostly red points. But for waaQ, most strains have solid color in the top panel, but only a few strains in the bottom panel are red. So solid color in the top does not indicate a variant allele? And why are there so many solid alleles; are these all indels? Even if so, for kgtP, the same colors (i.e., nucleotides) seem to seamlessly continue into the bottom, pale part of the top panel. How are these strains different genotypically? Are these blocks of solid color counted as one indel or several SNPs, or somehow as k-mer differences? As the authors can see, these figures are really hard to understand and should be reworked. The same comment applies to Figure 5, where it seems that all (!) strains have the "variant"?

      We thank the reviewer for pointing out some limitations with our visualizations, most importantly with the way we explained how to read those two figures. We have amended the captions to more explicitly explain what is shown. The solid colors in the “sequence pseudo-alignment” panels indicate the focal cis variant, which is indicated in red in the corresponding “predicted transcription rate” panels below. In the case of Figure 5, the solid color indicates instead the position of the TFBS in the reference.

      1. Figure 1A & B: It would be helpful to add the total number of analyzed genes somewhere so that the numbers denoted in the colored outer rings can be interpreted in comparison to the total.

      We have added the total number of genes being considered for either species in the legend.

      1. Figure 1C & D: It would be better to spell out the COG names in the figure, as it is cumbersome for the reader to have to look up what the letters stand for in a supplementary table in a separate file.

      While we do not disagree with the awkwardness of having to move to a supplementary table to identify the full name of a COG category, we also would like to point out that the very long names of each category would clutter the figure to a degree that would make it difficult to read. We had indeed attempted something similar to what the reviewer suggests in early drafts of this manuscript, leading to small and hard to read labels. We have therefore left the full names of each COG category in Supplementary Table 3.

      1. Line 107: "Similarly," does not fit here as the following example (with one differentially expressed gene in an operon) is conceptually different from the one before, where all genes in the operon were differentially expressed.

      We agree and have amended the sentence accordingly.

      1. Figure 5 bottom panel: it is odd that on the left the swarm plots (i.e., the dots) are on the inside of the boxplots while on the right they are on the outside.

      We have fixed the position of the dots so that they are centered with respect to the underlying boxplots.

      1. It is not clear to me how only one or a few genes in an operon can show differential mRNA abundance. Aren't all genes in an operon encoded by the same mRNA? If so, shouldn't this mRNA be up- or downregulated in the same manner for all genes it encodes? As I am not closely familiar with bacterial systems, it is well possible that I am missing some critical fact about bacterial gene expression here. If this is not an analysis artifact, the authors could briefly explain how this observation is possible.

      We thanks the reviewer for their comment, which again echoes one of the main concerns from reviewer #2. As noted in our reply above, it has been established in multiple studies (see the three we have indicated above in our reply to reviewer #2) how bacteria encode for multiple “non-canonical” transcriptional units (i.e. operons), due to the presence of accessory terminators and promoters. This, together with other biological effects such as the presence of mRNA molecules of different lengths due to active transcription and degradation and technical noise induced by RNA isolation and sequencing can result in variability in the estimation of abundance for each gene.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance. A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

      Major comments:

      1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

      2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

      3. I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

      4. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?
      5. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.
      6. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?
      7. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.
      8. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?
      9. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?
      10. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?
      11. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.
      12. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.
      13. How were the DEG p-values adjusted for multiple testing?
      14. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.
      15. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?
      16. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.

      Minor comments:

      1. I wasn't able to understand the top panels in Figure 4. For ulaE, most strains have the solid colors, and the corresponding bottom panel shows mostly red points. But for waaQ, most strains have solid color in the top panel, but only a few strains in the bottom panel are red. So solid color in the top does not indicate a variant allele? And why are there so many solid alleles; are these all indels? Even if so, for kgtP, the same colors (i.e., nucleotides) seem to seamlessly continue into the bottom, pale part of the top panel. How are these strains different genotypically? Are these blocks of solid color counted as one indel or several SNPs, or somehow as k-mer differences? As the authors can see, these figures are really hard to understand and should be reworked. The same comment applies to Figure 5, where it seems that all (!) strains have the "variant"?
      2. Figure 1A & B: It would be helpful to add the total number of analyzed genes somewhere so that the numbers denoted in the colored outer rings can be interpreted in comparison to the total.
      3. Figure 1C & D: It would be better to spell out the COG names in the figure, as it is cumbersome for the reader to have to look up what the letters stand for in a supplementary table in a separate file.
      4. Line 107: "Similarly," does not fit here as the following example (with one differentially expressed gene in an operon) is conceptually different from the one before, where all genes in the operon were differentially expressed.
      5. Figure 5 bottom panel: it is odd that on the left the swarm plots (i.e., the dots) are on the inside of the boxplots while on the right they are on the outside.
      6. It is not clear to me how only one or a few genes in an operon can show differential mRNA abundance. Aren't all genes in an operon encoded by the same mRNA? If so, shouldn't this mRNA be up- or downregulated in the same manner for all genes it encodes? As I am not closely familiar with bacterial systems, it is well possible that I am missing some critical fact about bacterial gene expression here. If this is not an analysis artifact, the authors could briefly explain how this observation is possible.

      Significance

      To my knowledge, this work represents the first cis-eQTL mapping in bacteria. As such, it is a useful and interesting exploration of this space that complements the large body of literature on this question in eukaryotic systems. This expansion to bacterial systems is especially interesting given the unique features of bacterial compared to eukaryotic genomes, including a small (10-15%) noncoding fraction of the genome and gene organization in operons. The work will be of interest to readers in the fields of complex trait genetics, gene expression, and regulatory variation. For context of this assessment, I am an expert in genomics and the study of genetic variation in gene expression in eukaryotic systems. I have limited knowledge about bacterial genetics and biology, as well as of antibiotic resistance.

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      Referee #2

      Evidence, reproducibility and clarity

      In their manuscript "Cis non-coding genetic variation drives gene expression changes in the E. coli and P. aeruginosa pangenomes", Damaris and co-authors present an extensive meta-analysis, plus some useful follow up experiments, attempting to apply GWAS principles to identify the extent to which differences in gene expression between different strains within a given species can be directly assigned to cis-regulatory mutations. The overall principle, and the question raised by the study, is one of substantial interest, and the manuscript here represents a careful and fascinating effort at unravelling these important questions. I want to preface my review below (which may otherwise sound more harsh than I intend) with the acknowledgment that this is an EXTREMELY difficult and challenging problem that the authors are approaching, and they have clearly put in a substantial amount of high quality work in their efforts to address it. I applaud the work done here, I think it presents some very interesting findings, and I acknowledge fully that there is no one perfect approach to addressing these challenges, and while I will object to some of the decisions made by the authors below, I readily admit that others might challenge my own suggestions and approaches here. With that said, however, there is one fundamental decision that the authors made which I simply cannot agree with, and which in my view undermines much of the analysis and utility of the study: that decision is to treat both gene expression and the identification of cis-regulatory regions at the level of individual genes, rather than transcriptional units. Below I will expand on why I find this problematic, how it might be addressed, and what other areas for improvement I see in the manuscript:

      In the entire discussion from lines roughly 100-130, the authors frequently dissect out apparently differentially expressed genes from non differentially expressed genes within the same operons... I honestly wonder whether this is a useful distinction. I understand that by the criteria set forth by the authors it is technically correct, and yet, I wonder if this is more due to thresholding artifacts (i.e., some genes passing the authors' reasonable-yet-arbitrary thresholds whereas others in the same operon do not), and in the process causing a distraction from an operon that is in fact largely moving in the same direction. The authors might wish to either aggregate data in some way across known transcriptional units for the purposes of their analysis, and/or consider a more lenient 'rescue' set of significance thresholds for genes that are in the same operons as differentially expressed genes. I would favor the former approach, performing virtually all of their analysis at the level of transcriptional units rather than individual genes, as much of their analysis in any case relies upon proper assignment of genes to promoters, and this way they could focus on the most important signals rather than get lots sometimes in the weeds of looking at every single gene when really what they seem to be looking at in this paper is a property OF THE PROMOTERS, not the genes. (of course there are phenomena, such as rho dependent termination specifically titrating expression of late genes in operons, but I think on the balance the operon-level analysis might provide more insights and a cleaner analysis and discussion).

      This also leads to a more general point, however, which I think is potentially more deeply problematic. At the end of the day, all of the analysis being done here centers on the cis regulatory logic upstream of each individual open reading frame, even though in many cases (i.e., genes after the first one in multi-gene operons), this is not where the relevant promoter is. This problem, in turn, raises potentially misattributions of causality running in both directions, where the causal impact on a bona fide promoter mutation on many genes in an operon may only be associated with the first gene, or on the other side, where a mutation that co-occurs with, but is causally independent from, an actual promoter mutation may be flagged as the one driving an expression change. This becomes an especially serious issue in cases like ulaE, for genes that are not the first gene in an operon (at least according to standard annotations, the UlaE transcript should be part of a polycistronic mRNA beginning from the ulaA promoter, and the role played by cis-regulatory logic immediately upstream of ulaE is uncertain and certainly merits deeper consideration. I suspect that many other similar cases likewise lurk in the dataset used here (perhaps even moreso for the Pseudomonas data, where the operon definitions are likely less robust). Of course there are many possible explanations, such as a separate ulaE promoter only in some strains, but this should perhaps be carefully stated and explored, and seems likely to be the exception rather than the rule. Another issue with the current definition of regulatory regions, which should perhaps also be accounted for, is that it is likely that for many operons, the 'regulatory regions' of one gene might overlap the ORF of the previous gene, and in some cases actual coding mutations in an upstream gene may contaminate the set of potential regulatory mutations identified in this dataset. Taken together, I feel that all of the above concerns need to be addressed in some way. At the absolute barest minimum, the authors need to acknowledge the weaknesses that I have pointed out in the definition of cis-regulatory logic at a gene level. I think it would be far BETTER if they performed a re-analysis at the level of transcriptional units, which I think might substantially strengthen the work as a whole, but I recognize that this would also constitute a substantial amount of additional effort. Having reached the end of the paper, and considering the evidence and arguments of the authors in their totality, I find myself wondering how much local x background interactions - that is, the effects of cis regulatory mutations (like those being considered here, with or without the modified definitions that I proposed above) IN THE CONTEXT OF A PARTICULAR STRAIN BACKGROUND, might matter more than the effects of the cis regulatory mutations per se. This is a particularly tricky problem to address because it would require a moderate number of targeted experiments with a moderate number of promoters in a moderate number of strains (which of course makes it maximally annoying since one can't simply scale up hugely on either axis individually and really expect to tease things out). I think that trying to address this question experimentally is FAR beyond the scope of the current paper, but I think perhaps the authors could at least begin to address it by acknowledging it as a challenge in their discussion section, and possibly even identify candidate promoters that might show the largest divergence of activities across strains when there IS no detectable cis regulatory mutation (which might be indicative of local x background interactions), or those with the largest divergences of effect for a given mutation across strains. A differential expression model incorporating shrinkage is essential in such analysis to avoid putting too much weight on low expression genes with a lot of Poisson noise.

      I also have some more minor concerns and suggestions, which I outline below: It seems that the differential expression analysis treats the lab reference strains as the 'centerpoint' against which everything else is compared, and yet I wonder if this is the best approach... it might be interesting to see how the results differ if the authors instead take a more 'average' strain (either chosen based on genetics or transcriptomics) as a reference and compared everything else to that.

      Line 104 - the statement about the differentially expressed genes being "part of operons with diverse biological functions" seems unclear - it is not apparent whether the authors are referring to diversity of function within each operon, or between the different operons, and in any case one should consider whether the observation reflects any useful information or is just an apparently random collection of operons. Line 292 - I find the argument here somewhat unconvincing, for two reasons. First, the fact that only half of the observed changes went in the same direction as the GWAS results would indicate, which is trivially a result that would be expected by random chance, does not lend much confidence to the overall premise of the study that there are meaningful cis regulatory changes being detected (in fact, it seems to argue that the background in which a variant occurs may matter a great deal, at least as much as the cis regulatory logic itself). Second, in order to even assess whether the GWAS is useful to "find the genetic determinants of gene expression changes" as the authors indicate, it would be necessary to compare to a reasonable, non-straw-man, null approach simply identifying common sequence variants that are predicted to cause major changes in sigma 70 binding at known promoters; such a test would be especially important given the lack of directional accuracy observed here. Along these same lines, it is perhaps worth noting, in the discussion beginning on line 329, that the comparison is perhaps biased in favor of the GWAS study, since the validation targets here were prioritized based on (presumably strong) GWAS data.

      I don't find the Venn diagrams in Fig 7C-D useful or clear given the large number of zero-overlap regions, and would strongly advocate that the authors find another way to show these data.

      In the analysis of waa operon gene expression beginning on line 400, it is perhaps important to note that most of the waa operon doesn't do anything in laboratory K12 strains due to the lack of complete O-antigen... the same is not true, however, for many wild/clinical isolates. It would be interesting to see how those results compare, and also how the absolute TPMs (rather than just LFCs) of genes in this operon vary across the strains being investigated during TOB treatment.

      I don't think that the second conclusion on lines 479-480 is fully justified by the data, given both the disparity in available annotation information between the two species, AND the fact that only two species were considered.

      Line 118: "Double of DEGs"

      Line 288 - presumably these are LOG fold changes

      Fig 6b - legend contains typos

      Line 661 - please report the read count (more relevant for RNA-seq analysis) rather than Gb

      Source code - I appreciate that the authors provide their source code on github, but it is very poorly documented - both a license and some top-level documentation about which code goes with each major operation/conclusion/figure should be provided. Also, ipython notebooks are in general a poor way in my view to distribute code, due to their encouragement of nonlinear development practices; while they are fine for software development, actual complete python programs along with accompanying source data would be preferrable.

      Significance

      Overall the key strength of the study is the heroic merging of large genetic and transcriptomic datasets to address the question of how much variation in gene expression can be assigned to cis regulatory mutations in E. coli and in P. aeruginosa. The authors find that only a minority of genes can have such an assignment explaining expression variation, which highlights both the many factors (local and global) impacting gene expression, and the difficulty in trying to predict and understand expression patterns in different strains. I believe that with suitable modification, the manuscript will be of great interest to a broad audience interested in bacterial genomics, gene regulation, and systems/synthetic biology.

      Reviewer Expertise: I consider myself a bacterial systems biologist and routinely use high throughput experiments to understand bacterial gene regulation.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Damaris et al. perform what is effectively an eQTL analysis on microbial pangenomes of E. coli and P. aeruginosa. Specifically, they leverage a large dataset of paired DNA/RNA-seq information for hundreds of strains of these microbes to establish correlations between genetic variants and changes in gene expression. Ultimately, their claim is that this approach identifies non-coding variants that affect expression of genes in a predictable manner and explain differences in phenotypes. They attempt to reinforce these claims through use of a widely regarded promoter calculator to quantify promoter effects, as well as some validation studies in living cells. Lastly, they show that these non-coding variations can explain some cases of antibiotic resistance in these microbes.

      Major comments

      Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

      The authors convincingly demonstrate that they can identify non-coding variation in pangenomes of bacteria and associate these with phenotypes of interest. What is unclear is the extent by which they account for covariation of genetic variation? Are the SNPs they implicate truly responsible for the changes in expression they observe? Or are they merely genetically linked to the true causal variants. This has been solved by other GWAS studies but isn't discussed as far as I can tell here.

      They need to justify why they consider the 30bp downstream of the start codon as non-coding. While this region certainly has regulatory impact, it is also definitely coding. To what extent could this confound results and how many significant associations to expression are in this region vs upstream?

      The claim that promoter variation correlates with changes in measured gene expression is not convincingly demonstrated (although, yes, very intuitive). Figure 3 is a convoluted way of demonstrating that predicted transcription rates correlate with measured gene expression. For each variant, can you do the basic analysis of just comparing differences in promoter calculator predictions and actual gene expression? I.e. correlation between (promoter activity variant X)-(promoter activity variant Y) vs (measured gene expression variant X)-(measured gene expression variant Y). You'll probably have to

      Figure 7 it is unclear what this experiment was. How were they tested? Did you generate the data themselves? Did you do RNA-seq (which is what is described in the methods) or just test and compare known genomic data?

      Are the data and the methods presented in such a way that they can be reproduced?

      No, this is the biggest flaw of the work. The RNA-Seq experiment to start this project is not described at all as well as other key experiments. Descriptions of methods in the text are far too vague to understand the approach or rationale at many points in the text. The scripts are available on github but there is no description of what they correspond to outside of the file names and none of the data files are found to replicate the plots.

      Figure 8B is intended to show that the WaaQ operon is connected to known Abx resistance genes but uses the STRING method. This requires a list of genes but how did they build this list? Why look at these known ABx genes in particular? STRING does not really show evidence, these need to be substantiated or at least need to justify why this analysis was performed.

      Are the experiments adequately replicated and statistical analysis adequate?

      An important claim on MIC of variants for supplementary table 8 has no raw data and no clear replicates available. Only figure 6, the in vitro testing of variant expression, mentions any replicates.

      Minor comments

      Specific experimental issues that are easily addressable.. Are prior studies referenced appropriately?

      There should be a discussion of eQTLs in this. Although these have mostly been in eukaryotes a. https://doi.org/10.1038/s41588-024-01769-9 ; https://doi.org/10.1038/nrg3891

      Line 67. Missing important citation for Ireland et al. 2020 https://doi.org/10.7554/eLife.55308 Line 69. Should mention Johns et al. 2018 (https://doi.org/10.1038/nmeth.4633) where they study promoter sequences outside of E. coli Line 90 - replace 'hypothesis-free' with unbiased Line 102 - state % of DEGs relative to the entire pan-genome Figure 1A is not discussed in the text Line 111: it is unclear what enrichment was being compared between, FIgures 1C/D have 'Gene counts' but is of the total DEGs? How is the p-value derived? Comparing and what statistical test was performed? Comparing DEG enrichment vs the pangenome? K12 genome? Line 122-123: State what letters correspond to these COG categories here Line 155: Need to clarify how you use k-mers in this and how they are different than SNPs. are you looking at k-mer content of these regions? K-mers up to hexamers or what? How are these compared. You can't just say we used k-mers. Line 172: It would be VERY helpful to have a supplementary figure describing these types of variants, perhaps a multiple-sequence alignment containing each example Figure 4: THis figure is too small. Why are WaaQ and UlaE being used as examples here when you are supposed to be explicitly showing variants with strong positive correlations? Figure 4: Why is there variation between variants present and variant absent? Is this due to other changes in the variant? Should mention this in the text somewhere Line 359: Need to talk about each supplementary figure 4 to 9 and how they demonstrate your point.

      Are the text and figures clear and accurate? Figure 4 too small Acronyms are defined multiple times in the manuscript, sometimes not the first time they are used (e.g. SNP, InDel) Figure 8A - Remove red box, increase label size Figure 8B - Low resolution, grey text is unreadable and should be darker and higher resolution Line 35 - be more specific about types of carbon metabolism and catabolite repression Line 67 - include citation for ireland et al. 2020 https://doi.org/10.7554/eLife.55308 Line 74 - You talk about looking in cis but don't specify how mar away cis is Line 75 - we encoded genetic variants..... It is unclear what you mean here Line 104 - 'were apart of operons' should clarify you mean polycistronic or multi-gene operons. Single genes may be considered operonic units as well. Figure 2: THere is no axis for the percents and the percents don't make sense relative to the bars they represent?? Figure 2: Figure 2B legend should clarify that these are individual examples of Differential expression between variants Line 198-199: This sentence doesn't make sense, 'encoded using kmers' is not descriptive enough Line 205: Should be upfront about that you're using the Promoter Calculator that models biophysical properties of promoter sequences to predict activity. Line 251: 'Scanned the non-coding sequences of the DEGs'. This is far too vague of a description of an approach. Need to clarify how you did this and I didn't see in the method. Is this an HMM? Perfect sequence match to consensus sequence? Some type of alignment? Line 257-259: This sentence lacks clarity Line346: How were the E. coli isolates tested? Was this an experiment you did? This is a massive undertaking (1600 isolates * 12 conditions) if so so should be clearly defined Figure 6A: The tile plot on the right side is not clearly labeled and it is unclear what it is showing and how that relates to the bar plots. FIgure 6B: typo in legend 'Downreglation' Line 398: Need to state rationale for why Waaq operon is being investigated here. WHy did you look into individual example? Figure 8: Can get rid of red box Line 463 - 'account for all kinds' is too informal Mix of font styles throughout document

      Significance

      Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested. The following aspects are important:General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

      This study applies eQTL concepts to bacterial pangenomes to understand how genetic variation in non-coding regions contributes to microbial phenotypes, which is clever and has not been done in bacterial communities (although has been done in yeast isolates, see citation above). They characterize these same variants using in silico promoter predictions, in vitro experiments, layer biological mechanism via transcription factor binding site mapping, and study associated antibiotic resistance phenotypes. These are all good ideas, but none of these points are very developed. The antibiotic work in particular was a missed opportunity as this is the most impactful demonstration of their approach. For instance, to what extent do these eQTLs explain resistance across isolates vs coding changes? Are non-coding variants more responsible for antibiotic resistance than coding variants? Given how easy it is to adapt gene expression vs establishing other mechanisms, this is plausible. How could knowing this change how we treat infections? While a general overview of their strategy is fine, the approaches are under-described and unclear so difficult to truly assess.

      Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

      To my knowledge and from a cursory search, this is the first pan-genome mapping of non-coding variants to transcriptional changes in bacteria. This is a good idea that could be applied to any microbe for which large transcriptomic datasets of strains are available or could be generated and is helpful for understanding genetic variation and the architecture of bacterial regulatory systems.

      Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

      This would be of interest to individuals interested in population genetics, gene regulation, and microbial evolution. It could inspire similar studies of other microbes to understand the contribution of non-coding changes to phenotypes across whole genomes.

      Please 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.

      I am an expert on bacterial gene regulation, especially concerning how promoter activity is encoded within sequences. I have less experience on using GWAS.

  2. Feb 2026
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      Reply to the reviewers

      In our manuscript, we describe a role for the nuclear mRNA export factor UAP56 (a helicase) during metamorphic dendrite and presynapse pruning in flies. We characterize a UAP56 ATPase mutant and find that it rescues the pruning defects of a uap56 mutant. We identify the actin severing enzyme Mical as a potentially crucial UAP56 mRNA target during dendrite pruning and show alterations at both the mRNA and protein level. Finally, loss of UAP56 also causes presynapse pruning defects with actin abnormalities. Indeed, the actin disassembly factor cofilin is required for pruning specifically at the presynapse.

      We thank the reviewers for their constructive comments, which we tried to address experimentally as much as possible. To summarize briefly, while all reviewers saw the results as interesting (e. g., Reviewer 3's significance assessment: "Understanding how post-transcriptional events are linked to key functions in neurons is important and would be of interest to a broad audience") and generally methodologically strong, they thought that our conclusions regarding the potential specificity of UAP56 for Mical mRNA was not fully covered by the data. To address this criticism, we added more RNAi analyses of other mRNA export factors and rephrased our conclusions towards a more careful interpretation, i. e., we now state that the pruning process is particularly sensitive to loss of UAP56. In addition, reviewer 1 had technical comments regarding some of our protein and mRNA analyses. We added more explanations and an additional control for the MS2/MCP system. Reviewers 2 and 3 wanted to see a deeper characterization of the ATPase mutant provided. We generated an additional UAP56 mutant transgene, improved our analyses of UAP56 localization, and added a biochemical control experiment. We hope that our revisions make our manuscript suitable for publication.

      1. 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. *

      • *

      Comments by reviewer 1.

      Major comments

      1.

      For Figure 4, the MS2/MCP system is not quantitative. Using this technique, it is impossible to determine how many RNAs are located in each "dot". Each of these dots looks quite large and likely corresponds to some phase-separated RNP complex where multiple RNAs are stored and/or transported. Thus, these data do not support the conclusion that Mical mRNA levels are reduced upon UAP56 knockdown. A good quantitative microscopic assay would be something like smFISH. Additinally, the localization of Mical mRNA dots to dendrites is not convincing as it looks like regions where there are dendritic swellings, the background is generally brighter.

      Our response

      We indeed found evidence in the literature that mRNPs labeled with the MS2/MCP or similar systems form condensates (Smith et al., JCB 2015). Unfortunately, smFISH is not established for this developmental stage and would likely be difficult due to the presence of the pupal case. To address whether the Mical mRNPs in control and UAP56 KD neurons are comparable, we characterized the MCP dots in the respective neurons in more detail and found that their sizes did not differ significantly between control and UAP56 KD neurons. To facilitate interpretability, we also increased the individual panel sizes and include larger panels that only show the red (MCP::RFP) channel. We think these changes improved the figure. Thanks for the insight.

      Changes introduced: Figure 5 (former Fig. 4): Increased panel size for MCP::RFP images, left out GFP marker for better visibility. Added new analysis of MCP::RFP dot size (new Fig. 5 I).

      1.

      Alternatively, levels of Mical mRNA could be verified by qPCR in the laval brain following pan-neuronal UAP56 knockdown or in FACS-sorted fluorescently labeled da sensory neurons. Protein levels could be analyzed using a similar approach.

      Our response

      We thank the reviewer for this comment. Unfortunately, these experiments are not doable as neuron-wide UAP56 KD is lethal (see Flybase entry for UAP56). From our own experience, FACS-sorting of c4da neurons would be extremely difficult as the GFP marker fluorescence intensity of UAP56 KD neurons is weak - this would likely result in preferential sorting of subsets of neurons with weaker RNAi effects. In addition, FACS-sorting whole neurons would not discriminate between nuclear and cytoplasmic mRNA.

      The established way of measuring protein content in the Drosophila PNS system is immunofluorescence with strong internal controls. In our case, we also measured Mical fluorescence intensity of neighboring c1da neurons that do not express the RNAi and show expression levels as relative intensities compared to these internal controls. This procedure rules out the influence of staining variation between samples and is used by other labs as well.

      1.

      In Figure 5, the authors state that Mical expression could not be detected at 0 h APF. The data presented in Fig. 5C, D suggest the opposite as there clearly is some expression. Moreover, the data shown in Fig. 5D looks significantly brighter than the Orco dsRNA control and appears to localize to some type of cytoplasmic granule. So the expression of Mical does not look normal.

      Our response

      We thank the reviewer for this comment. In the original image in Fig. 5 C, the c4da neuron overlaps with the dendrite from a neighboring PNS neuron (likely c2da or c3da). The latter neuron shows strong Mical staining. We agree that this image is confusing and exchanged this image for another one from the same genotype.

      Changes introduced: Figure 5 L (former Fig. 5 C): Exchanged panel for image without overlap from other neuron.

      1.

      Sufficient data are not presented to conclude any specificity in mRNA export pathways. Data is presented for one export protein (UAP56) and one putative target (Mical). To adequately assess this, the authors would need to do RNA-seq in UAP56 mutants.

      Our response

      We thank the reviewer for this comment. To address this, we tested whether knockdown of three other mRNA export factors (NXF1, THO2, THOC5) causes dendrite pruning defects, which was not the case (new Fig. S1). While these data are consistent with specific mRNA export pathways, we agree that they are not proof. We therefore toned down our interpretation and removed the conclusion about specificity. Instead, we now use the more neutral term "increased sensibility (to loss of UAP56)".

      Changes introduced: Added new Figure S1: RNAi analyses of NXF1, THO2 and THOC5 in dendrite pruning. Introduced concluding sentence at the end of first Results paragraph: We conclude that c4da neuron dendrite pruning is particularly sensitive to loss of UAP56. (p. 6)

      1.

      In summary, better quantitative assays should be used in Figures 4 and 5 in order to conclude the expression levels of either mRNA or protein. In its current form, this study demonstrates the novel finding that UAP56 regulates dendrite and presynaptic pruning, potentially via regulation of the actin cytoskeleton. However, these data do not convincingly demonstrate that UAP56 controls these processes by regulating of Mical expression and defintately not by controlling export from the nucleus.

      Our response

      We hope that the changes we introduced above help clarify this.

      1.

      While there are clearly dendrites shown in Fig. 1C', the cell body is not readily identifiable. This makes it difficult to assess attachment and suggests that the neuron may be dying. This should be replaced with an image that shows the soma.

      Our response

      We thank the reviewer for this comment. Changes introduced: we replaced the picture in the panel with one where the cell body is more clearly visible.

      1.

      The level of knockdown in the UAS56 RNAi and P element insertion lines should be determined. It would be useful to mention the nature of the RNAi lines (long/short hairpin). Some must be long since Dcr has been co-expressed. Another issue raised by this is the potential for off-target effects. shRNAi lines would be preferable because these effects are minimized.

      Our response

      We thank the reviewer for this comment. Assessment of knockdown efficiency is a control to make sure the manipulations work the way they are intended to. As mRNA isolation from Drosophila PNS neurons is extremely difficult, RNAi or mutant phenotypes in this system are controlled by performing several independent manipulations of the same gene. In our case, we used two independent RNAi lines (both long hairpins from VDRC/Bloomington and an additional insertion of the VDRC line, see Table S1) as well as a mutant P element in a MARCM experiment, i. e., a total of three independent manipulations that all cause pruning defects, and the VDRC RNAi lines do not have any predicted OFF targets (not known for the Bloomington line). If any of these manipulations would not have matched, we would have generated sgRNA lines for CRISPR to confirm.

      Minor comments:

      1.

      The authors should explain what EB1:GFP is marking when introduced in the text.


      Our response

      We thank the reviewer for this comment. Changes introduced: we explain the EB1::GFP assay in the panel with one where the cell body is more clearly visible.

      1.

      The da neuron images throughout the figures could be a bit larger.

      Our response

      We thank the reviewer for this comment. Changes introduced: we changed the figure organization to be able to use larger panels:

      • the pruning analysis of the ATPase mutations (formerly Fig. 2) is now its own figure (Figure 3).

      • we increased the panel sizes of the MCP::RFP images (Figure 5 A - I, formerly Fig. 4).

      Reviewer #1 (Significance (Required)):

      Strengths:

      The methodology used to assess dendrite and presynaptic prunings are strong and the phenotypic analysis is conclusive.

      Our response

      We thank the reviewer for this comment.

      Weakness:

      The evidence demonstrating that UAP56 regulates the expression of Mical is unconvincing. Similarly, no data is presented to show that there is any specificity in mRNA export pathways. Thus, these major conclusions are not adequately supported by the data.

      Our response

      We hope the introduced changes address this comment.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

      In this paper, the authors describe dendrite pruning defects in c4da neurons in the DEXD box ATPase UAP56 mutant or in neuronal RNAi knockdown. Overexpression UAP56::GFP or UAP56::GFPE194Q without ATPase activity can rescue dendrite pruning defects in UAP56 mutant. They further characterized the mis-localization of UAP56::GFPE194Q and its binding to nuclear export complexes. Both microtubules and the Ubiquitin-proteasome system are intact in UAP56RNAi neurons. However, they suggest a specific effect on MICAL mRNA nuclear export shown by using the MS2-MCP system., resulting in delay of MICAL protein expression in pruned neurons. Furthermore, the authors show that UAP56 is also involved in presynaptic pruning of c4da neuros in VNC and Mica and actin are also required for actin disassembly in presynapses. They propose that UAP56 is required for dendrite and synapse pruning through actin regulation in Drosophila. Following are my comments.

      Major comments

      1.

      The result that UAP56::GFPE194Q rescues the mutant phenotype while the protein is largely mis-localized suggests a novel mechanism or as the authors suggested rescue from combination of residual activities. The latter possibility requires further support, which is important to support the role mRNA export in dendrite and pre-synapse pruning. One approach would be to examine whether other export components like REF1, and NXF1 show similar mutant phenotypes. Alternatively, depleting residual activity like using null mutant alleles or combining more copies of RNAi transgenes could help.

      Our response

      We thank the reviewer for this comment. We agree that the mislocalization phenotype is interesting and could inform further studies on the mechanism of UAP56. To further investigate this and to exclude that this could represent a gain-of-function due to the introduced mutation, we made and characterized a new additional transgene, UAP56::GFP E194A. This mutant shows largely the same phenotypes as E194Q, with enhanced interactions with Ref1 and partial mislocalization to the cytoplasm. In addition, we tested whether knockdown of THO2, THOC5 or NXF1 causes pruning defects (no).

      Changes introduced:

      • added new Figure S1: RNAi analyses of NXF1, THO2 and THOC5 in dendrite pruning.

      • made and characterized a new transgene UAP56 E194A (new Fig. 2 B, E, E', 3 C, C', E, F).

      1.

      The localization of UAP56::GFP (and E194Q) should be analyzed in more details. It is not clear whether the images in Fig. 2A and 2B are from confocal single sections or merged multiple sections. The localization to the nuclear periphery of UAP56::GFP is not clear, and the existence of the E194Q derivative in both nucleus and cytosol (or whether there is still some peripheral enrichment) is not clear if the images are stacked.

      Our response

      We thank the reviewer for this comment. It is correct that the profiles in the old Figure 2 were from single confocal sections from the displayed images. As it was difficult to create good average profiles with data from multiple neurons, we now introduce an alternative quantification based on categories (nuclear versus dispersed) which includes data from several neurons for each genotype, including the new E194A transgene (new Fig 3 G). Upon further inspection, the increase at the nuclear periphery was not always visible and may have been a misinterpretation. We therefore removed this statement.

      Changes introduced:

      • added new quantitative analysis of UAP56 wt and E/A, E/Q mutant localization (new Fig 3 G).

      1.

      The Ub-VV-GFP is a new reagent, and its use to detect active proteasomal degradation is by the lack of GFP signals, which could be also due to the lack of expression. The use of Ub-QQ-GFP cannot confirm the expression of Ub-VV-GFP. The proteasomal subunit RPN7 has been shown to be a prominent component in the dendrite pruning pathway (Development 149, dev200536). Immunostaining using RPN7 antibodies to measure the RPN expression level could be a direct way to address the issue whether the proteasomal pathway is affected or not.

      Our response

      We thank the reviewer for this comment. We agree that it is wise to not only introduce a positive control for the Ub-VV-GFP sensor (the VCP dominant-negative VCP QQ), but also an independent control. As mutants with defects in proteasomal degradation accumulate ubiquitinated proteins (see, e. g., Rumpf et al., Development 2011), we stained controls and UAP56 KD neurons with antibodies against ubiquitin and found that they had similar levels (new Fig. S3).

      Changes introduced:

      • added new ubiquitin immunofluorescence analysis (new Fig. S3).

      1.

      Using the MS2/MCP system to detect the export of MICAL mRNA is a nice approach to confirm the UAP56 activity; lack of UAP56 by RNAi knockdown delays the nuclear export of MS2-MICAL mRNA. The rescue experiment by UAS transgenes could not be performed due to the UAS gene dosage, as suggested by the authors. However, this MS2-MICAL system is also a good assay for the requirement of UAP56 ATPase activity (absence in the E194Q mutant) in this process. Could authors use the MARCM (thus reduce the use of UAS-RNAi transgene) for the rescue experiment? Also, the c4da neuronal marker UAS-CD8-GFP used in Fig4 could be replaced by marker gene directly fused to ppk promoter, which can save a copy of UAS transgene. The results from the rescue experiment would test the dependence of ATPase activity in nuclear export of MICAL mRNA.

      Our response

      We thank the reviewer for this comment. This is a great idea but unfortunately, this experiment was not feasible due to the (rare) constraints of Drosophila genetics. The MARCM system with rescue already occupies all available chromosomes (X: FLPase, 2nd: FRT, GAL80 + mutant, 3rd: GAL4 + rescue construct), and we would have needed to introduce three additional ones (MCP::RFP and two copies of unmarked genomic MICAL-MS2, all on the third chromosome) that would have needed to be introduced by recombination. Any Drosophilist will see that this is an extreme, likely undoable project :-(

      1.

      The UAP56 is also involved in presynaptic pruning through regulating actin assembly, and the authors suggest that Mical and cofilin are involved in the process. However, direct observation of lifeact::GFP in Mical or cofilin RNAi knockdown is important to support this conclusion.

      Our response

      We thank the reviewer for this comment. In response, we analyzed the lifeact::GFP patterns of control and cofilin knockdown neurons and found that loss of cofilin also leads to actin accumulation (new Fig. 7 I, J).

      Changes introduced:

      • new lifeact analysis (new Fig. 7 I, J).

      Minor comments:

      1.

      RNA localization is important for dendrite development in larval stages (Brechbiel JL, Gavis ER. Curr Biol. 20;18(10):745-750). Yet, the role of UAP56 is relatively specific and shown only in later-stage pruning. It would need thorough discussion.


      Our response

      We thank reviewer 2 for this comment. We added the following paragraph to the discussion: "UAP56 has also been shown to affect cytoplasmic mRNA localization in Drosophila oocytes (Meignin and Davis, 2008), opening up the possibility that nuclear mRNA export and cytoplasmic transport are linked. It remains to be seen whether this also applies to dendritic mRNA transport (Brechbiel and Gavis, 2008)." (p.13)

      1.

      Could authors elaborate on the possible upstream regulators that might be involved, as described in "alternatively, several cofilin upstream regulators have been described (Rust, 2015) which might also be involved in presynapse pruning and subject to UAP56 regulation" in Discussion?

      Our response

      We thank reviewer 2 for this comment. In the corresponding paragraph, we cite as example now that cofilin is regulated by Slingshot phosphatases and LIM kinase (p.14).

      1.

      In Discussion, the role of cofilin in pre- and post-synaptic processes was described. The role of Tsr/Cofilin regulating actin behaviors in dendrite branching has been described in c3da and c4da neurons (Nithianandam and Chien, 2018 and other references) should be included in Discussion.

      Our response

      We thank reviewer 2 for this comment. In response we tested whether cofilin is required for dendrite pruning and found that this, in contrast to Mical, is not the case (new Fig. S6). We cite the above paper in the corresponding results section (p.12).

      Changes introduced:

      • new cofilin dendrite pruning analysis (new Fig. S6).

      • added cofilin reference in Results.

      1.

      The authors speculate distinct actin structures have to be disassembled in dendrite and presynapse pruning in Discussion. What are the possible actin structures in both sites could be elaborated.

      Our response

      We thank reviewer 2 for this comment. In response, we specify in the Discussion: "As Mical is more effective in disassembling bundled F-actin than cofilin (Rajan et al., 2023), it is interesting to speculate that such bundles are more prevalent in dendrites than at presynapses." (p14)

      Reviewer #2 (Significance (Required)):

      The study initiated a genetic screen for factors involved in a dendrite pruning system and reveals the involvement of nuclear mRNA export is an important event in this process. They further identified the mRNA of the actin disassembly factor MICAL is a candidate substrate in the exporting process. This is consistent with previous finding that MICAL has to be transcribed and translated when pruning is initiated. As the presynapses of the model c4da neuron in this study is also pruned, the dependence on nuclear export and local actin remodeling were also shown. Thus, this study has added another layer of regulation (the nuclear mRNA export) in c4da neuronal pruning, which would be important for the audience interested in neuronal pruning. The study is limited for the confusing result whether ATPase activity of the exporting factor is required.

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

      Summary: In the manuscript by Frommeyer, Gigengack et al. entitled "The UAP56 mRNA Export Factor is Required for Dendrite and Synapse Pruning via Actin Regulation in Drosophila" the authors surveyed a number of RNA export/processing factors to identify any required for efficient dendrite and/or synapse pruning. They describe a requirement for a general poly(A) RNA export factor, UAP56, which functions as an RNA helicase. They also study links to aspects of actin regulation.

      Overall, while the results are interesting and the impact of loss of UAP56 on the pruning is intriguing, some of the data are overinterpreted as presented. The argument that UAP56 may be specific for the MICAL RNA is not sufficiently supported by the data presented. The two stories about poly(A) RNA export/processing and the actin regulation seem to not quite be connected by the data presented. The events are rather distal within the cell, making connecting the nuclear events with RNA to events at the dendrites/synapse challenging.

      Our response

      We thank reviewer 3 for this comment. To address this, we tested whether knockdown of three other mRNA export factors (NXF1, THO2, THOC5) causes dendrite pruning defects, which was not the case (new Fig. S1). While these data are consistent with specific mRNA export pathways, we agree that they are not proof. We therefore toned down our interpretation and removed the conclusion about specificity. Instead, we now use the more neutral term "increased sensibility (to loss of UAP56)".

      We agree that it is a little hard to tie cofilin to UAP56, as we currently have no evidence that cofilin levels are affected by loss of UAP56, even though both seem to affect lifeact::GFP in a similar way (new Fig. 7 I, J). However, a dysregulation of cofilin can also occur through dysregulation of upstream cofilin regulators such as Slingshot and LIM kinase, making such a relationship possible.

      Changes introduced:

      • added new Figure S1: RNAi analyses of NXF1, THO2 and THOC5 in dendrite pruning.

      • introduced concluding sentence at the end of first Results paragraph: "We conclude that c4da neuron dendrite pruning is particularly sensitive to loss of UAP56." (p. 6)

      • add new lifeact::GFP analysis of cofilin KD (new Fig. I, J).

      • identify potential other targets from the literature in the Discussion (Slingshot phosphatases and LIM kinase, p.14).

      There are a number of specific statements that are not supported by references. See, for example, these sentences within the Introduction- "Dysregulation of pruning pathways has been linked to various neurological disorders such as autism spectrum disorders and schizophrenia. The cell biological mechanisms underlying pruning can be studied in Drosophila." The Drosophila sentence is followed by some specific examples that do include references. The authors also provide no reference to support the variant that they create in UAP56 (E194Q) and whether this is a previously characterized fly variant or based on an orthologous protein in a different system. If so, has the surprising mis-localization been reported in another system?

      Our response

      We thank reviewer 3 for this comment. We added the following references on pruning and disease:

      1) Howes, O.D., Onwordi, E.C., 2023. The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol. Psychiatry 28, 1843-1856.

      2) Tang, G., et al., 2014. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron 83, 1131-43.

      To better introduce the E194 mutations, we explain the position of the DECD motif in the Walker B domain, give the corresponding residues in the human and yeast homologues and cite papers demonstrating the importance of this residue for ATPase activity:

      3) Saguez, C., et al., 2013. Mutational analysis of the yeast RNA helicase Sub2p reveals conserved domains required for growth, mRNA export, and genomic stability. RNA 19:1363-71.

      4) Shen, J., et al., 2007. Biochemical Characterization of the ATPase and Helicase Activity of UAP56, an Essential Pre-mRNA Splicing and mRNA Export Factor. J. Biol. Chem. 282, P22544-22550.

      We are not aware of other studies looking at the relationship between the UAP56 ATPase and its localization. Thank you for pointing this out!

      Specific Comments:

      Specific Comment 1: Figure 1 shows the impact of loss of UAP56 on neuron dendrite pruning. The experiment employs both two distinct dsRNAs and a MARCM clone, providing confidence that there is a defect in pruning upon loss of UAP56. As the authors mention screening against 92 genes that caused splicing defects in S2 cells, inclusion of some examples of these genes that do not show such a defect would enhance the argument for specificity with regard to the role of UAP56. This control would be in addition to the more technical control that is shown, the mCherry dsRNA.

      Our response

      We thank reviewer 3 for this comment. To address this, we included the full list of screened genes with their phenotypic categorization regarding pruning (103 RNAi lines targeting 64 genes) as Table S1. In addition, we also tested four RNAi lines targeting the nuclear mRNA export factors Nxf1, THO2 and THOC5 which do not cause dendrite pruning defects (Fig. S1).

      Changes introduced:

      • added RNAi screen results as a list in Table S1.

      • added new Figure S1: RNAi analyses of NXF1, THO2 and THOC5 in dendrite pruning.

      Specific Comment 2: Later the authors demonstrate a delay in the accumulation of the Mical protein, so if they assayed these pruning events at later times, would the loss of UAP56 cause a delay in these events as well? Such a correlation would enhance the causality argument the authors make for Mical levels and these pruning events.

      Our response

      We thank reviewer 3 for this comment. Unfortunately, this is somewhat difficult to assess, as shortly after the 18 h APF timepoint, the epidermal cells that form the attachment substrate for c4da neuron dendrites undergo apoptosis. Where assessed (e. g., Wang et al., 2017, Development) 144: 1851–1862), this process, together with the reduced GAL4 activity of our ppk-GAL4 during the pupal stage (our own observations), eventually leads to pruning, but the causality cannot be easily attributed anymore. We therefore use the 18 h APF timepoint essentially as an endpoint assay.

      Specific Comment 3: Figure 2 provides data designed to test the requirement for the ATPase/helicase activity of UAP56 for these trimming events. The first observation, which is surprising, is the mislocalization of the variant (E194Q) that the authors generate. The data shown does not seem to indicate how many cells the results shown represent as a single image and trace is shown the UAP56::GFP wildtype control and the E194Q variant.

      Our response

      We thank reviewer 3 for this comment. It is correct that the traces shown are from single confocal sections. To better display the phenotypic penetrance, we now added a categorical analysis that shows that the UAP56 E194Q mutant is completely mislocalized in the majority of cells assessed (and the newly added E194A mutant in a subset of cells).

      Changes introduced:

      • added categorical quantification of UAP56 variant localization (new Fig. 2 G).

      __Specific Comment 4: __Given the rather surprising finding that the ATPase activity is not required for the function of UAP56 characterized here, the authors do not provide sufficient references or rationale to support the ATPase mutant that they generate. The E194Q likely lies in the Walker B motif and is equivalent to human E218Q, which can prevent proper ATP hydrolysis in the yeast Sub2 protein. There is no reference to support the nature of the variant created here.

      Our response

      We thank reviewer 3 for this comment. To better introduce the E194 mutations, we explain the position of the DECD motif in the Walker B domain, give the corresponding residues in the human and yeast homologues (Sub2) and cite papers demonstrating the importance of this residue for ATPase activity:

      1) Saguez, C., et al., 2013. Mutational analysis of the yeast RNA helicase Sub2p reveals conserved domains required for growth, mRNA export, and genomic stability. RNA 19:1363-71.

      2) Shen, J., et al., 2007. Biochemical Characterization of the ATPase and Helicase Activity of UAP56, an Essential Pre-mRNA Splicing and mRNA Export Factor. J. Biol. Chem. 282, P22544-22550.

      __Specific Comment 5: __Given the surprising results, the authors could have included additional variants to ensure the change has the biochemical effect that the authors claim. Previous studies have defined missense mutations in the ATP-binding site- K129A (Lysine to Alanine): This mutation, in both yeast Sub2 and human UAP56, targets a conserved lysine residue that is critical for ATP binding. This prevents proper ATP binding and consequently impairs helicase function. There are also missense mutations in the DEAD-box motif, (Asp-Glu-Ala-Asp) involved in ATP binding and hydrolysis. Mutations in this motif, such as D287A in yeast Sub2 (corresponding to D290A in human UAP56), can severely disrupt ATP hydrolysis, impairing helicase activity. In addition, mutations in the Walker A (GXXXXGKT) and Walker B motifs are can impair ATP binding and hydrolysis in DEAD-box helicases. Missense mutations in these motifs, like G137A (in the Walker A motif), can block ATP binding, while E218Q (in the Walker B motif)- which seems to be the basis for the variant employed here- can prevent proper ATP hydrolysis.

      Our response

      We thank reviewer 3 for this comment. Our cursory survey of the literature suggested that mutations in the Walker B motif are the most specific as they still preserve ATP binding and their effects have not well been characterized overall. In addition, these mutations can create strong dominant-negatives in related helicases (e. g., Rode et al., 2018 Cell Reports, our lab). To better characterize the role of the Walker B motif in UAP56, we generated and characterized an alternative mutant, UAP56 E194A. While the E194A variant does not show the same penetrance of localization phenotypes as E194Q, it also is partially mislocalized, shows stronger binding to Ref1 and also rescues the uap56 mutant phenotypes without an obvious dominant-negative effect, thus confirming our conclusions regarding E194Q.

      Changes introduced:

      • added biochemical, localization and phenotypic analysis of newly generated UAP56 E194A variant (new Figs. 2 B, 2 E, E', 3 C, C'). categorical quantification of UAP56 variant localization (new Fig. 2 G).

      __Specific Comment 6: __The co-IP results shown in Figure 2C would also seem to have multiple potential interpretations beyond what the authors suggest, an inability to disassemble a complex. The change in protein localization with the E194Q variant could impact the interacting proteins. There is no negative control to show that the UAP56-E194Q variant is not just associated with many, many proteins. Another myc-tagged protein that does not interact would be an ideal control.

      Our response

      We thank reviewer 3 for this comment. To address this comment, we tried to co-IP UAP56 wt or UAP56 E194Q with a THO complex subunit THOC7 (new Fig. S2). The results show that neither UAP56 variant can co-IP THOC7 under our conditions (likely because the UAP56/THO complex intermediate during mRNA export is disassembled in an ATPase-independent manner (Hohmann et al., Nature 2025)).

      Changes introduced:

      • added co-IP experiment between UAP56 variants and THOC7 (new Fig. S2).

      __Specific Comment 7: __With regard to Figure 3, the authors never define EB1::GFP in the text of the Results, so a reader unfamiliar with this system has no idea what they are seeing. Reading the Materials and Methods does not mitigate this concern as there is only a brief reference to a fly line and how the EB1::GFP is visualized by microscopy. This makes interpretation of the data presented in Figure 3A-C very challenging.

      Our response

      We thank reviewer 3 for pointing this out. We added a description of the EB1::GFP analysis in the corresponding Results section (p.8).

      __Specific Comment 8: __The data shown for MICAL MS2 reporter localization in Figure 4 is nice, but is also fully expected on many former studies analyzing loss of UAP56 or UAP56 hypomorphs in different systems. While creating the reporter is admirable, to make the argument that MICAL localization is in some way preferentially impacted by loss of UAP56, the authors would need to examine several other transcripts. As presented, the authors can merely state that UAP56 seems to be required for the efficient export of an mRNA transcript, which is predicted based on dozens of previous studies dating back to the early 2000s.

      Our response

      Firstly, thank you for commenting on the validity of the experimental approach! The primary purpose of this experiment was to test whether the mechanism of UAP56 during dendrite pruning conforms with what is known about UAP56's cellular role - which it apparently does. We also noted that our statements regarding the specificity of UAP56 for Mical over other transcripts are difficult. While our experiments would be consistent with such a model, they do not prove it. We therefore toned down the corresponding statements (e. g., the concluding sentence at the end of first Results paragraphis now: "We conclude that c4da neuron dendrite pruning is particularly sensitive to loss of UAP56." (p. 6)).

      Minor (and really minor) points:

      In the second sentence of the Discussion, the word 'developing' seems to be mis-typed "While a general inhibition of mRNA export might be expected to cause broad defects in cellular processes, our data in develoing c4da neurons indicate that loss of UAP56 mainly affects pruning mechanisms related to actin remodeling."

      Sentence in the Results (lack of page numbers makes indicating where exactly a bit tricky)- "We therefore reasoned that Mical expression could be more challenging to c4da neurons." This is a complete sentence as presented, yet, if something is 'more something'- the thing must be 'more than' something else. Presumably, the authors mean that the length of the MICAL transcript could make the processing and export of this transcript more challenging than typical fly transcripts (raising the question of the average length of a mature transcript in flies?).

      Our response

      Thanks for pointing these out. The typo is fixed, page numbers are added. We changed the sentence to: "Because of the large size of its mRNA, we reasoned that MICAL gene expression could be particularly sensitive to loss of export factors such as UAP56." (p.9) We hope this is more precise language-wise.

      Reviewer #3 (Significance (Required)):

      Understanding how post-transcriptional events are linked to key functions in neurons is important and would be of interest to a broad audience.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In the manuscript by Frommeyer, Gigengack et al. entitled "The UAP56 mRNA Export Factor is Required for Dendrite and Synapse Pruning via Actin Regulation in Drosophila" the authors surveyed a number of RNA export/processing factors to identify any required for efficient dendrite and/or synapse pruning. They describe a requirement for a general poly(A) RNA export factor, UAP56, which functions as an RNA helicase. They also study links to aspects of actin regulation.

      Overall, while the results are interesting and the impact of loss of UAP56 on the pruning is intriguing, some of the data are overinterpreted as presented. The argument that UAP56 may be specific for the MICAL RNA is not sufficiently supported by the data presented. The two stories about poly(A) RNA export/processing and the actin regulation seem to not quite be connected by the data presented. The events are rather distal within the cell, making connecting the nuclear events with RNA to events at the dendrites/synapse challenging.

      There are a number of specific statements that are not supported by references. See, for example, these sentences within the Introduction- "Dysregulation of pruning pathways has been linked to various neurological disorders such as autism spectrum disorders and schizophrenia. The cell biological mechanisms underlying pruning can be studied in Drosophila." The Drosophila sentence is followed by some specific examples that do include references. The authors also provide no reference to support the variant that they create in UAP56 (E194Q) and whether this is a previously characterized fly variant or based on an orthologous protein in a different system. If so, has the surprising mis-localization been reported in another system?

      Specific Comments:

      Figure 1 shows the impact of loss of UAP56 on neuron dendrite pruning. The experiment employs both two distinct dsRNAs and a MARCM clone, providing confidence that there is a defect in pruning upon loss of UAP56. As the authors mention screening against 92 genes that caused splicing defects in S2 cells, inclusion of some examples of these genes that do not show such a defect would enhance the argument for specificity with regard to the role of UAP56. This control would be in addition to the more technical control that is shown, the mCherry dsRNA. Later the authors demonstrate a delay in the accumulation of the Mical protein, so if they assayed these pruning events at later times, would the loss of UAP56 cause a delay in these events as well? Such a correlation would enhance the causality argument the authors make for Mical levels and these pruning events.

      Figure 2 provides data designed to test the requirement for the ATPase/helicase activity of UAP56 for these trimming events. The first observation, which is surprising, is the mislocalization of the variant (E194Q) that the authors generate. The data shown does not seem to indicate how many cells the results shown represent as a single image and trace is shown the UAP56::GFP wildtype control and the E194Q variant.

      Given the rather surprising finding that the ATPase activity is not required for the function of UAP56 characterized here, the authors do not provide sufficient references or rationale to support the ATPase mutant that they generate. The E194Q likely lies in the Walker B motif and is equivalent to human E218Q, which can prevent proper ATP hydrolysis in the yeast Sub2 protein. There is no reference to support the nature of the variant created here.

      Given the surprising results, the authors could have included additional variants to ensure the change has the biochemical effect that the authors claim. Previous studies have defined missense mutations in the ATP-binding site- K129A (Lysine to Alanine): This mutation, in both yeast Sub2 and human UAP56, targets a conserved lysine residue that is critical for ATP binding. This prevents proper ATP binding and consequently impairs helicase function. There are also missense mutations in the DEAD-box motif, (Asp-Glu-Ala-Asp) involved in ATP binding and hydrolysis. Mutations in this motif, such as D287A in yeast Sub2 (corresponding to D290A in human UAP56), can severely disrupt ATP hydrolysis, impairing helicase activity. In addition, mutations in the Walker A (GXXXXGKT) and Walker B motifs are can impair ATP binding and hydrolysis in DEAD-box helicases. Missense mutations in these motifs, like G137A (in the Walker A motif), can block ATP binding, while E218Q (in the Walker B motif)- which seems to be the basis for the variant employed here- can prevent proper ATP hydrolysis.

      The co-IP results shown in Figure 2C would also seem to have multiple potential interpretations beyond what the authors suggest, an inability to disassemble a complex. The change in protein localization with the E194Q variant could impact the interacting proteins. There is no negative control to show that the UAP56-E194Q variant is not just associated with many, many proteins. Another myc-tagged protein that does not interact would be an ideal control.

      With regard to Figure 3, the authors never define EB1::GFP in the text of the Results, so a reader unfamiliar with this system has no idea what they are seeing. Reading the Materials and Methods does not mitigate this concern as there is only a brief reference to a fly line and how the EB1::GFP is visualized by microscopy. This makes interpretation of the data presented in Figure 3A-C very challenging. The data shown for MICAL MS2 reporter localization in Figure 4 is nice, but is also fully expected on many former studies analyzing loss of UAP56 or UAP56 hypomorphs in different systems. While creating the reporter is admirable, to make the argument that MICAL localization is in some way preferentially impacted by loss of UAP56, the authors would need to examine several other transcripts. As presented, the authors can merely state that UAP56 seems to be required for the efficient export of an mRNA transcript, which is predicted based on dozens of previous studies dating back to the early 2000s.

      Minor (and really minor) points:

      In the second sentence of the Discussion, the word 'developing' seems to be mis-typed "While a general inhibition of mRNA export might be expected to cause broad defects in cellular processes, our data in develoing c4da neurons indicate that loss of UAP56 mainly affects pruning mechanisms related to actin remodeling."

      Sentence in the Results (lack of page numbers makes indicating where exactly a bit tricky)- "We therefore reasoned that Mical expression could be more challenging to c4da neurons." This is a complete sentence as presented, yet, if something is 'more something'- the thing must be 'more than' something else. Presumably, the authors mean that the length of the MICAL transcript could make the processing and export of this transcript more challenging than typical fly transcripts (raising the question of the average length of a mature transcript in flies?).

      Significance

      Understanding how post-transcriptional events are linked to key functions in neurons is important and would be of interest to a broad audience.

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      Referee #2

      Evidence, reproducibility and clarity

      In this paper, the authors describe dendrite pruning defects in c4da neurons in the DEXD box ATPase UAP56 mutant or in neuronal RNAi knockdown. Overexpression UAP56::GFP or UAP56::GFPE194Q without ATPase activity can rescue dendrite pruning defects in UAP56 mutant. They further characterized the mis-localization of UAP56::GFPE194Q and its binding to nuclear export complexes. Both microtubules and the Ubiquitin-proteasome system are intact in UAP56RNAi neurons. However, they suggest a specific effect on MICAL mRNA nuclear export shown by using the MS2-MCP system., resulting in delay of MICAL protein expression in pruned neurons. Furthermore, the authors show that UAP56 is also involved in presynaptic pruning of c4da neuros in VNC and Mica and actin are also required for actin disassembly in presynapses. They propose that UAP56 is required for dendrite and synapse pruning through actin regulation in Drosophila. Following are my comments.

      Major comments

      1. The result that UAP56::GFPE194Q rescues the mutant phenotype while the protein is largely mis-localized suggests a novel mechanism or as the authors suggested rescue from combination of residual activities. The latter possibility requires further support, which is important to support the role mRNA export in dendrite and pre-synapse pruning. One approach would be to examine whether other export components like REF1, and NXF1 show similar mutant phenotypes. Alternatively, depleting residual activity like using null mutant alleles or combining more copies of RNAi transgenes could help.

      2. The localization of UAP56::GFP (and E194Q) should be analyzed in more details. It is not clear whether the images in Fig. 2A and 2B are from confocal single sections or merged multiple sections. The localization to the nuclear periphery of UAP56::GFP is not clear, and the existence of the E194Q derivative in both nucleus and cytosol (or whether there is still some peripheral enrichment) is not clear if the images are stacked.

      3. The Ub-VV-GFP is a new reagent, and its use to detect active proteasomal degradation is by the lack of GFP signals, which could be also due to the lack of expression. The use of Ub-QQ-GFP cannot confirm the expression of Ub-VV-GFP. The proteasomal subunit RPN7 has been shown to be a prominent component in the dendrite pruning pathway (Development 149, dev200536). Immunostaining using RPN7 antibodies to measure the RPN expression level could be a direct way to address the issue whether the proteasomal pathway is affected or not.

      4. Using the MS2/MCP system to detect the export of MICAL mRNA is a nice approach to confirm the UAP56 activity; lack of UAP56 by RNAi knockdown delays the nuclear export of MS2-MICAL mRNA. The rescue experiment by UAS transgenes could not be performed due to the UAS gene dosage, as suggested by the authors. However, this MS2-MICAL system is also a good assay for the requirement of UAP56 ATPase activity (absence in the E194Q mutant) in this process. Could authors use the MARCM (thus reduce the use of UAS-RNAi transgene) for the rescue experiment? Also, the c4da neuronal marker UAS-CD8-GFP used in Fig4 could be replaced by marker gene directly fused to ppk promoter, which can save a copy of UAS transgene. The results from the rescue experiment would test the dependence of ATPase activity in nuclear export of MICAL mRNA.

      5. The UAP56 is also involved in presynaptic pruning through regulating actin assembly, and the authors suggest that Mical and cofilin are involved in the process. However, direct observation of lifeact::GFP in Mical or cofilin RNAi knockdown is important to support this conclusion.

      Minor comments

      1. RNA localization is important for dendrite development in larval stages (Brechbiel JL, Gavis ER. Curr Biol. 20;18(10):745-750). Yet, the role of UAP56 is relatively specific and shown only in later-stage pruning. It would need thorough discussion.

      2. Could authors elaborate on the possible upstream regulators that might be involved, as described in "alternatively, several cofilin upstream regulators have been described (Rust, 2015) which might also be involved in presynapse pruning and subject to UAP56 regulation" in Discussion?

      3. In Discussion, the role of cofilin in pre- and post-synaptic processes was described. The role of Tsr/Cofilin regulating actin behaviors in dendrite branching has been described in c3da and c4da neurons (Nithianandam and Chien, 2018 and other references) should be included in Discussion.

      4. The authors speculate distinct actin structures have to be disassembled in dendrite and presynapse pruning in Discussion. What are the possible actin structures in both sites could be elaborated.

      Significance

      The study initiated a genetic screen for factors involved in a dendrite pruning system and reveals the involvement of nuclear mRNA export is an important event in this process. They further identified the mRNA of the actin disassembly factor MICAL is a candidate substrate in the exporting process. This is consistent with previous finding that MICAL has to be transcribed and translated when pruning is initiated. As the presynapses of the model c4da neuron in this study is also pruned, the dependence on nuclear export and local actin remodeling were also shown. Thus, this study has added another layer of regulation (the nuclear mRNA export) in c4da neuronal pruning, which would be important for the audience interested in neuronal pruning. The study is limited for the confusing result whether ATPase activity of the exporting factor is required.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This manuscript by Frommeyer et al. explores the role of the helicase and regulator of nuclear export, UAP56, in the control of dendrite and presynaptic pruning in Drosophila larval da sensory neurons. The authors present evidence showing that UAP56 regulates these processes via the actin cytoskeleton and suggest that this is occurs by controlling the expression of the actin severing enzyme, Mical.

      Major comments:

      The most signficant issue with the manuscript is that some of the major conclusions are not supported by the data. Additional experiment would need to be completed in order support these claims. These (and other) major comments are as follows:

      1. For Figure 4, the ms2/MCP system is not quantitative. Using this technique, it is impossible to determine how many RNAs are located in each "dot". Each of these dots looks quite large and likely corresponds to some phase-separated RNP complex where multiple RNAs are stored and/or transported. Thus, these data do not support the conclusion that Mical mRNA levels are reduced upon UAP56 knockdown. A good quantitative microscopic assay would be something like smFISH. Additinally, the localization of Mical mRNA dots to dendrites is not convincing as it looks like regions where there are dendritic swellings, the background is generally brighter.

      2. Alternatively, levels of Mical mRNA could be verified by qPCR in the laval brain following pan-neuronal UAP56 knockdown or in FACS-sorted fluorescently labeled da sensory neurons. Protein levels could be analyzed using a similar approach.

      3. In Figure 5, the authors state that Mical expression could not be detected at 0 h APF. The data presented in Fig. 5C, D suggest the opposite as there clearly is some expression. Moreover, the data shown in Fig. 5D looks significantly brighter than the Orco dsRNA control and appears to localize to some type of cytoplasmic granule. So the expression of Mical does not look normal.

      4. Sufficient data are not presented to conclude any specificity in mRNA export pathways. Data is presented for one export protein (UAP56) and one putative target (Mical). To adequately assess this, the authors would need to do RNA-seq in UAP56 mutants.

      5. In summary, better quantitative assays should be used in Figures 4 and 5 in order to conclude the expression levels of either mRNA or protein. In its current form, this study demonstrates the novel finding that UAP56 regulates dendrite and presynaptic pruning, potentially via regulation of the actin cytoskeleton. However, these data do not convincingly demonstrate that UAP56 controls these processes by regulating of Mical expression and defintately not by controlling export from the nucleus.

      6. While there are clearly dendrites shown in Fig. 1C', the cell body is not readily identifiable. This makes it difficult to assess attachment and suggests that the neuron may be dying. This should be replaced with an image that shows the soma.

      7. The level of knockdown in the UAS56 RNAi and P element insertion lines should be determined. It would be useful to mention the nature of the RNAi lines (long/short hairpin). Some must be long since Dcr has been co-expressed. Another issue raised by this is the potential for off-target effects. shRNAi lines would be preferable because these effects are minimized.

      Minor comments:

      1. The authors should explain what EB1:GFP is marking when introduced in the text.

      2. The da neuron images througout the figures could be a bit larger.

      Significance

      Strengths:

      The methodology used to assess dendrite and presynaptic prunings are strong and the phenotypic analysis is conclusive.

      Weakness:

      The evidence demonstrating that UAP56 regulates the expression of Mical is unconvincing. Similarly, no data is presented to show that there is any specificity in mRNA export pathways. Thus, these major conclusions are not adequately supported by the data.

      Advance:

      The findings that UAP56 regulate dendrite and synaptic pruning are novel. As is its specific regulation of the actin cytoskeleton. These findings are restricted to a phenotypic analysis and do not show that it is not simply due to the disruption of general mRNA export.

      Audience:

      In its current form the manuscript whould be of interest to an audience who specializes in the study of RNA binding proteins in the control of neurodevelopment. This would include scientists who work in invertebrate and vertebrate model systems.

      My expertise:

      My lab uses Drosophila to study the role of RNA binding proteins in neurodevelopment and neurodegeneration. Currently, we use flies as a model to better understand the molecular pathogenesis of neurodevelopmenal disorders such as FXS and ASD.

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      Reply to the reviewers

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

      *Using genetics and microscopy approaches, Cabral et al. investigate how fission yeast regulates its length and width in response to osmotic, oxidative, or low glucose stress. Miller et al. have recently found that the cell cycle regulators Cdc25, Cdc13 and Cdr2 integrate information about cell volume, time and cell surface area into the cellular decision when to divide. Cabral now build on this work and test how disruption of these regulators affects cell size adaptation. They find that each stress condition shows a distinct dependence on the individual regulators, suggesting that the complex size control network enables optimized size adaptation for each condition. Overall, the manuscript is clear and the detailed methods ensure that the experiments can be replicated.

      Major comments:

      1.) It would be much easier to follow the authors' conclusions, if in addition to surface area to volume ratio, length and width, they would also plot cell volume at division in Figs. 1-4.*

      AUTHOR RESPONSE: Due to space constraints in the main (and supplemental) figures, we focused on SA:Vol ratio together with cell length and width, which directly define cell geometry in rod-shaped fission yeast. Surface area and volume are derived from these measurements and can be misleading when considered alone, as similar surface area or volume values can arise from distinct combinations of length and width. The SA:Vol ratio therefore serves as a robust integrative metric for capturing coordinated changes in length and width that reshape cell geometry. We would be happy to include individual surface area and volume plots if requested.

      2.) To me, it seems that maybe even more than upon osmotic stress, the cdc13-2x strain differs qualitatively from WT in low glucose conditions, where the increased SA-V ratio is almost completely abolished.

      AUTHOR RESPONSE: We agree with the reviewer and have revised the manuscript text to point out this difference. The newly added text states: “Under low glucose, cdc13-2x cells also showed a WT-like response, decreasing length and increasing in SA:Vol ratio (Figures 3B-D). However, this SA:Vol increase was reduced compared to WT (1% vs 8.5%; Figures 1D and 3B), suggesting impaired geometric remodeling under glucose limitation.”

      3.) It is not entirely clear to me why two copies of Cdc13 would qualitatively affect the responses. Shouldn't the extra copy behave similarly to the endogenous one and therefore only lead to quantitative changes? Maybe the authors can discuss this more clearly or even test a strain in which Cdc13 function is qualitatively disrupted.

      AUTHOR RESPONSE: Increased Cdc13 protein concentration in cdc13-2x cells disrupts the typical time-scaling of Cdc13 protein. Consistent with this, cdc13-2x cells enter mitosis at a smaller cell size. We have modified the text to clarify this point. The new text states: “To access the role of the Cdc13 time-sensing pathway, we disrupted Cdc13 protein abundance by creating a cdc13-2x strain carrying an additional copy of cdc13 integrated at an exogenous locus. cdc13-2x cells divided at a smaller size than WT, reflecting accelerated mitotic entry upon disruption of typical time-scaling of Cdc13 protein (Figure S1A).”

      4.) I don't see why the authors come to the conclusion that under osmotic stress cells would maximize cell volume. It leads to a decreased cell length, doesn't it?

      AUTHOR RESPONSE: WT cells under osmotic stress do decrease in length, but this is accompanied by an increase in cell width. Because width contributes disproportionately to cell volume in rod-shaped cells, this change results in a modest but reproducible reduction in the SA:Vol ratio relative to WT cells in control medium (Figure 1D). We note that the degree of this change under osmotic stress is small (-0.4%), although statistically significant (p * Likewise, in Figure 2B, they interpret tiny changes in the SA/V. By my estimation, the difference between control and osmotic stress is only 2% (1.195/1.17), less that the wild-type case, which appears to be twice that (which is still pretty modest). The small amplitude of these changes is obscured by the fact that the graphs do not have a baseline at zero, which, as a matter of good data-presentation practice, they should.

      *

      AUTHOR RESPONSE: We appreciate the reviewer’s distinction between statistical and biological significance and agree that this is an important point to clarify. We now note in the revised text that changes in SA:Vol ratio under osmotic stress are numerically small and should not be overinterpreted. Our revised text now states: “Under oxidative and osmotic stress, the SA:Vol ratio decreased, indicating greater cell volume expansion relative to surface area (Figure 1D). However, we note that the reduction in SA:Vol under osmotic stress, while statistically significant, was modest in magnitude (−0.4%).”

      Although small in absolute terms, even subtle geometric changes can be biologically meaningful in fission yeast due to the small size of these cells, where minor shifts in length or width translate into measurable differences in membrane area relative to cytoplasmic volume. Importantly, in Figure 2B, the key observation is not the magnitude of the change but its direction: cdc25-degron-DaMP cells exhibit a ~2% increase in SA:Vol ratio under osmotic stress, in contrast to the decrease observed in WT cells under the same condition. This opposite response reflects altered cell geometry and is supported by corresponding changes in cell length and width. We have revised the Results text to emphasize both the modest magnitude and the directional nature of these effects: “Under osmotic stress, cdc25-degron-DaMP cells exhibited a ~2% increase in SA:Vol ratio, opposite to the modest decrease observed in WT cells. This increase arose from increased cell length and reduced width (Figures 2B-D).”

      Regarding data presentation, because SA:Vol ratios vary over a narrow numerical range, setting the y-axis minimum to zero would compress the data and obscure all detectable differences. Instead, we have modifed our SA:Vol ratio graphs in Fig. 1-4 to have consistent axis scaling across panels to accurately convey relative changes while maintaining visual clarity. We are happy to provide full data tables and statistical outputs upon request.

      * I am also concerned about the use of manual measurement of width at a single point along the cell. This approach is very sensitive to the choice of width point and to non-cylindrical geometries, several of which are evident in the images presented. MATLAB will return the ??? as well as the length from a mask, but even better, one can more accurately calculate the surface area and volume by assuming rotational symmetry of the mask. Given that surface area and volume calculation need to be redone anyway, as discussed below, I encourage the authors to calculate them directly from the mask, instead of using the cylindrical assumption.*

      AUTHOR RESPONSE: In initial experiments to calculate surface area and volume of fission yeast cells for prior work (Miller et al., 2023, Current Biology) we found that automated width measurements by MATLAB or ImageJ were inaccurate for a subset of cells leading to noisy cell surface area and volume values. Measuring cell width by hand and assuming that each cell in a given strain had the same cell radius (average of population) for calculation of cell surface area and volume gave more consistent results and recapitulated established conclusions regarding size control mechanisms.

      In this previous work and the current study, abnormally skinny or wide regions of a cell were avoided when drawing a line to measure the cell width by hand. For each strain and condition, an average cell width was determined per independent experiment and used for surface area and volume calculations. Additionally, previous analysis demonstrated that this approach yields results consistent with a rotation method derived directly from cell masks, which does not assume a cylindrical cell shape (Facchetti et al., 2019, Current Biology; Miller et al., 2023, Current Biology).

      To test the validity of our size measurements and confirm the robustness of our results in this study we compared the surface area and volume of cells by this rotation method. We have added this additional information to our revised methods section and also added SA:Vol ratio graphs generated from the rotation size measurement to our revised Figure S1 E-J. Importantly, both approaches used to measure cell size gave consistent results and supported the same conclusions.*

      The authors also need to be more careful about their claims about size-dependent scaling. The concentration of both Cdc13 and Cdc25 scale with size (perhaps indirectly, in the case of Cdc13), but Cdr2 does not. Cdr2 activity has been proposed to scale with size, and its density at cortical nodes has been reported to scale with size, although that claim has been challenged .*

      AUTHOR RESPONSE: We have modified text in the Introduction and Results to address this point. Our revised text in the introduction states: “Recent work has shown that Cdk1 activation integrates size- and time-dependent inputs: the Wee1-inhibitory kinase Cdr2 cortical node density scales with cell surface area (Pan et al., 2014; Facchetti et al., 2019); Cdc25 nuclear accumulation scales with cell volume; and cyclin Cdc13 accumulates over time in the nucleus (Miller et al., 2023) (Figure 1B).” Our revised text in the results section states: “Cdr2 functions as a cortical scaffold that regulates Wee1 activity in relation to cell size, with Cdr2 nodal density reported to scale with cell surface area, enforcing a surface area threshold for mitotic entry (Pan et al., 2014; Allard et al., 2018; Facchetti et al., 2019; Sayyad and Pollard, 2022).”*

      Even taking the authors approach at face value, there are observations that do not seem to make sense, which led me to realize that the wrong formulae were used to calculate surface area and volume.

      In Figure 1E,F, the KCl-treated cells get shorter and wider; surely, that should result in a lower SA/V ratio. However, as noted above, in Figure 1D, they are shown to have a similar ratio. As a sanity check, I eye-balled the numbers off of the figure (control: 14 µm x 3.6 µm and KCl: 11 µm x 3.8 µm) and calculated their surface area and volume using the formula for a capsule (i.e., a cylinder with hemispheric ends).

      SA = the surface area of the two hemispheres + the surface are of the cylinder in between = 4*pi*(width/2)^2 + pi*width*(length-width), the length-width term calculates the side length of the capsule (length without the hemispheres) from the full length of the capsule (length including the hemispheres)

      V = the volume of the two hemispheres + the volume of the cylinder in between = 4/3*pi*(width/2)^3 + pi*(width/2)^2*(length-width).

      I got SA/V ratios of around 2, which are way off from what is presented in Figure 1D, but my calculated ratio goes down in KCl, as expected, but not as reported.

      To make sure I was not doing something wrong, I was going to repeat my calculations with the formulae in Table 1, which made me realize both are incorrect. The stated formula for the cell surface area-2*pi*RL-only represents to surface area of the cylindrical side of the cells, not its hemispherical ends. And it is not even the correct formula for the surface area of the side, because that calls for L to be the length of the side (without the hemispherical ends) not the length of the cell (which includes the hemispherical ends). L here is stated to be cell length (which is what is normally measured in the field, and which is consistent with the reported length of control cells in Figure 1E being 14 µm). The formula for the volume of a capsule in the form use in Table 1 (volume of a cylinder of length L - the volume excluded from the hemispherical ends) is pi*R^2*L - (8-(4/3*pi))*R^3.

      Given these problems, I think I spent too much time thinking about the rest of the paper, because all of the calculations, and perhaps their interpretations, need to be redone.*

      AUTHOR RESPONSE: The surface area and volume equations for a cylinder with hemispherical ends used in our study and listed in our table are correct and widely used in other work with fission yeast cells (Navarro and Nurse, 2012; Pan et al., 2014; Facchetti et al., 2019; BayBay et al., 2020; and Miller et al., 2023). We write our equations with variables for cell length and radius because these are biologically relevant and measured parameters for fission yeast cells. Cell length (L) refers to the total tip-to-tip length of the cell, including the hemispherical ends, and radius (R) refers to half the measured cell width. We have revised the Methods section to clarify this definition and avoid ambiguity (Please see methods section “Cell geometry measurements”)

      Additionally, SA or Vol calculations were performed using the length of each individual cell and the average cell radius of the population. We did not use mean cell length of the population for our calculations like the reviewer assumed in their “sanity check” above. Please see methods section “Cell geometry measurements”. We hope that these clarifications and text revisions improve transparency and reproducibility.

      * Minor Points:

      Strains should be identified by strain number is the text and figure legends.*

      AUTHOR RESPONSE: For clarity and readability, we refer to strains by genotype in the main text and figure legends, which we believe is more informative for readers than strain numbers. All strain numbers corresponding to each genotype are provided in Table S1, ensuring traceability and reproducibility without compromising clarity in data presentation.*

      In the Introduction, "Most cell control their size" should be "Most eukaryotic cell control their size".*

      • *

      AUTHOR RESPONSE: The text has been corrected as suggested.*

      Reviewer #2 (Significance (Required)):

      Nothing to add.*

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

      Summary This manuscript reports that fission yeast cells exhibit distinct cell size and geometry when exposed to osmotic, oxidative, or low-glucose stress. Based on quantitative measurements of cell length and width, the authors propose that different stress conditions trigger specific 'geometric adaptation' patterns, suggesting that cell size homeostasis is flexibly modulated depending on environmental cues. The study provides phenotypic evidence that multiple environmental stresses lead to distinct outcomes in the balance between cell surface area and volume, which the authors interpret as stress-specific modes of size control.

      Major comments 1) The authors define the 48-hour time point as the 'long-term response', but no justification is provided for why 48 hours represents a physiologically relevant adaptation phase. It is unclear whether the size-control mode has stabilized by that time, or whether it may continue to change afterward. At minimum, the authors should provide a rationale (e.g., growth recovery dynamics, transcriptional adaptation plateau, or pilot time-course observations) to demonstrate that 48 hours corresponds to the steady-state adaptive phase rather than an arbitrarily selected time point.*

      AUTHOR RESPONSE: We thank the reviewer for this important point and agree that the definition of the long-term response should be clarified. We have addressed this with new experiments and revised text. We now incorporate growth curve data and doubling time analyses for all yeast strains grown under control and stress conditions (See new Figure S3). These analyses show that following an initial transient stress-induced cell cycle delay, growth rates stabilize well before 48 hours. Notably, the slowest growth rate observed was in 1M KCl, with a doubling time of ~4 hours across all yeast strains tested. Thus, by 48 hours, cells in this condition have undergone more than 12 generations of growth, while cells in all other conditions with shorter doubling times have undergone even more divisions. So by allowing cells to grow for 48 hours prior to imaging, we are capturing cells that have resumed sustained cell cycle progression following transient stress-induced cell cycle delays. Because cell size control is tightly linked to the cell cycle, we define 48 hours as a physiologically relevant time point where cells have adapted to stress conditions.

      Our revised methods now states: “Cultures were incubated at 25°C while shaking at 180 rpm for 48 h prior to imaging. This time point was chosen to ensure that cells had progressed beyond the initial transient stress response and reached a stable, condition-specific growth state, as confirmed by growth curve and doubling time analyses showing stabilization well before 48 h (Figure S3), including in the slowest growing condition (1 M KCl; doubling time ~4 h).”

      * 2*)Related to the above comment, the authors propose that different stresses lead to distinct cell size adaptations, yet the rationale for the chosen stress intensities and exposure times is insufficiently described. It remains unclear whether the osmotic, oxidative, and low-glucose conditions used here induce comparable levels of cellular stress. Dose-response and time-course analyses would greatly strengthen the conclusions. Without such analyses, it is difficult to support the interpretation that geometry modulation represents a direct adaptive response.

      AUTHOR RESPONSE: * *We selected the specific stress conditions based on previously published work showing that these doses elicit robust responses while preserving overall cell viability and the capacity for recovery. We note that osmotic, oxidative, and low glucose conditions perturb fundamentally different cellular systems (turgor pressure and cell wall mechanics, redox balance, and metabolism etc.) and therefore do not generate directly comparable levels of cellular stress in a quantitative sense. Our goal was not to equalize stress intensity across conditions, but to examine how cells change their geometry in response to distinct classes of stressors.

      We have clarified the rationale for specific stress conditions in the revised methods: “These stress intensities were selected based on prior studies demonstrating robust cellular responses while preserving cell viability and the capacity for recovery (Fantes and Nurse, 1977, Shiozaki and Russell, 1995, Degols, et al., 1996; López-Avilés et al., 2008; Sansó et al., 2008; Satioh et al., 2015, Salat-Canela et al., 2021, Bertaux et al., 2023).”

      * 3) The authors describe stress-induced size changes as an 'adaptive' response. While this is an appealing hypothesis, the presented data do not demonstrate that the change in cell size itself confers a fitness advantage. Evidence showing that blocking the size change reduces stress survival-or that the altered size improves growth recovery- would be required to support this claim. Without such data, the use of the term 'geometric adaptation' seems overstated.*

      AUTHOR RESPONSE: We have revised the text to remove the term “adaptive” and now describe stress-induced size changes in descriptive terms. As discussed further in response to Comment 4, new growth curve and doubling time analyses show that defects in surface area or volume expansion do not uniformly impair growth or survival over the stress exposure examined here, reinforcing the decision to avoid fitness-based language.*

      4) The authors conclude that mutants exhibit no major defects in growth or viability during 48-hour stress exposure based on comparable septation index values (Fig. S2). However, septation index alone does not fully capture growth performance or cell-cycle progression and is not sufficient to support claims regarding fitness or robustness of proliferation. If the authors intend to make statements about 'growth', 'viability', or 'cell-cycle progression', additional quantitative measures (e.g., growth curves, doubling time, colony-forming units, or microcolony growth measurements) would be necessary. Alternatively, the claims should be toned down to align with the measurements currently provided.*

      AUTHOR RESPONSE: We have addressed this concern with new experiments and revised text. In addition to septation index measurements (now analyzed using chi-square tests of proportions; Figure S2), we performed growth curve experiments and doubling time analyses for all genotypes under control and stress conditions (new Figure S3). These additional data show that growth rates are largely comparable across genotypes in control, oxidative, and low-glucose conditions, with more pronounced genotype-dependent differences emerging under osmotic stress. Defects in surface area or volume expansion did not uniformly correspond to impaired population growth, indicating that geometric remodeling is not strictly required for proliferation over the 48-hour stress exposure examined here. We have refined our conclusion to emphasize that defects in surface area or volume expansion do not uniformly impair growth or survival. See revised Results text under the heading “Defects in surface area or volume expansion do not uniformly compromise growth or survival”.*

      5) Related to the above comment, the manuscript does not adequately rule out the possibility that the decreased division size simply results from slower growth or delayed cell-cycle progression rather than a shift in the size-control mechanism. Measurements and normalizations of growth rate are required; without them, the interpretation remains speculative.*

      AUTHOR RESPONSE: We agree that changes in growth rate or altered cell cycle timing are important to consider. We have revised our text: “Changes in growth rate or cell cycle progression under stress may influence division size by altering mitotic regulator accumulation. Future studies measuring mitotic regulator dynamics alongside growth rates will be needed to distinguish direct changes in size control mechanisms from growth- or timing-dependent effects.”

      * 6) Regarding the phenotypes of wee1-2x cells, it is interesting that they increase the SA:Vol ratio under all stress conditions and show phenotypes distinct from cdr2Δ cells. From these observations, the authors claims that Cdr2 and Wee1 function as a surface-area-sensing module that complements the volume-sensing and time-sensing pathways to maintain geometric homeostasis. To support this interpretation, the authors could consider additional experiments, such as analyzing cdr2Δ + wee1-2x cells under the same stress conditions. Such data would test whether increased Wee1 can rescue or modify the cdr2Δ phenotype, providing functional evidence for the proposed Cdr2-Wee1-Cdk1 regulatory relationship. Measurements of cell length, width, SA:Vol ratio, and, if feasible, Cdk1 activity markers in the strain would greatly strengthen the mechanistic claims.*

      AUTHOR RESPONSE: We thank the reviewer for this insightful suggestion. While analysis of a cdr2Δ wee1-2x strain could provide additional mechanistic detail, such experiments address a distinct question beyond the scope of our current study, which focuses on how cell geometry changes under different stress conditions in cells with perturbed surface area-, volume-, or time-sensing pathways. Our conclusions regarding a surface area-sensing role for Cdr2-Wee1 signaling are based on previous studies (Pan et al., 2014; Facchetti et al., 2019; Miller et al., 2023) and the cell geometry phenotypes we observe of cdr2Δ and wee1-2x cells under stress conditions. *

      Minor comments 1) The manuscript focuses on adaptation through changes in the surface-to-volume ratio; however, only the ratio is shown. Presenting the underlying values of surface area and volume would clarify which geometric parameter primary contributes to the observed changes.*

      AUTHOR RESPONSE: Please see our response to Reviewer 1 major comment 1.*

      *2) Statistical analysis for Fig.S2 should be provided.

      AUTHOR RESPONSE: We have completed this. See revised Figure S2 and methods.*

      3) The paper by Kellog and Levin 2022 is missing from the reference list.*

      AUTHOR RESPONSE: Thank you for catching this. This reference has now been added. *

      **Referees cross-commenting**

      After reading the other reviewer's reports, I recognize that focal points differ, but they appear sequential rather than contradictory.

      Reviewer 2 raises concerns regarding the surface area/volume calculations, which-if incorrect-would influence many of the quantitative conclusions. I agree that confirming the validity of these calculations (and recalculating if necessary) should be the top priority before evaluating the biological interpretations.

      Reviewer 1 raises more mechanistic biological questions. These are certainly important, but in my view they depend on the robustness of the quantitative analysis highlighted by Reviewer 2.

      Therefore, I regard the reports as complementary rather than conflicting. Once the analytical issue pointed out by Reviewer 2 is resolved, the field will be in a better position to assess the significance of the mechanistic points raised by Reviewer 1 (as well as those in my own report).

      Reviewer #3 (Significance (Required)):

      General assessment One of the major strengths of this manuscript is its quantitative, side-by-side comparison of multiple environmental stresses under a unified experimental and analytical framework. The authors provide well-controlled morphometric measurements, allowing direct comparison of geometry changes that would otherwise be difficult to evaluate across studies. The observation that different stress types generate distinct geometric outcomes is particularly intriguing and has the potential to stimulate new conceptual thinking in the field of size control. However, the strength of the conceptual conclusion is currently limited by several aspects of the experimental design and interpretation. In particular, it remains unclear whether the observed geometry changes represent active adaptive responses rather than non-specific consequences of prolonged or string stress exposure. Demonstrating whether geometry remodeling provides a fitness advantage, clarifying whether the changes reach a steady-state rather than reflecting slow drift over time, or identifying upstream stress pathways that govern the response would substantially strengthen the conceptual advance. Even if additional mechanistic or fitness-related data cannot be added, refining the interpretation so that it remains aligned with the present evidence will enhance the clarity, and impact of the study.

      Advance Previous study - including the 2023 publication by the James B. Moseley group - established that fission yeast integrates distinct size-control pathways related to surface area, volume, and time under normal growth conditions. The present manuscript extends this line of work to stressed environments and argues that each stress condition elicits a distinct size-control pattern. To our knowledge, a systematic comparison of cell geometry across multiple stress types in the context of size-control pathways has not been reported, and this represents a potentially valuable conceptual advance. The advance is primarily phenomenological and conceptual rather than mechanistic: the work presents new correlation between stress types and geometry but does not yet elucidate the pathways governing these responses or demonstrate a functional advantage. With additional evidence - or with qualifiers ensuring that claims match the current data - the study could make an important contribution to understanding how cells integrate environmental cues into size-control strategies.

      Audience Although the primary audience consists of researchers in the fields of cell growth, cell-cycle control, and stress responses in yeast, the conceptual contribution may interest broader fields such as growth homeostasis, metabolic adaptation, and pathological cell size changes in higher eukaryotes. Beyond yeast biology, the modular view of size regulation proposed here may inspire new investigations in stem cell biology, cancer research, and biotechnology where environmental adaptation and cell size are closely linked.

      Expertise: nuclear morphology; cell morphology; cell growth; cell cycle; cytoskeleton*

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript reports that fission yeast cells exhibit distinct cell size and geometry when exposed to osmotic, oxidative, or low-glucose stress. Based on quantitative measurements of cell length and width, the authors propose that different stress conditions trigger specific 'geometric adaptation' patterns, suggesting that cell size homeostasis is flexibly modulated depending on environmental cues. The study provides phenotypic evidence that multiple environmental stresses lead to distinct outcomes in the balance between cell surface area and volume, which the authors interpret as stress-specific modes of size control.

      Major comments

      1) The authors define the 48-hour time point as the 'long-term response', but no justification is provided for why 48 hours represents a physiologically relevant adaptation phase. It is unclear whether the size-control mode has stabilized by that time, or whether it may continue to change afterward. At minimum, the authors should provide a rationale (e.g., growth recovery dynamics, transcriptional adaptation plateau, or pilot time-course observations) to demonstrate that 48 hours corresponds to the steady-state adaptive phase rather than an arbitrarily selected time point.

      2)Related to the above comment, the authors propose that different stresses lead to distinct cell size adaptations, yet the rationale for the chosen stress intensities and exposure times is insufficiently described. It remains unclear whether the osmotic, oxidative, and low-glucose conditions used here induce comparable levels of cellular stress. Dose-response and time-course analyses would greatly strengthen the conclusions. Without such analyses, it is difficult to support the interpretation that geometry modulation represents a direct adaptive response.

      3) The authors describe stress-induced size changes as an 'adaptive' response. While this is an appealing hypothesis, the presented data do not demonstrate that the change in cell size itself confers a fitness advantage. Evidence showing that blocking the size change reduces stress survival-or that the altered size improves growth recovery- would be required to support this claim. Without such data, the use of the term 'geometric adaptation' seems overstated.

      4) The authors conclude that mutants exhibit no major defects in growth or viability during 48-hour stress exposure based on comparable septation index values (Fig. S2). However, septation index alone does not fully capture growth performance or cell-cycle progression and is not sufficient to support claims regarding fitness or robustness of proliferation. If the authors intend to make statements about 'growth', 'viability', or 'cell-cycle progression', additional quantitative measures (e.g., growth curves, doubling time, colony-forming units, or microcolony growth measurements) would be necessary. Alternatively, the claims should be toned down to align with the measurements currently provided.

      5) Related to the above comment, the manuscript does not adequately rule out the possibility that the decreased division size simply results from slower growth or delayed cell-cycle progression rather than a shift in the size-control mechanism. Measurements and normalizations of growth rate are required; without them, the interpretation remains speculative.

      6) Regarding the phenotypes of wee1-2x cells, it is interesting that they increase the SA:Vol ratio under all stress conditions and show phenotypes distinct from cdr2Δ cells. From these observations, the authors claims that Cdr2 and Wee1 function as a surface-area-sensing module that complements the volume-sensing and time-sensing pathways to maintain geometric homeostasis. To support this interpretation, the authors could consider additional experiments, such as analyzing cdr2Δ + wee1-2x cells under the same stress conditions. Such data would test whether increased Wee1 can rescue or modify the cdr2Δ phenotype, providing functional evidence for the proposed Cdr2-Wee1-Cdk1 regulatory relationship. Measurements of cell length, width, SA:Vol ratio, and, if feasible, Cdk1 activity markers in the strain would greatly strengthen the mechanistic claims.

      Minor comments

      1) The manuscript focuses on adaptation through changes in the surface-to-volume ratio; however, only the ratio is shown. Presenting the underlying values of surface area and volume would clarify which geometric parameter primary contributes to the observed changes.

      2) Statistical analysis for Fig.S2 should be provided.

      3) The paper by Kellog and Levin 2022 is missing from the reference list.

      Referees cross-commenting

      After reading the other reviewer's reports, I recognize that focal points differ, but they appear sequential rather than contradictory.

      Reviewer 2 raises concerns regarding the surface area/volume calculations, which-if incorrect-would influence many of the quantitative conclusions. I agree that confirming the validity of these calculations (and recalculating if necessary) should be the top priority before evaluating the biological interpretations.

      Reviewer 1 raises more mechanistic biological questions. These are certainly important, but in my view they depend on the robustness of the quantitative analysis highlighted by Reviewer 2.

      Therefore, I regard the reports as complementary rather than conflicting. Once the analytical issue pointed out by Reviewer 2 is resolved, the field will be in a better position to assess the significance of the mechanistic points raised by Reviewer 1 (as well as those in my own report).

      Significance

      General assessment

      One of the major strengths of this manuscript is its quantitative, side-by-side comparison of multiple environmental stresses under a unified experimental and analytical framework. The authors provide well-controlled morphometric measurements, allowing direct comparison of geometry changes that would otherwise be difficult to evaluate across studies. The observation that different stress types generate distinct geometric outcomes is particularly intriguing and has the potential to stimulate new conceptual thinking in the field of size control. However, the strength of the conceptual conclusion is currently limited by several aspects of the experimental design and interpretation. In particular, it remains unclear whether the observed geometry changes represent active adaptive responses rather than non-specific consequences of prolonged or string stress exposure. Demonstrating whether geometry remodeling provides a fitness advantage, clarifying whether the changes reach a steady-state rather than reflecting slow drift over time, or identifying upstream stress pathways that govern the response would substantially strengthen the conceptual advance. Even if additional mechanistic or fitness-related data cannot be added, refining the interpretation so that it remains aligned with the present evidence will enhance the clarity, and impact of the study.

      Advance

      Previous study - including the 2023 publication by the James B. Moseley group - established that fission yeast integrates distinct size-control pathways related to surface area, volume, and time under normal growth conditions. The present manuscript extends this line of work to stressed environments and argues that each stress condition elicits a distinct size-control pattern. To our knowledge, a systematic comparison of cell geometry across multiple stress types in the context of size-control pathways has not been reported, and this represents a potentially valuable conceptual advance. The advance is primarily phenomenological and conceptual rather than mechanistic: the work presents new correlation between stress types and geometry but does not yet elucidate the pathways governing these responses or demonstrate a functional advantage. With additional evidence - or with qualifiers ensuring that claims match the current data - the study could make an important contribution to understanding how cells integrate environmental cues into size-control strategies.

      Audience

      Although the primary audience consists of researchers in the fields of cell growth, cell-cycle control, and stress responses in yeast, the conceptual contribution may interest broader fields such as growth homeostasis, metabolic adaptation, and pathological cell size changes in higher eukaryotes. Beyond yeast biology, the modular view of size regulation proposed here may inspire new investigations in stem cell biology, cancer research, and biotechnology where environmental adaptation and cell size are closely linked.

      Expertise: nuclear morphology; cell morphology; cell growth; cell cycle; cytoskeleton.

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      Referee #2

      Evidence, reproducibility and clarity

      Cabral et al. present a analysis of the effects of environmental stress of cellular geometry in the fission yeast S. pombe. The stresses they study-oxidative, osmotic and nutritional-have previously been shown to affect cell size in fission yeast. Here, the authors do a more sophisticated analysis, measuring surface area as well as volume (for which length had previously been used as a proxy, assuming fission yeast cells are cylinders of constant width). In addition, they investigate the effect of mutations in three cell-cycle control proteins that have been proposed to regulate cell geometry: Cdc13, Cdc25 and Cdr2. It is an interesting study that could provide insight into cell-size control and environmental-stress response in fission yeast. However, I have serious concerns about the analysis of the data. In fact, as I was writing up my concerns, I noticed that the formulae in Table 1 for surface area and volume are incorrect, so the whole paper appears to require reanalysis.

      One general problem is that the authors seem to confuse statistical significance with biological significance. They claim that both oxidative and osmotic stress cause a reduction in SA/V ratio. For oxidative stress, the difference is evident, but the control and KCl-treated cells look to have indistinguishable distributions. Perhaps there is a significant statistical difference between the, but I am skeptical. (I would ask for the data table to try out the stats myself, but given the revelation below that the number will all need to be recalculated, that point is moot). In any case, the difference is certainly not biologically significant.

      Likewise, in Figure 2B, they interpret tiny changes in the SA/V. By my estimation, the difference between control and osmotic stress is only 2% (1.195/1.17), less that the wild-type case, which appears to be twice that (which is still pretty modest). The small amplitude of these changes is obscured by the fact that the graphs do not have a baseline at zero, which, as a matter of good data-presentation practice, they should.

      I am also concerned about the use of manual measurement of width at a single point along the cell. This approach is very sensitive to the choice of width point and to non-cylindrical geometries, several of which are evident in the images presented. MATLAB will return the ??? as well as the length from a mask, but even better, one can more accurately calculate the surface area and volume by assuming rotational symmetry of the mask. Given that surface area and volume calculation need to be redone anyway, as discussed below, I encourage the authors to calculate them directly from the mask, instead of using the cylindrical assumption.

      The authors also need to be more careful about their claims about size-dependent scaling. The concentration of both Cdc13 and Cdc25 scale with size (perhaps indirectly, in the case of Cdc13), but Cdr2 does not. Cdr2 activity has been proposed to scale with size, and its density at cortical nodes has been reported to scale with size, although that claim has been challenged <https://pubmed.ncbi.nlm.nih.gov/36093997>.

      Even taking the authors approach at face value, there are observations that do not seem to make sense, which led me to realize that the wrong formulae were used to calculate surface area and volume.

      In Figure 1E,F, the KCl-treated cells get shorter and wider; surely, that should result in a lower SA/V ratio. However, as noted above, in Figure 1D, they are shown to have a similar ratio. As a sanity check, I eye-balled the numbers off of the figure (control: 14 µm x 3.6 µm and KCl: 11 µm x 3.8 µm) and calculated their surface area and volume using the formula for a capsule (i.e., a cylinder with hemispheric ends).

      SA = the surface area of the two hemispheres + the surface are of the cylinder in between = 4pi(width/2)^2 + piwidth(length-width), the length-width term calculates the side length of the capsule (length without the hemispheres) from the full length of the capsule (length including the hemispheres)

      V = the volume of the two hemispheres + the volume of the cylinder in between = 4/3pi(width/2)^3 + pi(width/2)^2(length-width).

      I got SA/V ratios of around 2, which are way off from what is presented in Figure 1D, but my calculated ratio goes down in KCl, as expected, but not as reported.

      To make sure I was not doing something wrong, I was going to repeat my calculations with the formulae in Table 1, which made me realize both are incorrect. The stated formula for the cell surface area-2piRL-only represents to surface area of the cylindrical side of the cells, not its hemispherical ends. And it is not even the correct formula for the surface area of the side, because that calls for L to be the length of the side (without the hemispherical ends) not the length of the cell (which includes the hemispherical ends). L here is stated to be cell length (which is what is normally measured in the field, and which is consistent with the reported length of control cells in Figure 1E being 14 µm). The formula for the volume of a capsule in the form use in Table 1 (volume of a cylinder of length L - the volume excluded from the hemispherical ends) is piR^2L - (8-(4/3pi))R^3.

      Given these problems, I think I spent too much time thinking about the rest of the paper, because all of the calculations, and perhaps their interpretations, need to be redone.

      Minor Points:

      Strains should be identified by strain number is the text and figure legends.

      In the Introduction, "Most cell control their size" should be "Most eukaryotic cell control their size".

      Significance

      Nothing to add.

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      Referee #1

      Evidence, reproducibility and clarity

      Using genetics and microscopy approaches, Cabral et al. investigate how fission yeast regulates its length and width in response to osmotic, oxidative, or low glucose stress. Miller et al. have recently found that the cell cycle regulators Cdc25, Cdc13 and Cdr2 integrate information about cell volume, time and cell surface area into the cellular decision when to divide. Cabral now build on this work and test how disruption of these regulators affects cell size adaptation. They find that each stress condition shows a distinct dependence on the individual regulators, suggesting that the complex size control network enables optimized size adaptation for each condition. Overall, the manuscript is clear and the detailed methods ensure that the experiments can be replicated.

      Major comments:

      1. It would be much easier to follow the authors' conclusions, if in addition to surface area to volume ratio, length and width, they would also plot cell volume at division in Figs. 1-4.
      2. To me, it seems that maybe even more than upon osmotic stress, the cdc13-2x strain differs qualitatively from WT in low glucose conditions, where the increased SA-V ratio is almost completely abolished.
      3. It is not entirely clear to me why two copies of Cdc13 would qualitatively affect the responses. Shouldn't the extra copy behave similarly to the endogenous one and therefore only lead to quantitative changes? Maybe the authors can discuss this more clearly or even test a strain in which Cdc13 function is qualitatively disrupted.
      4. I don't see why the authors come to the conclusion that under osmotic stress cells would maximize cell volume. It leads to a decreased cell length, doesn't it?

      Significance

      Fission yeast has long been used as a model for eukaryotic cell size regulation. So far, this research has been mostly focused on steady state size regulation. While it has long been clear that cells across organisms adapt their size in response to environmental changes, little is known about how these external inputs are processed through the size control network. Dissecting how disruption of the various branches of the size control network affects size adaptation is an important step towards a mechanistic understanding of this process. Future studies will have to build on these observations and investigate how each stress mechanistically affects the respective regulator(s). While the details of the molecular players and their contribution to size adaptation are likely specific to fission yeast, the concept of stress type-specific size adaptation that is mediated through different regulators is likely conserved and thus of broader relevance.

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      Reply to the reviewers

      We sincerely appreciate the feedback, attention to detail and timeliness of the referees for our manuscript. Below, we provide a point-by-point response to all comments from the referees, detailing the changes we have already made, and those that are in progress. Referee's comments will appear in bolded text, while our responses will be unbolded. Any text quoted directly from the manuscript will be italicised and contained within "quotation marks". Additionally, we have grouped all comments into four categories (structural changes, minor text changes, experimental changes, figure changes), comments are numbered 1-n in each of these categories. Please note: this response to reviewer's comments included some images that cannot be embedded in this text-only section.

      1. General Statements

      We appreciate the overall highly positive and enthusiastic comments from all reviewers, who clearly appreciated the technical difficulty of this study, and noted amongst other things that this study represents" a major contribution to the future advancement of oocyst-sporozoite biology" and the development of the segmentation score for oocysts as a "major advance[ment]". We apologise for the omission of line numbers on the document sent to reviewers, we removed these for the bioRxiv submission without considering that this PDF would be transferred across to Review Commons.

      We have responded to all reviewers comments through a variety of text changes, experimental inclusions, or direct query response. Significant changes to the manuscript since initial submission are as follows:

      1. Refinement of rhoptry biogenesis model: Reviewers requested more detail around the content of the AORs, which we had previously suggested were a vehicle for rhoptry biogenesis as we saw they carried the rhoptry neck protein RON4. To address this, we first attempted to address this using antibodies against rhoptry bulb proteins but were unsuccessful. We then developed a * berghei* line where there rhoptry bulb protein RhopH3 was GFP-tagged. Using this parasite line, we observed that the earliest rhoptry-like structure, which we had previously interpreted as an AOR contained RhopH3. By contrast, RhopH3 was absent from AORs. Reflecting these observations we have renamed this initial structure the 'pre-rhoptry' and suggested a model for rhoptry biogenesis where rhoptry neck cargo are trafficked via the AOR but rhoptry bulb cargo are trafficked by small vesicles that move along the rootlet fibre (previously observed by EM).
      2. Measurement of rhoptry neck vs bulb: While not directly suggested by the reviewers, we have also included an analysis that estimates the proportion of the sporozoite rhoptry that represents the rhoptry neck. By contrast to merozoites, which we show are overwhelmingly represented by the rhoptry bulb, the vast majority of the sporozoite rhoptry represents the rhoptry neck.
      3. Measurement of subpellicular microtubules: One reviewer asked if we could measure the length of subpellicular microtubules where we had previously observed that they were longer on one side of the sporozoite than the other. We have now provided absolute and relative (% sporozoite length) length measurements for these subpellicular microtubules and also calculated the proportion of the microtubule that is polyglutamylated.
      4. More detailed analysis of RON11cKD rhoptries: Multiple comments suggested a more detailed analysis of the rhoptries that were formed/not formed in RON11cKD We have included an updated analysis that shows the relative position of these rhoptries in sporozoites.

      2. Point-by-point description of the revisions

      Reviewer #1

      Minor text changes (Reviewer #1)

      1. __Text on page 12 could be condensed to highlight the new data of ron4 staining of the AOR. __

      We agree with the reviewer that it is a reasonable suggestion. After obtaining additional data on the contents of the AOR (as described in General Statements #1), this section has been significantly rewritten to highlight these findings. 2.

      __Add reference on page 3 after 'disrupted parasites' __

      This sentence has been rewritten slightly with some references included and now reads: "Most data on these processes comes from electron microscopy studies 6-8, with relatively few functional reports on gene deleted or disrupted parasites9-11. 3.

      __Change 'the basal complex at the leading edge' - this seems counterintuitive __

      This change has been made. 4.

      __Change 'mechanisms underlying SG are poorly' - what mechanisms? of invasion or infection? __

      This was supposed to read "SG invasion" and has now been fixed. 5.

      __On page 4: 'handful of proteins' __

      This error has been corrected. 6.

      __What are the 'three microtubule spindle structures'? __

      The three microtubule spindle structures: hemispindle, mitotic spindle, and interpolar spindle are now listed explicitly in the text. 7.

      __On page 5: 'little is known' - please describe what is known, also in other stages. At the end of the paper I would like to know what is the key difference to rhoptry function in other stages? __

      The following sentence already detailed that we had recently used U-ExM to visualise rhoptry biogenesis in blood-stage parasites, but the following two sentences have been added to provide extra detail on these findings: "In that study, we defined the timing of rhoptry biogenesis showing that it begun prior to cytokinesis and completed approximate coincident with the final round of mitosis. Additionally, we observed that rhoptry duplication and inheritance was coupled with centriolar plaque duplication and nuclear fission." 8.

      __change 'rhoptries golgi-derived, made de novo' __

      This has been fixed. 9.

      __change 'new understand to' __

      This change has been made 10.

      __'rhoptry malformations' seem to be similar in sporozoites and merozoites. Is that surprising/new? __

      We assume this is in reference to mention of "rhoptry malformations" in the abstract. In the RON11 merozoite study (PMID:39292724) the authors noted no gross rhoptry malformations, only that one was not formed/missing. The abstract sentence has been changed to the following to better reflect this nuance: "*We show that stage-specific disruption of RON11 leads to a formation of sporozoites that only contain half the number of rhoptries of controls like in merozoites, however unlike in merozoites the majority of rhoptries appear grossly malformed."

      * 11.

      __What is known about crossing the basal lamina. Where rhoptries thought to be involved in this process? Or is it proteins on the surface or in other secretory organelles? __

      We are unaware of any studies that specifically look at sporozoites crossing the SG basal lamina. A review, although now ~15 years old stated that "No information is available as to how the sporozoites traverse the basal lamina" (PMID:19608457) and we don't know any more information since then. To try and better define our understanding of rhoptry secretion during SG invasion, we have added the following sentence:

      "It is currently unclear precisely when during these steps of SG invasion rhoptry proteins are required, but rhoptry secretion is thought to begin before in the haemolymph before SG invasion16." 12.

      __On page change/specify: 'wide range of parasite structures' __

      The structures observed have been listed: centriolar plaque, rhoptry, apical polar rings, rootlet fibre, basal complex, apicoplast. 13.

      __On page 7: is Airyscan2 a particular method or a specific microscope? __

      Airyscan2 is a detector setup on Zeiss LSM microscopes, this was already detailed in the materials and methods sections, but figure legends have been clarified to read: "...imaged by an LSM900 microscopy with an Airyscan2 detector". 14.

      __how large is RON11? __

      RON11 is 112 kDa in * berghei*, as noted in the text. 15.

      __There is no causal link between ookinete invasion and oocyst developmental asynchrony __

      We have deleted the sentence that implied that ookinete invasion was responsible for oocyst asynchrony. This section now simply states that "Development of each oocyst within a midgut is asynchronous..." 16.

      __First sentence of page 24 appears to contradict what is written in results____ I don't understand the first two sentences in the paragraph titled Comparison between Plasmodium spp __

      This sentence was worded confusingly, making it appear contradictory when that was not the intention. The sentence has been changed to more clearly support what is written in the discussion and now reads: "Our extensive analysis only found one additional ultrastructural difference between Plasmodium spp."

      __On page 25 or before the vast number of electron microscopy studies should be discussed and compared with the authors new data. __

      It is not entirely clear which new data should be specifically discussed based on this comment. However, we have added a new paragraph that broadly compares MoTissU-ExM and our findings with other imaging methods previously used on mosquito-stage malaria parasites:

      "*Comparison of MoTissU-ExM and other imaging modalities

      Prior to the development of MoTissU-ExM, imaging of mosquito-stage malaria parasites in situ had been performed using electron microscopy7,8,11,28, conventional immunofluorescence assays (IFA)10, and live-cell microscopy25. MoTissU-ExM offers significant advantages over electron microscopy techniques, especially volume electron microscopy, in terms of accessibility, throughput, and detection of multiple targets. While we have benchmarked many of our observations against previous electron microscopy studies, the intracellular detail that can be observed by MoTissU-ExM is not as clear as electron microscopy. For example, previous electron microscopy studies have observed Golgi-derived vesicles trafficking along the rootlet fibre8 and distinguished the apical polar rings44; both of which we could not observe using MoTissU-ExM. Compared to conventional IFA, MoTissU-ExM dramatically improves the number and detail of parasite structures/organelles that can be visualised while maintaining the flexibility of target detection. By contrast, it can be difficult or impossible to reliably quantify fluorescence intensity in samples prepared by expansion microscopy, something that is routine for conventional IFA. For studying temporally complex processes, live-cell microscopy is the 'gold-standard' and there are some processes that fundamentally cannot be studied or observed in fixed cells. We attempt to increase the utility of MoTissU-ExM in discerning temporal relationships through the development of the segmentation score but note that this cannot be applied to the majority of oocyst development. Collectively, MoTissU-ExM offers some benefits over these previously applied techniques but does not replace them and instead serves as a novel and complementary tool in studying the cell biology of mosquito-stage malaria parasites.**"

      *

      __First sentence on page 27: there are many studies on parasite proteins involved in salivary gland invasion that could be mentioned/discussed. __

      The sentence in question is "To the best of our knowledge, the ability of sporozoites to cross the basal lamina and accumulate in the SG intercellular space has never previously been reported."

      This sentence has now been changed to read as follows: "While numerous studies have characterized proteins whose disruption inhibited SG invasion9,10,15,59-63, to the best of our knowledge the ability of sporozoites to cross the basal lamina and accumulate in the SG intercellular space has never previously been reported ."

      __On page 10 I suggest to qualify the statement 'oocyst development has typcially been inferred by'. There seem a few studies that show that size doesn't reflect maturation. __

      In our opinion, this statement is already qualified in the following sentence which reads: "Recent studies have shown that while oocysts increase in size initially, their size eventually plateaus (11 days pot infection (dpi) in P. falciparum4)."

      __On page 16 the authors state that different rhoptries might have different function. This is an interesting hypothesis/result that could be mentioned in the abstract. __

      The abstract already contains the following statement: "...and provide the first evidence that rhoptry pairs are specialised for different invasion events." We see this as an equivalent statement.


      Experimental changes (Reviewer #1)

      1. On page 19: do the parasites with the RON11 knockout only have the cytoplasmic or only the apical rhoptries?

      The answer to this is not completely clear. We have added the following data to Figures 6 and 8 where we quantify the proportion of rhoptries that are either apical or cytoplasmic: In both wildtype parasites and RON11ctrl parasites, oocyst spz rhoptries are roughly 50:50 apical:cytoplasmic (with a small but consistent majority apical), while almost all rhoptries are found at the apical end (>90%) in SG spz. Presumably, after the initial apical rhoptries are 'used up' during SG invasion, the rhoptries that were previously cytoplasmic take their place. In RON11cKD the ratio of apical:cytoplasmic rhoptries is fairly similar to control oocyst spz. In RON11cKD SG spz, the proportion of cytoplasmic rhoptries decreases but not to the same extent as in wildtype or RON11Ctrl. From this, we infer that the two rhoptries that are lost/not made in RON11cKD sporozoites are likely a combination of both the apical and cytoplasmic rhoptries we find in control sporozoites.

      __in panel G: Are the dense granules not micronemes? What are the dark lines? Rhoptries?? __

      We have labelled all of Figure 1 more clearly to point out that the 'dark lines' are indeed rhoptries. Additionally, we have renamed the 'protein-dense granules' to 'protein-rich granules', as it seems we are suggesting that these structures are dense granules the secretory organelle. At this stage we simply do not know what all of these granules are. The observation that some but not all of these granules contain CSP (Supplementary Figure 2) suggests that they may represent heterogenous structures. It is indeed possible that some are micronemes, however, we think it is unlikely that they are all micronemes for a number of reasons: (1) micronemes are not nearly this protein dense in other Plasmodium lifecycle stages, (2) some of them carry CSP which has not been demonstrated to be micronemal, (3) very few of these granules are present in SG sporozoites, which would be unexpected because microneme secretion is required for hepatocyte invasion.

      __Figure 2 seems to add little extra compared to the following figures and could in my view go to the supplement. __

      We agree that Figure 2b adds little and so have moved that to Supplementary Figure 2, but think that the relative ease at which it can be distinguished if sporozoites are in the secretory cavity or SG epithelial cell is a key observation because of the difficulty in doing this by conventional IFA.

      __On page 8 the authors mention a second layer of CSP but do not further investigate it. It is likely hard to investigate this further but to just let it stand as it is seems unsatisfactory, considering that CSP is the malaria vaccine. What happens if you add anti-CSP antibodies? I would suggest to shorten the opening paragraphs of this paper and to focus on the rhoptries. This could be done be toning down the text on all aspects that are not rhoptries and point to the open question some of the observations such as the CSP layers raise for future studies. __

      When writing the manuscript, we were unsure whether to include this data at all as it is a purely incidental finding. We had no intention of investigating CSP specifically, but anti-CSP antibodies were included in most of the salivary gland imaging experiments so we could more easily find sporozoites. Given the tremendous importance of CSP to the field, we figured that these observations were potentially important enough that they should be reported in the literature even though they are not something we have the intention or resources to investigate subsequently. Additionally, after consultation with other microscopists we think there is a reasonable chance that this double-layer effect could be a product of chemical fixation. To account for this, we have qualified the paragraph on CSP with this sentence:

      "We cannot determine if there is any functional significance of this second CSP layer and considering that it has not been observed previously it may well represent an artefact of chemical (paraformaldehyde) fixation."

      __Maybe include more detail of the differences between species on rhoptry structure into Figure 4. I would encourage to move the Data on rhoptries in Figure S6 to the main text ie to Figure 4. __

      We have moved the images of developing rhoptries in * falciparum *(previously Figure S6a and b) into figure 4, which now looks as follows:

      Figure S8 (previously S6c) now consists only of the MG spz rhoptry quantification

      Manuscript structural changes (Reviewer #1)

      1. Abstract: don't focus on technique but on the questions you tried to answer (ie rewrite or delete the 3rd and 4th sentence)

      2. 'range of cell biology processes' - I understand the paper that the key discovery concerns rhoptry biogenesis and function, so focus on that, all other aspects appear rather peripheral.

      3. 'Much of this study focuses on the secretory organelles': I would suggest to rewrite the intro to focus solely on those, which yield interesting findings.

      4. Page 11: I am tempted to suggest the authors start their study with Figure 3 and add panel A from Figure 2 to it. This leads directly to their nice work on rhoptries. Other features reported in Figures 1 and 2 are comparatively less exciting and could be moved to the supplement or reported in a separate study.____ Page 23: I suggest to delete the first sentence and focus on the functional aspects and the discoveries.

      5. __Maybe add a conclusion section rather than a future application section, which reads as if you want to promoted the use of ultrastructure expansion microscopy. To my taste the technological advance is a bit overplayed considering the many applications of this techniques over the last years, especially in parasitology, where it seems widely used. In any case, please delete 'extraordinarily' __

      Response to Reviewer#1 manuscript structural changes 1-5: This reviewer considers the findings related to rhoptry biology as the most significant aspect of the study and suggests rewriting the manuscript to emphasize these findings specifically. Doing so might make the key findings easier to interpret. However, in our view, this approach could misrepresent how the study originated and what we see as the most important outcomes. We did not develop MoTissU-ExM specifically to investigate rhoptry biology. Instead, this technique was created independently of any particular biological question, and once established, we asked what questions it could answer, using rhoptry biology as a proof of concept. Given the authors' previous work and available resources, we chose to focus on rhoptry biology. Since this was driven by basic research rather than a specific hypothesis, it's important to acknowledge this in the manuscript. While we agree that the findings related to rhoptry biology are valuable, we believe that highlighting the technique's ability to observe organelles, structures, and phenotypes with unprecedented ease and detail is more important than emphasizing the rhoptry findings alone. For these reasons, we have decided not to restructure the manuscript as suggested.


      Reviewer #2

      Minor text changes (Reviewer #2)

      1. __The 'image Z-depth' value indicated in the figures is ambiguous. It is not clear whether this refers to the distance from the coverslip surface or the starting point of the z-stack image acquisition. A precise definition of this parameter would be beneficial. __

      In the legend of Figure 1, the image Z-depth has been clarified as "sum distance of Z-slices in max intensity projection". 2.

      __Paragraph 3 of the introduction - line 7, "handful or proteins" should be handful of proteins __

      This has been corrected. 3.

      __Paragraph 5 of the introduction - line 7, "also able to observed" should be observe __

      This has been changed. 4.

      __In the final paragraph of the introduction - line 1, "leverage this new understand" should be understanding __

      This has been fixed. 5.

      __The first paragraph of the discussion summary contains an incomplete sentence on line 7, "PbRON11ctrl-infected SGs." __

      This has been removed. 6.

      __The second paragraph of the discussion - line 10, "until cytokinesis beings" should be begins __

      This mistake has been corrected. 7.

      __One minor point that author suggest that oocyst diameter is not appropriate for the development of sporozoite develop. This is not so true as oocyst diameter tells between cell division and cell growth so it is important parameter especially where the proliferation with oocyst does not take place but the growth of oocyst takes place. __

      We agree that this was not highlighted enough in the text. The final sentence of the results section about this now reads:

      "While diameter is a useful readout for oocyst development in the early stages of its growth, this suggests that diameter is a poor readout for oocyst development once sporozoite formation has begun and highlights the usefulness of the segmentation score as an alternative.", and the final sentence of the discussion section about this now reads "Considering that oocyst size does not plateau until cytokinesis begins4, measuring oocyst diameter may represent a useful biological clock specifically when investigating the early stages of oocyst development." 8.

      __How is the apical polarity different to merozoite as some conoid genes are present in ookinete and sporozoite but not in merozoite. __

      Our hypothesis is that apical polarity is established by the positioning and attachment of the centriolar plaque to the parasite plasma membrane in both forming merozoites and sporozoites. While the apical polar ring proteins are obviously present at the apical end, and have important functions, we think that they themselves are unlikely to regulate polarity establishment directly. Additionally, it seems that the apical polar rings are visible in forming sporozoites far before the comparable stages of merozoite formation. An important note here is that at this point, this is largely inferences based on observational differences and there is relatively little functional data on proteins that regulate polarity establishment at any stage of the Plasmodium 9.

      __Therefore, I think that electron microscopy remains essential for the observation of such ultra-fine structures __

      We have added a paragraph in the discussion that provides a more clear comparison between MoTissU-ExM and other imaging modalities previously applied on mosquito-stage parasites (see response to Reviewer#1 (Minor text changes) comment #17). 10.

      __The author have not mentioned that sometimes the stage oocyst development is also dependent on the age of mosquito and it vary between different mosquito gut even if the blood feed is done on same day. __

      In our opinion this can be inferred through the more general statement that "development of each oocyst within a midgut is asynchronous..."


      Figure changes (Reviewer #2)

      1. __Fig 3B: stage 2 and 6 does not show the DNA cyan, it would-be good show the sate of DNA at that particular stage, especially at stage 2 when APR is visible. And box the segment in the parent picture whose subset is enlarged below it. __

      We completely agree with the reviewer that the stage 2 image would benefit from the addition of a DNA stain. Many of the images in Figure 3b were done on samples that did not have a DNA stain and so in these * yoelii samples we did not find examples of all segmentation scores with the DNA stain. Examples of segmentation score 2 and 6 for P. berghei, and 6 for P. falciparum* can be found with DNA stains in Figure S8. 2.

      __For clarity, it would be helpful to add indicators for the centriolar plaques in Figure 1b, as their locations are not immediately obvious. __

      The CPs in Figure 1a and 1b have been circled on the NHS ester only panel for clarity. +

      __Regarding Figure 1c, the authors state that 'the rootlet fiber is visible'. However, such a structure cannot be confirmed from the provided NHS ester image. Can the authors present a clearer image where the rootlet fibre is more distinct? Furthermore, please provide the basis for identifying this structure as a rootlet fiber based on the NHS ester observation alone. __

      The image in Figure 1c has been replaced with one that more clearly shows the rootlet fibre.

      Based on electron microscopy studies, the rootlet fibre has been defined as a protein dense structure that connects the centriolar plaque to the apical polar rings (PMID: 17908361). Through NHS ester and tubulin staining, we could identify the apical polar rings and centriolar plaque as sites on the apical end of the parasite and nucleus that microtubules are nucleated from. There is a protein dense fibre that connects these two structures. Based on the fact that the protein density of this structure was previously considered sufficient for its identification by electron microscopy, we consider its visualisation by NHS ester staining sufficient for its identification by U-ExM.

      __Fig 1B - could the tubulin image in the hemispindle panel be made brighter? __

      The tubulin staining in this panel was not saturated, and so this change has been made.

      __Fig 4A - the green text in the first image panel is not visible. Also, the cyan text in the 3rd image in Fig 1A is also difficult to see. There's a few places where this is the case __

      We have made all microscopy labels legible at least when printed in A4/Letter size.

      __Fig 6A - how do the authors know ron11 expression is reduced by 99%? Did they test this themselves or rely on data from the lab that gifted them the construct? Also please provide mention the number of oocyst and sporozoites were observed. __

      The way Figure 6a was previously designed and described was an oversight, that wrongly suggested we had quantified a >99% reduction in *ron11 * The 99% reduction has been removed from Figure 6a and the corresponding part of the figure legend has been rewritten to emphasise that this was previously established:

      "(a) Schematic showing previously established Ron11Ctrl and Ron11cKD parasite lines where ron11 expression was reduced by >99%9."

      As to the second part of the question, we did not independently test either protein or RNA level expression of RON11, but we were gifted the clonal parasite lines established by Prof. Ishino's lab in PMID: 31247198 not just the genetic constructs.

      __Fig 6E - are the data point colours the wrong way round on this graph? Just looking at the graph it looks as though the RON11cKD has more rhoptries than the control which does not match what is said in the text. __

      Thank you for pointing out this mistake, the colours have now been corrected.

      __Fig S8C, PbRON11 ctrl, pie chart shows 89.7 % spz are present in the secretory cavity while the text shows 100 %, 35/35 __

      The text saying 100% (35/35) only considered salivary glands that were infected (ie. Uninfected SGs were removed from the count. The two sentences that report this data have been clarified to reflect this better:

      "Of *PbRON11ctrl SGs that were infected (35/39), 100% (35/35) contained sporozoites in the secretory cavity (Figure S8c). Conversely of infected PbRON11cKD SGs (59/82), only 24% (14/59) contained sporozoites within the secretory cavity (Figure S9d)."

      *

      __Fig S9D shows that RON11 ckd contains 17.1% sporozoites in secretory cavity while the text says 24%. __

      Please see the response to Reviewer#2 Figure Changes Comment #8 where this was addressed.


      Experimental changes (Reviewer #2)

      1. __Why do the congruent rhoptries have similar lengths to each other, while the dimorphic rhoptries have different lengths? Is this morphological difference related to the function of these rhoptries? __

      We hypothesise that this morphological difference arises because the congruent rhoptries are 'used' during SG invasion, while the dimorphic rhoptries are utilized during hepatocyte invasion. It is not straightforward to test this functionally at this point, as no protein is known to have differential localization between the two. Additionally, RON11 is likely directly involved in both SG and hepatocyte invasion through a secreted portion of the protein (as seen in RBC invasion). Therefore, RON11cKD sporozoites may have combined defects, meaning we cannot assume any defect is solely due to the absence of two rhoptries. Determining this functionally is of high interest to our research groups and remains an area of ongoing study, but it is beyond the scope of this study. 2.

      Would it be possible to show whether RON11 localises to the dimorphic rhoptries, the congruent rhoptries, or both, by using expansion microscopy and a parasite line that expresses RON11 tagged with GFP or a peptide tag?

      __ __We do not have access to a parasite line that expresses a tagged copy of RON11, or anti-PbRON11 antibodies. Based on previously published localisation data, however, it seems likely that RON11 localises to both sets of rhoptries. Below are excerpts from Figure 1c of PMID: 31247198, where RON11 (in green) seems to have a more basally-extended localisation in midgut (MG) sporozoites than in salivary gland (SG) sporozoites. From this we infer that in the MG sporozoite you're seeing RON11 in both pairs of rhoptries, but only the one remaining pair in the SG sporozoite.


      __The knockdown of RON11 disrupts the rhoptry structure, making the dimorphic and congruent rhoptries indistinguishable. Does this suggest that RON11 is important for the formation of both types of rhoptries? I believe that it would be crucial to confirm whether RON11 localises to all rhoptries or is restricted to specific rhoptries for a more precise discussion of RON11's function. __

      Based on our analysis, it does indeed seem that RON11 is important for both types of rhoptries as when RON11 isn't expressed sporozoites still have both apical and cytoplasmic rhoptries (ie. Not just one pair is lost; see Reviewer #1 Experimental changes comment #1).

      __The authors state that 64% of RON11cKD SG sporozoites contained no rhoptries at all. Does this mean RON11cKD SG sporozoites used up all rhoptries corresponding to the dimorphic and congruent pairs during SG invasion? If so, this contradicts your claims that sporozoites are 'leaving the dimorphic rhoptries for hepatocyte invasion' and that 'rhoptry pairs are specialized for different invasion events'. If that is not the case, does it mean that RON11cKD sporozoites failed to form the rhoptries corresponding to the dimorphic pair? A more detailed discussion would be needed on this point and, as I mentioned above, on the specific role of RON11 in the formation of each rhoptry pair. __

      We do not agree that this constitutes a contradiction; instead, more nuance is needed to fully explain the phenotype. As shown in the new graph added in response to Reviewer#1 Figure changes comment #1 in RON11cKD oocyst sporozoites, 64% of all rhoptries are located at the apical end. Our hypothesis is that these rhoptries are used for SG invasion and, therefore, would not be present in RON11cKD SG sporozoites. Consequently, the fact that 64% of RON11cKD sporozoites lack rhoptries is exactly what we would expect. Essentially, we predict three slightly different 'pathways' for RON11cKD sporozoites: If they had 2 apical rhoptries in the oocyst, we predict they would have zero rhoptries in the SG. If they had 2 cytoplasmic rhoptries in the oocyst, we predict they would have two rhoptries in the SG. If they had one apical and one cytoplasmic rhoptry in the oocyst, we predict they would have one rhoptry in the SG. In any case, we expect the apical rhoptries to be 'used up,' which appears to be supported by the data.

      __Out of pure curiosity, is it possible to measure the length and number of subpellicular microtubules in the sporozoites observed in this study using expansion microscopy? __

      We have performed an analysis of subpellicular microtubules which is now included as Supplementary Figure 2. We could not always distinguish every SPMT from each other and so have not quantified SPMT number. We have, however, quantified their absolute length on both the 'long side' and 'short side', their relative length (as % sporozoite length) and the degree to which they are polyglutamylated.

      A description of this analysis is now found in the results section as follows: "*We quantified the length and degree of polyglutamylation of SPMTs on the 'long side' and 'short side' of the sporozoite (Figure S2). 'Short side' SPMTs were on average 33% shorter (mean = 3.6 µm {plus minus}SD 1.0 µm) than 'long side' SPMTs (mean = 5.3 µm {plus minus}SD 1.5 µm) and extended 17.4% less of the total sporozoite length. While 'short side' SPMTs were significantly shorter, a greater proportion of their length (87.9% {plus minus}SD 11.2%) was polyglutamylated compared to 'long side' SPMTs (69.4% {plus minus}SD 13.8%)." *

      Supplementary Figure 2: Analysis of sporozoite subpellicular microtubules. Isolated P. yoelii salivary gland sporozoites were prepared by U-ExM and stained with anti-tubulin (microtubules) and anti-PolyE (polyglutamylated SPMTs) antibodies. SPMTs were defined as being on either the 'long side' (nucleus distant from plasma membrane) or 'short side' (nucleus close to plasma membrane) of the sporozoite as depicted in Figure 1f. (a) SPMT length along with (b) SPMT length as a proportion of sporozoite length were both measured. (c) Additionally, the proportion of the SPMT that was polyglutamylated was measured. Analysis comprises 25 SPMTs (11 long side, 14 short side) from 6 SG sporozoites. ** = p The following section has also been added to the methods to describe this analysis: * "Subpellicular microtubule measurement

      • To measure subpellicular microtubule length and polyglutamylation maximum intensity projections were made of sporozoites stained with NHS Ester, anti-tubulin and anti-PolyE antibodies, and SYTOX Deep Red. The side where the nucleus was closest to the parasite plasma membrane was defined as the 'short side', while the side where the nucleus was furthest from the parasite plasma membrane was defined as the 'long side'. Subpellicular microtubules were then measured using a spline contour from the apical end of the sporozoite to the basal-most end of the microtubule with fluorescence intensity across the contour plotted (Zeiss ZEN 3.8). Sporozoite length was defined as the distance from the sporozoite apical polar rings to the basal complex, measuring through the centre of the cytoplasm. The percentage of the subpellicular microtubule that was polyglutamylated was determined by assessing when along the subpellicular microtubule contour the anti-PolyE fluorescence intensity last dropped below a pre-defined threshold."

      *

      __In addition to the previous point, in the text accompanying Figure 7a, the authors claim that "64% of PbRON11cKD SG sporozoites contained no rhoptries at all, while 9% contained 1 rhoptry and 27% contained 2 rhoptries". Could this data be used to infer which rhoptry pair are missing from the RON11cKD oocyst sporozoites? Can it be inferred that the 64% of salivary gland sporozoites that had no rhoptries in fact had 2 congruent rhoptries in the oocyst sporozoite stage and that these have been discharged already? __

      Please see the response to Reviewer #2 Experimental Changes Comment #4.

      __Is it possible that the dimorphic rhoptries are simply precursors to the congruent rhoptries? Could it be that after the congruent rhoptries are used for SG invasion, new congruent rhoptries are formed from the dimorphic ones and are then used for the next invasion?____ Would it be possible to investigate this by isolating sporozoites some time after they have invaded the SG and performing expansion microscopy? This would allow you to confirm whether the dimorphic rhoptries truly remain in the same form, or if new congruent rhoptries have been formed, or if there have been any other changes to the morphology of the dimorphic rhoptries. __

      In theory, it is possible that the dimorphic rhoptries are precursors to the uniform rhoptries, specifically how the larger one of the two in the dimorphic pair might be a precursor. Maybe the smaller one is, but we have no evidence to suggest that this rhoptry lengthens after SG invasion. We are interested in isolating sporozoites from SGs to add a temporal perspective, but currently, this isn't feasible. When sporozoites are isolated from SGs, they are collected at all stages of invasion. Additionally, we don't know how long each step of SG invasion takes, so a time-based method might not be effective either. We are developing an assay to better determine the timing of events during SG invasion with MoTissU-ExM, but this is beyond the scope of this study.

      __In the section titled "Presence of PbRON11cKD sporozoites in the SG intercellular space", the authors state that "the majority of PbRON11cKD-infected mosquitoes contained some sporozoites in their SGs, but these sporozoites were rarely inside either the SG epithelial cell or secretory cavity". - this is suggestive of an invasion defect as the authors suggest. Could the authors collect these sporozoites and see if liver hepatocyte infection can be established by the mutant sporozoites? They previously speculate that the two different types of rhoptries (congruent and dimorphic) may be specific to the two invasion events (salivary gland epithelial cell and liver cell infection). __

      It has already been shown that RON11cKD sporozoites fail hepatocyte invasion (PMID: 31247198), even when isolated from the haemolymph and so it seems very unlikely that they would be invasive following SG isolation. As mentioned in the discussion, RON11 in merozoites has a 'dual-function' where it is partially secreted during merozoite invasion in addition to its rhoptry biogenesis functions. Assuming this is also the case in sporozoites, using the RON11cKD parasite line we cannot differentiate these two functions and therefore cannot ascribe invasion defects purely to issues with rhoptry biogenesis. In order to answer this question functionally, we would need to identify a protein that only has roles in rhoptry biogenesis and not invasion directly.

      Reviewer #3

      Minor text changes (Reviewer #3)

      1. __Page 3 last paragraph: ...the molecular mechanisms underlying SG (invasion?) are poorly understood. __

      This has been corrected 2.

      __The term "APR" does not refer to a tubulin structure per se, but rather to the proteinaceous structure to which tubulin anchors. Are there any specific APR markers that can be used in Figure 1C? If not, I recommend avoiding the use of "APR" in this context. __

      The text does not state that the APR is a tubulin structure. Given that it is a proteinaceous structure, we visualise the APRs through protein density (NHS Ester). It has been standard for decades to define APRs by protein density using electron microscopy, and it has previously been sufficient in Plasmodium using expansion microscopy (PMIDs: 41542479, 33705377) so it is unclear why it should not be done so in this study. 3.

      __I politely disagree with the bold statements ‚ Little is known about cell biology of sporozoite formation.....from electron microscopy studies now decades old' (p.3, 2nd paragraph); ‚To date, only a handful of (instead of ‚or') proteins have been implicated in SG invasion' (p. 4, 1st paragraph). These claims may overlook existing studies; a more thorough review of the literature is recommended. __

      This study includes at least 50 references from papers broadly related to sporozoite biology, covering publications from every decade since the 1970s. The most recent review that discusses salivary gland invasion cites 11 proteins involved in SG invasion. We have replaced "handful" with a more precise term, as it is not the best adjective, but it is hardly an exaggeration.


      Figure changes (Reviewer #3)

      1. __The hypothesis that Plasmodium utilizes two distinct rhoptry pairs for invading the salivary gland and liver cells is intriguing but remains clearly speculative. Are the "cytoplasmic pair" and "docked pair" composed of the same secretory proteins? Are the paired rhoptries identical? How does the parasite determine which pair to use for salivary gland versus liver cell invasion? Is there any experimental evidence showing that the second pair is activated upon successful liver cell invasion? Without such data this hypothesis seems rather premature. __

      We are unaware of any direct protein localisation evidence suggesting that the rhoptry pairs may carry different cargo. However, only a few proteins have been localised in a way that would allow us to determine if they are associated with distinct rhoptry pairs, so this possibility cannot be ruled out either. It seems unlikely that the parasite 'selects' a specific pair, as rhoptries are typically always found at the apical end. What appears more plausible is that the "docked pair" forms first and immediately occupies the apical docking site, preventing the cytoplasmic pair from docking there. Regarding any evidence that the second pair is activated during liver cell invasion, it has been well documented over decades that rhoptries are involved in hepatocyte invasion. If the dimorphic rhoptries are the only ones present in the parasite during hepatocyte invasion, then they must be used for this process. 2.

      __The quality of the "Roolet fibre" image is not good and resembles background noise from PolyE staining. Additional or alternative images should be provided to convincingly demonstrate that PolyE staining indeed visualizes the Roolet fibre. It is puzzling that the structure is visible with PolyE staining but not with tubulin staining. __

      This is a logical misinterpretation based on the image provided in Figure 1c. Our intention was not to imply that PolyE staining enables us to see the rootlet fibre but that PolyE and tubulin allow us to see the APR to which the rootlet fibre is connected. There is some PolyE staining that likely corresponds to the early SPMTs that in 1c appears to run along the rootlet fibre but this is a product of the max-intensity projection. Please see Reviwer#2 Figure Changes Comment #3 for the updated Figure 1c. 3.

      __More arrows should be added to Figures 6b and 6c to guide readers and improve clarity. __

      We have added arrows to Figure 6b and 6c which point out what we have defined as normal and aberrant rhoptries more clearly. These panels now look like this: 4.

      __Figure 2a zoomed image of P. yoelii infected SG is different than the highligted square. __

      We agree that the highlighted square and the zoomed area appear different, but this is due to the differing amounts of light captured by the objectives used in these two panels. The entire SG panel was captured with a 5x objective, while the zoomed panel was captured with a 63x objective. Because of this difference, the plane of focus of the zoomed area is hard to distinguish in the whole SG image. The zoomed image is on the 'top' of the SG (closest to the coverslip), while most of the signal you see in the whole SG image comes from the 'middle' of the SG. To demonstrate this more clearly, we have provided the exact region of interest shown in the 63x image alongside a 5x image and an additional 20x image, all of which are clearly superimposable.__

      __ 5.

      __Figure 3 legend: "P. yoelii infected midguts harvested on day 15" should be corrected. More general, yes, "...development of each oocyst within a single midgut is asynchronous." but it is still required to provide the dissection days. __

      We are unsure what the suggested change here is. We do not know what is wrong with the statement about day 15 post infection, that is when these midguts were dissected. __ Experimental Changes (Reviewer #3)__

      1. __The proposed role of AOR in rhoptry biogenesis appears highly speculative. It is unclear how the authors conclude that "AORs carry rhoptry cargo" solely based on the presence of RON4 within the structure. Inclusion of additional markers to characterize the content of AOR and rhoptries will be essential to substantiate the hypothesis that this enigmatic structure supports rhoptry biogenesis. __

      It is important to note that the hypothesis that AORs, or rhoptry anlagen, carry rhoptry cargo and serve as vehicles of rhoptry biogenesis was proposed long before this study (PMID: 17908361). In that study, it was assumed that structures now called AORs or rhoptry anlagen were developing rhoptries. Although often visualised by EM and presumed to carry rhoptry cargo (PMID: 33600048, 26565797, 25438048), it was only more recently that AORs became the subject of dedicated investigation (PMID: 31805442), where the authors stated that "...AORs could be immature rhoptr[ies]...". Our observation that AORs contain the rhoptry protein RON4, which is not known to localize to any other organelle, we therefore consider sufficient to conclude that AORs carry rhoptry cargo and are thus vehicles for rhoptry biogenesis. 2.

      __The study of RON11 appears to be a continuation of previous work by a collaborator in the same group. However, neither this study nor the previous one adequately addresses the evolutionary context or structural characteristics of RON11. Notably, the presence of an EF-hand motif is an important feature, especially considering the critical role of calcium signaling in parasite stage conversion. Given the absence of a clear ortholog, it would be interesting to know whether other Apicomplexan parasites harbor rhoptry proteins with transmembrane domains and EF-hand motifs, and if these proteins might respond similarly to calcium stimulation. Investigating mutations within the EF-hand domain could provide valuable functional insights into RON11. __

      We are unsure what suggests that RON11 lacks a clear orthologue. RON11 is conserved across all apicomplexans and is also present in Vitrella brassicaformis (OrthoMCL orthogroup: OG7_0028843). A phylogenetic comparison of RON11 across apicomplexans has previously been performed (PMID: 31247198), and this study provides a structural prediction of PbRON11 with the dual EF-hand domains annotated (Supplementary Figure 9). 3.

      __The study cannot directly confirm that membrane fusion occurs between rhoptries and AORs. __

      This is already stated verbatim in the results "Our data cannot directly confirm that membrane fusion occurs between rhoptries and AORs..." 4.

      __It is unclear what leads to the formation of the aberrant rhoptries observed in RON11cKD sporozoites. Since mosquitoes were not screened for infection prior to salivary gland dissection, The defect reports and revisited of RON11 knockdown does not aid in interpreting rhoptry pair specialization, as there was no consistent trend as to which rhoptry pair was missing in RON11cKD oocyst sporozoites. The notion that RON11cKD parasites likely have ‚combinatorial defects that effect both rhoptry biogenesis and invasion' poses challenges to understand the molecular role(s) of RON11 on biogenesis versus invasion. Of note, RON11 also plays a role in merozoite invasion. __

      We are unclear about the comment or suggestion here, as the claims that RON11cKD does not help interpret rhoptry pair specialization, and that these parasites have combined defects, are both directly stated in the manuscript. 5.

      __Do all SG PbRON11cKD sporozoites lose their reduced number of rhoptries during SG invasion as in Figure 7a (no rhoptries)? __

      Not all RON11cKD SG sporozoites 'use up' their rhoptries during SG invasion. This is quantified in both Figure 7a and the text, which states: "64% of *PbRON11cKD SG sporozoites contained no rhoptries at all, while 9% contained 1 rhoptry and 27% contained 2 rhoptries."

      * 6.

      Different mosquito species/strains are used for P. yoelii, P. berghei, and P. falciparum. Does it effect oocyst sizes/stages? Is it ok to compare?

      __ __We agree that a direct comparison between for example * yoelii and P. berghei *oocyst size would be inappropriate, however Figure 3c and Supplementary Figure 4 are not direct comparisons between two species, but a summation of all oocysts measured in this study to indicate that the trends we observe transcend parasite/mosquito species differences. Our study was not set up with the experimental power to determine if mosquito host species alter oocyst size. 7.

      __While I acknowledge that UExM has significantly advanced resolution capabilities in parasite studies, the value of standard microscopy technique should not be overlooked. Particularly, when discussing the function of RON11, relevant IFA and electron microscopy (EM) images should be included to support claims about RON11's role in rhoptry biogenesis. This would complement the UExM data and substantially strengthen the conclusions. Importantly, UExM can sometimes produce unexpected localization patterns due to the denaturation process, which warrants caution. __

      The purpose of this study is not to discredit, undermine, or supersede other imaging techniques. It is simply to use U-ExM to answer biological questions that cannot or have not been answered using other techniques. Please refer to Reviewer # 1 Minor text changes comment#17 to see the new paragraph "Comparison of MoTissU-ExM and other imaging modalities" that addresses this

      Both conventional IFA and immunoEM have already been performed on RON11 in sporozoites before (PMID: 31247198). When assessing defects caused by RON11 knockdown, conventional IFA isn't especially helpful because it doesn't allow visualization of individual rhoptries. Thin-section TEM also doesn't provide the whole-cell view needed to draw these kinds of conclusions. Volume EM could likely support these observations, but we don't have access to or expertise in this technique, and we believe it is beyond the scope of this study. It's also important to note that for the defect we observe-missing or abnormal rhoptries-the visualization with NHS ester isn't significantly different from what would be seen with EM-based techniques, where rhoptries are easily identified based on their protein density.

      The statement that "UExM can sometimes produce unexpected localisation patterns due to the denaturation process..." is partially correct but lacks important nuance in this context. Based on our extensive experience with U-ExM, there are two main reasons why the localisation of a single protein may look different when comparing U-ExM and traditional IFA images. First, denaturation: in conventional IFAs, antibodies need to recognize conformational epitopes to bind to their target, whereas in U-ExM, antibodies must recognize linear epitopes. This doesn't mean the target protein's localisation changes, only that the antibody's ability to recognize it does. Second, antibody complexes seem unable to freely diffuse out of the gel, which can result in highly fluorescent signals not related to the target protein appearing in the image, as we have previously reported (PMID: 36993603). Importantly, neither of these factors applies to our phenotypic analysis of RON11 knockdown. All phenotypes described are based solely on NHS Ester (total protein) staining, so the considerations about changes in the localisation of individual proteins are not relevant.

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      Referee #3

      Evidence, reproducibility and clarity

      Overall, the manuscript is well-written and -structured. However, I would like to raise several major points for consideration:

      1. While I acknowledge that UExM has significantly advanced resolution capabilities in parasite studies, the value of standard microscopy technique should not be overlooked. Particularly, when discussing the function of RON11, relevant IFA and electron microscopy (EM) images should be included to support claims about RON11's role in rhoptry biogenesis. This would complement the UExM data and substantially strengthen the conclusions. Importantly, UExM can sometimes produce unexpected localization patterns due to the denaturation process, which warrants caution.
      2. The proposed role of AOR in rhoptry biogenesis appears highly speculative. It is unclear how the authors conclude that "AORs carry rhoptry cargo" solely based on the presence of RON4 within the structure. Inclusion of additional markers to characterize the content of AOR and rhoptries will be essential to substantiate the hypothesis that this enigmatic structure supports rhoptry biogenesis.
      3. The hypothesis that Plasmodium utilizes two distinct rhoptry pairs for invading the salivary gland and liver cells is intriguing but remains clearly speculative. Are the "cytoplasmic pair" and "docked pair" composed of the same secretory proteins? Are the paired rhoptries identical? How does the parasite determine which pair to use for salivary gland versus liver cell invasion? Is there any experimental evidence showing that the second pair is activated upon successful liver cell invasion? Without such data this hypothesis seems rather premature.
      4. The study of RON11 appears to be a continuation of previous work by a collaborator in the same group. However, neither this study nor the previous one adequately addresses the evolutionary context or structural characteristics of RON11. Notably, the presence of an EF-hand motif is an important feature, especially considering the critical role of calcium signaling in parasite stage conversion. Given the absence of a clear ortholog, it would be interesting to know whether other Apicomplexan parasites harbor rhoptry proteins with transmembrane domains and EF-hand motifs, and if these proteins might respond similarly to calcium stimulation. Investigating mutations within the EF-hand domain could provide valuable functional insights into RON11.
      5. The study cannot directly confirm that membrane fusion occurs between rhoptries and AORs.
      6. It is unclear what leads to the formation of the aberrant rhoptries observed in RON11cKD sporozoites. Since mosquitoes were not screened for infection prior to salivary gland dissection, The defect reports and revisited of RON11 knockdown does not aid in interpreting rhoptry pair specialization, as there was no consistent trend as to which rhoptry pair was missing in RON11cKD oocyst sporozoites. The notion that RON11cKD parasites likely have ‚combinatorial defects that effect both rhoptry biogenesis and invasion' poses challenges to understand the molecular role(s) of RON11 on biogenesis versus invasion. Of note, RON11 also plays a role in merozoite invasion. I like the introduction of a segmentation score to Plasmodium oocyst maturation.

      Minor comments:

      1. The term "APR" does not refer to a tubulin structure per se, but rather to the proteinaceous structure to which tubulin anchors. Are there any specific APR markers that can be used in Figure 1C? If not, I recommend avoiding the use of "APR" in this context.
      2. The quality of the "Roolet fibre" image is not good and resembles background noise from PolyE staining. Additional or alternative images should be provided to convincingly demonstrate that PolyE staining indeed visualizes the Roolet fibre. It is puzzling that the structure is visible with PolyE staining but not with tubulin staining.
      3. Figure 2a zoomed image of P. yoelii infected SG is different than the highligted square.
      4. Figure 3 legend: "P. yoelii infected midguts harvested on day 15" should be corrected. More general, yes, "...development of each oocyst within a single midgut is asynchronous." but it is still required to provide the dissection days.
      5. More arrows should be added to Figures 6b and 6c to guide readers and improve clarity.
      6. Do all SG PbRON11cKD sporozoites lose their reduced number of rhoptries during SG invasion as in Figure 7a (no rhoptries)?
      7. Different mosquito species/strains are used for P. yoelii, P. berghei, and P. falciparum. Does it effect oocyst sizes/stages? Is it ok to compare?
      8. I politely disagree with the bold statements ‚ Little is known about cell biology of sporozoite formation.....from electron microscopy studies now decades old' (p.3, 2nd paragraph); ‚To date, only a handful of (instead of ‚or') proteins have been implicated in SG invasion' (p. 4, 1st paragraph). These claims may overlook existing studies; a more thorough review of the literature is recommended.
      9. Page 3 last paragraph: ...the molecular mechanisms underlying SG (invasion?) are poorly understood.

      Significance

      In this study, the authors explore Ultrastructure Expansion Microscopy (U-ExM) in Plasmodium-infected mosquito tissue with the aim to enhance the visualization of parasite ultrastructure. For this purpose, they revisit sporogony, the maturation of sporozoites inside oocysts, and sporozoite invasion of salivary glands, which has been studied both by cell biological methods and experimental genetics over four decades. They focus their analysis on the biogenesis and function of key secretory organelles, termed rhoptries, which are central to parasite invasion and, again, have been studied extensively.

      This study is a follow-up of a previous study by the same authors (Ref. 19). In the former study the authors showed that U-ExM allows to visualize subcellular structures in sporozoites, including the nucleus, rhoptries, Golgi, apical polar rings (APR), and basal complex, as well as midgut-associated oocysts with developing sporozoites. Here, the authors claim a new finding by stating that sporozoites possess two distinct rhoptry pairs. Supposedly, only one pair is utilized during salivary gland invasion. The authors suggest specialization of rhoptries for different cell invasion events. The authors also revisit a RON11 knock-down parasite line, which was previously shown to be deficient in salivary gland invasion, host cell attachment, gliding locomotion, and liver invasion (Ref. 14).

      I find it difficult to estimate the significance. Obviously, attention will be limited to Plasmodium researchers only, as this study is descriptive and revisits a well-studied aspect of the Plasmodium life cycle in the Anopheles vector.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Liffner et al have used the modified expansion microscopy as they term Mosquito Tissue Ultrastructure Expansion Microscopy (MoTissU-ExM) to study a cell biology of temporal development of malaria parasite sporozoite biogenesis within mosquito host. They employed three different malaria parasite models Plasmodium yoelii, P.beghei and P falciparum and infected them in mosquito host.

      The application of MoTissU-ExM to infected mosquito tissues is a significant technical advance, enabling visualizations previously only achievable with electron microscopy.

      The major conclusion and advances are as following

      • The establishment of a "segmentation score" as a great tool for staging asynchronous oocyst development.
      • The location of Centriolar plaques, rootlet and other structures which are difficult to analyse
      • The first detailed timeline for sporozoite rhoptry biogenesis.
      • Clear quantification showing that sporozoites possess four rhoptries and utilise two during salivary gland (SG) invasion.
      • A characterization of the RON11 knockout phenotype, linking it to defects in rhoptry biogenesis and a specific block in SG epithelial cell invasion. The following points are intended to further strengthen the paper for publication.

      Points for Revision

      1. For clarity, it would be helpful to add indicators for the centriolar plaques in Figure 1b, as their locations are not immediately obvious.
      2. The 'image Z-depth' value indicated in the figures is ambiguous. It is not clear whether this refers to the distance from the coverslip surface or the starting point of the z-stack image acquisition. A precise definition of this parameter would be beneficial.
      3. Regarding Figure 1c, the authors state that 'the rootlet fiber is visible'. However, such a structure cannot be confirmed from the provided NHS ester image. Can the authors present a clearer image where the rootlet fibre is more distinct? Furthermore, please provide the basis for identifying this structure as a rootlet fiber based on the NHS ester observation alone.
      4. Why do the congruent rhoptries have similar lengths to each other, while the dimorphic rhoptries have different lengths? Is this morphological difference related to the function of these rhoptries?
      5. Would it be possible to show whether RON11 localises to the dimorphic rhoptries, the congruent rhoptries, or both, by using expansion microscopy and a parasite line that expresses RON11 tagged with GFP or a peptide tag?
      6. The knockdown of RON11 disrupts the rhoptry structure, making the dimorphic and congruent rhoptries indistinguishable. Does this suggest that RON11 is important for the formation of both types of rhoptries? I believe that it would be crucial to confirm whether RON11 localises to all rhoptries or is restricted to specific rhoptries for a more precise discussion of RON11's function.
      7. The authors state that 64% of RON11cKD SG sporozoites contained no rhoptries at all. Does this mean RON11cKD SG sporozoites used up all rhoptries corresponding to the dimorphic and congruent pairs during SG invasion? If so, this contradicts your claims that sporozoites are 'leaving the dimorphic rhoptries for hepatocyte invasion' and that 'rhoptry pairs are specialized for different invasion events'. If that is not the case, does it mean that RON11cKD sporozoites failed to form the rhoptries corresponding to the dimorphic pair? A more detailed discussion would be needed on this point and, as I mentioned above, on the specific role of RON11 in the formation of each rhoptry pair.
      8. Out of pure curiosity, is it possible to measure the length and number of subpellicular microtubules in the sporozoites observed in this study using expansion microscopy?
      9. Is it possible that the dimorphic rhoptries are simply precursors to the congruent rhoptries? Could it be that after the congruent rhoptries are used for SG invasion, new congruent rhoptries are formed from the dimorphic ones and are then used for the next invasion? Would it be possible to investigate this by isolating sporozoites some time after they have invaded the SG and performing expansion microscopy? This would allow you to confirm whether the dimorphic rhoptries truly remain in the same form, or if new congruent rhoptries have been formed, or if there have been any other changes to the morphology of the dimorphic rhoptries.
      10. In addition to the previous point, in the text accompanying Figure 7a, the authors claim that "64% of PbRON11cKD SG sporozoites contained no rhoptries at all, while 9% contained 1 rhoptry and 27% contained 2 rhoptries". Could this data be used to infer which rhoptry pair are missing from the RON11cKD oocyst sporozoites? Can it be inferred that the 64% of salivary gland sporozoites that had no rhoptries in fact had 2 congruent rhoptries in the oocyst sporozoite stage and that these have been discharged already?
      11. In the section titled "Presence of PbRON11cKD sporozoites in the SG intercellular space", the authors state that "the majority of PbRON11cKD-infected mosquitoes contained some sporozoites in their SGs, but these sporozoites were rarely inside either the SG epithelial cell or secretory cavity". - this is suggestive of an invasion defect as the authors suggest. Could the authors collect these sporozoites and see if liver hepatocyte infection can be established by the mutant sporozoites? They previously speculate that the two different types of rhoptries (congruent and dimorphic) may be specific to the two invasion events (salivary gland epithelial cell and liver cell infection).

      There are a few typing errors in the document:

      1. Paragraph 3 of the introduction - line 7, "handful or proteins" should be handful of proteins
      2. Paragraph 5 of the introduction - line 7, "also able to observed" should be observe
      3. In the final paragraph of the introduction - line 1, "leverage this new understand" should be understanding
      4. The first paragraph of the discussion summary contains an incomplete sentence on line 7, "PbRON11ctrl-infected SGs."
      5. The second paragraph of the discussion - line 10, "until cytokinesis beings" should be begins

      Some suggestions for figures

      Fig 1B - could the tubulin image in the hemispindle panel be made brighter?

      Fig 3B: stage 2 and 6 does not show the DNA cyan, it would-be good show the sate of DNA at that particular stage, especially at stage 2 when APR is visible. And box the segment in the parent picture whose subset is enlarged below it.

      Fig 4A - the green text in the first image panel is not visible. Also, the cyan text in the 3rd image in Fig 1A is also difficult to see. There's a few places where this is the case

      Fig 6A - how do the authors know ron11 expression is reduced by 99%? Did they test this themselves or rely on data from the lab that gifted them the construct? Also please provide mention the number of oocyst and sporozoites were observed.

      Fig 6E - are the data point colours the wrong way round on this graph? Just looking at the graph it looks as though the RON11cKD has more rhoptries than the control which does not match what is said in the text.

      Fig S8C, PbRON11 ctrl, pie chart shows 89.7 % spz are present in the secretory cavity while the text shows 100 %, 35/35

      Fig S9D shows that RON11 ckd contains 17.1% sporozoites in secretory cavity while the text says 24%.

      Some point to discuss

      1.One minor point that author suggest that oocyst diameter is not appropriate for the development of sporozoite develop. This is not so true as oocyst diameter tells between cell division and cell growth so it is important parameter especially where the proliferation with oocyst does not take place but the growth of oocyst takes place.<br /> 2. The author have not mentioned that sometimes the stage oocyst development is also dependent on the age of mosquito and it vary between different mosquito gut even if the blood feed is done on same day. 3. How is the apical polarity different to merozoite as some conoid genes are present in ookinete and sporozoite but not in merozoite.

      Significance

      The following aspects are important:

      This is novel and more cell biology approach to study the challenging stage of malaria parasite within mosquito. By using MoTissU-ExM, the authors have enabled the three-dimensional observation of ultrastructures of oocyst-sporozoite development that were previously difficult to observe with conventional electron microscopy alone. This includes the developmental process and entire ultrastructure of oocysts and sporozoites, and even the tissue architecture of the mosquito salivary gland and its epithelia cells.

      Advances:

      By observing sporozoites formation within the oocyst and the overall ultrastructure of the sporozoite with MoTissU-ExM, the authors have provided detailed descriptions of the complete structure and three-dimensional spatial relationships of the rhoptries, rootlet fibre, nucleus, and other organelles. Furthermore, their detailed localisation analysis of sporozoites within the salivary gland is also a great achievement. Considering that such observations were technically and laboriously very difficult with conventional electron microscopy, enabling these analyses with higher efficiency and relatively lower difficulty represents a major contribution to the future advancement of oocyst-sporozoite biology. The development of the 'segmentation score' for sporozoite formation within the oocyst is another major advance. I think this will enable detailed descriptions of structural changes at each developmental stage and of the molecular mechanisms involved in the development of oocysts-sporozoites This has its advantages if antibodies can be used and somewhat reduces the need for immuno-EM. Secondly, in terms of sporozoite rhoptry biology, the Schrevel et al Parasitology 2007 seems to only focus on oocyst sporozoite rhoptries as they say that the sporozoites have 4 rhoptries. This study on the other hand also looks at salivary gland sporozoites and shows that there are potentially important differences between the two - namely the reduction from 4 rhoptries to two. This also leads to further questions about the different types of rhoptries in oocyst sporozoites and whether they're adapted to invasion of different cell types (sal gland epithelial cells or liver hepatocytes)

      Limitation

      It would be that expansion microscopy alone still has its limits when it comes to observing ultra-fine structures. For example, visualising the small vesicular structures that Schrevel et al. observed in detail with electron microscopy, or seeing ultra-high resolution details such as the fusion of membrane structures and their interactions with structures like the rootlet fibre and microtubules. Therefore, I think that electron microscopy remains essential for the observation of such ultra-fine structures The real impact of this work is mostly cell biologist working with malaria parasite and more in mosquito stages. But the approaches can be applied to any material from any species where temporal dynamics need to be studied with tissue related structures and where UExM can be applied. I am parasite cell biologist working with parasites stages within mosquito vector host.

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper the authors use ultrastructure expansion microscopy to investigate the mosquito stages of the malaria parasite, specifically the stage called oocyst and the process of sporozoite development. They report a number of observations of which the ones concerning rhoptries are the most interesting. There are four of these organelles in the first form of sporozoites in the oocyst and only two in the mature form in the salivary gland. Using a gene knockout of a protein that was reported to be important for rhoprty formation in merozoites, the parasites invading into human red blood cells, they found that fewer rhoptries are formed also in sporozoites and that these cannot enter into the salivary gland cells any more. The presented data are in my view conclusive and no additional experiments are needed for this work to be published. The described experiments should be readily reproducible and have a high statistical power. The text is mostly clearly written but could be improved to make it more concise and more precise and to avoid overstatements. Some references could be added. It would have helped to have line numbers in the manuscript. My suggestions are as following:

      Abstract: don't focus on technique but on the questions you tried to answer (ie rewrite or delete the 3rd and 4th sentence)

      Add reference on page 3 after 'disrupted parasites' Change 'the basal compelx at the leading edge' - this seems counterintuitive Change 'mechanisms underlying SG are poorly' - what mechanisms? of invasion or infection? On page 4: 'handful of proteins' 'range of cell biology processes' - I understand the paper that the key discovery concerns rhoptry biogenesis and function, so focus on that, all other aspects appear rather peripheral. what are the 'three microtubule spindle structures'? 'Much of this study focuses on the secretory organelles': I would suggest to rewrite the intro to focus solely on those, which yield interesting findings. On page 5: 'little is known' - please describe what is known, also in other stages. At the end of the paper I would like to know what is the key difference to rhoptry function in other stages? change 'rhoptries golgi-derived, made de novo' change 'new understand to' 'rhoptry malformations' seem to be similar in sporozoites and merozoites. Is that surprising/new? What is known about crossing the basal lamina. Where rhoptries thought to be involved in this process? Or is it proteins on the surface or in other secretory organelles? On page change/specify: 'wide range of parasite structures' On page 7: is Airyscan2 a particular method or a specific microscope? what are the dark lines in panel E? in panel G: Are the dense granules not micronemes? What are the dark lines? Rhoptries?? On page 8 the authors mention a second layer of CSP but do not further investigate it. It is likely hard to investigate this further but to just let it stand as it is seems unsatisfactory, considering that CSP is the malaria vaccine. What happens if you add anti-CSP antibodies? I would suggest to shorten the opening paragraphs of this paper and to focus on the rhoptries. This could be done be toning down the text on all aspects that are not rhoptries and point to the open question some of the observations such as the CSP layers raise for future studies. Figure 2 seems to add little extra compared to the following figures and could in my view go to the supplement. On page 10 I suggest to qualify the statement 'oocyst development has typcially been inferred by'. There seem a few studies that show that size doesn't reflect maturation. Page 11: I am tempted to suggest the authors start their study with Figure 3 and add panel A from Figure 2 to it. This leads directly to their nice work on rhoptries. Other features reported in Figures 1 and 2 are comparatively less exciting and could be moved to the supplement or reported in a separate study. Text on page 12 could be condensed to highlight the new data of ron4 staining of the AOR. Maybe include more detail of the differences between species on rhoptry structure into Figure 4. I would encourage to move the Data on rhoptries in Figure S6 to the main text ie to Figure 4. On page 16 the authors state that different rhoptries might have different function. This is an interesting hyopthesis/result that could be mentioned in the abstract. how large is RON11? On page 19: do the parasites with the RON11 knockout only have the cytoplasmic or only the apical rhoptries? Page 23: I suggest to delete the first sentence and focus on the functional aspects and the discoveries. There is no causal link between ookinete invasion and oocyst developmental asynchrony First sentence of page 24 appears to contradict what is written in results I don't understand the first two sentences in the paragraph titled Comparison between Plasmodium spp On page 25 or before the vast number of electron microscopy studies should be discussed and compared with the authors new data. First sentence on page 27: there are many studies on parasite proteins involved in salivary gland invasion that could be mentioned/discussed. Maybe add a conclusion section rather than a future application section, which reads as if you want to promoted the use of ultrastructure expansion microscopy. To my taste the technological advance is a bit overplayed considering the many applications of this techniques over the last years, especially in parasitology, where it seems widely used. In any case, please delete 'extraordinarily'

      Significance

      This interesting study investigates the development of malaria parasites in the mosquito using ultrastructure expansion microscopy adapted to mosquito tissue. It provides new and beautiful views of the process of sporozoite formation. The authors discovered that four secretory vesicles called rhoptires are formed in the sporozoites with two pairs being important for distinct functions, one pair functions during invasion of the salivary glands of the mosquito and the other in liver infection, although the latter is not shown but inferred from prior data.

      This study will thus be of interest to scientists investigating malaria parasites in the mosquito as well as to scientist working on vesicle secretion and invasion in these parasites.

      The authors use a previously generated parasite line that lack a protein to investigate its function in rhoptry biogenesis and find that its absence leads to fewer rhoptries which impacts the capacity of the parasite to enter into salivary gland cells. This is a nice functional addition to an otherwise largely descriptive study, but mimics largely the previously reported results from the blood stages. It is not clear to this reviewer how much the study advances the field over the many previous electron microscopy studies. This could be better elaborated in the text.

      Strength of the study: beautiful microscopy, new insights into rhoptry formation and function, new technique to study malaria parasites in the mosquito

      Weakness of the study: Some loose ends in the description of spindles and CSP layers, text could be more focussed on the key advancements reported

  3. Jan 2026
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      Reply to the reviewers

      Reviewer #1 Reviewer 1 Point 1- The authors describe cortical neuronal counts across several mammalian species, which is quite impressive, but the information on the methods of counting is lacking: how representative are the data used / shown; how many individuals / brains / sections were used for each species considered? Much more detailed description of the quantifications should be provided to judge the validity of this first conclusion.

      Response: We sincerely thank the reviewer for this insightful and constructive suggestion. We agree that the methodological description of our comparative histological analysis, which is the fundamental basis of this study, was insufficient in the original manuscript. Following the reviewer’s advice, we have extensively revised the Materials and Methods section entitled “Nissl staining and neuronal cell number count” (Page 32, Line 15).

      Reviewer 1 Point 2- The authors use several markers of cortical neuron identity to confirm their neuron number measurements, but from the data shown in Figure 1D,E it seems that only some markers (Satb2) show species-differences while others do not (CTIP2 / Tbr1). How do the authors explain this discrepancy - does this mean that it is mainly Satb2 neurons that are increased in number? But if so how to explain the relative increase in subcortical projections shown in Figure S7?

      Response: We appreciate the reviewer’s insightful comments regarding the marker expression patterns. Upon re-evaluating our data in light of your feedback, we agree that the species differences in deep-layer (DL) markers such as Ctip2 and Tbr1 in the adult stage appear relatively modest compared to the robust differences observed in Satb2 and the projection data shown in Figure S8.

      To address this point, we have incorporated a comparison between the adult data (Figure 1) and our findings from P7 (Figure S2). As shown in the revised manuscript, the species differences for all markers are significantly more pronounced at P7 than in the adult. Notably, in the lower layers, rats exhibit a significantly higher number of marker-positive cells across all markers, including those newly added in this revision, compared to mice.

      We offer the following interpretation regarding these temporal differences:

      1. Developmental Relevance: The marker molecules analyzed are well-established regulators of neuronal subtype fate and projection identity during development. Their critical fate-determining functions are primarily exercised during the migration and maturation phases of nascent neurons.
      2. Postnatal Expression Shifts: Whether these molecules maintain functional roles in the fully matured adult brain remains less certain. It is plausible that marker expression may diminish in certain neuronal populations during late postnatal development, leading to the attenuated species differences observed in adults. Consequently, we believe the strong correlation between P7 quantitative data and projection fate provides a biologically sound validation of our hypothesis.

      While we have kept the discussion in the main text concise to maintain focus for the general reader, we have provided comprehensive data in Figure 1 and Figure S2. This ensures that the necessary evidence is readily available for specialists interested in these developmental dynamics.

      Reviewer 1 Point 3- The authors focus their study almost exclusively on somatosensory cortex, but can they comment on other areas (motor, visual for instance)? It would be nice to provide additional comparative data on other areas, at least for some of the parameters examined across mouse and rat. Alternatively the authors should be more explicit in the abstract and description of the study that it is limited to a single area.

      Response: We sincerely appreciate the reviewer’s insightful comment. As suggested, we have revised the Abstract to explicitly state that our current analysis is focused on the somatosensory cortex. Furthermore, as demonstrated in Figure 1B, we have added a discussion regarding the possibility that the species differences observed in the primary somatosensory cortex may be a general feature shared across the entire cerebral cortex, as follows: “This DL-biased thickening in rats was evident in the primary somatosensory area, but is consistently observed throughout the rostral-caudal cortical regions. (Page 19, Lines 29-31)“

      Reviewer 1 Point 4- The authors provide convincing evidence of increased Wnt signaling pathway in the rat. They should show more explicitly how other classical pathways of neurogenic balance / temporal patterning are expressed in their mouse and rat transcriptome data sets. These would include Notch, FGF, BMP, for which all the data should be available to provide meaningful species comparison.

      Response: We sincerely thank the reviewer for this insightful suggestion. Following your advice, we have newly included comparative data on key signaling pathways essential for cortical development—namely Wnt, FGF, NOTCH, mTOR, SHH, and BMP—across different species. These results are now presented in Figure S17. Rat progenitors show comparable patterns to other species for FGF, mTOR, and Notch signaling, but elevated Wnt and BMP expression, especially at early stages. A detailed heatmap of raw Wnt pathway gene expression across species is also included in the same supplementary figure. We believe these additions provide a more comprehensive evolutionary perspective and significantly strengthen our findings.

      Reviewer 1 Point 5- The alignment of mouse and rat trajectories is very nicely showing a delay at early-mid-corticogenesis. But there is also heterochronic transcriptome at latest stages (end of 5). How can this be interpreted? Does this mean potentially prolonged astrogliogenesis in the rat cortex?

      Response: We sincerely appreciate the reviewer’s insightful comment and the meticulous attention given to our data. Regarding the heterochronic shift observed at Day 5, we agree that this point was not sufficiently addressed in the original manuscript.

      We would like to clarify the two primary reasons for this omission, which are inherent to the current study’s design:

      1. Resolution of Stage Alignment at Temporal Extremes: In our developmental stage alignment analysis, corresponding stages are defined by pairs showing the highest transcriptomic similarity within the sampled range. By definition, the precision of this alignment tends to decrease at the earliest and latest time points of a dataset. Since the "true" biological equivalent might lie outside our sampling window, we must be cautious in interpreting shifts at these temporal boundaries.
      2. Difference in Validation Rigor: Our study prioritized the early stages of deep-layer (DL) neuron production. Consequently, we rigorously defined the onset of neurogenesis in rats (Day 1) using multiple independent methods, including clonal analysis, immunohistochemistry, and gene expression. In contrast, Day 5 was defined simply as five days post-initiation of neurogenesis, without equivalent multi-modal validation. Given that our primary focus is the early phase of neurogenesis, the precision of the transition from late neurogenesis to gliogenesis is relatively lower. For these reasons, we believe that an in-depth discussion of the heterochronic shift at Day 5 might lead to over-interpretation. To reflect this more accurately and avoid misleading the reader, we have revised Figure 6F to de-emphasize the Day 5 shift. In addition, we revised the manuscript as “Importantly, while this analysis identified stage pairs with the highest similarity, the correspondence at the edges of the temporal sampling window is inherently less certain than at the center. Consequently, we focus on the notable reflection point at the center of our dataset. (Page 13, Lines 37-39)”.

      We believe these changes more faithfully represent the biological scope of our data while maintaining the scientific integrity of our primary conclusions.

      Reviewer 1 Point 6- Figure 7: description implies that module 3 is a subset of module 4, but this is not obvious at all from the panels shown. Please clarify.

      Response: We sincerely appreciate the reviewer’s careful reading of our manuscript. As suggested, we have revised Figure 7 to clarify the hierarchical relationship between Module 3 and Module 4, ensuring that their inclusion is now explicitly presented.


      Reviewer #2 Reviewer 2 Point 1. The introduction lacks sufficient background and fails to convey the significance of the study. Specifically, why the research was undertaken, what knowledge gap it addresses, and how the findings could be applied. Addressing these questions already in the introduction would enhance the impact of the work and broaden its readership.

      Response: We sincerely appreciate the reviewer’s insightful comment on this point. Our study reports evolutionary insights gained through an unconventional approach: a single-cell level comparison between mice and rats. We agree that clarifying the necessity of this specific approach is crucial for the manuscript. Accordingly, we have added the following two points to the Introduction:

      1. At the end of the first paragraph, we emphasized the current lack of research on the evolutionary adaptation of cortical circuits, despite the established functional importance of evolutionarily conserved circuits. (Page 3, Lines 7-10); “Paradoxically, despite the importance of these variations, research has predominantly focused on the conserved aspects of cortical architecture. Consequently, the degree of evolutionary plasticity inherent in these circuits and the cell-intrinsic mechanisms driving their modification remain profoundly enigmatic.”)
      2. At the end of the third paragraph, we revised and added text (Page3, Lines 26-27; “This lack of comparative insight represents a significant gap in our understanding of how conserved developmental programs give rise to species-specific brain architectures.”).

      Reviewer 2 Point 2. In figure 5 the authors conclude that "differences in cell cycle kinetics and indirect neurogenesis are unlikely to be the primary factors driving the species-specific variation in DL neuron production. Instead, the temporal regulation of progenitor neurogenic competence, which determines the duration of the DL production phase, provides a more plausible explanation for the greater number of DL subtypes observed in rats". It is not clear to this reviewer how the authors come to this conclusion. Authors observe a significant proportion of mitotic cells in rat VZ from day 1, and a higher constant proportion of mitotic progenitors in SVZ rats compared to mouse (Figure 5C). This points to an early difference in mitotic progenitors that may also lead to increased IP numbers, and potentially an increased number in DL cells, even before day 1. In addition, the higher abundance of IPs in the G2/S phase (statistically significant in 4 of the 7 time points) (Figure 5F), would suggest that this difference might play a role in the species-specific variation of DL neuron production. The authors should estimate cell cycle length instead of just measuring proportions to conclude something about cell cycle kinetics. They can then model growth curves to predict the effect caused if there were differences in cell cycle length between equivalent cell types across species.

      Response: We sincerely thank the reviewer for their careful reading of our manuscript and for pointing out the overstatements in our original descriptions. We agree that a more nuanced interpretation of the data was necessary. In response to these constructive suggestions, we have made the following revisions:

      1. Refinement of Descriptions: We have revised the text to more accurately reflect our findings, specifically noting that the increase in RG division on Day 1 and IP proliferation throughout the neurogenic period showed a significant trend. These features are now described more fairly and cautiously in the revised manuscript. (Page 11, Lines 42-46; “Remarkably, while the temporal dynamics of mitotic density were strikingly conserved between the two species, subtle yet discernible species-specific signatures emerged. Specifically, rats exhibited a higher ratio of mitotic cells in the VZ at the onset of neurogenesis, the precise period when DL subtypes are generated in both species. Further assessment of G2/S-phase cells via pulse-EdU labeling (Figure 5D, E) “)
      2. Inclusion of Time-lapse Imaging Data: The reviewer is correct that measuring the proportions of M and G2/S phases provides only a limited snapshot of cell cycle dynamics. To gain a more precise insight, we performed primary cultures of neural progenitor cells (NPCs) from Day 1 and conducted live-cell time-lapse imaging. This allowed us to directly quantify the cell cycle duration of mouse and rat NPCs (Figure S9A-C).
      3. Comparative Analysis and Mathematical Modeling: Our new data revealed that the cell cycle lengths of the two species are remarkably similar, with no significant differences observed under these culture conditions. Furthermore, to validate the impact of these findings on overall brain development, we developed a mathematical model based on our experimental data. This model predicts the total number of cells produced over the five-day neurogenic period, providing a more robust theoretical framework for our conclusions (Figure S9D). We believe these additions significantly strengthen the manuscript and address the reviewer's concerns regarding the physiological relevance of our observations.

      Reviewer 2 Point 3. In Figure 6 the authors focus only on the mouse and rat datasets. Given the availability of datasets from primates that the author used already for Figure 7, it would give the reader a broader prospective if also these datasets would be integrated in the analysis done for Figure 6, particularly it would be interesting to integrate them in the pseudotime alignment of cortical progenitor. How do human and/or macaque early and late neurogenic phase would compare to mouse and rat in this model?

      Response: We sincerely appreciate the reviewer’s insightful suggestion. In accordance with this comment, we have now incorporated pseudotime alignments of cortical progenitors between primates (human, macaque) and rodents (mouse, rat), presented as pairwise gene expression distance matrices with dynamic time warping in Figure S13. These heatmaps illustrate temporal compression or stretching in progenitor gene expression progression across species. Notably, macaque progenitors show no definitive deviations from rodents, whereas human progenitors exhibit distinct protraction relative to rats and even more so to mice. These additions provide a more comprehensive cross-species perspective without altering the study's core conclusions.

      Reviewer 2 Point 4. In Figures 6C and 6D, the authors distinguish between cycling and non-cycling NECs and RGCs. Could the authors clarify the rationale behind making this distinction? Could the authors comment on how they interpret the impact of cycling versus non-cycling states on species-specific non-uniform scaling? Do they consider the observed non-linear correspondences to be driven by differences in cell cycle activity?

      Response: We are grateful to the reviewer for their insightful observation. We agree that our initial classification of neural progenitor cell (NPC) populations based on proliferation marker expression levels followed a convention used in other studies but was, in the context of this work, unnecessary and potentially misleading. To avoid further confusion and focus on the core biological question, we have re-organized the data by pooling these populations into a single group. Regarding the concern about species differences in cell cycle kinetics, we believe there is no significant divergence between mice and rats that could explain the observed developmental patterns in temporal progression of neurogenesis. This is supported by two lines of evidence:

      1. Quantitative analysis of pH3-positive cells (Figure 5).
      2. New time-lapse imaging data of primary cultured NPCs, which shows no substantial difference in cell cycle length between the two species (Figure S9). These results indicate that the species-specific differences in deep-layer (DL) neuron production are not driven by cell division kinetics. Consequently, we conclude that the non-linear developmental progression of NPCs occurs independently of cell cycle regulation.

      Reviewer 2 Point 5. For the non-uniform scaling in Figure 6F, the authors identify critical inflection points and mention that "the largest delay in rat progenitors occurring where Day 1 and Day 3 progenitors overlapped". It would be good if the authors could discuss what they think all the inflection points represents. How much can it be explained by the heterogeneity within progenitors per time point? There is a clear higher spread of histograms at days 3 and 5, and the histogram at day 5 almost overlaps with day 1. I wonder if the same conclusion about non-uniform scaling would be detected if the distance matrix was built separately for specific cell types, for example only looking at NECs or RGCs.

      Response: We sincerely appreciate the reviewer’s insightful perspective on this point. In alignment with the suggestions from both this reviewer and Reviewer 1 (Point 5), we have updated the manuscript to discuss all identified inflection points. Specifically, we have clarified why our discussion focuses on the correspondence between Mouse D1 and Rat Day 3.

      A recognized limitation of our current analytical approach is that it identifies the closest matching expression profiles within the specific timeframes sampled for each species. For stages at the beginning or end of our sampling window, the "true" corresponding stage in the other species may lie outside our sampled range, which naturally limits the strength of any conclusions regarding those boundary points. Consequently, while we can confidently confirm the correspondence between Mouse Day 1 and Rat Day 3—both of which sit centrally within our sampled window—we have intentionally avoided over-interpreting data near the temporal boundaries.

      Regarding the cell types analyzed, this specific analysis was conducted exclusively on NECs and RGs (now shown in Figure 6F). Extensive prior research (Susan McConnell lab, Sally Temple lab, Fumio Matsuzaki lab, Dennis Jabaudon lab, and more) has established that the time-dependent mechanisms governing the fate determination of cortical excitatory neuron subtypes are encoded within RGs. Therefore, we focused our investigation on these lineages and did not include other cell types in this study. We believe this focused approach maintains the highest degree of biological relevance for our conclusions.

      Reviewer 2 Point 6. The authors conclude that the elevated and prolonged expression of Wnt-ligand genes in rat RGs extend the DL neurogenic window and contribute to rat-specific expansion of deep cortical layer. In order to validate this finding it would be good for the authors to perform a perturbation experiment and reduce Wnt signalling/ Axin 2 levels in rats or depleted the Lmx1a and Lhx2 double-positive population. Response: __We thank the reviewer for this insightful suggestion. We agree that providing direct experimental evidence is crucial to demonstrating that elevated Wnt signaling in RG progenitors drives the production of DL subtype neurons in rats. To address this, we performed a functional intervention on Day 3, a stage when Wnt signaling (indicated by Axin2 expression) is significantly higher in rats than in mice (__Figure 7C, D). By introducing a dominant-negative form of TCF7L2 (dnTCF7L2) to inhibit Wnt signaling specifically in RG progenitors, we tracked the fate of the resulting neurons (Figure 7I, J). Our results showed a clear reduction in the proportion of DL neurons, accompanied by a reciprocal increase in upper-layer (UL) neurons. These findings demonstrate that maintained high levels of Wnt signaling are essential for the prolonged neurogenic capacity for DL neurons in rats. This new data has been incorporated into Figure 7.

      Reviewer 2 Point 7. The authors conclude that Wnt signaling is a rat specific effect since they did not observe any clear temporal change in wnt receptors in gyrencephalic species, and only a subset of RG in rats co-express Lmx1a and Lhx2. However, specific Wntligands and receptors (Wnt5a, Fzd and Lrp6) seem to be upregulated in human as well (Fig 7G), non RG cells could act as wnt ligand inducers in other species, and it has not been demonstrated that Lmx1a and Lhx2 are the source for Wntligand production. I wonder if the authors can completely rule out a role for Wnt in the protracted neurogenesis of other species.

      Response: We sincerely appreciate the reviewer’s insightful and broad perspective regarding Wnt signaling dynamics across diverse species. In this study, our primary focus was to elucidate the specific mechanisms underlying the differences between mice and rats. Consequently, we did not initially explore Wnt dynamics in other species or their roles in developmental timing in great depth in the original manuscript. We fully acknowledge that lineage-specific adaptations occur at the individual gene level; for instance, Silver and colleagues have reported that human-specific upregulation of Wnt receptor gene FZD8 modulates neural progenitor behavior (Boyd et al., Current Biology 2008, Liu et al., Nature 2025). However, our comparative analysis of five mammalian species—carefully aligned by developmental stage—reveals a distinct global trend. While individual gene variations exist like human FZD8, the expression levels of multiple Wnt-related genes, particularly ligands, are markedly higher in rats than in the other four species.

      Following the reviewer’s insightful suggestion, we examined the potential role of Lmx1a in activating Wnt ligand transcription in rat cortical progenitors by analyzing their expression correlation at the single-cell level. Our analysis revealed that several Wnt ligand genes are co-expressed with Lmx1a with a remarkably strong positive correlation. While we have not yet experimentally demonstrated the direct transcriptional activation of Wnt ligands by Lmx1a in these cells, this robust correlation at single-cell resolution strongly suggests that Lmx1a regulates Wnt ligand expression. These new findings are now included in Figure 7 and Figure S16, and the corresponding results section (Page 15, Lines 42-44) has been revised accordingly.

      __Reviewer 2 Point 8 __Minor comments: The RNAscope experiment is currently qualitative. Is it the mRNA copy number per cell equal in both species but more cells are positive in rat, or are there differences in number of mRNA molecules as well? It is not indicated if the RNAscopeprobes are the same for mouse and rat.

      Response: We sincerely thank the reviewer for this insightful suggestion. Following the comment, we performed RNAscope analysis for Axin2 in both mice and rats and quantified the results (now included in Figure 7D). The new data successfully validate the species differences initially observed in our scRNAseq analysis: specifically, the period of high-level Axin2 expression is significantly extended in rats compared to mice. These findings provide histological evidence that reinforces our conclusions regarding the distinct temporal dynamics between the two species.

      Regarding probe design, the Axin2 RNAscope probes target conserved and corresponding sequences between mouse and rat, with species-specific probes optimized for each organism to ensure maximal specificity and sensitivity. We have updated the Methods section ("Fluorescent in situ hybridization with RNAscope") to include these details.

      Reviewer #3

      Reviewer 3 Point 1. Satb2 is also widely recognized as a deep layer marker. The authors need to perform analysis and quantification in Figs 1 and 4 with other II/III and IV markers such as Cux1 and Rorb.

      Response: We thank the reviewer for their insightful comments regarding the marker specificity. We fully agree that while Satb2 is a robust marker for callosal projection identity, its broad distribution across both deep and upper layers limits its utility as a layer-specific marker. As the reviewer suggested, Cux1 (Layers 2/3) and Rorb (Layer 4) are indeed superior markers for defining laminar identity.

      To address this, we have incorporated new immunohistochemical data for these markers in both the quantification of somatosensory cortical neurons (Figure S2) and the birth-dating analysis (Figure 4).

      Our new findings are as follows:

      1. Layer Quantification (Figure S2): By utilizing Cux1 and Rorb as more specific upper-layer (UL) markers, we confirmed that there are no significant differences in the number of these neurons between mice and rats.
      2. Birth-dating Analysis (Figure 4): These markers allowed us to more precisely define the timing of Cux1/Rorb-positive cell generation, revealing subtle but important differences between the two species. While these additions do not alter the fundamental narrative of the original manuscript, they have significantly enhanced the precision and rigor of our analysis. We are grateful to the reviewer for guiding us toward this more robust validation.

      Reviewer 3 Point 2. Rats have larger cortices. Therefore, quantification of neurons should also be normalized to cortical thickness in Fig 1E and also represented with individual data points.

      Response: We sincerely appreciate the reviewer’s constructive suggestion. We agree that normalizing the number of cortical neurons by thickness provides a more rigorous comparison. Accordingly, we have calculated the neuronal density (cell count per unit thickness) for Tbr1- and Ctip2-positive cells and included these data in Figure S2C. Our analysis confirms that these populations are distributed at a significantly higher density in mice compared to rats.

      Furthermore, we have updated the visualization in Figure 1E to display individual data points, ensuring full transparency of the underlying distribution. We believe these revisions, prompted by the reviewer’s insight, have substantially strengthened the clarity and persuasiveness of our manuscript.

      Reviewer 3 Point 3. The clonal analysis in Figs 2 and 3 quantifies GFP and RFP and reports these as neurons. However, without using cell-specific markers, it seems the authors cannot exclude that some progeny are also glia derived from a radial glial progeny. I don't expect all experiments to have this but they must have some measures of both populations to address this possibility. This needs to be addressed to build confidence in the conclusion that there is clonal production of neurons.

      Related to this, the relationship between position and fate is not always 1 to 1. The data summarized in Fig 2G are based on position and not using subtype markers. They should include assessment of markers as they do in Fig 4.

      Response: We sincerely thank the reviewer for this insightful comment. We agree that a clear definition of cell types is essential for the accuracy of clonal analysis.

      In this study, we primarily identified neurons based on their distinct morphological characteristics and performed measurements specifically on these cells. To validate this approach, we confirmed that the vast majority of cells identified as neurons were positive for NeuN and cortical excitatory neuron markers, while remaining negative for glial markers such as Olig2 and SOX9. (Notably, at postnatal day 7, most cells in the glial lineage exist as undifferentiated Olig2-positive progenitors). These observations support our conclusion that the cells analyzed based on morphology are indeed cortical excitatory neurons.

      As the reviewer rightly pointed out, evaluating cell composition using fate-specific marker expression is the ideal approach. However, our current experimental setup required multiple fluorescence channels for DAPI staining (to assess tissue architecture) and immunostaining for GFP and RFP (to identify labeled clones). Due to these technical constraints regarding available detection channels and host species compatibility, we relied on morphological criteria for the primary analysis.

      To address this concern and ensure the reliability of our findings, we performed additional analyses using a subset of samples. By co-staining retrovirally labeled neurons with cell-fate markers, we obtained results consistent with our other data (Figures 1 and 4) regarding laminar position and marker expression. Based on this consistency, we are confident that our classification based on morphology and laminar position does not alter the fundamental conclusions of this study.

      Reviewer 3 Point 4. In Fig 5, the authors use PH3 as well as EdU to measure differences in indirect neurogenesis. Using EdU and Tbr2 they report more dividing IPs. However they need to measure this over the total number of Tbr2 cells as it is not normalized to differences in Tbr2 cells between species. Are there total differences in Tbr2+ cells when normalized to DAPI as well? Moreover, little analyses is performed to measure any impact on radial glia. As no striking differences were observed in IPs this leaves the cellular mechanism a bit unclear and begs the impact on radial glia. Measuring PH3+ cells in VZ and SVZ is not cell specific nor does it yield information to support the prolonged neurogenesis.

      Response: We sincerely thank the reviewer for this insightful suggestion. We agree that quantifying Tbr2+/EdU+ double-positive cells alone was insufficient to fully capture the IP dynamics. Following the reviewer’s advice, we have now quantified the total population of Tbr2+ cells, normalized to the number of DAPI-stained nuclei. This new analysis reveals that mice and rats exhibit nearly indistinguishable temporal dynamics (Figure S10). When integrated with the original Tbr2+/EdU+ data in Figure 5, these findings suggest that rats maintain a slightly higher IP pool throughout the neurogenic period. This implies that the increased neuronal production in rats is not restricted to a specific phase, but rather occurs consistently across all developmental stages. We believe these additional data significantly strengthen our conclusions.


      Reviewer 3 Point 5. The sc-seq is done in rat and compared to published mouse data from corresponding stages. They conclude species specific differences in progenitor gene expression. I am unsure how appropriate this is. Are similar sequencing platforms used? Can they find similar results if using multiple dataset? There are other datasets that may be used to validate these findings beyond DiBella et al.

      Response: We sincerely thank the reviewer for this insightful comment. We agree that establishing the validity of our analytical approach is crucial for the reader’s confidence in our findings. To address this, we have explicitly stated in the revised manuscript that both our rat scRNAseq data and the publicly available datasets were generated using consistent experimental platforms. This ensures that the integration process is technically sound.

      Revised text (Page 13, Lines 16-18): “After quality control, we integrated these profiles with previously published mouse cortical cell data from corresponding neurogenic stages, which is prepared using the consistent platform with ours (35) (Figure S11).”

      Furthermore, to ensure the robustness of our comparative analysis, we have incorporated an additional independent dataset (Ruan et al., PNAS 2021) in addition to the Di Bella et al. Nature 2021 data used in the original manuscript. We confirmed that the results obtained using this second dataset are highly consistent with our initial findings, further validating our conclusions across different studies (Figure S13A).

      Reviewer 3 Point 6. Wnt ligand analysis requires validation in situ across developmental stages, to support their conclusions. Ideally they might consider doing some manipulations to provide context to this observation.

      Response: We sincerely thank the reviewer for these insightful suggestions. We agree that validating the spatial expression patterns of Wnt ligands and confirming their expression in rat-specific RG, as suggested by our scRNAseq data, is crucial for strengthening our conclusions.

      Regarding the expression of Wnt3a, a key ligand in cortical development: although immunohistochemical analysis clearly identified Wnt3a expression in the cortical hem, the expression levels in RG within the cortical area were substantially lower than those in the hem, making definitive visualization challenging. To complement these findings and provide more robust evidence, we performed the following additional experiments:

      1. Validation of Wnt signaling levels: Using RNAscope-based in situ hybridization for Axin2, we successfully confirmed the elevated Wnt signaling levels in rat-specific RG (Figure 7C, D), consistent with our scRNAseq findings.
      2. Elucidating strikingly high correlated expressions of Lmx1a and Wnt ligand genes in the rat cortical progenitors in our scRNAseq dataset (Figure S16B).
      3. Functional analysis: To test the functional significance of this signaling, we inhibited Wnt signaling by electroporating dominant-negative TCF7L2 into rat RG at E15.5. This manipulation resulted in a subtype shift of the generated neurons toward an upper-layer identity (Figure 7I, J). These new results demonstrate that the rat-specific extension of high Wnt signaling levels serves as a fundamental mechanism for the prolonged production of deep-layer (DL) neurons. We are grateful to the reviewer for these suggestions; these additional data have significantly strengthened our core argument that the heterochronic regulation of Wnt signaling states drives the evolution of cortical neuronal composition.

      __Reviewer 3 Point 7 __Minor concerns-1

      Please separate images in Fig 1D it is very strange to have them all on top of each other.

      Response: We sincerely thank the reviewer for this suggestion. As requested, we have provided individual channel images alongside the merged multicolor panels. We agree that this modification significantly enhances the clarity of our data and makes the results much easier to interpret.

      __Reviewer 3 Point 8 __Minor concerns-2

      Are data in Fig 4E Edu+Tbr1+EdU+? This should be clarified and would be most accurate.

      Response: We appreciate the reviewer’s suggestion. We added the label of Y axes of the plots in Figure 4E-K. The procedure of cell count in these analyses are documented in the caption of Figure 4E-K, “Normalized counts of neurons colabeled for EdU and projection-specific markers, relative to the peak of EdU+ and marker+ cells.”.

      __Reviewer 3 Point 9 __Minor concerns-3

      Fig 4 graphs only have titles without Y axis. Please adjust location of title or repeat for clarity.

      Response: We thank the reviewer for this helpful suggestion. To clarify the definition of the Y-axis, we have now added a descriptive label to the axis in the revised figure.

      __Reviewer 3 Point 10 __Minor concerns-4

      Fig 4A implies cumulative incorporation which I don't think is being performed here. They should clarify this in the figure.

      Response: We appreciate the reviewer’s insightful comment. To avoid any potential misunderstanding regarding the additivity of the effect, we have revised the illustration in Figure 4A for greater clarity.

      __Reviewer 3 Point 11 __Minor concerns-5

      Fig 5 needs labels for the actual stages assayed, as illustrated in Fig 4A.

      Response: We thank the reviewer for this helpful suggestion. Following your comment, we have added the developmental stage information (expressed as embryonic days) for both mice and rats in the revised manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study the authors investigate differences between two closely related species, rats and mice, in terms of cortical development and neuronal composition. They first perform comparative analysis of cortical layers which revealed the density and markers of deep layer neurons of rats is disproportionately larger compared to adult mice. They then use retroviruses for lineage analysis from embryonic stages to P7. They find in general that there are temporal differences in when mice and rats produce upper versus deep layer neurons, with the process being protracted in rats. EdU injections were used to report differences in the timing of cortical neuron generation between species and they note no striking differences in IPs. Sc-sequencing of rat cortices at different stages was then used to measure temporal changes in gene expression and compared to published mouse data. They note that rats have sustained Wnt ligand expression in radial glia highlighting that as a potential mechanism of action.

      Major concerns 1. Satb2 is also widely recognized as a deep layer marker. The authors need to perform analysis and quantification in Figs 1 and 4 with other II/III and IV markers such as Cux1 and Rorb. 2. Rats have larger cortices. Therefore, quantification of neurons should also be normalized to cortical thickness in Fig 1E and also represented with individual data points. 3. The clonal analysis in Figs 2 and 3 quantifies GFP and RFP and reports these as neurons. However, without using cell-specific markers, it seems the authors cannot exclude that some progeny are also glia derived from a radial glial progney. I don't expect all experiments to have this but they must have some measures of both populations to address this possibility. This needs to be addressed to build confidence in the conclusion that there is clonal production of neurons. Related to this, the relationship between position and fate is not always 1 to 1. The data summarized in Fig 2G are based on position and not using subtype markers. They should include assessment of markers as they do in Fig 4. 4. In Fig 5, the authors use PH3 as well as EdU to measure differences in indirect neurogenesis. Using EdU and Tbr2 they report more dividing IPs. However they need to measure this over the total number of Tbr2 cells as it is not normalized to differences in Tbr2 cells between species. Are there total differences in Tbr2+ cells when normalized to DAPI as well? Moreover, little analyses is performed to measure any impact on radial glia. As no striking differences were observed in IPs this leaves the cellular mechanism a bit unclear and begs the impact on radial glia. Measuring PH3+ cells in VZ and SVZ is not cell specific nor does it yield information to support the prolonged neurogenesis. 5. The sc-seq is done in rat and compared to published mouse data from corresponding stages. They conclude species specific differences in progenitor gene expression. I am unsure how appropriate this is. Are similar sequencing platforms used? Can they find similar results if using multiple dataset? There are other datasets that may be used to validate these findings beyond DiBella et al. 6. Wnt ligand analysis requires validation in situ across developmental stages, to support their conclusions. Ideally they might consider doing some manipulations to provide context to this observation.

      Minor concerns 1. Please separate images in Fig 1D it is very strange to have them all on top of each other. 2. Are data in Fig 4E Edu+Tbr1+EdU+? This should be clarified and would be most accurate. 3. Fig 4 graphs only have titles without Y axis. Please adjust location of title or repeat for clarity. 4. Fig 4A implies cumulative incorporation which I don't think is being performed here. They should clarify this in the figure. 5. Fig 5 needs labels for the actual stages assayed, as illustrated in Fig 4A.

      Significance

      Strengths:

      The finding that there are differences in cortical composition between rats and mice and that this is linked to prolonged neurogenesis in rats Use of careful and detailed lineage analysis to define differences in temporal production of neurons Inclusion of single cell sequencing

      Limitations:

      Largely descriptive Requires additional investigation to support some conclusions about neurons Concerns about inferring too much from single cell sequencing done by the authors but compared to publication

      Advance: Finding that there are differences in neurogenesis between closely related species is interesting and provides insight into mechanisms of cortical evolution.

      Audience: Evolution, cortical development

      Expertise: Cortical development, evolution

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Yamauchi et al. performed a comparative anatomical analysis of the layer architecture in the primary somatosensory cortex across 8 mammalian species. Unlike primates, which show an expansion of upper layers (UL), rodents, especially rats, display a pronounced thickening of deep layers (DL). In this study they focus on comparing rats and mice, given the higher abundance of DL neuron subtypes in rats. Using histological analysis, they showed that rats possess significantly more DL neurons per cortical column than mice, while UL neuron counts remain similar. Clonal lineage tracing showed that rat radial glial (RG) progenitors generate more DL neurons, indicating species-specific differences in progenitor neurogenic activity. Birth dating assays confirmed an extended DL neurogenesis phase in rats, followed by a conserved UL generation phase. Single-cell RNA sequencing further revealed that rats maintain an early progenitor state longer than mice, marked by sustained expression of DL-associated genes. Specifically, rat RG progenitors exhibit prolonged and elevated expression of Wnt signaling genes, particularly Wnt ligands. Comparative analysis of published single-cell RNA-Seq across species highlighted that this extended Wnt-high period in rats is exceptional, suggesting a species-specific extension of a conserved neurogenic program.

      Major comments:

      This reviewer thinks the topic is exciting, and the experiments elegant, insightful and well described. The paper is well written and follows a very logical flow, the conclusion for each experiment is supported by the data and they are carefully stated. This reviewer really appreciated the summary illustration included as a panel in each figure, they think that this greatly enhanced the clarity and accessibility of the data presented, especially because species comparison can be difficult to follow.

      In this reviewer's opinion, there are some aspects of the findings that the authors would need to clarify/address to explain in clarify the phenotype observed and to enhance the overall significance of this very well-made paper: 1. The introduction lacks sufficient background and fails to convey the significance of the study. Specifically, why the research was undertaken, what knowledge gap it addresses, and how the findings could be applied. Addressing these questions already in the introduction would enhance the impact of the work and broaden its readership. 2. In figure 5 the authors conclude that "differences in cell cycle kinetics and indirect neurogenesis are unlikely to be the primary factors driving the species-specific variation in DL neuron production. Instead, the temporal regulation of progenitor neurogenic competence, which determines the duration of the DL production phase, provides a more plausible explanation for the greater number of DL subtypes observed in rats". It is not clear to this reviewer how the authors come to this conclusion. Authors observe a significant proportion of mitotic cells in rat VZ from day 1, and a higher constant proportion of mitotic progenitors in SVZ rats compared to mouse (Figure 5C). This points to an early difference in mitotic progenitors that may also lead to increased IP numbers, and potentially an increased number in DL cells, even before day 1. In addition, the higher abundance of IPs in the G2/S phase (statistically significant in 4 of the 7 time points) (Figure 5F), would suggest that this difference might play a role in the species-specific variation of DL neuron production. The authors should estimate cell cycle length instead of just measuring proportions to conclude something about cell cycle kinetics. They can then model growth curves to predict the effect caused if there were differences in cell cycle length between equivalent cell types across species. 3. In Figure 6 the authors focus only on the mouse and rat datasets. Given the availability of datasets from primates that the author used already for Figure 7, it would give the reader a broader prospective if also these datasets would be integrated in the analysis done for Figure 6, particularly it would be interesting to integrate them in the pseudotime alignment of cortical progenitor. How do human and/or macaque early and late neurogenic phase would compare to mouse and rat in this model? 4. In Figures 6C and 6D, the authors distinguish between cycling and non-cycling NECs and RGCs. Could the authors clarify the rationale behind making this distinction? Could the authors comment on how they interpret the impact of cycling versus non-cycling states on species-specific non-uniform scaling? Do they consider the observed non-linear correspondences to be driven by differences in cell cycle activity? 5. For the non-uniform scaling in Figure 6F, the authors identify critical inflection points and mention that "the largest delay in rat progenitors occurring where Day 1 and Day 3 progenitors overlapped". It would be good if the authors could discuss what they think all the inflection points represents. How much can it be explained by the heterogeneity within progenitors per time point? There is a clear higher spread of histograms at days 3 and 5, and the histogram at day 5 almost overlaps with day 1. I wonder if the same conclusion about non-uniform scaling would be detected if the distance matrix was built separately for specific cell types, for example only looking at NECs or RGCs. 6. The authors conclude that the elevated and prolonged expression of Wnt-ligand genes in rat RGs extend the DL neurogenic window and contribute to rat-specific expansion of deep cortical layer. In order to validate this finding it would be good for the authors to perform a perturbation experiment and reduce Wnt signalling/ Axin 2 levels in rats or depleted the Lmx1a and Lhx2 double-positive population. 7. The authors conclude that Wnt signaling is a rat specific effect since they did not observe any clear temporal change in wnt receptors in gyrencephalic species, and only a subset of RG in rats co-express Lmx1a and Lhx2. However, specific Wnt ligands and receptors (Wnt5a, Fzd and Lrp6) seem to be upregulated in human as well (Fig 7G), non RG cells could act as wnt ligand inducers in other species, and it has not been demonstrated that Lmx1a and Lhx2 are the source for Wnt ligand production. I wonder if the authors can completely rule out a role for Wnt in the protracted neurogenesis of other species.

      Minor comments:

      The RNAscope experiment is currently qualitative. Is it the mRNA copy number per cell equal in both species but more cells are positive in rat, or are there differences in number of mRNA molecules as well? It is not indicated if the RNAscope probes are the same for mouse and rat.

      Significance

      How different species achieve such remarkable differences in brain shape and size remains poorly understood. A critical aspect of this process is the duration of the neurogenic phase: the period during which neural progenitors generate neurons. This phase tends to be extended in species with larger brains and contains multiple neuronal stem cell types in varying proportions. It is thought that this accounts for their increased neuronal numbers. In their search for mechanisms that prolong neurogenesis across species, the authors propose a rat-specific role for Wnt ligands in expanding the neurogenic period in the rat brain. Importantly, they rule out that this mechanism operates in other species, such as primates or ferrets, to achieve similar extensions.

      The study is of high quality, incorporating rigorous lineage-tracing experiments in two species and single-cell RNA sequencing. Previous work established a role for Wnt signaling in regulating early neurogenesis in mice. Here, the authors characterize a novel population of radial glial cells (Lmx1a and Lhx2 double-positive) that may explain increased Wnt ligand secretion in rats. However, functional validation of this mechanism is still lacking. To strengthen its evolutionary relevance, it would be important to determine whether similar effects occur during earlier neural stages in other species (such as neuroepithelium thickening), or whether other cell types have co-opted the proposed Lmx1a-Lhx2 regulatory module in other species.

      From the perspective of a researcher with a stem cell and developmental background focused on neural evo-devo, this manuscript represents a solid and novel contribution. The proposed model of a rat-specific mechanism for extending the neurogenic phase contrasts with the prevailing concept of convergence in mechanisms underlying species-specific cortical development. This raises intriguing questions about how multiple molecular pathways have been co-opted to achieve similar developmental outcomes. Furthermore, we know very little about what determines the duration of specific developmental processes. This work suggests that extended Wnt signaling may account for prolonged neurogenesis in rats compared to mice. Future studies should aim to validate the proposed rat-specific co-option of an Lmx1a-Wnt ligand cascade in cortical radial glia, potentially through relief of Lhx2-mediated repression of Lmx1a.

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      Referee #1

      Evidence, reproducibility and clarity

      Yamauchi and colleagues explore how species-specific differences in timing of neurogenesis may contribute to cell composition in the mature brain, using rat and mouse cortex as a main model of study. They first estimate and compare among 8 mammalian species the number of cortical neurons corresponding to deep layer (DL) and upper layer (UL) neurons. They find a species-specific relative increase of DL/UL neurons in rats, compared with all other species tested. They then explore the cellular mechanisms underlying these differences in mouse and rat, using retrovirus-based clonal analyses and EdU nuclear labeling, as well as axonal projection retrograde tracing. They conclude that the increased number of DL neurons in the rat is correlated with an increase in the period of DL neuron generation at early stages of corticogenesis. They also report a lack of obvious difference in cell cycle kinetics and indirect vs direct neurogenesis that could explain the DL/UL differences. Finally, they perform comparative scRNAseq analysis in mouse vs rat embryonic cortical cells. This first confirms at the transcriptomic level an apparent prolonged period of early neurogenesis in the rat cortex. Moreover they find among modules of co-expression detectable at these stages an increase in genes corresponding to Wnt signalling, a pathway previously linked to increased self-renewal and delayed differentiation of radial glial progenitors. They thus conclude that the species-differences in neuronal number in the rat is linked to increased Wnt signaling at a critical time of corticogenesis.

      Overall this is a thorough and elegant study focused on a timely and interesting topic. The data shown are convincing and carefully interpreted. I have however a couple of comments and questions to make the study fully clear and convincing.

      • The authors describe cortical neuronal counts across several mammalian species, which is quite impressive, but the information on the methods of counting is lacking: how representative are the data used / shown; how many individuals / brains / sections were used for each species considered? Much more detailed description of the quantifications should be provicded to judge the validity of this first conclusion.
      • The authors use several markers of cortical neuron identity to confirm their neuron number measurements, but from the data shown in Figure 1D,E it seems that only some markers (Satb2) show species-differences while others do not (CTIP2 / Tbr1). How do the authors explain this discrepancy - does this mean that it is mainly Satb2 neurons that are increased in number? But if so how to explain the relative increase in subcortical projections shown in Figure S7?
      • The authors focus their study almost exclusively on somatosensory cortex, but can they comment on other areas (motor, visual for instance)? It would be nice to provide additional comparative data on other areas, at least for some of the parameters examined acros mouse and rat. Alternatively the authors should be more explicit in the abstract and description of the study that it is limited to a single area.
      • The authors provide convincing evidence of increased Wnt signaling pathway in the rat. They should show more explicitely how other classical pathways of neurogenic balance / temporal patterning are expressed in their mouse and rat transcriptome data sets. These would include Notch, FGF, BMP, for which all the data should be available to provide meaningful species comparison.
      • The alignment of mouse and rat trajectories is very nicely showing a delay at early-mid-corticogenesis. But there is also heterochronic transcriptome at latest stages (end of 5). How can this be interpreted? Does this mean potentially prolonged astrogliogenesis in the rat cortex?
      • Figure 7: description implies that module 3 is a subset of module 4, but this is not obvious at all from the panels shown. Please clarify.

      Significance

      The topic of the study if of general interest and original, and the conclusions original and important. The approaches used are state of the art and applied in an elegant fashion to the topic. This study should be of broad interest to developmental neurobiologists, but also developmental biologists interesting in temporal patterning and developmental timing across species.

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      Reply to the reviewers

      We thank the reviewers and editors for their careful evaluation of our manuscript and their positive comments on the importance and rigor of the work. Below you will find our point-by-point response to each reviewer's suggestions. We believe that we have addressed (in the response and the revised manuscript) all of the concerns. Please note that in some cases, we have numbered a reviewer's comments for clarity, however beyond this, we have not altered any of the reviewers' text.

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

      Lo et al., report a high-throughput functional profiling study on the gene encoding for argininosuccinate synthase (ASS1), done in a yeast experimental system. The study design is robust (see lines 141-143, main text, Methods), whereby "approximately three to four independent transformants of each variant would be isolated and assayed." (lines 140 - 141, main text, Methods). Such a manner of analysis will allow for uncertainty of the functional readout for the tested variants to be accounted for.

      This is an outstanding study providing insights on the functional landscape of ASS1. Functionally impaired ASS1 may cause citrullinemia type I, and disease severity varies according to the degree of enzyme impairment (line 30, main text; Abstract). Data from this study forms a valuable resource in allowing for functional interpretation of protein-altering ASS1 variants that could be newly identified from large-scale whole-genome sequencing efforts done in biobanks or national precision medicine programs. I have some suggestions for the Authors to consider:

      1. The specific function of ASS1 is to condense L-citrulline and L-aspartate to form argininosuccinate. Instead of measuring either depletion of substrate or formation of product, the Authors elected to study 'growth' of the yeast cells. This is a broader phenotype which could be determined by other factors outside of ASS1. Whereas i agree that the experiments were beautifully done, the selection of an indirect phenotype such as ability of the yeast cells to grow could be more vigorously discussed.

      We appreciate the reviewer's point regarding the indirect nature of growth as a functional readout. In our system, yeast growth is tightly and specifically coupled to ASS enzymatic activity. The strains used are isogenic and lack the native yeast argininosuccinate synthetase, such that arginine biosynthesis, and therefore yeast replication on minimal medium lacking arginine, depends exclusively on the activity of human ASS1. Under these defined and limiting conditions, growth provides a quantitative proxy for ASS1 function. However, we acknowledge that this assay does not resolve specific molecular mechanisms underlying reduced function, such as altered catalytic activity versus effects on protein stability. We have updated the text to clarify these points.

      "While growth is an indirect phenotype relative to direct measurement of substrate turnover or product formation, it is tightly coupled to ASS enzymatic activity in this system and is expected to be impaired by amino acid substitutions that reduce catalytic activity or protein stability. Therefore, growth on minimal medium lacking arginine is a quantitative measure of ASS enzyme function, allowing the impact of ASS1 missense variants to be assessed at scale through a high-throughput growth assay, in a single isogenic strain background, under controlled, defined conditions that limit confounding factors unrelated to ASS1 activity. We expect that the assay will detect reductions in both catalytic activity and protein stability but will not distinguish between these mechanisms."

      1. One of the key reasons why studies such as this one are valuable is due to the limitations of current variant classification methods that rely on 'conservation' status of amino acid residues to predict which variants might be 'pathogenic' and which variants might be 'likely benign'. However, there are serious limitations, and Figures 2 and 6 in the main text shows this clearly. Specifically, there is an appreciable number of variants that, despite being classified as "ClinVar Pathogenic", were shown by the assay to unlikely be functionally impaired. This should be discussed vigorously. Could these inconsistencies be potentially due to the read out (growth instead of a more direct evaluation of ASS1 function)?

      We interpret this discrepancy as reflecting a sensitivity limitation of the growth-based readout rather than a fundamental disagreement between functional effect and clinical annotation. Specifically, we believe that our assay is unable to resolve the very mildest hypomorphic variants from true wild type, i.e., the residual activity of these variants is sufficient to fully support yeast growth under the conditions used. On this basis, we have chosen not to treat wild-type-like growth in our assay as informative for benignity; conversely, reduced growth provides evidence supporting pathogenicity (all clinically validated variants examined in this range are pathogenic).

      We have revised the manuscript to clarify this point explicitly and to frame these variants as lying outside the effective resolution limit of the assay rather than representing true false positives. Additional discussion of this limitation and its implications is provided in our responses to Reviewer 2 (points 1 and 4) along with specific changes made to the text.

      1. Figure 3 is very interesting, showing a continuum of functional readout ranging from 'wild-type' to 'null'. It is very interesting that the Authors used a threshold of less than 0.85 as functionally hypomorphic. What does this mean? It would be very nice if they have data from patients carrying two hypomorphic ASS1 alleles, and correlate their functional readout with severity of clinical presentation. The reader might be curious as to the clinical presentation of individuals carrying, for example, two ASS1 alleles with normalized growth of 0.7 to 0.8.

      I hope you will find these suggestions helpful.

      We thank the reviewer for this thoughtful comment. Figure 3 indeed illustrates a continuum of functional effects, and we agree that careful interpretation of the thresholds used is important. To clarify the rationale for the hypomorphic threshold, the interpretation of intermediate growth values, and to emphasize that these labels reflect only behavior in the functional assay, we have rewritten the relevant section of the Results:

      "The normalized growth scores of the 2,193 variants tested in our functional assay form a clear bimodal distribution (Figure 3), with two distinct peaks corresponding to functional extremes, as is commonly reported in large-scale functional assays of protein function [9, 10]. The smaller peak, centered around the null control (normalized growth = 0), represents variants that fail to support growth in the assay (growth 0.85). Variants with growth values falling between these two peak-based thresholds display partial functional impairment and are classified as functionally hypomorphic (n = 323). Crucially, these classifications are entirely derived from the observed peaks in the distribution of growth values and reflect differences in functional activity under the assay conditions. They do not provide direct evidence for clinical pathogenicity or benignity and should not be used for clinical variant interpretation without proper benchmarking against clinical reference datasets, as implemented below within an OddsPath framework."

      We agree with the reviewer that correlating functional measurements with clinical severity in individuals carrying two hypomorphic ASS1 alleles would be highly informative, particularly given that ASS1 deficiency is an autosomal recessive disorder. While mild hypomorphic variants (for example, variants with normalized growth values of 0.7-0.8 in our assay) could plausibly contribute to disease when paired with a complete loss-of-function allele, systematic analysis of combinatorial genotype effects and genotype-phenotype correlations is beyond the scope of the present study, which focuses on the functional effects of individual variants. We view this as an important direction for future work.

      Reviewer #1 (Significance (Required)):

      This is an outstanding study providing insights on the functional landscape of ASS1. Functionally impaired ASS1 may cause citrullinemia type I, and disease severity varies according to the degree of enzyme impairment (line 30, main text; Abstract). Data from this study forms a valuable resource in allowing for functional interpretation of protein-altering ASS1 variants that could be newly identified from large-scale whole-genome sequencing efforts done in biobanks or national precision medicine programs.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Lo et al characterize the phenotypic effect of ~90% of all possible ASS1 missense mutations using an elegant yeast-based system, and use this dataset to aid the interpretation of clinical ASS1 variants. Overall, the manuscript is well-written and the experimental data are interpretated rigorously. Of particular interest is the identification of pairs of deleterious alleles that rescue ASS1 activity in trans. My comments mainly pertain to the relevance of using a yeast screening methodology to infer functional effects of human ASS1 mutations.

      1. Since human ASS1 is heterologously expressed in yeast for this mutational screen, direct comparison of native expression levels between human cells and yeast is not possible. Could the expression level of human ASS1 (driven by the pARG1 promoter) in yeast alter the measured fitness defect of each variant? For instance, if ASS1 expression in yeast is sufficiently high to mask modest reductions in catalytic activity, such variants may be misclassified as hypomorphic rather than amorphic. Conversely, if expression is intrinsically low, even mild catalytic impairments could appear deleterious. While it is helpful that the authors used non-human primate SNV data to calibrate their assay, experiments could be performed to directly address this possibility.

      The nature of the relationship between yeast growth and availability of functional ASS1 could also influence the interpretation of results from the yeast-based screen. Does yeast growth scale proportionately with ASS1 enzymatic activity?

      We completely agree that the expression level of human ASS1 in yeast could influence the measured fitness effects of individual variants. We expect the rank ordering of variants in our growth assay to reflect their relative enzymatic activity (i.e. a monotonic relationship) but acknowledge that the precise mapping between activity and growth is unknown and may include ceiling and floor effects that limit the assay's dynamic range. As the reviewer notes, under high expression conditions moderate loss-of-function variants could appear indistinguishable from wild type (ceiling effect), whereas under lower expression the same variants could behave closer to the null control (floor effect).

      In our system, ASS1 is expressed from the pARG1 promoter, chosen under the assumption that the native expression level of ARG1 (the yeast ASS1 ortholog) is appropriately tuned for yeast growth. Crucially, rather than assuming a fixed mapping from assay growth to clinical pathogenicity (given potential nonlinearities in the relationship between ASS function and growth) we benchmark the assay against external data, including known pathogenic and benign variants and non-human primate SNVs, to calibrate thresholds and guide interpretation within an OddsPath framework. This benchmarking indicates that ceiling effects are likely present, with some mild loss-of-function pathogenic variants appearing indistinguishable from wild type in the growth assay. We explicitly account for this by not using high-growth scores as evidence toward benignity. We have made the following changes the manuscript:

      "A subset of clinically pathogenic ASS1 variants exhibit near-wild-type growth in our yeast assay. In general, we expect a monotonic relationship between ASS function and yeast growth, but with the potential for floor and ceiling effects that constrain the assay's dynamic range. In this context, we interpret high-growth pathogenic variants as likely causing mild loss of function that cannot be distinguished from wild type in our assay"

      "Based on these findings and given that 22/56 pathogenic variants show >85% growth, we conclude that growth above this threshold should not be used as evidence toward benignity."

      1. It would be helpful to add an additional diagram to Figure 1A explaining how the screen was performed, in particular: when genotype and phenotype were measured, relative to plating on selective vs non-selective media? This is described in "Variant library sequence confirmation" and "Measuring the growth of individual isolates" of the Methods section but could also be distilled into a diagram.

      We thank the reviewer for this helpful suggestion. We have updated Figure 1 by adding a new schematic panel (Figure 1C) that distills the experimental workflow into a visual overview. This diagram is intended to complement the detailed descriptions in the Methods and improve clarity for the reader.

      1. The authors rationalize the biochemical consequences of ASS1 mutations in the context of ASS1 per se - for example, mutations in the active site pocket impair substrate binding and therefore catalytic activity, which is expected. Does ASS1 physically interact with other proteins in human cells, and could these interactions be altered in the presence of specific ASS1 mutations? Such effects may not be captured by performing mutational scanning in yeast.

      We are not aware of any specific protein-protein interactions involving ASS that are required for its enzymatic function. However, we agree that ASS could engage in non-essential interactions with other human proteins that might be altered by specific missense variants and that such interactions would not necessarily be captured in a yeast-based assay.

      Importantly, our complementation system depends on human ASS providing the essential enzymatic activity required for arginine biosynthesis in yeast. If ASS1 required obligate human-specific protein interactions to function, even the wild-type enzyme would fail to support yeast growth, which is clearly not the case. We therefore conclude that the assay robustly reports on the intrinsic enzymatic activity of ASS, while acknowledging that non-essential human-specific interactions may not be assessed. We have updated the manuscript to reflect this point.

      "Importantly, successful functional complementation indicates that ASS enzymatic activity does not depend on any obligate human-specific protein interactions."

      1. The authors note that only a small number (2/11) of mutations at the ASS1 monomer-monomer interface lead to growth defects in yeast. It would be helpful for the authors to discuss this further.

      As discussed in response to the reviewer's comments on the relationship between ASS activity and yeast growth (point 1 above), we expect growth to be a monotonic but nonlinear function of enzymatic activity, with potential ceiling effects at high activity. Under this model, variants causing weak or moderate loss of function may remain indistinguishable from wild type when residual activity is sufficient to support normal growth. We favor this explanation for the observation that only 2/11 interface variants show reduced growth, as many pathogenic interface substitutions are associated with milder disease presentations, consistent with higher residual enzyme function. Consistent with this interpretation, variants affecting the active site, where substitutions are expected to cause large reductions in catalytic activity, are readily detected by the assay.

      Although we cannot exclude partial buffering of dimerization defects in yeast, we interpret the reduced sensitivity to interface variants primarily as a general limitation of growth-based assays. Accordingly, our decision not to use growth >85% as evidence toward benignity is conservative relative to approaches that would classify high-growth variants as benign except at the monomer-monomer interface, avoiding reliance on structural subclassification and minimizing the risk of false benign interpretation. Reduced growth, by contrast, provides strong evidence of loss of ASS1 function and pathogenicity, validated under the OddsPath framework.

      We have updated the Results and Discussion sections to clarify these points (also see response to the reviewer's point 1).

      "A subset of clinically pathogenic ASS1 variants exhibit near-wild-type growth in our yeast assay. In general, we expect a monotonic relationship between ASS function and yeast growth, but with the potential for floor and ceiling effects that constrain the assay's dynamic range. In this context, we interpret high-growth pathogenic variants as likely causing mild loss of function that cannot be distinguished from wild type in our assay. Consistent with this view, many pathogenic variants with high assay growth are located at the monomer-monomer interface rather than the active site, and are associated with milder or later-onset clinical presentations, suggesting partial enzymatic impairment that is clinically relevant in humans but not resolved by the yeast assay."

      "Based on these findings and given that 22/56 pathogenic variants show >85% growth, we conclude that growth above this threshold should not be used as evidence toward benignity. Notably, this approach is conservative relative to treating high-growth variants as benign except at the monomer-monomer interface, avoiding reliance on structural subclassification and minimizing the risk of false benign interpretation arising from assay ceiling effects. Conversely, the variants with

      Reviewer #2 (Significance (Required)):

      This study presents the first comprehensive mutational profiling of human ASS1 and would be of broad interest to clinical geneticists as well as those seeking biochemical insights into the enzymology of ASS1. The authors' use of a yeast system to profile human mutations would be particularly useful for researchers performing deep mutational scans, given that it provides functional insights in a rapid and inexpensive manner.

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

      Section 1 - Evidence, reproducibility, and clarity Summary This manuscript presents a comprehensive functional profiling of 2,193 ASS1 missense variants using a yeast complementation assay, providing valuable data for variant interpretation in the rare disease citrullinemia type I. The dataset is extensive, technically sound, and clinically relevant. The demonstration of intragenic complementation in ASS1 is novel and conceptually important. Overall, the study represents a substantial contribution to functional genomics and rare disease variant interpretation.

      Major comments 1. This is an exciting paper as it can provide support to clinicians to make actionable decisions when diagnosing infants. I have a few major comments, but I want to emphasize the label of "functionally unimpaired" variants to be misleading. The authors explain that there are several pathogenic ClinVar variants that fall into this category (above the >.85 growth threshold) but I think this category needs a more specific name and I would ask the authors to reiterate the shortcomings of the assay again in the Discussion section.

      We thank the reviewer for raising this important point. We agree that the label "functionally unimpaired" could be misleading if interpreted as implying clinical benignity rather than assay behavior. We have therefore clarified that this designation refers strictly to variant behavior in the yeast growth assay and does not imply absence of pathogenicity.

      In addition, we have expanded the Discussion to explicitly address the existence of clinically pathogenic variants with high growth scores (>0.85), emphasizing that these likely reflect a ceiling effect of the assay and represent a key limitation for interpretation. This clarification reiterates that high-growth scores should not be used as evidence toward benignity, while reduced growth provides strong functional evidence of pathogenicity. Relevant revisions are described in our responses to Reviewers 1 and 2.

      1. I think there's an important discussion to be had here, is the assay detecting variants that alter the function of ASS or is it detecting a complete ablation of enzymatic activity? The results might be strengthened with a follow-up experiment that identifies stably expressed ASS1 variants.

      We agree with the review that distinguishing between stability and enzyme activity would be valuable information. Unfortunately, we do not currently have the resources to perform this type of large-scale study. We have acknowledged in the text that our assay does not distinguish between enzyme activity and protein stability:

      "We expect that the assay will detect reductions in both catalytic activity and protein stability, but will not distinguish between these mechanisms."

      At the very least, it would be great to see the authors replicate some of their interesting results from the high-throughput screen by down-selecting to ~12 variants of uncertain significance that could be newly considered pathogenic.

      We have included new analysis of all 25 VUS variants falling in the pathogenic range of our assay (Supplemental Table S7). Reclassification under current guidelines (in the absence of our data) shifts six variants to Pathogenic/Likely Pathogenic and 11 more are reclassified to Likely Pathogenic with the application of our functional data as PS3_Supporting. The remaining eight VUS are all reclassified to Likely Pathogenic when inclusion of homozygous PrimateAI-benign variants allows the assay to satisfy full PS3 criteria.

      1. I would ask the authors to provide more citations of the literature in the introduction of the manuscript. I would be especially interested in knowing more about human ASS being identified as a homolog of yeast ARG1, as they share little sequence similarity (27.5%) at the protein level. That said, I find the yeast complementation assay exciting.

      We thank the reviewer for this suggestion. Human ASS and yeast Arg1 catalyze the same biochemical reaction and share approximately 49% amino acid sequence identity. We have revised the Introduction to clarify this relationship and to note explicitly that the Saccharomyces Genome Database (SGD) identifies the human gene encoding argininosuccinate synthase (ASS1) as the ortholog of yeast ARG1. An appropriate citation has been added to support this statement. The protein alignments have been provided as File S2.

      "This assay is based on the ability of human ASS to functionally replace (complement) its yeast ortholog (Arg1) in S. cerevisiae (Saccharomyces Genome Database, 2026). Importantly, successful functional complementation indicates that ASS enzymatic activity does not depend on any obligate human-specific protein interactions. At the protein level, human ASS and yeast Arg1 display 49% sequence identity (File S2) and share identical enzymatic roles in converting citrulline and aspartate into argininisuccinate."

      1. I appreciate the efforts made by the authors to share their work and make this study more reproducible, such as sharing the hASS1 and yASS1 plasmids being shared on NCBI Genbank (Line 121) and publishing the ONT reads on SRA (Line 154). I made a requests for additional data to be shared, such as the custom method/code for codon optimization and a table of Twist variant cassettes that were ordered. I would also love to see these results shared on MaveDB.org.

      We thank the reviewer for these suggestions regarding data sharing and reproducibility. As requested, we have provided the custom codon optimization script as File S1 and the amino acid alignment used to perform codon harmonization as File S2. The sequence of the underlying variant cassette is included in the corresponding GenBank entry, and we have clarified this point in the legend of Figure 1. For each amino acid substitution, Twist Bioscience used a yeast-specific codon scheme with a single consistent codon per amino acid; accordingly, the sequence of each variant cassette can be inferred from the base construct and the specified amino acid change. A complete list of variant amino acid substitutions used in this study is provided in Table S3.

      1. I find this manuscript very exciting as the authors have a compelling assay that identifies pathogenic variants, but I was generally disappointed by the quality and organization of the figures. For example, Figure 4 provides very little insight, but could be dramatically improved with an overlay of the normalized growth score data or highlighting variants surrounding the substrate or ATP interfaces. There are some very interesting aspects of this manuscript that could be shine through with some polished figures.

      We thank the reviewer for this feedback and agree that clear and well-organized figures are essential for conveying the key results of the study. In response, we have substantially revised Figure 4 by adding colored overlays showing residue conservation and median normalized growth scores (new panels Figure 4C and 4D), which more directly link structural context to functional outcomes and highlight patterns surrounding the active site and substrate interfaces.

      I would also encourage the authors to generate a heatmap of the data represented in Figure 2 (see Fowler and Fields 2014 PMID 25075907, Figure 2), this would be more helpful reference to the readers.

      The reviewer also suggested that a heatmap representation, similar to that used in Fowler and Fields (2014), might aid interpretation of the data shown in Figure 2. Because our dataset consists of sparse single-amino acid substitutions rather than a complete mutational scan, such heatmaps are inherently less dense and less effective at conveying patterns than in saturation mutagenesis studies. Nevertheless, to aid readers who may find this visualization useful, we have generated and included a single-nucleotide variant heatmap as Supplemental Figure S1.

      My major comments are as follows: 6. Citations needed - especially in the introduction and for establishing that hASS is a homolog of yARG1

      We have added the requested citations and clarified the ASS1-ARG1 orthology in the Introduction, as described in our response to point 3 above.

      1. Generally, the authors do a nice job distinguishing the ASS1 gene from the ASS enzyme, though I found some ambiguities (Line 685). Please double-check the use of each throughout the manuscript.

      We have edited the manuscript to ensure consistent and unambiguous use of gene and enzyme nomenclature throughout.

      1. Generally, I'm confused about what strain was used for integrating all these variants, was is the arg1 knock-out strain from the yeast knockout collection or was it FY4? I think FY4 was used for the preliminary experiments, then the KO collection strain was used for making the variant library but I think this could be made more clear in the text and figures. Lines 226-229 describes introducing the hASS1 and yASS1 sequences into the native ARG1 locus in strain FY4, but the Fig1A image depicts the ASS1 variants going into arg1 KO locus. Fig1A should be moved to Fig2.

      We agree that the strain construction steps were not described as clearly as they could have been. We have therefore clarified the strain construction workflow in the Materials & Methods and Results sections, as well as in the Figure 1 legend, to explicitly distinguish preliminary experiments performed in strain FY4 from construction of the variant library in the arg1 knockout background.

      As we have also added an additional panel to Figure 1 that schematically explains how the screen was performed (per Reviewer #2's request), we believe that Figure 1A is appropriately placed and should remain in Figure 1.

      1. Line 303 - "We classify these variants as 'functionally unimpaired'", this is not an accurate description of these variants as Figure 2 highlights 24 pathogenic ClinVar variants that would fall into this category of "functionally unimpaired". The yeast growth assay appears to capture pathogenic variants, but there is likely some nuance of human ASS functionality that is not being assessed here. I would make the language more specific, e.g. "complementary to Arg1" or "growth-compatible".

      We agree that the label "functionally unimpaired" could be misinterpreted if read as implying clinical benignity. We have therefore clarified within the manuscript that this designation refers strictly to variant behavior in the yeast growth assay (i.e., wild-type-like growth under assay conditions) and does not imply absence of pathogenicity. We also expanded the Discussion to explicitly address the subset of clinically pathogenic variants with high growth scores (>0.85), consistent with a ceiling effect of the assay and a key limitation for interpretation. See response to reviewer #3 point 1. Relevant revisions are also discussed in our responses to Reviewers #1 and #2.

      1. Lines 345-355 - It is interesting that there are variants that appear functional at the substrate interfacing sites. Is there anything common across these variants? Are they maintaining the polarity or hydrophobicity of the WT residue? Are any of these variants included in ClinVar or gnomAD? Are pathogenic variants found at any of these sites

      Yes. For highly sensitive active-site residues that have few permissible variants, the vast majority of amino acid substitutions that do retain activity preserve key physicochemical properties of the wild-type residue, such as hydrophobicity or charge. We have added this important observation to the manuscript:

      "Any variants at these sensitive residues that are permissive for activity in our assay retain hydrophobicity or charged states relative to the original amino acid side chain (Figure 5A & Table S5)."

      None of these variants are present in ClinVar. Only L15V and E191D are present in gnomAD (Table S4).

      1. Lines 423-430 - The OddsPath calculation would seem to rely heavily on the thresholds of .85 for normalized growth. The OddsPath calculation could be bolstered with some additional analysis that emphasizes the robustness to alternative thresholds.

      We agree that the sensitivity of the OddsPath calculation to the choice of growth thresholds is an important consideration. In our assay, benign ClinVar variants and non-human primate variants are observed exclusively within the peak centered on wild-type growth, whereas clinically annotated variants falling below this peak are exclusively pathogenic. On this basis, we defined the upper boundary of the assay range interpreted as supporting pathogenicity as the lower boundary of the wild-type-centered peak in the growth distribution (as defined in Figure 3), rather than selecting a cutoff by direct optimization of the OddsPath. This choice reflects the observed concordance, in our dataset, between the onset of measurable functional impairment in the assay and clinical pathogenic annotation. Importantly, in practice the OddsPath value is locally robust to the precise placement of this boundary, remaining invariant across the range 0.82-0.88. Supporting our chosen threshold of 0.85, the lowest-growth benign or primate variant observed has a normalized growth value of 0.88, while the lowest growth observed among variants present as homozygotes in gnomAD was 0.86. We have clarified this rationale and analysis in the revised manuscript.

      "Notably, the "Among all nine of the human ASS1 missense variants observed as homozygotes in gnomAD which were tested as amino acid substitutions in our assay, the lowest observed growth value was 0.86 (Ala258Val) consistent with the lower boundary of the PrimateAI variants which was a growth value of 0.87 (Ala81Thr) (Figure 6) and with our use of a 0.85 classification threshold."

      "If we treat PrimateAI variants as benign (solely for OddsPath calculation purposes), the OddsPath for growth

      1. Lines 432-441 - This is an interesting idea to use variants observed in primates, has ACMG weighed in on this? I understand that CTLN1 is an autosomal recessive disorder but I'd still be interested in seeing how the observed ASS1 missense variants in gnomAD perform in your growth assay, possibly a supplemental figure?

      To our knowledge, the ACMG/AMP guidelines do not currently address the use of homozygous missense variants observed in non-human primates. We are currently in discussion with two ClinGen working groups to discuss the possibility of formalizing the use of this data source.

      We agree that comparison with human population data is also important. Accordingly, total gnomAD allele counts and homozygous counts for all applicable ASS1 missense variants are provided in Table S4, and the growth behavior of ASS1 missense variants observed in the homozygous state in gnomAD is shown in Figure 6. These homozygous variants uniformly exhibit high growth in our assay, consistent with the absence of strong loss-of-function effects. We have updated the manuscript text to clarify these points.

      Minor comments 1. Lines 53-59 - This paragraph needs to cite the literature, especially lines 56, 57, and 59 2. Line 61 - no need to repeat "citrullinemia type I", just use the abbreviation as it was introduced in the paragraph above 3. Lines 61-71 - again, this paragraph needs more literature citations 4. Line 62 - change to "results"

      The changes suggested in points 1-4 have all been implemented in the revised manuscript.

      1. Line 74-75 - "RUSP" acronym not needed as it's never used in the manuscript, the same goes for "HHS"

      We agree that the acronyms "RUSP" and "HHS" are not reused elsewhere in the manuscript. We have nevertheless retained them at first mention, alongside the expanded names, because these acronyms are commonly used in newborn screening and public health policy contexts and may be more familiar to some readers than the expanded terms. We would be happy to remove the acronyms if preferred.

      1. Line 86 - "ASS1" I think is referring to the enzyme and should just be "ASS"? If referring to the gene then italicize to "ASS1"
      2. Lines 91-93 - It would be helpful to mention this is a functional screen in yeast
      3. Line 101 - It would be helpful to the readers to define SD before using the acronym, consider changing to "minimal synthetic defined (SD) medium" and afterwards can refer to as "SD medium"
      4. 109-114 - It would be great if you could share your method for designing the codon-harmonized yASS1 gene, consider sharing as a supplemental script or creating a GitHub repository linked to a Zenodo DOI for publication.

      The changes suggested in points 6-9 have all been implemented in the revised manuscript. The codon harmonization script has been provided as File S1.

      1. Lines 135-137 - I think it's helpful to provide a full table of the cassettes ordered from Twist as well as the primers used to amplify them, consider a supplemental table.

      Details of Twist cassette and the primer sequences used for amplification have been added to the Materials & Methods.

      1. Line 138 - "standard methods" is a bit vague, I'm guessing this is a Geitz and Schiestl 2007 LiAc/ssDNA protocol (PMID 17401334)? Also, was ClonNAT used to select for natMX colonies?

      The reviewer is correct about which protocol was used, and we have added the citation. We have also clarified that selection was carried out based on resistance to nourseothricin.

      1. Line 150 - change to "sequence the entire open reading frame, as previously described [4]."
      2. Line 222-223 - remove "replace" and just use "complement" (and remove the parenthesis)
      3. Line 249 - It would be great to see a supplemental alignment of the hASS1 and yASS1 sequences.
      4. Line 261 - spelling "citrullemia" should be corrected to "citrullinemia"
      5. Line 280 - "using Oxford Nanopore sequencing" is a bit vague, I suggest specifying the equipment used (e.g. Oxford Nanopore Technologies MinION platform) or simplify to "via long-read sequencing (see Materials & Methods)"

      The changes suggested in points 12-16 have all been implemented in the revised manuscript. An alignment of the ASS and Arg1 protein sequences has been provided as File S2.

      1. Line 287-289 - It would be great to see the average number of isolates per variant, as well as a plot of the variant growth estimate vs individual isolate growth.

      We agree with the reviewer that conveying measurement precision is important. The number of isolates assayed per variant is provided in Table S4, and we have added explicit mention of this in the text. Because variants were assayed with a mixture of 1, 2, or {greater than or equal to}3 independent isolates, a scatterplot of variant-level growth estimates versus individual isolate measurements would be difficult to interpret and potentially misleading. Instead, we report standard error estimates for each variant in Table S4, derived from the linear model used to estimate growth effects, which more appropriately summarizes measurement uncertainty given the experimental design.

      1. Lines 324-25 - consider removing the last sentence of this paragraph, it is redundant as the following paragraph starts with the same statement.

      We have removed this sentence.

      1. Lines 327-335 - This is interesting and would benefit from its own subpanel or plot in which the normalized growth score is plotted against variants that are at conserved or diverse residues in human ASS, and see if there's a statistical difference in score between the two groupings.

      As suggested by the reviewer, we have added Supplemental Figure 2 (Figure S2) in which the normalized growth score of each variant is plotted against the conservation of the corresponding residue, as measured by ConSurf. The manuscript already includes a statistical analysis of the relationship between residue conservation and functional impact, showing that amorphic variants occur significantly more frequently at highly conserved residues than unimpaired variants do (one-sided Fisher's exact test). We now refer to this new supplemental figure in the relevant Results section.

      1. Lines 339-341 - As written, it is unclear if aspartate interacts with all of the same residues as citrulline or just Asn123 and Thr119.
      2. Lines 345-355 - As with my above comment, I find this interesting and would
      3. Line 353 - add a period to "al" in "Diez-Fernandex et al."

      The issues raised in points 20 and 22 have all addressed. Point 21 appears to be truncated.

      1. Figure 1 a. Remove "Figure" from the subpanels and show just "A" and "B" (as you do for Figure 4) and combine the two images into a single image. Also make this correction to Figure 5 and Figure 8. b. Panel A - I thought the hASS1 and yASS1 were dropped into FY4, not the arg1 KO strain. This needs clarification. c. Panel A - I'm assuming the natMX cassette contains its own promoter, you could use a right-angled arrow to indicate where the promotors are in your construct. d. Panel B - I'm not sure the bar graph is necessary, it would be more helpful to see calculations of the colony size (or growth curves for each strain) and plot the raw values (maybe pixel counts?) for each replicate rather than normalizing to yeast ARG1. I would be great to have a supplemental figure showing all the replicates side-by-side. e. Panel B - Would be helpful to denote the pathogenic and benign ClinVar variants with an icon or colored text.

      f. Figure 1 Caption - make "A)" and "B)" bold.

      We have implemented the requested changes in Figure 1 with the following exceptions. We have retained panels A and B as separate subfigures because they illustrate distinct experimental concepts. In addition, we respectfully disagree with point (d). The bar graph is intended to provide a clear, high-level comparison of functional complementation by hASS1 versus yASS1 and to illustrate the gross differences in growth between benign and pathogenic proof-of-principle variants. As the bar graph includes error bars for standard deviations, presenting raw colony size measurements or growth curves for individual replicates would substantially complicate the figure without materially improving interpretability for this purpose.

      1. Figure 2 a. "Shown in magenta are amino acid substitutions corresponding to ClinVar pathogenic, pathogenic/likely pathogenic, and likely pathogenic variants" is repeated in the figure caption. b. "Shown in green are amino acid substitutions corresponding to ClinVar benign and likely benign variants." I don't see any green points. c. Identify the colors used for ASS1 substrate binding residues. d. This plot would benefit from a depiction of the human ASS secondary structure and any protein domains (nucleotide-binding domain, synthase domain, and C-terminal helix from Fig4B)

      e. Line 685 675 - "ASS1" is being used in reference to the enzyme, is this correct or should it be "ASS"?

      We have made the requested changes to Figure 2. The repeated caption text has been removed, and references to green points have been corrected to orange points to match the figure. The colors used to indicate ASS substrate-binding residues are explicitly described in the figure key. Secondary structure annotations have been added. References to the enzyme have been corrected to "ASS" rather than "ASS1" where appropriate.

      1. Figure 3 a. Rename the "unimpaired" category as there are several pathogenic ClinVar variants that fall into this category.

      To address this point, we have clarified the labeling by adding "in our yeast assay" to the figure legend, making explicit that the "unimpaired" category refers only to wild-type-like behavior under assay conditions and does not imply clinical benignity. See also response to Reviewer #3, Major Comment 1.

      1. Figure 4 a. List the PDB or AlphaFold accession used for this structure b. Panel A - state which colors are used for to depict each monomer. It is confusing to see several shades of pink/purple used to depict a single monomer in Panel A. c. It is very difficult to make out the aspartate and citrulline substrates in the catalytic binding activity, consider making an inset zooming-in on this domain and displaying a ribbon diagram of the structure rather than the surface. d. Generally, it would be more helpful here to label any particular residues that were identified as pathogenic from your screen, or to overlay average grow scores per residue data onto the structure

      We have implemented the requested changes to Figure 4. The relevant PDB/AlphaFold accession is now listed, and the colors used to depict each monomer in Panel A are clarified in the figure legend. An inset focusing on the active site has been added to improve visualization of the citrulline and aspartate substrates. In addition, we have added new panels (Figure 4C and 4D) overlaying pathogenic residues and average growth scores onto the structure to more directly link structural context with functional data.

      1. Figure 5 a. Line 716 - Insert a page break to place Figure 5 on its own page b. I suggest using a heatmap for this type of plot, as it is very difficult to track which color corresponds to which residue.

      c. Fig5A - This plot could be improved by identifying which residue positions interface with which substrate.

      We have placed Figure 5 on its own page and added information to the legend identifying which residue positions interface with each substrate. We have retained the active-site variant strip charts raised in point (b), as we believe they effectively illustrate how the distribution of variant effects differs between residues. In addition, we have provided a supplemental heatmap showing variant growth across the entire protein (Figure S1), and individual variant scores for all residues are provided in Table S4.

      1. Figure 7 a. Line 735 - Insert page break to place figure on a new page

      List the PDB accession used for these images. c. For clarity I would mention "human ASS" in the figure title d. State the colors of the substrates e. Panels A and B could be combined into a single panel, making it easier to distinguish the active site and dimerization variants.

      f. Could be interesting to get SASA scores for the ClinVar structural variants to determine if they are surface-accessible

      We have implemented the requested changes in Figure 7 with the following exceptions. For point (e), there is no single orientation of the structure that allows a clear simultaneous view of both active-site and dimerization variants; accordingly, we have retained panels A and B as separate subfigures to preserve clarity. With respect to point (f), we agree that solvent accessibility analysis could be informative in other contexts. However, such an analysis does not integrate naturally with the functional and assay-based framework of the present study and was therefore not included.

      1. Figure 8 a. Panel B - overlay a square frame in the larger protein structure that depicts where the below inset is focused, and frame inset image as well.

      We have framed the inset image as requested. We did not add a corresponding frame to the full protein structure, as doing so obscured structural details in the region of interest.

      Reviewer #3 (Significance (Required)):

      Section 2 - Significance This study represents a substantial technical, functional, and translational advance in the interpretation of missense variation in ASS1, a gene of high clinical relevance for the rare disease citrullinemia type I. Its principal strength lies in the generation of an experimentally validated functional atlas of ASS1 missense variants that covers ~90% of all SNV-accessible substitutions. The scale, internal reproducibility, and careful benchmarking of the yeast complementation assay against known pathogenic and benign variants provide a robust foundation for identifying pathogenic ASS1 variants. Particularly strong aspects include the rigorous quality control of variant identities, the quantitative nature of the functional readout, and the thoughtful integration of results into the ACMG/AMP OddsPath framework. The discovery of intragenic complementation between variants affecting distinct structural regions of the enzyme is a notable conceptual and mechanistic contribution. Limitations include the assay's reduced sensitivity to variants impacting oligomerization or subtle folding defects, and the use of yeast as a heterologous system, which may mask disease-relevant mechanisms as several pathogenic ClinVar variants were found to be "functionally unimpaired". Future work extending functional testing to additional cellular contexts or expanding genotype-level combinatorial analyses would further enhance clinical applicability. Relative to prior studies, which have relied on small numbers of patient-derived variants or low-throughput biochemical assays, this work extends the field decisively by delivering a comprehensive, variant-resolved functional map for ASS1. To the best of my current knowledge, this is the first systematic functional screen of ASS1 at this scale and the first direct experimental demonstration that ASS active sites span multiple subunits, enabling intragenic complementation consistent with Crick and Orgel's classic variant sequestration model. As such, the advance is simultaneously technical (high-throughput functional genomics), mechanistic (defining structural contributors to catalysis and epistasis), and clinical (enabling evidence-based reclassification of VUS). I find the use of homozygous non-human primate variants as an orthogonal benign calibration set both creative and controversial, my hope would be that this manuscript will prompt a productive discussion.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript presents a comprehensive functional profiling of 2,193 ASS1 missense variants using a yeast complementation assay, providing valuable data for variant interpretation in the rare disease citrullinemia type I. The dataset is extensive, technically sound, and clinically relevant. The demonstration of intragenic complementation in ASS1 is novel and conceptually important. Overall, the study represents a substantial contribution to functional genomics and rare disease variant interpretation.

      Major comments

      This is an exciting paper as it can provide support to clinicians to make actionable decisions when diagnosing infants. I have a few major comments, but I want to emphasize the label of "functionally unimpaired" variants to be misleading. The authors explain that there are several pathogenic ClinVar variants that fall into this category (above the >.85 growth threshold) but I think this category needs a more specific name and I would ask the authors to reiterate the shortcomings of the assay again in the Discussion section. I think there's an important discussion to be had here, is the assay detecting variants that alter the function of ASS or is it detecting a complete ablation of enzymatic activity? The results might be strengthened with a follow-up experiment that identifies stably expressed ASS1 variants. At the very least, it would be great to see the authors replicate some of their interesting results from the high-throughput screen by down-selecting to ~12 variants of uncertain significance that could be newly considered pathogenic. I would ask the authors to provide more citations of the literature in the introduction of the manuscript. I would be especially interested in knowing more about human ASS being identified as a homolog of yeast ARG1, as they share little sequence similarity (27.5%) at the protein level. That said, I find the yeast complementation assay exciting. I appreciate the efforts made by the authors to share their work and make this study more reproducible, such as sharing the hASS1 and yASS1 plasmids being shared on NCBI Genbank (Line 121) and publishing the ONT reads on SRA (Line 154). I made a requests for additional data to be shared, such as the custom method/code for codon optimization and a table of Twist variant cassettes that were ordered. I would also love to see these results shared on MaveDB.org. I find this manuscript very exciting as the authors have a compelling assay that identifies pathogenic variants, but I was generally disappointed by the quality and organization of the figures. For example, Figure 4 provides very little insight, but could be dramatically improved with an overlay of the normalized growth score data or highlighting variants surrounding the substrate or ATP interfaces. There are some very interesting aspects of this manuscript that could be shine through with some polished figures. I would also encourage the authors to generate a heatmap of the data represented in Figure 2 (see Fowler and Fields 2014 PMID 25075907, Figure 2), this would be more helpful reference to the readers.

      My major comments are as follows:

      1. Citations needed - especially in the introduction and for establishing that hASS is a homolog of yARG1
      2. Generally, the authors do a nice job distinguishing the ASS1 gene from the ASS enzyme, though I found some ambiguities (Line 685). Please double-check the use of each throughout the manuscript
      3. Generally, I'm confused about what strain was used for integrating all these variants, was is the arg1 knock-out strain from the yeast knockout collection or was it FY4? I think FY4 was used for the preliminary experiments, then the KO collection strain was used for making the variant library but I think this could be made more clear in the text and figures. Lines 226-229 describes introducing the hASS1 and yASS1 sequences into the native ARG1 locus in strain FY4, but the Fig1A image depicts the ASS1 variants going into arg1 KO locus. Fig1A should be moved to Fig2.
      4. Line 303 - "We classify these variants as 'functionally unimpaired'", this is not an accurate description of these variants as Figure 2 highlights 24 pathogenic ClinVar variants that would fall into this category of "functionally unimpaired". The yeast growth assay appears to capture pathogenic variants, but there is likely some nuance of human ASS functionality that is not being assessed here. I would make the language more specific, e.g. "complementary to Arg1" or "growth-compatible".
      5. Lines 345-355 - It is interesting that there are variants that appear functional at the substrate interfacing sites. Is there anything common across these variants? Are they maintaining the polarity or hydrophobicity of the WT residue? Are any of these variants included in ClinVar or gnomAD? Are pathogenic variants found at any of these sites
      6. Lines 423-430 - The OddsPath calculation would seem to rely heavily on the thresholds of <.05 and >.85 for normalized growth. The OddsPath calculation could be bolstered with some additional analysis that emphasizes the robustness to alternative thresholds.
      7. Lines 432-441 - This is an interesting idea to use variants observed in primates, has ACMG weighed in on this? I understand that CTLN1 is an autosomal recessive disorder but I'd still be interested in seeing how the observed ASS1 missense variants in gnomAD perform in your growth assay, possibly a supplemental figure?

      Minor comments

      1. Lines 53-59 - This paragraph needs to cite the literature, especially lines 56, 57, and 59
      2. Line 61 - no need to repeat "citrullinemia type I", just use the abbreviation as it was introduced in the paragraph above
      3. Lines 61-71 - again, this paragraph needs more literature citations
      4. Line 62 - change to "results"
      5. Line 74-75 - "RUSP" acronym not needed as it's never used in the manuscript, the same goes for "HHS"
      6. Line 86 - "ASS1" I think is referring to the enzyme and should just be "ASS"? If referring to the gene then italicize to "ASS1"
      7. Lines 91-93 - It would be helpful to mention this is a functional screen in yeast
      8. Line 101 - It would be helpful to the readers to define SD before using the acronym, consider changing to "minimal synthetic defined (SD) medium" and afterwards can refer to as "SD medium"
      9. 109-114 - It would be great if you could share your method for designing the codon-harmonized yASS1 gene, consider sharing as a supplemental script or creating a GitHub repository linked to a Zenodo DOI for publication.
      10. Lines 135-137 - I think it's helpful to provide a full table of the cassettes ordered from Twist as well as the primers used to amplify them, consider a supplemental table
      11. Line 138 - "standard methods" is a bit vague, I'm guessing this is a Geitz and Schiestl 2007 LiAc/ssDNA protocol (PMID 17401334)? Also, was ClonNAT used to select for natMX colonies?
      12. Line 150 - change to "sequence the entire open reading frame, as previously described [4]."
      13. Line 222-223 - remove "replace" and just use "complement" (and remove the parenthesis)
      14. Line 249 - It would be great to see a supplemental alignment of the hASS1 and yASS1 sequences
      15. Line 261 - spelling "citrullemia" should be corrected to "citrullinemia"
      16. Line 280 - "using Oxford Nanopore sequencing" is a bit vague, I suggest specifying the equipment used (e.g. Oxford Nanopore Technologies MinION platform) or simplify to "via long-read sequencing (see Materials & Methods)"
      17. Line 287-289 - It would be great to see the average number of isolates per variant, as well as a plot of the variant growth estimate vs individual isolate growth
      18. Lines 324-25 - consider removing the last sentence of this paragraph, it is redundant as the following paragraph starts with the same statement
      19. Lines 327-335 - This is interesting and would benefit from its own subpanel or plot in which the normalized growth score is plotted against variants that are at conserved or diverse residues in human ASS, and see if there's a statistical difference in score between the two groupings
      20. Lines 339-341 - As written, it is unclear if aspartate interacts with all of the same residues as citrulline or just Asn123 and Thr119.
      21. Lines 345-355 - As with my above comment, I find this interesting and would
      22. Line 353 - add a period to "al" in "Diez-Fernandex et al."
      23. Figure 1

      a. Remove "Figure" from the subpanels and show just "A" and "B" (as you do for Figure 4) and combine the two images into a single image. Also make this correction to Figure 5 and Figure 8

      b. Panel A - I thought the hASS1 and yASS1 were dropped into FY4, not the arg1 KO strain. This needs clarification

      c. Panel A - I'm assuming the natMX cassette contains its own promoter, you could use a right-angled arrow to indicate where the promotors are in your construct

      d. Panel B - I'm not sure the bar graph is necessary, it would be more helpful to see calculations of the colony size (or growth curves for each strain) and plot the raw values (maybe pixel counts?) for each replicate rather than normalizing to yeast ARG1. I would be great to have a supplemental figure showing all the replicates side-by-side

      e. Panel B - Would be helpful to denote the pathogenic and benign ClinVar variants with an icon or colored text

      f. Figure 1 Caption - make "A)" and "B)" bold 24. Figure 2

      a. "Shown in magenta are amino acid substitutions corresponding to ClinVar pathogenic, pathogenic/likely pathogenic, and likely pathogenic variants" is repeated in the figure caption

      b. "Shown in green are amino acid substitutions corresponding to ClinVar benign and likely benign variants." I don't see any green points

      c. Identify the colors used for ASS1 substrate binding residues

      d. This plot would benefit from a depiction of the human ASS secondary structure and any protein domains (nucleotide-binding domain, synthase domain, and C-terminal helix from Fig4B)

      e. Line 685 - "ASS1" is being used in reference to the enzyme, is this correct or should it be "ASS"? 25. Figure 3

      a. Rename the "unimpaired" category as there are several pathogenic ClinVar variants that fall into this category 26. Figure 4

      a. List the PDB or AlphaFold accession used for this structure

      b. Panel A - state which colors are used for to depict each monomer. It is confusing to see several shades of pink/purple used to depict a single monomer in Panel A

      c. It is very difficult to make out the aspartate and citrulline substrates in the catalytic binding activity, consider making an inset zooming-in on this domain and displaying a ribbon diagram of the structure rather than the surface.

      d. Generally, it would be more helpful here to label any particular residues that were identified as pathogenic from your screen, or to overlay average grow scores per residue data onto the structure 27. Figure 5

      a. Line 716 - Insert a page break to place Figure 5 on its own page

      b. I suggest using a heatmap for this type of plot, as it is very difficult to track which color corresponds to which residue

      c. Fig5A - This plot could be improved by identifying which residue positions interface with which substrate 28. Figure 7

      a. Line 735 - Insert page break to place figure on a new page

      b. List the PDB accession used for these images

      c. For clarity I would mention "human ASS" in the figure title

      d. State the colors of the substrates

      e. Panels A and B could be combined into a single panel, making it easier to distinguish the active site and dimerization variants

      f. Could be interesting to get SASA scores for the ClinVar structural variants to determine if they are surface-accessible 29. Figure 8

      a. Panel B - overlay a square frame in the larger protein structure that depicts where the below inset is focused, and frame inset image as well.

      Significance

      This study represents a substantial technical, functional, and translational advance in the interpretation of missense variation in ASS1, a gene of high clinical relevance for the rare disease citrullinemia type I. Its principal strength lies in the generation of an experimentally validated functional atlas of ASS1 missense variants that covers ~90% of all SNV-accessible substitutions. The scale, internal reproducibility, and careful benchmarking of the yeast complementation assay against known pathogenic and benign variants provide a robust foundation for identifying pathogenic ASS1 variants. Particularly strong aspects include the rigorous quality control of variant identities, the quantitative nature of the functional readout, and the thoughtful integration of results into the ACMG/AMP OddsPath framework. The discovery of intragenic complementation between variants affecting distinct structural regions of the enzyme is a notable conceptual and mechanistic contribution. Limitations include the assay's reduced sensitivity to variants impacting oligomerization or subtle folding defects, and the use of yeast as a heterologous system, which may mask disease-relevant mechanisms as several pathogenic ClinVar variants were found to be "functionally unimpaired". Future work extending functional testing to additional cellular contexts or expanding genotype-level combinatorial analyses would further enhance clinical applicability.

      Relative to prior studies, which have relied on small numbers of patient-derived variants or low-throughput biochemical assays, this work extends the field decisively by delivering a comprehensive, variant-resolved functional map for ASS1. To the best of my current knowledge, this is the first systematic functional screen of ASS1 at this scale and the first direct experimental demonstration that ASS active sites span multiple subunits, enabling intragenic complementation consistent with Crick and Orgel's classic variant sequestration model. As such, the advance is simultaneously technical (high-throughput functional genomics), mechanistic (defining structural contributors to catalysis and epistasis), and clinical (enabling evidence-based reclassification of VUS). I find the use of homozygous non-human primate variants as an orthogonal benign calibration set both creative and controversial, my hope would be that this manuscript will prompt a productive discussion.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Lo et al characterize the phenotypic effect of ~90% of all possible ASS1 missense mutations using an elegant yeast-based system, and use this dataset to aid the interpretation of clinical ASS1 variants. Overall, the manuscript is well-written and the experimental data are interpretated rigorously. Of particular interest is the identification of pairs of deleterious alleles that rescue ASS1 activity in trans. My comments mainly pertain to the relevance of using a yeast screening methodology to infer functional effects of human ASS1 mutations.

      • Since human ASS1 is heterologously expressed in yeast for this mutational screen, direct comparison of native expression levels between human cells and yeast is not possible. Could the expression level of human ASS1 (driven by the pARG1 promoter) in yeast alter the measured fitness defect of each variant? For instance, if ASS1 expression in yeast is sufficiently high to mask modest reductions in catalytic activity, such variants may be misclassified as hypomorphic rather than amorphic. Conversely, if expression is intrinsically low, even mild catalytic impairments could appear deleterious. While it is helpful that the authors used non-human primate SNV data to calibrate their assay, experiments could be performed to directly address this possibility.
      • The nature of the relationship between yeast growth and availability of functional ASS1 could also influence the interpretation of results from the yeast-based screen. Does yeast growth scale proportionately with ASS1 enzymatic activity?
      • It would be helpful to add an additional diagram to Figure 1A explaining how the screen was performed, in particular: when genotype and phenotype were measured, relative to plating on selective vs non-selective media? This is described in "Variant library sequence confirmation" and "Measuring the growth of individual isolates" of the Methods section but could also be distilled into a diagram.
      • The authors rationalize the biochemical consequences of ASS1 mutations in the context of ASS1 per se - for example, mutations in the active site pocket impair substrate binding and therefore catalytic activity, which is expected. Does ASS1 physically interact with other proteins in human cells, and could these interactions be altered in the presence of specific ASS1 mutations? Such effects may not be captured by performing mutational scanning in yeast.
      • The authors note that only a small number (2/11) of mutations at the ASS1 monomer-monomer interface lead to growth defects in yeast. It would be helpful for the authors to discuss this further.

      Significance

      This study presents the first comprehensive mutational profiling of human ASS1 and would be of broad interest to clinical geneticists as well as those seeking biochemical insights into the enzymology of ASS1. The authors' use of a yeast system to profile human mutations would be particularly useful for researchers performing deep mutational scans, given that it provides functional insights in a rapid and inexpensive manner.

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      Referee #1

      Evidence, reproducibility and clarity

      Lo et al., report a high-throughput functional profiling study on the gene encoding for argininosuccinate synthase (ASS1), done in a yeast experimental system. The study design is robust (see lines 141-143, main text, Methods), whereby "approximately three to four independent transformants of each variant would be isolated and assayed." (lines 140 - 141, main text, Methods). Such a manner of analysis will allow for uncertainty of the functional readout for the tested variants to be accounted for.

      This is an outstanding study providing insights on the functional landscape of ASS1. Functionally impaired ASS1 may cause citrullinemia type I, and disease severity varies according to the degree of enzyme impairment (line 30, main text; Abstract). Data from this study forms a valuable resource in allowing for functional interpretation of protein-altering ASS1 variants that could be newly identified from large-scale whole-genome sequencing efforts done in biobanks or national precision medicine programs. I have some suggestions for the Authors to consider:

      1. The specific function of ASS1 is to condense L-citrulline and L-aspartate to form argininosuccinate. Instead of measuring either depletion of substrate or formation of product, the Authors elected to study 'growth' of the yeast cells. This is a broader phenotype which could be determined by other factors outside of ASS1. Whereas i agree that the experiments were beautifully done, the selection of an indirect phenotype such as ability of the yeast cells to grow could be more vigorously discussed.
      2. One of the key reasons why studies such as this one are valuable is due to the limitations of current variant classification methods that rely on 'conservation' status of amino acid residues to predict which variants might be 'pathogenic' and which variants might be 'likely benign'. However, there are serious limitations, and Figures 2 and 6 in the main text shows this clearly. Specifically, there is an appreciable number of variants that, despite being classified as "ClinVar Pathogenic", were shown by the assay to unlikely be functionally impaired. This should be discussed vigorously. Could these inconsistencies be potentially due to the read out (growth instead of a more direct evaluation of ASS1 function)?
      3. Figure 3 is very interesting, showing a continuum of functional readout ranging from 'wild-type' to 'null'. It is very interesting that the Authors used a threshold of less than 0.85 as functionally hypomorphic. What does this mean? It would be very nice if they have data from patients carrying two hypomorphic ASS1 alleles, and correlate their functional readout with severity of clinical presentation. The reader might be curious as to the clinical presentation of individuals carrying, for example, two ASS1 alleles with normalized growth of 0.7 to 0.8.

      I hope you will find these suggestions helpful.

      Significance

      This is an outstanding study providing insights on the functional landscape of ASS1. Functionally impaired ASS1 may cause citrullinemia type I, and disease severity varies according to the degree of enzyme impairment (line 30, main text; Abstract). Data from this study forms a valuable resource in allowing for functional interpretation of protein-altering ASS1 variants that could be newly identified from large-scale whole-genome sequencing efforts done in biobanks or national precision medicine programs.

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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 study. It is really gratifying to read that the Reviewers believe that this work is interesting, novel and of interest to broad audience. Therefore, we believe that it will be suitable for a high profile journal. Further, the experiments suggested by the reviewers have added value to the work and have substantiated our findings. It is important to highlight that we have performed all the suggested experiments. Please find below the detailed point by point response to Reviewer’s Comments.

      2. Point-by-point description of the revisions

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

      • The manuscript entitled, "IP3R2 mediated inter-organelle Ca2+ signaling orchestrates melanophagy" is a rather diffuse study of the relationship between IP3R2 and melanin production. While this is an interesting and understudied area, the study lacks a clear focus. The model seems to be that IP3R2 is essential for mitochondrial calcium loading. And that its absence increases lysosomal calcium loading. There are also a number of incomplete and/or unconvincing links to autophagy/melanophagy, TMEM165, TRPML1 and even gene transcription. In this kind of diffuse study, each step needs to be convincing to get to the next one, which is not the case here. There are also references to altered proteasome function, despite the total absence of any direct data on the proteasome. Finally, I felt it was sometimes unclear whether the authors were referring to melanosomes or lysosomes at various points throughout the study.*

      While I suspect that, somewhere in here, there are some novel relationships worthy of further investigation, this is a case where the many parts make the overall product less convincing. What effects here are directly relevant to IP3R2? This study should stop there, leaving investigations of peripheral factors for future investigations, as the further you get from where you start, the less clear what you are studying becomes. And the less direct.

      Response: We thank the Reviewer for finding our study interesting and recognizing that this is an understudied area. Further, we appreciate the constructive feedback given by the Reviewer. We have addressed all the Reviewer’s comments. Please find below point-wise responses to the comments.

      Specific Comments:

      __ Comment 1.__ The separation of Figures 1F and 1J makes it impossible to assess the effect of αMSH on IP3R2 expression. This presentation makes interpretation difficult; a simple 4 lane Western would be more informative.

      Response: We apologize to the Reviewer for not being very clear. Actually, we have separated these data sets because these are two independent experimental conditions. The Figure 1F illustrates data from the LD-based pigmentation model, whereas Supplementary Figure 1K (Previously Fig 1J) depicts data from α-MSH–induced pigmentation model.

      Comment 2. One of the most attractive points made by this study is that there is a specific link between IP3R2 and melanin production. In my opinion, the null hypothesis is that this is just about the amount of IP3Rs expressed per cell. To reject this concept, the authors should show data demonstrating the relative expression of all 3 IP3Rs. Without this information, the null hypothesis that IP3R2 is the most expressed IP3R isoform and that's why its knockdown has the most dramatic effect cannot be rejected It would also be helpful to show where the different IP3Rs are expressed within the cell.

      Response: We thank the Reviewer for raising this interesting point and for the constructive comment. As suggested, we would like to clarify that the relative expression of all three IP₃R isoforms has already been analyzed in our study. Specifically, in Figure 1B, we demonstrate the expression pattern of IP₃R isoforms in our experimental system, where IP₃R2 shows the highest expression level, followed by IP₃R3 and IP₃R1 (IP₃R2 > IP₃R3 > IP₃R1). Further, in the revised manuscript, we additionally analyzed publicly available datasets for IP₃Rs expression. “The Human Protein Atlas” reports a higher expression of IP₃R2 in melanocytes compared to the other IP₃R isoforms (Supplementary Fig 1A). Therefore, we agree with the Reviewer’s proposed concept that the relatively higher expression of IP₃R2 can be one of the important factors that regulate pigmentation levels. Indeed, our analysis of microarray dataset from African vs Caucasian skin revealed a greater IP₃R2 expression in African skin compared to Caucasian skin (__Figure 1L). __

      With respect to subcellular localization, all three IP₃R isoforms are predominantly localized to the endoplasmic reticulum, consistent with their established role as ER-resident Ca²⁺ release channels. However, their expression levels are known to be highly cell and tissue specific (Bartok et al., Nature Communications 2019), supporting the idea that higher IP₃R2 levels play a functionally specialized role in melanogenesis.

      Comment 3. It would be helpful to label Figs 3F-I with the conditions used. The description in the text is of increased LC3II levels, however, the ratio of LC3I to LC3II might be more meaningful. Irrespective, although the graph shows an increase in LC3II, the Western really doesn't show much. As a standalone finding, I don't find this figure to be very convincing; there are better options to demonstrate this proposed relationship between IP3R2 and autophagy than what is shown.

      Response: We sincerely thank the Reviewer for this thoughtful and critical evaluation, which has helped us improve the clarity and precision of this analysis. To address this concern, in the revised manuscript, we have now labeled ‘LD’ in the Supplementary Fig 2A-B (Previously, Fig 4F-I) with the corresponding experimental conditions for clarity. In addition, we reanalyzed the data by calculating the LC3II/LC3I ratio in all the figures of the revised manuscript that include LC3II expression, which provides a more meaningful and robust assessment of autophagic flux. This revised analysis yields a clearer representation of LC3 dynamics and strengthens the interpretation of the western blotting data in support of the relationship between IP₃R2 and autophagy. Further, we have shown by confocal imaging that IP3R2 silencing significantly reduced GFP/RFP ratio of the pMRX-IP-GFP-LC3-RFP reporter system in comparison to control condition in Fig 4M-N to demonstrate the relationship between IP3R2 and autophagy. Collectively, these autophagy flux assays and biochemical experiments clearly demonstrate a direct relationship between IP3R2 and autophagy.

      Comment 4. The following statement at the beginning of page 22 "We observed an impaired proteasomal degradation of critical melanogenic proteins localized on melanosomes in the IP3R2 knockdown condition" is insufficiently supported by data to be made. Even if I was convinced that autophagy was enhanced, there is no data of any kind about the proteasome in this manuscript.

      Response: We appreciate the Reviewer’s careful scrutiny of this statement and the opportunity to clarify and strengthen our interpretation. To directly address the concern regarding proteasomal involvement, in the revised manuscript, we performed additional experiments using MG132, a well-established inhibitor of proteasomal degradation. These experiments were designed to assess whether the altered stability of melanogenic proteins observed upon IP₃R2 knockdown could be attributed to changes in proteasome-mediated turnover.

      In the revised manuscript, our new data show that treatment with MG132 leads to a marked reduction in the levels of melanosome-associated melanogenic proteins, including GP100 and DCT, compared to the DMSO control (Fig. 4A–D). This response contrasts with that of non-melanosomal proteins, such as IP₃R2 and Calnexin, which are localized to the endoplasmic reticulum and exhibits increased accumulation upon MG132 treatment (Fig. 4E–H), consistent with canonical proteasomal inhibition. These differential outcomes suggest that melanosome-resident proteins respond distinctly to proteasomal blockade, likely due to their compartmentalized localization on melanosomes.

      Previous studies have shown that impairment of proteasomal function can activate autophagy as a compensatory, cytoprotective mechanism (Williams et al, 2013; Li et al, 2019; Su & Wang, 2020; Pan et al, 2020). Indeed, we observed a significant increase in LC3II/LC3I levels in IP3R2 knockdown plus MG132 treatment condition in comparison to IP3R2 knockdown plus the DMSO control (Fig. 4I–J).

      To investigate whether impairment of proteasomal degradation upon IP3R2 silencing alone or together with MG132 selectively triggers melanophagy, we assessed melanophagy using melanophagy reporter, mCherry-Tyrosinase-eGFP following IP3R2 silencing along with MG132 treatment. Our observations revealed an increase in melanophagy flux with IP3R2 silencing and MG132 treatment compared to siNT with DMSO control (Fig 5K-L). This suggests that IP3R2 silencing induced inhibition of proteasomal degradation activates melanophagy. Taken together, these findings indicate that compromised proteasomal degradation engages the autophagy machinery, providing a mechanistic link between proteasome dysfunction, enhanced autophagy, and altered melanogenic protein turnover.

      Comment 5. In figure 5, the authors create a new ratiometric dye to detect melanosome stability based on the principle that tyrosinase is exclusively found in melanosomes. Unfortunately, there is no validation that this new construct is found exclusively in melanosomes upon expression. In addition, there is discussion about the pH of lysosomes, but not of melanosomes. Ultimately, this data cannot be considered at face value without any type of validation; I also note that the pictures lack sufficient detail to support identification of these structures as melanosomes. * While I maintain the above concerns, I note that, the data in supplemental figure 3 is MUCH more convincing than what is in the figure. Both the writing and the figure design should be rethought.*

      Response: We appreciate the Reviewer’s thorough evaluation and constructive critique of Figure 5, which has helped us to better clarify and validate this aspect of the study. In the revised manuscript, we directly address the concern regarding the subcellular specificity of the ratiometric probes, we performed detailed colocalization analysis using established melanosome markers. Specifically, we assessed the localization of the melanophagy detection probes mCherry–Tyr–eGFP and tyrosinase–mKeimaN1 with the melanosome-resident protein GP100 detected by anti-HMB45 (Supplementary Fig 2E-F and 2K-L). These analyses revealed a very high degree of colocalization, reflected by strong Pearson’s correlation and overlap coefficients, thereby validating that the expressed probes are predominantly localized to melanosomes.

      Regarding Lysosome/Melanosomal pH considerations, our melanophagy detection ratiometric probes: mCherry–Tyrosinase–eGFP (sensitive to acidic pH via eGFP) and tyrosinase mKeimaN1 (sensitive to acidic pH via Keima) are specifically designed to identify melanosome degradation, which happens upon melanosome fusion with lysosome. Consequently, the observed signal shifts indicate melanosome turnover rather than merely reflecting the lysosomal pH.

      To further corroborate the microscopic observations, we performed biochemical assays to study melanophagy flux upon IP3R2 silencing. We employed Bafilomycin A1, an inhibitor of autophagosome-lysosome fusion, to examine melanosomal protein accumulation. Upon Bafilomycin A1 treatment, IP3R2 silenced cells showed enhanced accumulation of melanosomes, as indicated by elevated tyrosinase levels compared with siNT controls (Supplementary Fig 3C-D), indicating elevated melanophagy flux upon IP3R2 knockdown. In the revised manuscript, we employed additional melanophagy detection strategies to further strengthen our findings. Specifically, we used Retagliptin phosphate (RTG), a well-established selective inducer of melanophagy, and observed a marked increase in melanophagy using the mCherry–Tyrosinase–eGFP melanophagy probe (Supplementary Fig 2G-H). Additionally, we performed independent validation by assessing colocalization of the melanosome (recognized by anti-HMB45 ab that identifies melanosomal structural protein GP100) with LC3 (Supplementary Fig 3A-B). This analysis revealed a significant increase in melanosomes colocalization with LC3 upon IP₃R2 silencing compared to control conditions.

      Collectively, these independent approaches clearly demonstrate that the melanophagy probes localize to melanosomes and detect melanophagy (by responding to melanosome fusion to lysosomes).

      Comment 6. Given the increase in ER Ca2+ content after IP3R2 knockdown, ER calcium content should be emptied before attempting to estimate lysosomal Ca2+ content with GPN or Bafilomycin. Otherwise, the source of calcium is less than clear.

      Response____: We appreciate the Reviewer’s careful consideration of Ca²⁺ source, which is critical for accurate interpretation of these experiments. Therefore, as suggested, in the revised manuscript, we conducted experiments involving Thapsigargin (Tg) pre-treatment to deplete ER Ca²⁺ reserves before examining lysosomal Ca²⁺ release using GPN or Bafilomycin (Supplementary Fig 6I-N). Even under these conditions, we noted increased lysosomal Ca²⁺ release in IP₃R2 knockdown cells, thus confirming that the observed Ca²⁺ signals originate from lysosomes rather than any remaining ER Ca²⁺. Importantly, this approach allowed us to minimize ER-derived Ca²⁺ contributions to changes in the lysosomal Ca²⁺ release.


      Reviewer #1 (Significance (Required)):

      The manuscript entitled, "IP3R2 mediated inter-organelle Ca2+ signaling orchestrates melanophagy" is a rather diffuse study of the relationship between IP3R2 and melanin production. While this is an interesting and understudied area, the study lacks a clear focus. The model seems to be that IP3R2 is essential for mitochondrial calcium loading. And that its absence increases lysosomal calcium loading. There are also a number of incomplete and/or unconvincing links to autophagy/melanophagy, TMEM165, TRPML1 and even gene transcription. In this kind of diffuse study, each step needs to be convincing to get to the next one, which is not the case here. There are also references to altered proteasome function, despite the total absence of any direct data on the proteasome. Finally, I felt it was sometimes unclear whether the authors were referring to melanosomes or lysosomes at various points throughout the study.

      Response____: We thank the Reviewer for finding our work interesting and appreciating that this is an understudied field. Further, we thank him/her for the constructive feedback on our study. We have performed several additional experiments and significantly revised the manuscript to address all the comments of the Reviewer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In the present manuscript, Saurav et al. identify IP3R2-mediated ER calcium release as a key suppressor of melanophagy, thereby sustaining pigmentation in melanocytes. Using in vitro (B16 murine melanoma cells, primary human melanocytes) and in vivo (zebrafish) models, the authors report that IP3R2 expression is positively correlated with pigmentation. They then investigate the impact of IP3R2 knockdown and find that IP3R2 silencing enhances the stability of melanogenic proteins, while also inducing autophagic degradation of melanosomes (i.e., melanophagy). Concomitantly, they find that IP3R2 silencing decreases mitochondrial calcium uptake, increases lysosomal calcium loading, and lowers lysosomal pH. They propose a pathway wherein in IP3R2 knockdown cells impaired mitochondrial calcium uptake induces the activation of AMPK-ULK1, and increased lysosomal calcium activates TRPML1 via TMEM165 and closer proximity interactions between ER and lysosomes, TFEB nuclear translocation, and upregulation of melanophagy-related genes, namely OPTN and RCHY1. The work is placed within the context of emerging roles of organelle calcium signaling in pigmentation biology, where extracellular calcium influx pathways are known regulators, but the contribution of ER-mitochondria-lysosome crosstalk to melanosome turnover remains largely unknown.

      Response____: We thank the Reviewer for appreciating our work and highlighting that the contribution of ER-mitochondria-lysosome crosstalk to melanosome turnover remains largely unappreciated.

      Major comments:

      Comment 1- The central finding is that IP3R2 knockdown induces melanophagy and reduces pigmentation. However, the manuscript does not identify any physiological or pathological context in which IP3R2 expression or activity is naturally downregulated in melanocytes. Without such context, the knockdown may represent an artificial perturbation that broadly alters ER calcium handling and triggers melanophagy as part of a general stress-induced autophagy response. This raises uncertainty about whether the pathway operates in vivo under normal or disease conditions. It would strengthen the study to identify upstream cues that reduce IP3R2 function and to test whether these also trigger melanophagy through the proposed mechanism.


      Response____: We thank the Reviewer for asking such an important question. The Reviewer asked to identify any physiological or pathological context in which IP3R2 expression is naturally downregulated in melanocytes. To address this question, in the revised manuscript, we analyzed publicly available microarray datasets comparing skin samples from Caucasian and African populations (Yin et al., Experimental Dermatology 2014). This unbiased analysis revealed considerably lower IP₃R2 expression in the Caucasian skin as compared to African skin (Fig. 1L). This data support a physiological correlation between IP₃R2 expression and pigmentation level, reinforcing the physiological relevance of the proposed pathway.


      Comment 2- While the data link IP3R2 knockdown to decreased pigmentation and increased melanophagy, the causality between altered organelle calcium dynamics and the melanophagy induction is inferred from correlation and partial rescue experiments. More direct interventions in the proposed downstream pathways (e.g., acute mitochondrial calcium uptake restoration, lysosomal calcium buffering) would strengthen mechanistic claims.

      Response____: We appreciate the Reviewer’s recommendation on strengthening the mechanistic causality between organelle Ca²⁺ dynamics and melanophagy. As suggested, in the revised manuscript, we restored acute mitochondrial Ca²⁺ uptake by MCU over-expression in the IP₃R2 knockdown background, which resulted in a marked reduction in melanophagy along with increased mitochondrial Ca²⁺ uptake in comparison to control (Fig 6I-L). This data clearly demonstrates that downstream of IP₃R2 silencing mitochondrial Ca²⁺ restoration rescues the melanophagy phenotype thereby revealing a mechanistic causality between mitochondrial Ca²⁺ dynamics and melanophagy.

      Similarly, to assess the causality between lysosomal Ca²⁺ dynamics and melanophagy, we silenced TMEM165 in the IP₃R2 knockdown background. Excitingly, upon TMEM165 knockdown we observed reduction in melanophagy, concomitant with decrease in lysosomal Ca²⁺ levels under IP₃R2 silencing conditions (Supplementary Fig 7I-L). Together, these direct manipulations support a causal role for altered organelle Ca²⁺ dynamics in driving melanophagy.


      We believe that these experiments would have addressed the concern of the Reviewer. However, if there are any other specific experiments that the Reviewer would like us to perform, we would be happy to carry out them as well.

      __Comment 3____- __Zebrafish assays convincingly show altered pigmentation with altered IP3R2 levels, but do not connect this to in vivo melanophagy measurements or TRPML1/TFEB activity, which would link the cell biology to organismal phenotype more directly.

      Response____: We thank the Reviewer for appreciating our in vivo zenrafish experiments. Futher, we acknowledge the Reviewer’s point of linking the cellular mechanisms to organismal phenotypes in vivo. Therefore, as suggested, we activated TRPML1 in the zebrafish model system. In the revised manuscript, we investigated role of the TRPML1–TFEB axis in pigmentation in vivo by pharmacological activation of TRPML channels with MLSA1. The MLSA1 treatment resulted in a marked reduction in zebrafish pigmentation compared to vehicle-treated controls (Fig. 8M). This phenotypic change was further substantiated by quantitative melanin content assays, which confirmed a significant decrease in melanin levels following MLSA1 treatment (Fig. 8M–N). These in vivo findings support the involvement of TRPML1-mediated lysosomal signaling in pigmentation regulation.

      Comment 4- The work suggests therapeutic potential for pigmentary disorders, but no disease models are tested. It is unclear whether the observed mechanisms operate under physiological stressors.

      Response____: We appreciate the Reviewer’s comment regarding physiological relevance and disease context. As addressed in Comment 1, we examined publicly available human skin microarray datasets for IP₃R2 expression in Caucasian and African population. This analysis revealed a positive correlation between IP₃R2 expression and human skin pigmentation, supporting that modulation of IP₃R2 occurs under physiological conditions rather than representing an artificial perturbation.

      While formal pigmentary disease models were not examined in this study, the observed correlation between IP₃R2 expression and physiological pigmentation differences along with our robust in vivo zebrafish data suggests that IP₃R2 plays an important role in physiological pigmentation. As highlighted by Reviewer 1 and Reviewer 3, the manuscript is already too long. Therefore, we plan to delineate the precise role of IP₃R2 in pigmentary disorders as an independent study.

      Comment 5- The paradox between the observed enhanced stability of melanogenic proteins and increased melanophagy is insufficiently addressed. DCT, Tyrosinase and GP100 are all melanosome-associated and their stability or degradation is in prior literature often interpreted as reflecting melanosome biogenesis and turnover. This discrepancy needs to be resolved, as it complicates interpretation of melanophagy assays.

      Response____: We appreciate the Reviewer’s careful consideration of this apparent paradox. This point was also raised by Reviewer 1. We have addressed the query in detail in response to Comment 4 of Reviewer 1. Briefly, the enhanced stability of melanosome-associated proteins reflects impaired proteasomal degradation and prolonged protein half-life, while the concurrent increase in melanophagy represents a compensatory turnover mechanism for degrading such dysfunctional melanosomes.

      Thus, increased melanophagy and apparent stabilization of melanogenic proteins are not contradictory but instead represent parallel outcomes of disrupted proteostasis. This interpretation is supported by our proteasomal inhibition experiments (Fig 4A-H) and autophagy analyses (Fig 4I-P), which collectively reconcile the observed protein stability with enhanced melanosome turnover.


      Comment 6- The authors propose that mitophagy and ER-phagy are reduced in IP3R2 knockdown cells, suggesting specific induction of melanophagy, but the rationale for why increased autophagic flux only targets melanosomes is insufficiently addressed. Also, these conclusions are solely based on Keima assays, and positive controls for mitophagy and ER-phagy are lacking.

      Response: We appreciate the Reviewer’s critical assessment of the specificity of autophagic targeting in the IP₃R2 knockdown condition and the need for appropriate validation controls. In the revised manuscript, we have repeated both the mitophagy and ER-phagy assays with well-established positive controls. Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) was employed as a positive control to robustly induce mitophagy (Supplementary Fig 4E-F), while 4-phenylbutyric acid (4PBA) was used as a positive control for ER-phagy/reticulophagy (Supplementary Fig 4G-H). Secondly, we have validated the microscopy data with biochemical assays by examining levels of ER (Fig 4E-H) and mitochondria resident protein MCU.

      To provide a mechanistic rationale for the specific induction of melanophagy, we examined recently identified regulators of melanophagy, RCHY1 and OPTN (Lee et al., PNAS 2024). Bioinformatic analysis identified multiple TFEB binding sites on the promoters of both genes, which was supported by increased RCHY1 and OPTN expression following IP₃R2 knockdown. Further, in the revised manuscript, we performed additional loss-of-function experiments to demonstrate that co-silencing IP3R2 along with RCHY1 or OPTN significantly reduced melanophagy flux compared to IP₃R2 knockdown alone (Fig. 9H–K). Taken together, these data explain why enhanced autophagic flux downstream of IP₃R2 silencing is preferentially directed toward melanosomes.

      Comment 7- The melanophagy probes are novel and validated with rapamycin/bafilomycin, but quantitative calibration of GFP/mCherry or Keima signal to actual lysosomal delivery rates is missing; photobleaching, pH heterogeneity (incl., observed decrease in lysosomal pH), and melanin autofluorescence (see below) could confound ratios. Also, side-by-side comparison with other melanophagy detection approaches (e.g., colocalization of melanosomes with LC3) is lacking.

      __Response____: __We appreciate the Reviewer’s careful evaluation of the melanophagy probes and the potential technical confounders. In the revised manuscript, we have performed a variety of experiments to further characterize and validate the probes. First of all, the melanophagy detection ratiometric probes (mCherry–Tyrosinase–eGFP and tyrosinase mKeimaN1) are built on well-established and extensively validated backbones. Further, we used appropriate controls (empty vectors/non-targeting siRNAs/vehicle controls) in all experiments to analyze the relative fluorescence changes in the test condition v/s control. The confounding factors, if any, should be present for both test and control. Therefore, we initially did not perform side-by-side comparison with other melanophagy detection approaches.

      In the revised manuscript, as suggested by the reviewer, we employed additional melanophagy detection strategies to further strengthen our findings. Specifically, we used Retagliptin phosphate (RTG), a well-established selective inducer of melanophagy, and observed a marked increase in melanophagy using the mCherry–Tyrosinase–eGFP melanophagy probe (Supplementary Fig 2G-H). Additionally, we performed independent validation by assessing colocalization of the melanosome (recognized by anti-HMB45 ab that identifies melanosomal structural protein GP100) with LC3 (Supplementary Fig 3A-B). This analysis revealed a significant increase in melanosomes colocalization with LC3 upon IP₃R2 silencing compared to control conditions. Further, to minimize the contribution of melanin autofluorescence, non-transfected cells were imaged under identical settings, and background signals obtained from these cells were subtracted during fluorescence quantitation from all acquired images. Potential effects of photobleaching and pH heterogeneity were minimized by uniform acquisition parameters and ratiometric analysis. Taken together, we believe these complementary approaches address the Reviewer’s concerns and reinforce the robustness of our melanophagy measurements.

      Comment 8- Melanosomes exhibit broad autofluorescence, particularly upon excitation at 405-488 nm and extending into the red channel. This signal can overlap with the detection ranges for GFP, mCherry, and mKeima reporters, potentially confounding quantitative readouts unless appropriate controls (e.g., untransfected cells, spectral unmixing) are used. Throughout this manuscript, it is not addressed how melanosome autofluorescence was controlled for or excluded in the reported fluorescence measurements.

      __Response____: __We apologize to the Reviewer for not clearly stating that melanosome autofluorescence was controlled by imaging non-transfected cells under identical settings, and these background signals were subtracted during quantitation from the acquired images. Specifically, to rigorously control this issue, autofluorescence was systematically evaluated using non-transfected control cells imaged under identical excitation and emission settings used for GFP, mCherry, and mKeima reporters. These controls allowed us to define the baseline autofluorescence profile arising from melanosomes across the relevant spectral ranges. These details are included in the methods section.

      Comment 9- While OPTN and RCHY1 expression is elevated upon IP3R2 knockdown, functional engagement (e.g., OPTN localization to melanosomes, melanosome ubiquitination by RCHY1), or necessity (e.g., siRNA knockdown of these in the IP3R2-deficient background), are not tested.

      Response: We appreciate the Reviewer’s point on establishing necessity of OPTN and RCHY1 in IP₃R2 knockdown–induced melanophagy. In the revised manuscript, we performed targeted loss of function analyses for both OPTN and RCHY1 in the IP₃R2-deficient background. We assessed melanophagy using the mCherry–Tyrosinase–eGFP melanophagy probe following co-silencing of IP₃R2 with either OPTN or RCHY1. Quantitative analysis revealed a significant reduction in melanophagy flux upon co-silencing of either gene compared to IP₃R2 silencing alone (Fig. 9H–K). These findings establish the functional requirement of OPTN and RCHY1 downstream of IP₃R2 loss to drive melanophagy. Since functional engagement of OPTN and RCHY1 on melanosomes is already well-established (Lee et al. PNAS 2024 and Park et al. Autophagy 2024), we have not repeated these experiments. Taken together, our data demonstrates that OPTN and RCHY1 are not only overexpressed but also act as critical mediators of melanophagy downstream of IP₃R2 silencing.

      __Comment 10- __While siRNA/shRNA efficacy is shown, functional rescue with pore-dead mutants sometimes fails to return to control values. The possibility of partial off-target or compensatory effects is not fully excluded.

      Response: We thank the Reviewer for raising for this point. In this study, we employed pore-dead mutants of IP₃R2 (IP₃R2-M) and TRPML1 (TRPML1-M), both of them are well characterized, widely validated and extensively used by a number of leading groups in the field. Upon meticulous literature analysis, we came across multiple studies wherein partial rescue effect was reported with these pore-dead mutants. Therefore, we believe it is not surprising that we are also observing partial rescue in some of our assays.

      Actually, it is important to note that we observe rescue of the function and phenotype in every single experiment carried out with the mutants. We agree with the Reviewer that the extent of rescue is not up to control levels in few experiments. This can be attributed to the differences in the extend of expression of mutants across different experiments. However, we have validated the results with multiple independent approaches. Collectively, the use of multiple independent approaches along with genetic silencing, pharmacological inhibition/activation supports the specificity of the observed phenotypes.

      Comment 11- The mitochondrial and lysosomal calcium measurements are largely endpoint peak quantifications; kinetic analyses and buffering capacity measurements would provide more mechanistic depth, especially for the TMEM165 contribution. Also, TMEM165 necessity for melanophagy induction upon IP3R2 knockdown has not been directly addressed.

      Response: We appreciate the Reviewer’s request for greater mechanistic depth regarding organelle Ca²⁺ dynamics and the specific contribution of TMEM165. Consistent with this, we had previously demonstrated that TMEM165 silencing decreases lysosomal Ca²⁺ levels using Oregon BAPTA–dextran–based measurements (Supplementary Fig 7C-D), establishing its role in regulating lysosomal Ca²⁺ buffering. Building on this, in the revised manuscript, we performed kinetic analyses of lysosomal Ca²⁺ levels following IP₃R2 and TMEM165 silencing. These kinetic analyses validated our end point measurements that IP₃R2 knockdown leads to increase in lysosomal Ca²⁺ levels, whereas TMEM165 silencing results in decrease in lysosomal Ca²⁺ content in comparison to control. Therefore, highlighting distinct and opposing effects of IP₃R2 and TMEM165 on lysosomal Ca²⁺ kinetics.

      Further, we directly evaluated the necessity of TMEM165 for melanophagy induction in the IP₃R2-deficient background. TMEM165 knockdown alone resulted in a significant reduction in melanophagy (Supplementary Fig 7G-H). Further, co-silencing of TMEM165 with IP₃R2 also attenuated melanophagy compared to IP₃R2 knockdown alone (Supplementary Fig 7K-L). Collectively, these kinetic Ca²⁺ assays and genetic loss-of-function analyses provide mechanistic depth to the organelle Ca²⁺ measurements and establish TMEM165 as a critical regulator of melanophagy downstream of IP₃R2 silencing.

      Comment 12- The proximity ligation assay between VAP-A and LAMP1 is interpreted as showing increased ER-lysosome contacts in IP3R2 knockdown cells. However, additional controls are needed and quantitative TEM should be included to substantiate changes in organelle contact frequency and distance.

      Response: We thank the Reviewer’s for his/her emphasis on strengthening the validation of the proximity ligation assay (PLA) findings and on providing ultrastructural evidence to support altered organelle interactions. The PLA data revealed a significant increase in VAP-A–LAMP1 interaction signals in IP₃R2-silenced cells compared to control conditions (Fig. 7L–M). In the revised manuscript, this increase was not observed upon treatment with bafilomycin A1, a specific inhibitor of lysosomal acidification, or when one of the primary antibodies was omitted, confirming the specificity of the PLA signal (Fig. 7L–M). These controls support the interpretation that IP₃R2 downregulation enhances ER–lysosome interactions.

      To further substantiate the changes in organelle contact frequency and distance, we performed ultrastructural analyses using transmission electron microscopy (TEM). The quantitative TEM measurements revealed no significant change in the frequency of ER–mitochondria or ER–lysosome contacts upon IP₃R2 silencing (Fig. 7N–P). Similarly, ER–mitochondria distances remained unchanged. However, we observed a significant reduction in the distance between the ER and lysosomes in IP₃R2 knockdown cells compared to control (Fig. 7N, 7Q–R). Together, these complementary approaches demonstrate that IP₃R2 silencing specifically increases ER–lysosome proximity without altering overall contact frequency, thereby strengthening the conclusion that IP₃R2 regulates ER–lysosome coupling.

      Comment 13- Some assays report small biological n (e.g., three independent experiments with relatively small per-condition cell counts).

      __Response:____ __We appreciate the Reviewer’s comment regarding sample size. All experiments were performed with a minimum of three independent biological replicates, which is consistent with standard practice in the field. For imaging-based assays, multiple fields of view and cells were analyzed per condition in each independent experiment, and quantitative analyses were performed on pooled data across replicates. As suggested by the Reviewer, we have increased the cell numbers in some experiments. The detailed information on biological replicates and cell numbers analyzed is provided in the respective figure legends.

      Minor comments:

      • Comment 1- The title "IP3R2-mediated inter-organelle Ca2+ signaling orchestrates melanophagy" could be misread as indicating IP3R2 'promotes' melanophagy; consider rewording to make clear that IP3R2 suppresses melanophagy to maintain pigmentation. Similarly, the running title "IP3R2 negatively regulates melanophagy" would be clearer as "IP3R2 suppresses melanophagy".*

      __Response____: __As suggested by the Reviewer, we have modified the title and running title in the revised manuscript.

      Comment 2- Unify the framing of "positively regulates pigmentation" vs. "negatively regulates melanophagy" in the Introduction/Discussion.

      Response: As recommended, we have unified the framing in the suggested sections.

      Comment 3- Adding schematic flow diagrams summarizing each pathway at the end of relevant results (figure) sections could help accessibility.

      Response____: __We appreciate the Reviewer’s suggestion to improve accessibility of the presented pathways. Accordingly, we have included schematic diagrams at the end of the relevant figures. These schematics summarize: (i) ER–mitochondria interactions in the context of melanophagy (__Fig. 6P); (ii) differences in Ca²⁺ and pH regulation between wild-type and IP₃R2-silenced cells (Fig. 7S); and (iii) TRPML1-mediated Ca²⁺ release driving melanophagy via TFEB translocation (Fig. 9L). Together, these diagrams provide a concise visual overview of the key mechanistic pathways described in the study.

      Comment 4- While the introduction summarizes extracellular calcium signaling in pigmentation, there is less coverage of recent work on selective autophagy of other lysosome-related organelles (e.g., platelet dense granules, lytic granules), which could provide broader mechanistic context.

      __Response____: __As suggested by the Reviewer, we have discussed selective autophagy of other lysosome-related organelles in the introduction.

      Reviewer #2 (Significance (Required)):

      This study addresses an important gap in pigmentation biology by identifying IP3R2-mediated ER calcium release as a suppressor of melanophagy and a positive regulator of pigmentation. The strongest aspects are the integration of in vitro and in vivo models, the multi-faceted mechanistic exploration linking altered organelle calcium dynamics to selective melanosome turnover, and the development of novel ratiometric fluorescent probes for live-cell melanophagy measurement. Conceptually, the work extends prior literature that has focused on extracellular calcium influx and melanosome biogenesis, revealing a new inter-organelle calcium signaling module that controls melanosome degradation via AMPK-ULK1 and TMEM165-TRPML1-TFEB pathways.

      • However, several limitations reduce the strength of the mechanistic claims. Some key pathway steps are inferred from correlation and partial rescue rather than direct necessity/sufficiency tests (e.g., mitochondrial calcium uptake restoration, lysosomal calcium buffering). The paradoxical observation that IP3R2 knockdown both increases melanophagy and stabilizes melanosome-resident protein (DCT, Tyrosinase, GP100) is not resolved, complicating interpretation of the melanophagy assays. The specificity for melanophagy over other selective autophagy pathways is asserted but not fully explained mechanistically, and positive controls for mitophagy/ER-phagy are missing. Potential technical confounds, such as melanin autofluorescence in the detection ranges of GFP, mCherry, and mKeima, are not explicitly addressed and alternative assays for these key data were insufficiently employed. In vivo results do not yet connect altered pigmentation to melanophagy readouts or downstream TRPML1/TFEB activation. Importantly, the study does not identify any physiological or pathological scenario in which IP3R2 expression or activity is naturally reduced in melanocytes. In the absence of such upstream cues, IP3R2 knockdown may represent an artificial perturbation that triggers melanophagy as part of a broader stress-induced autophagy response, raising questions about the in vivo relevance of the proposed pathway.*

      • The work's primary audience is specialized, cell biologists, autophagy researchers, and pigmentation/skin biology specialists, but the mechanistic framework on organelle crosstalk and selective autophagy will interest a broader basic research readership, including those studying lysosome-related organelles in other systems. The ratiometric probes could be adapted for future melanophagy research, and the pathway insights may guide translational studies in pigmentary disorders or melanoma. My expertise is in mitochondrial and lysosomal calcium signaling, autophagy, and microscopy-based functional assays; I do not have detailed expertise in zebrafish developmental genetics, though the phenotypic analysis appears sound.*

      Response____: We thank the Reviewer for appreciating our work and stating that our study “addresses an important gap in pigmentation biology”. Further, we thank him/her for believing that this work will be of interest to a broad basic research readership. Moreover, we thank him/her for valuing the importance and potential significance of the ratio-metric melanophagy probes generated in this study. Finally, we acknowledge the Reviewer’s constructive feedback on our study, which has helped us in enhancing the quality of our manuscript. We have performed variety of additional in vitro experiments, in vivo zebrafish studies and have significantly revised the manuscript to address all the comments of the Reviewer.

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

      This is a robust and extensive study showing that IP3R2 selectively initiates a calcium signalling pathway leading to melanophagy, that is the degradation of melanosomes. This reduces pigmentation and UV light protection. A strength of the paper is that it combines detailed cellular studies with in viva studies in the zebrafish model. They show that knockdown of IP3R2 reverses this process perhaps leading to a strategy to enhance melanosome number and hence to afford protection from UV irradiation. The authors use a battery of fluorescent probes (mainly genetically encoded reporters) in investigate the signalling cascade leading to melanophagy or its reduction. This involves reports for a number of different organelles involved in this process. The experiments are generally well performed with clear controls for the probes in many cases. My main issue is the panels contain too much data which may obscure the message, and a good deal could be moved to supplementary data. The manuscript investigates many mechanisms in distinct organelles which is remarkable for a two author paper. Particularly interesting was the design of novel fluorescent protein reporters for melanophagy itself. One area not explored is ion fluxes across melanosomes themselves which are lysosome-related organelles and may exhibit similar properties and signalsomes of lysosomes.

      Specifically, the authors show that a REDUCTION of IP3R2-mediated calcium release leads to a calcium flux from the ER by a different mechanism (possibly via TMBIM6). This increases calcium loading of the lysosome via TMEM165, at the expense of calcium transfer to mitochondria, and an acidification.

      • This leads to TRPML1 activation and the lysosomal calcium release activates TFEB translocation to the nucleus increases the transcription of autophagy/melanophagy genes and activation of the AMPK-ULK1 pathway (rather than mTOR). This is a complex pathway and evidence is presented for many of the steps involved.*

      • This is a tour de force investigating organelle communication during the process of melanophagy, that is little understood. It highlights many important organelle ion transport events that are important findings in their own right. For example, the importance of TMEM165 in calcium filling of lysosomes.*

      Response____: We thank the Reviewer for appreciating our study and thinking that it is a robust and extensive study in a highly understudied area. We appreciate the Reviewer’s acknowledgement that our manuscript combines detailed cellular studies with in vivo studies in the zebrafish model. Further, we thank the Reviewer for his/her constructive feedback on our work.

      __ Major points:__

      Comment 1- The authors state that TPC activation does not activate TFEB translocation the nucleus. This is now not the case and should be at least looked at. What is the role of endolysosomal channels on the melanosomes themselves in melanophagy.

      Response____: We appreciate the Reviewer’s comment regarding the potential contribution of TPC channels to TFEB activation and melanophagy. In the revised manuscript, we assessed Ca²⁺ release from TPC2 under IP₃R2 knockdown conditions using the selective TPC2 agonist TPC2-A1-N (Supplementary Fig 9G-H). Additionally, we evaluated TFEB nuclear translocation following TPC2-mediated Ca²⁺ release using TPC2-A1-N (Supplementary Fig 9I-J). Our analyses revealed no significant differences in TPC2 activity or TFEB nuclear translocation upon IP₃R2 silencing compared to control conditions. These findings suggest that, in our system, TPC2-mediated Ca²⁺ signaling does not contribute significantly to TFEB activation or melanophagy downstream of IP₃R2 silencing, indicating a more prominent role for TRPML1-dependent Ca²⁺ signaling in this context.

      Comment 2- How does reduction in IP3R2 mediated calcium fluxes enhance lysosomal acidity?

      Response____: We thank the Reviewer’s question regarding the mechanistic link between reduced IP₃R2-mediated Ca²⁺ flux and enhanced lysosomal acidity. In the revised manuscript, we show that IP₃R2 silencing results in a significant upregulation of the lysosomal proton pump H⁺-ATPase subunits: ATPV0D1 and ATP6V1H (Supplementary Fig 6E-F). Increased H⁺-ATPase expression is expected to promote proton influx into the lysosomal lumen, thereby enhancing lysosomal acidification. These findings provide a mechanistic basis for how IP₃R2 silencing can drive increased lysosomal acidity.

      Comment 3- What mediates the ER source for calcium filling of lysosomes?

      Response____: We appreciate the Reviewer’s interest in the mechanism underlying ER to lysosome Ca²⁺ transfer. Recently, an independent study also reported that IP₃R2 silencing enhances lysosomal Ca²⁺ levels and lysosomal Ca²⁺ release (Zheng et al. Cell 2022). Literature suggests that lysosomal Ca²⁺ refilling is depend on Ca²⁺ fluxes originating from the endoplasmic reticulum, particularly through ER Ca²⁺ leak pathways at ER–lysosome contact sites. In this context, ER-resident Ca²⁺ leak channels such as TMBIM6 (also known as Bax inhibitor-1) play an important role in maintaining basal cytosolic Ca²⁺ levels that can be subsequently taken up by lysosomes (Kim et al. Autophagy 2020). TMBIM6-mediated Ca²⁺ leak from the ER provides a continuous, low-level Ca²⁺ source that supports lysosomal Ca²⁺ loading, (Kim et al. Autophagy 2020). This mechanism allows lysosomes to replenish their Ca²⁺ stores via Ca²⁺ uptake systems operating at ER–lysosome contact sites. Thus, ER Ca²⁺ leak channels represent a key conduit linking ER Ca²⁺ homeostasis to lysosomal Ca²⁺ filling and function.

      Recently, lysosome localized TMEM165 was identified to play an important role in Ca²⁺ filling of lysosomes (Zajac et al. Science Advances 2024). Here, in our study, we observe that TMEM165 drives lysosomal Ca²⁺ influx in melanocytes.

      Comment 4- Oregon-green-dextran is not a great probe for lysosomal calcium. Its Kd is 170nM and even in the acidic environment this may be lowered to low micromolar which may not be great for measuring changes around luminal concentrations of around 500uM. Additionally, it is usual to correct for pH effects simultaneously since the dye is also a pH reporter and has been used as such. However, I take the point that they still see an increase in fluorescence whilst pH falls probably indicating an increase in luminal lysosomal calcium confirmed by increased perilysosomal calcium.

      Response____: We thank the Reviewer for the careful and balanced assessment of the Oregon Green–dextran measurements. We appreciate the acknowledgment that, despite the known limitations of this probe and its pH sensitivity, the observed increase in fluorescence concurrent with reduced lysosomal pH is consistent with elevated luminal lysosomal Ca²⁺ levels. We are grateful for this positive interpretation, which strengthens our conclusions when considered alongside the large amount of supporting data.

      Comment 5- The major point is to reduce the number of main data panels with consigment of some controls perhaps to supplementary. This would increase the comprehensibility of the paper.

      Response____: We thank the Reviewer for this constructive and positive suggestion. We appreciate the emphasis on reducing the data in the main figures. Therefore, as suggested, we have moved considerable data to the supplementary figures. However, due to the additional experiments performed to address the concerns of other Reviewers, the main data panels may still look little busy. We sincerely think that the Reviewer would understand our situation.

      Minor points

      Comment 1- Fig 10 needs a clear legend with symbols in the diagram explained. eg ER calcium release proteins.

      Response____: We thank the Reviewer for this helpful and constructive comment. Therefore, we have revised the Figure 10 legend to clearly explain all symbols used in the schematic illustration.

      Reviewer #3 (Significance (Required)):

      This is a tour de force investigating organelle communication during the process of melanophagy, that is little understood. It highlights many important organelle ion transport events that are important findings in their own right. For example, the importance of TMEM165 in calcium filling of lysosomes.

      Response____: We sincerely thank the Reviewer for considering our work as “a tour de force investigation” and appreciating that our study presents several important organelle ion transport events.

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      Referee #3

      Evidence, reproducibility and clarity

      This is a robust and extensive study showing that IP3R2 selectively initiates a calcium signalling pathway leading to melanophagy, that is the degradation of melanosomes. This reduces pigmentation and UV light protection. A strength of the paper is that it combines detailed cellular studies with in viva studies in the zebrafish model. They show that knockdown of IP3R2 reverses this process perhaps leading to a strategy to enhance melanosome number and hence to afford protection from UV irradiation. The authors use a battery of fluorescent probes (mainly genetically encoded reporters) in investigate the signalling cascade leading to melanophagy or its reduction. This involves reportes for a number of different organelles involved in this process. The experiments are generally well performed with clear controls for the probes in many cases. My main issue is the panels conatin to much data which may obscure the message, and a good deal could be moved to supplementary data. The manuscript investigates many mechanisms in distinct organelles which is remarkable for a two author paper. Particularly interesting was the design of novel fluorescent protein reporters for melanophagy itself. One area not explored is ion fluxes across melanosomes themselves which are lysosome-related organelles and may exhibit similar properties and signalsomes of lysosomes. Specifically the authors show that a REDUCTION of IP3R2-mediated calcium release leads to a calcium flux from the ER by a different mechanism (possibly via TMBIM6). This increases calcium loading of the lysosome via TMEM165, at the expense of calcium transfer to mitochondria, and an acidification. This leads to TRPML1 activation and the lysosomal calcium release activates TFEB translocation to the nucleus increases the transcription of autophagy/melanophagy genes and activation of the AMPK-ULK1 pathway (rather than mTOR). This is a complex pathway and evidence is presented for many of the steps involved.

      This is a tour de force investigating organelle communication during the process of melanophagy, that is little understood. It highlights many important organelle ion transport events that are important finmdings in their own right. For example, the importance of TMEM165 in calcium filling of lysosomes.

      Major points:

      1. The authors state that TPC activation does not activate TFEB translocation the the nucleus. This is now not the case and should be at least looked at. What is the role of endolysosomal channels on the melanosomes themselves in melanophagy.
      2. How does reduction in IP3R2 mediated calcium fluxes enhance lysosomal acidity?
      3. What mediates the ER source for calcium filling of lysosomes?
      4. Oregon-green-dextran is not a great probe for lysosomal calcium. Its Kd is 170nM and even in the acidic environment this may be lowered to low micromolar which may not be great for meaduring changes around luminal concentrations of around 500uM. Additionally, it is usual to correct for pH effects simulataneously since the dye is also a pH reporter and has been used as such. However, I take the point that they still see an increase in fluorescence whilst pH falls probably indicating an increase in luminal lysosomal calcium confirmed by increased perilysosomal calcium.

      The major point is to reduce the number of main data panels with consigment of some controls perhaps to supplementary. This would increase the comprehensibility of the paper.

      Minor points

      1. Fig 10 needs a clear legend with symbols in the diagram explained. eg ER calcium release proteins

      Significance

      This is a tour de force investigating organelle communication during the process of melanophagy, that is little understood. It highlights many important organelle ion transport events that are important findings in their own right. For example, the importance of TMEM165 in calcium filling of lysosomes.

    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 the present manuscript, Saurav et al. identify IP3R2-mediated ER calcium release as a key suppressor of melanophagy, thereby sustaining pigmentation in melanocytes. Using in vitro (B16 murine melanoma cells, primary human melanocytes) and in vivo (zebrafish) models, the authors report that IP3R2 expression is positively correlated with pigmentation. They then investigate the impact of IP3R2 knockdown and find that IP3R2 silencing enhances the stability of melanogenic proteins, while also inducing autophagic degradation of melanosomes (i.e., melanophagy). Concomitantly, they find that IP3R2 silencing decreases mitochondrial calcium uptake, increases lysosomal calcium loading, and lowers lysosomal pH. They propose a pathway wherein in IP3R2 knockdown cells impaired mitochondrial calcium uptake induces the activation of AMPK-ULK1, and increased lysosomal calcium activates TRPML1 via TMEM165 and closer proximity interactions between ER and lysosomes, TFEB nuclear translocation, and upregulation of melanophagy-related genes, namely OPTN and RCHY1. The work is placed within the context of emerging roles of organelle calcium signaling in pigmentation biology, where extracellular calcium influx pathways are known regulators, but the contribution of ER-mitochondria-lysosome crosstalk to melanosome turnover remains largely unknown.

      Major comments:

      • The central finding is that IP3R2 knockdown induces melanophagy and reduces pigmentation. However, the manuscript does not identify any physiological or pathological context in which IP3R2 expression or activity is naturally downregulated in melanocytes. Without such context, the knockdown may represent an artificial perturbation that broadly alters ER calcium handling and triggers melanophagy as part of a general stress-induced autophagy response. This raises uncertainty about whether the pathway operates in vivo under normal or disease conditions. It would strengthen the study to identify upstream cues that reduce IP3R2 function and to test whether these also trigger melanophagy through the proposed mechanism.
      • While the data link IP3R2 knockdown to decreased pigmentation and increased melanophagy, the causality between altered organelle calcium dynamics and the melanophagy induction is inferred from correlation and partial rescue experiments. More direct interventions in the proposed downstream pathways (e.g., acute mitochondrial calcium uptake restoration, lysosomal calcium buffering) would strengthen mechanistic claims.
      • Zebrafish assays convincingly show altered pigmentation with altered IP3R2 levels, but do not connect this to in vivo melanophagy measurements or TRPML1/TFEB activity, which would link the cell biology to organismal phenotype more directly.
      • The work suggests therapeutic potential for pigmentary disorders, but no disease models are tested. It is unclear whether the observed mechanisms operate under physiological stressors.
      • The paradox between the observed enhanced stability of melanogenic proteins and increased melanophagy is insufficiently addressed. DCT, Tyrosinase and GP100 are all melanosome-associated and their stability or degradation is in prior literature often interpreted as reflecting melanosome biogenesis and turnover. This discrepancy needs to be resolved, as it complicates interpretation of melanophagy assays.
      • The authors propose that mitophagy and ER-phagy are reduced in IP3R2 knockdown cells, suggesting specific induction of melanophagy, but the rationale for why increased autophagic flux only targets melanosomes is insufficiently addressed. Also, these conclusions are solely based on Keima assays, and positive controls for mitophagy and ER-phagy are lacking.
      • The melanophagy probes are novel and validated with rapamycin/bafilomycin, but quantitative calibration of GFP/mCherry or Keima signal to actual lysosomal delivery rates is missing; photobleaching, pH heterogeneity (incl., observed decrease in lysosomal pH), and melanin autofluorescence (see below) could confound ratios. Also, side-by-side comparison with other melanophagy detection approaches (e.g., colocalization of melanosomes with LC3) is lacking.
      • Melanosomes exhibit broad autofluorescence, particularly upon excitation at 405-488 nm and extending into the red channel. This signal can overlap with the detection ranges for GFP, mCherry, and mKeima reporters, potentially confounding quantitative readouts unless appropriate controls (e.g., untransfected cells, spectral unmixing) are used. Throughout this manuscript, it is not addressed how melanosome autofluorescence was controlled for or excluded in the reported fluorescence measurements.
      • While OPTN and RCHY1 expression is elevated upon IP3R2 knockdown, functional engagement (e.g., OPTN localization to melanosomes, melanosome ubiquitination by RCHY1), or necessity (e.g., siRNA knockdown of these in the IP3R2-deficient background), are not tested.
      • While siRNA/shRNA efficacy is shown, functional rescue with pore-dead mutants sometimes fails to return to control values. The possibility of partial off-target or compensatory effects is not fully excluded.
      • The mitochondrial and lysosomal calcium measurements are largely endpoint peak quantifications; kinetic analyses and buffering capacity measurements would provide more mechanistic depth, especially for the TMEM165 contribution. Also, TMEM165 necessity for melanophagy induction upon IP3R2 knockdown has not been directly addressed.
      • The proximity ligation assay between VAP-A and LAMP1 is interpreted as showing increased ER-lysosome contacts in IP3R2 knockdown cells. However, additional controls are needed and quantitative TEM should be included to substantiate changes in organelle contact frequency and distance.
      • Some assays report small biological n (e.g., three independent experiments with relatively small per-condition cell counts).

      Minor comments:

      • The title "IP3R2-mediated inter-organelle Ca2+ signaling orchestrates melanophagy" could be misread as indicating IP3R2 'promotes' melanophagy; consider rewording to make clear that IP3R2 suppresses melanophagy to maintain pigmentation. Similarly, the running title "IP3R2 negatively regulates melanophagy" would be clearer as "IP3R2 suppresses melanophagy".
      • Unify the framing of "positively regulates pigmentation" vs. "negatively regulates melanophagy" in the Introduction/Discussion.
      • Adding schematic flow diagrams summarizing each pathway at the end of relevant results (figure) sections could help accessibility.
      • While the introduction summarizes extracellular calcium signaling in pigmentation, there is less coverage of recent work on selective autophagy of other lysosome-related organelles (e.g., platelet dense granules, lytic granules), which could provide broader mechanistic context.

      Significance

      This study addresses an important gap in pigmentation biology by identifying IP3R2-mediated ER calcium release as a suppressor of melanophagy and a positive regulator of pigmentation. The strongest aspects are the integration of in vitro and in vivo models, the multi-faceted mechanistic exploration linking altered organelle calcium dynamics to selective melanosome turnover, and the development of novel ratiometric fluorescent probes for live-cell melanophagy measurement. Conceptually, the work extends prior literature that has focused on extracellular calcium influx and melanosome biogenesis, revealing a new inter-organelle calcium signaling module that controls melanosome degradation via AMPK-ULK1 and TMEM165-TRPML1-TFEB pathways.

      However, several limitations reduce the strength of the mechanistic claims. Some key pathway steps are inferred from correlation and partial rescue rather than direct necessity/sufficiency tests (e.g., mitochondrial calcium uptake restoration, lysosomal calcium buffering). The paradoxical observation that IP3R2 knockdown both increases melanophagy and stabilizes melanosome-resident proteisn (DCT, Tyrosinase, GP100) is not resolved, complicating interpretation of the melanophagy assays. The specificity for melanophagy over other selective autophagy pathways is asserted but not fully explained mechanistically, and positive controls for mitophagy/ER-phagy are missing. Potential technical confounds, such as melanin autofluorescence in the detection ranges of GFP, mCherry, and mKeima, are not explicitly addressed and alternative assays for these key data were insufficiently employed. In vivo results do not yet connect altered pigmentation to melanophagy readouts or downstream TRPML1/TFEB activation. Importantly, the study does not identify any physiological or pathological scenario in which IP3R2 expression or activity is naturally reduced in melanocytes. In the absence of such upstream cues, IP3R2 knockdown may represent an artificial perturbation that triggers melanophagy as part of a broader stress-induced autophagy response, raising questions about the in vivo relevance of the proposed pathway.

      The work's primary audience is specialized, cell biologists, autophagy researchers, and pigmentation/skin biology specialists, but the mechanistic framework on organelle crosstalk and selective autophagy will interest a broader basic research readership, including those studying lysosome-related organelles in other systems. The ratiometric probes could be adapted for future melanophagy research, and the pathway insights may guide translational studies in pigmentary disorders or melanoma. My expertise is in mitochondrial and lysosomal calcium signaling, autophagy, and microscopy-based functional assays; I do not have detailed expertise in zebrafish developmental genetics, though the phenotypic analysis appears sound.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript entitled, "IP3R2 mediated inter-organelle Ca2+ signaling orchestrates melanophagy" is a rather diffuse study of the relationship between IP3R2 and melanin production. While this is an interesting and understudied area, the study lacks a clear focus. The model seems to be that IP3R2 is essential for mitochondrial calcium loading. And that its absence increases lysosomal calcium loading. There are also a number of incomplete and/or unconvincing links to autophagy/melanophagy, TMEM165, TRPML1 and even gene transcription. In this kind of diffuse study, each step needs to be convincing to get to the next one, which is not the case here. There are also references to altered proteasome function, despite the total absence of any direct data on the proteasome. Finally, I felt it was sometimes unclear whether the authors were referring to melanosomes or lysosomes at various points throughout the study. While I suspect that, somewhere in here, there are some novel relationships worthy of further investigation, this is a case where the many parts make the overall product less convincing. What effects here are directly relevant to IP3R2? This study should stop there, leaving investigations of peripheral factors for future investigations, as the further you get from where you start, the less clear what you are studying becomes. And the less direct.

      Specific Comments:

      1. The separation of Figures 1F and 1J makes it impossible to assess the effect of αMSH on IP3R2 expression. This presentation makes interpretation difficult; a simple 4 lane Western would be more informative
      2. One of the most attractive points made by this study is that there is a specific link between IP3R2 and melanin production. In my opinion, the null hypothesis is that this is just about the amount of IP3Rs expressed per cell. To reject this concept, the authors should show data demonstrating the relative expression of all 3 IP3Rs. Without this information, the null hypothesis that IP3R2 is the most expressed IP3R isoform and that's why its knockdown has the most dramatic effect cannot be rejected It would also be helpful to show where the different IP3Rs are expressed within the cell.
      3. It would be helpful to label Figs 3F-I with the conditions used. The description in the text is of increased LC3II levels, however, the ratio of LC3I to LC3II might be more meaningful. Irrespective, although the graph shows an increase in LC3II, the Western really doesn't show much. As a standalone finding, I don't find this figure to be very convincing; there are better options to demonstrate this proposed relationship between IP3R2 and autophagy than what is shown.
      4. The following statement at the beginning of page 22 "We observed an impaired proteasomal degradation of critical melanogenic proteins localized on melanosomes in the IP3R2 knockdown condition" is insufficiently supported by data to be made. Even if I was convinced that autophagy was enhanced, there is no data of any kind about the proteasome in this manuscript.
      5. In figure 5, the authors create a new ratiometric dye to detect melanosome stability based on the principle that tyrosinase is exclusively found in melanosomes. Unfortunately, there is no validation that this new construct is found exclusively in melanosomes upon expression. In addition, there is discussion about the pH of lysosomes, but not of melanosomes. Ultimately, this data cannot be considered at face value without any type of validation; I also note that the pictures lack sufficient detail to support identification of these stuctures asmelanosomes.

      While I maintain the above concerns, I note that, the data in supplemental figure 3 is MUCH more convincing than what is in the figure. Both the writing and the figure design should be rethought. 6. Given the increase in ER Ca2+ content after IP3R2 knockdown, ER calcium content should be emptied before attempting to estimate lysosomal Ca2+ content with GPN or Bafilomycin. Otherwise, the source of calcium is less than clear.

      Significance

      The manuscript entitled, "IP3R2 mediated inter-organelle Ca2+ signaling orchestrates melanophagy" is a rather diffuse study of the relationship between IP3R2 and melanin production. While this is an interesting and understudied area, the study lacks a clear focus. The model seems to be that IP3R2 is essential for mitochondrial calcium loading. And that its absence increases lysosomal calcium loading. There are also a number of incomplete and/or unconvincing links to autophagy/melanophagy, TMEM165, TRPML1 and even gene transcription. In this kind of diffuse study, each step needs to be convincing to get to the next one, which is not the case here. There are also references to altered proteasome function, despite the total absence of any direct data on the proteasome. Finally, I felt it was sometimes unclear whether the authors were referring to melanosomes or lysosomes at various points throughout the study.

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      Reply to the reviewers

      We are grateful to the Review Commons reviewers for their constructive feedback, which has significantly strengthened the manuscript. In response, we have performed additional experiments, revised and expanded multiple figures, incorporated new statistical and functional analyses, and carefully edited the text to improve clarity and precision. A detailed point-by-point response to all reviewer comments, together with a summary of revised figures, is provided.

      To address the reviewers' suggestions, we have conducted additional experiments that are now incorporated into new figures, or we have added new images to several existing figures where appropriate.

      For this reason, please note that all figures have been renumbered to improve clarity and facilitate cross-referencing throughout the text. As recommended by Referee #3, all figure legends have been thoroughly revised to reflect these updates and are now labeled following the standard A-Z panel format, enhancing readability and ensuring easier identification. In addition, all figure legends now include the sample size for each statistical analysis.

      For clarity and ease of reference, we provide below a comprehensive list of all figures included in the revised version. Figures that have undergone modifications are underlined.

      Figure 1____. The first spermatogenesis wave in prepuberal mice.

      This figure now includes amplified images of representative spermatocytes and a summary schematic illustrating the timeline of spermatogenesis. In addition, it now presents the statistical analysis of spermatocyte quantification to support the visual data.

      __Figure 2.____ Cilia emerge across all stages of prophase I in spermatocytes during the first spermatogenesis wave. __

      The images of this figure remain unchanged from the original submission, but all the graphs present now the statistical analysis of spermatocyte quantification.

      Figure 3. Ultrastructure and markers of prepuberal meiotic cilia.

      This figure remains unchanged from the original submission; however, we have replaced the ARL3-labelled spermatocyte image (A) with one displaying a clearer and more representative signal.

      __Figure 4. Testicular tissue presents spermatocyte cysts in prepuberal mice and adult humans. __

      This figure remains unchanged from the original submission.

      __Figure 5. Cilia and flagella dynamics are correlated during prepuberal meiosis. __

      This figure remains unchanged from the original submission.

      __Figure 6. Comparative proteomics identifies potential regulators of ciliogenesis and flagellogenesis. __

      This figure remains unchanged from the original submission.

      Figure 7.____ Deciliation induces persistence of DNA damage in meiosis.

      This figure has been substantially revised and now includes additional experiments analyzing chloral hydrate treatment, aimed at more accurately assessing DNA damage under both control and treated conditions. Images F-I and graph J are new.

      Figure 8____. Aurora kinase A is a regulator of cilia disassembly in meiosis.

      This figure is remodelled as the original version contained a mistake in previous panel II, for this, graph in new Fig.8 I has been corrected. In addition, it now contains additional data of αTubulin staining in arrested ciliated metaphases I after AURKA inhibition (new panel L1´).

      __Figure 9. Schematic representation of the prepuberal versus adult seminiferous epithelium. __

      This figure remains unchanged from the original submission.

      __Supplementary Figure 1. Meiotic stages during the first meiotic wave. __

      This figure remains unchanged from the original submission.

      __Supplementary Figure 2 (new)____. __

      This is a new figure that includes additional data requested by the reviewers. It includes additional markers of cilia in spermatocytes (glutamylated Tubulin/GT335), and the control data of cilia markers in non-ciliated spermatocytes. It also includes now the separated quantification of ciliated spermatocytes for each stage, as requested by reviewers, complementing graphs included in Figure 2.

      Please note that with the inclusion of this new Supplementary Figure 2, the numbering of subsequent supplementary figures has been updated accordingly.

      Supplementary Figure 3 (previously Suppl. Fig. 2)__. Ultrastructure of prophase I spermatocytes. __

      This figure is equal in content to the original submission, but some annotations have been included.

      Supplementary Figure 4 (previously Suppl. Fig. 3).__ Meiotic centrosome under the electron microscope. __

      This figure remains unchanged from the original submission, but additional annotations have been included.

      Supplementary Figure 5 (previously Suppl. Fig. 4)__. Human testis contains ciliated spermatocytes. __

      This figure has been revised and now includes additional H2AX staining to better determine the stage of ciliated spermatocytes and improve their identification.

      Supplementary Figure 6 (previously Suppl. Fig. 5). GLI1 and GLI3 readouts of Hedgehog signalling are not visibly affected in prepuberal mouse testes.

      This figure has been remodeled and now includes the quantification of GLI1 and GLI3 and its corresponding statistical analysis. It also includes the control data for Tubulin, instead of GADPH.

      Supplementary Figure 7 (previously Suppl. Fig. 6)__. CH and MLN8237 optimization protocol. __

      This figure has been remodeled to incorporate control experiments using 1-hour organotypic culture treatment.

      Supplementary Figure 8 (previously Suppl. Fig. 7)__. Tracking first meiosis wave with EdU pulse injection during prepubertal meiosis. __This figure remains unchanged from the original submission.

      Supplementary Figure 9 (previously Suppl. Fig. 8)__. PLK1 and AURKA inhibition in cultured spermatocytes. __

      This figure has been remodeled and now includes additional data on spindle detection in control and AURKA-inhibited spermatocytes (both ciliated and non ciliated).

      DETAILED POINT-BY-POINT RESPONSE TO THE REVIEWERS

      We will submit both the PDF version of the revised manuscript and the Word file with tracked changes relative to the original submission. Each modification made in response to reviewers' suggestions is annotated in the Word document within the corresponding section of the text. all new figures have also been uploaded to the system.

      Response to the Referee #1

      In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

      Author response: We sincerely thank Ref #1 for the thorough and thoughtful evaluation of our manuscript. We are particularly grateful for the reviewer's careful reading and constructive feedback, which have helped us refine several sections of the text and strengthen our discussion. All comments and suggestions have been carefully considered and addressed, as detailed below.

      __Major comments: __

      1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

      Response:

      We thank Ref #1 for this valuable comment, which significantly contributed to improving both the design and interpretation of the cilia depolymerization assay.

      Following this suggestion, we repeated the experiment including 1-hour (immediately after culturing), and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). To ensure accurate staging, we now employ triple immunolabelling for γH2AX, SYCP3, and H1T, allowing clear distinction of zygotene (H1T−), early pachytene (H1T−), and late pachytene (H1T+) cells. The revised data (Figure 7) now provide a more complete and statistically robust analysis of DNA damage dynamics. These results confirm that CH-induced deciliation leads to persistence of the γH2AX signal at 24 hours, indicating impaired DNA repair progression in pachytene spermatocytes. The new images and graphs are included in the revised Figure 7.

      Regarding the reviewer's final point about the comparison of γH2AX levels between ciliated and non-ciliated cells, we regret that direct comparison of γH2AX levels between ciliated and non-ciliated cells is not technically feasible. To preserve cilia integrity, all cilia-related imaging is performed using the squash technique, which maintains the three-dimensional structure of the cilia but does not allow reliable quantification of DNA damage markers due to nuclear distortion. Conversely, the nuclear spreading technique, used for DNA damage assessment, provides optimal visualization of repair foci but results in the loss of cilia due to cytoplasmic disruption during the hypotonic step. Given that spermatocytes in juvenile testes form developmentally synchronized cytoplasmic cysts, we consider that analyzing a statistically representative number of spermatocytes offers a valid and biologically meaningful measure of tissue-level effects.

      In conclusion, we believe that the additional experiments and clarifications included in revised Figure 7 strengthen our conclusion that cilia depolymerization compromises DNA repair during meiosis. Further functional confirmation will be pursued in future works, since we are currently generating a conditional genetic model for a ciliopathy in our laboratory.

      The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

      Response:

      We thank Ref#1 for identifying this issue and for the careful examination of Figure 8. We discovered that the submitted version of Figure 8 contained a mismatch between the figure legend and the figure panels. The legend text was correct; however, the figure inadvertently included a non-corresponding graph (previously panel II-A), which actually belonged to Supplementary Figure 7 in the original submission. We apologize for this mistake.

      This error has been corrected in the revised version. The updated Figure 8 now accurately presents the distribution of EdU-labelled spermatocytes across prophase I substages in control and AURKA-inhibited cultures (previously Figure 8-II B, now Figure 8-A). The corrected data show no significant differences in the proportions of EdU-labelled spermatocytes among prophase I substages after 24 hours of AURKA inhibition, confirming that meiotic progression is not delayed and that no accumulation of zygotene cells occurs under this treatment. Therefore, the observed increase in ciliated zygotene spermatocytes upon AURKA inhibition (new Figure 8 H-I) is best explained by a delay in cilia disassembly, rather than by an arrest or slowdown in meiotic progression. The figure legend and main text have been revised accordingly.

      How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

      Response:

      We appreciate Ref#1 for this excellent suggestion. In the original submission (lines 446-447), we described that ciliated metaphase I spermatocytes in AURKA-inhibited samples exhibited monopolar spindle phenotypes. This description was based on previous reports showing that AURKA or PLK1 inhibition produces metaphases with monopolar spindles characterized by aberrant yet characteristic SYCP3 patterns, abnormal chromatin compaction, and circular bivalent alignment around non-migrated centrosomes (1). In our study, we observed SYCP3 staining consistent with these characteristic features of monopolar metaphases I.

      However, we agree with Ref #1 that this could be better sustained with data. Following the reviewer's suggestion, we performed additional immunostaining using α-Tubulin, which labels total microtubules rather than only the acetylated fraction. For clarity purposes, the revised Figure 8 now includes α-Tubulin staining in the same ciliated metaphase I cells shown in the original submission, confirming the presence of defective microtubule polymerization and defective spindle organization. For clarity, we now refer to these ciliated metaphases I as "arrested MI". This new data further support our conclusion that AURKA inhibition disrupts spindle bipolarization and prevents cilia depolymerization, indicating that cilia maintenance and bipolar spindle organization are mechanistically incompatible events during male meiosis. The abstract, results, and discussion section has been expanded accordingly, emphasizing that the persistence of cilia may interfere with microtubule polymerization and centrosome separation under AURKA inhibition. The Discussion has been expanded to emphasize that persistence of cilia may interfere with centrosome separation and microtubule polymerization, contrasting with invertebrate systems -e.g. Drosophila (2) and P. brassicae (3)- in which meiotic cilia persist through metaphase I without impairing bipolar spindle assembly.

      1. Alfaro, et al. EMBO Rep 22, (2021). DOI: 15252/embr.202051030 (PMID: 33615693)
      2. Riparbelli et al . Dev Cell (2012) DOI: 1016/j.devcel.2012.05.024 (PMID: 22898783)
      3. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 1002/cm.21755 (PMID: 37036073)

      The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

      Response:

      We thank Ref#1 for this valuable observation, with which we fully agree. To avoid overstatement, the original statement has been removed from the Abstract, Results, and Discussion, and replaced with a more accurate formulation indicating that cilia maintenance and bipolar spindle formation are mutually exclusive events during mouse meiosis.

      This revised statement is now directly supported by the new data presented in Figure 8, which demonstrate that AURKA inhibition prevents both spindle bipolarization and cilia depolymerization. We are grateful to the reviewer for highlighting this important clarification.

      Minor comments:

      The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

      Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

      In my opinion, these would be interesting points to discuss in the discussion section.

      Response:

      We thank the reviewer for these thoughtful observations, which we agree are indeed intriguing.

      We believe that our findings likely reflect a developmental role for primary cilia during testicular maturation. We hypothesize that primary cilia at this stage might act as signaling organelles, receiving cues from Sertoli cells or neighboring spermatocytes and transmitting them through the cytoplasmic cysts shared by spermatocytes. Such intercellular communication could be essential for coordinating tissue maturation and meiotic entry during puberty. Although speculative, this hypothesis aligns with the established role of primary cilia as sensory and signaling hubs for GPCR and RTK pathways regulating cell differentiation and developmental patterning in multiple tissues (e.g., 1, 2). The Discussion section has been expanded to include these considerations.

      1. Goetz et al, Nat Rev Genet (2010)- DOI: 1038/nrg2774 (PMID: 20395968)
      2. Naturky et al , Cell (2019) DOI: 1038/s41580-019-0116-4 (PMID: 30948801) Our study focuses on the first spermatogenic wave, which represents the transition from the juvenile to the reproductive phase. It is therefore plausible that the transient presence of longer cilia during this period reflects a developmental requirement for external signaling that becomes dispensable in the mature testis. Given that this is only the second study to date examining mammalian meiotic cilia, there remains a vast area of research to explore. We plan to address potential signaling cascades involved in these processes in future studies.

      On the other hand, while we cannot confirm that the cilia observed in zygotene spermatocytes persist until pachytene within the same cell, it is reasonable to speculate that they do, serving as longer-lasting signaling structures that facilitate testicular development during the critical pubertal window. In addition, the observation of ciliated spermatocytes at all prophase I substages at 20 dpp, together with our proteomic data, supports the idea that the emergence of meiotic cilia exerts a significant developmental impact on testicular maturation.

      In summary, although we cannot yet define specific prophase I functions for meiotic cilia in juvenile spermatocytes, our data demonstrate that the first meiotic wave differs from later waves in cilia dynamics, suggesting distinct regulatory requirements between puberty and adulthood. These findings underscore the importance of considering developmental context when using the first meiotic wave as a model for studying spermatogenesis.

      The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

      Response:

      We thank Ref#1 for this excellent question and for the opportunity to clarify our statement.

      The presence of intercellular bridges between spermatocytes is well known and has long been proposed to support germ cell communication and synchronization (1,2) as well as sharing mRNA (3) and organelles (4). A classic example is the Akap gene, located on the X chromosome and essential for the formation of the sperm fibrous sheath; cytoplasmic continuity through intercellular bridges allows Akap-derived products to be shared between X- and Y-bearing spermatids, thereby maintaining phenotypic balance despite transcriptional asymmetry (5). In addition, more recent work has further demonstrated that these bridges are critical for synchronizing meiotic progression and for processes such as synapsis, double-strand break repair, and transposon repression (6).

      In this context, and considering our proteomic data (Figure 6), our statement did not intend to imply direct cytoplasmic exchange between ciliated and flagellated cells. Although our current methods do not allow comprehensive tracing of cytoplasmic continuity from the basal to the luminal compartment of the seminiferous epithelium, we plan to address this limitation using high-resolution 3D and ultrastructural imaging approaches in future studies.

      Based on our current data, we propose that cytoplasmic continuity within developmentally synchronized spermatocyte cysts could facilitate the coordinated regulation of ciliogenesis, and similarly enable the sharing of regulatory factors controlling flagellogenesis within spermatid cysts. This coordination may occur through the diffusion of centrosomal or ciliary proteins, mRNAs, or signaling intermediates involved in the regulation of microtubule dynamics. However, we cannot exclude the possibility that such cytoplasmic continuity extends across all spermatocytes derived from the same spermatogonial clone, potentially providing a larger regulatory network.]] This mechanism could help explain the temporal correlation we observe between the appearance of meiotic cilia and the onset of flagella formation in adjacent spermatids within the same seminiferous segment.

      We have revised the Discussion to explicitly clarify this interpretation and to note that, although hypothetical, it is consistent with established literature on cytoplasmic continuity and germ cell coordination.

      1. Dym, et al. * Reprod.*(1971) DOI: 10.1093/biolreprod/4.2.195 (PMID: 4107186)
      2. Braun et al. Nature. (1989) DOI: 1038/337373a0 (PMID: 2911388)
      3. Greenbaum et al. * Natl. Acad. Sci. USA*(2006). DOI: 10.1073/pnas.0505123103 (PMID: 16549803)
      4. Ventelä et al. Mol Biol Cell. (2003) DOI: 1091/mbc.e02-10-0647 (PMID: 12857863)
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      6. Sorkin, et al. Nat Commun (2025). DOI: 1038/s41467-025-56742-9 (PMID: 39929837) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

      Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

      Response:

      Ref#1 is very right in this suggestion. We have revised Figure 1 to improve the quality of the H&E-stained testis sections and have added zoomed-in panels where spermatocytes, round spermatids, and elongated spermatids are clearly distinguishable. These additions significantly enhance the clarity and interpretability of the figure.

      In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

      Response:

      We thank the reviewer for this valuable observation. Indeed, the predominance of ciliated pachytene spermatocytes reflects the fact that most meiotic cells in juvenile testes are at the pachytene stage (Figure 1). We have clarified this point in the text and have added a new supplementary figure (Supplementary Figure 2, new figure) presenting a graph showing the proportion of spermatocytes at each prophase I substage that possess primary cilia. This visualization provides a clearer quantitative overview of ciliation dynamics across meiotic substages.

      I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

      Response:

      We thank the reviewer for this helpful suggestion. We have now added annotations to the EM images in Supplementary Figures 3 and 4 to facilitate their interpretation. These visual guides help readers more easily identify the relevant ultrastructural features described in the text.

      The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

      Response:

      We thank the reviewer for this valuable suggestion. Following this recommendation, Supplementary Figure 5 has been revised to include quantification of GLI1 and GLI3 protein levels, normalized to the loading control.

      After quantification, we observed statistically significant differences across developmental stages. Specifically, GLI1 expression is slightly higher at 21 dpp compared to 8 dpp. For GLI3, we performed two complementary analyses:

      • Total GLI3 protein (sum of full-length and repressor forms normalized to loading control) shows a progressive decrease during development, with the lowest levels at 60 dpp (Supplementary Figure 5D).
      • GLI3 activation status, assessed as the GLI3-FL/GLI3-R ratio, is highest during the 19-21 dpp window, compared to 8 dpp and 60 dpp. Although these results suggest a possible transient activation of GLI3 during testicular maturation, we caution that this cannot automatically be attributed to increased Hedgehog signaling, as GLI3 processing can also be affected by other processes, such as changes in ciliogenesis. Furthermore, because the analysis was performed on whole-testis protein extracts, these changes cannot be specifically assigned to ciliated spermatocytes.

      We have expanded the Discussion to address these findings and to highlight the potential involvement of the Desert Hedgehog (DHH) pathway, which plays key roles in testicular development, Sertoli-germ cell communication, and spermatogenesis (1, 2, 3). We plan to investigate these pathways further in future studies.

      1. Bitgood et al. Curr Biol. (1996). DOI: 1016/s0960-9822(02)00480-3 (PMID: 8805249)
      2. Clark et al. Biol Reprod. (2000) DOI: 1095/biolreprod63.6.1825 (PMID: 11090455)
      3. O'Hara et al. BMC Dev Biol. (2011) DOI: 1186/1471-213X-11-72 (PMID: 22132805) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

      There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

      Response:

      We thank the reviewer for detecting this. All typographical errors have been corrected, and figure callouts have been reviewed for consistency.

      Response to the Referee #2

      This study focuses on the dynamic changes of ciliogenesis during meiosis in prepubertal mice. It was found that primary cilia are not an intrinsic feature of the first wave of meiosis (initiating at 8 dpp); instead, they begin to polymerize at 20 dpp (after the completion of the first wave of meiosis) and are present in all stages of prophase I. Moreover, prepubertal cilia (with an average length of 21.96 μm) are significantly longer than adult cilia (10 μm). The emergence of cilia coincides temporally with flagellogenesis, suggesting a regulatory association in the formation of axonemes between the two. Functional experiments showed that disruption of cilia by chloral hydrate (CH) delays DNA repair, while the AURKA inhibitor (MLN8237) delays cilia disassembly, and centrosome migration and cilia depolymerization are mutually exclusive events. These findings represent the first detailed description of the spatiotemporal regulation and potential roles of cilia during early testicular maturation in mice. The discovery of this phenomenon is interesting; however, there are certain limitations in functional research.

      We thank Referee #2 for their careful reading of the manuscript and for highlighting important limitations regarding functional interpretation.

      Our primary objective in this study was to provide a rigorous structural, temporal, and developmental characterization of meiotic ciliogenesis in the mammalian testis, a process for which almost no prior data exist. Given this lack of foundational information, we focused on establishing when, where, and in which meiotic stages primary cilia form during prepubertal development, and on identifying candidate regulatory pathways using complementary imaging, proteomic, and pharmacological approaches.

      We agree that genetic ablation models would provide the most direct means to test ciliary function during spermatogenesis. However, we believe that such functional analyses must be preceded by a detailed developmental and phenotypic framework, which was previously unavailable. The present study therefore represents a necessary first step, defining the dynamics, ultrastructure, and molecular context of meiotic cilia during the transition from juvenile to adult spermatogenesis. We are currently generating conditional genetic models to directly address functional mechanisms in future work.

      Regarding the temporal coincidence between the emergence of meiotic cilia and the onset of flagellogenesis, we do not interpret this observation as evidence of stochastic or non-functional protein expression. Rather, we present it as a developmental correlation that may reflect shared regulatory constraints on axonemal assembly during testicular maturation. We have clarified in the revised manuscript that this relationship is descriptive and hypothesis-generating, and we avoid assigning direct causal roles.

      With respect to the proteomic analysis, we agree that proteomics alone cannot establish function. Our intent was not to assign causality, but to provide a developmental, hypothesis-generating dataset identifying candidate regulators that are enriched at the precise developmental window when both meiotic cilia and spermatid flagella first emerge. We have revised the text to explicitly frame these data as a resource for future mechanistic studies, rather than as direct functional evidence.

      Taken together, we believe that the revised manuscript now more accurately reflects the scope and limitations of the study, while providing a robust and much-needed developmental framework for future genetic and functional analyses of meiotic ciliogenesis in mammals. We would be happy to further clarify any aspect of these interpretations if the reviewer or editor considers it helpful.

      Major points:

      1. The prepubertal cilia in spermatocytes discovered by the authors lack specific genetic ablation to block their formation, making it impossible to evaluate whether such cilia truly have functions. Because neither in the first wave of spermatogenesis nor in adult spermatogenesis does this type of cilium seem to be essential. In addition, the authors also imply that the formation of such cilia appears to be synchronized with the formation of sperm flagella. This suggests that the production of such cilia may merely be transient protein expression noise rather than a functionally meaningful cellular structure.

      Response:

      We agree that a genetic ablation model would represent the ideal approach to directly test cilia function in spermatogenesis. However, given the complete absence of prior data describing the dynamics of ciliogenesis during testis development, our priority in this study was to establish a rigorous structural and temporal characterization of this process in the main mammalian model organism, the mouse. This systematic and rigorous phenotypic characterization is a necessary first step before any functional genetics could be meaningfully interpreted.

      To our knowledge, this study represents the first comprehensive analysis of ciliogenesis during prepubertal mouse meiosis, extending our previous work on adult spermatogenesis (1). Beyond these two contributions, only four additional studies have addressed meiotic cilia-two in zebrafish (2, 3), with Mytlys et al. also providing preliminary observations relevant to prepubertal male meiosis that we discuss in the present work, one in Drosophila (4) and a recent one in butterfly (5). No additional information exists for mammalian gametogenesis to date.

      1. López-Jiménez et al. Cells (2022) DOI: 10.3390/cells12010142 (PMID: 36611937)
      2. Mytlis et al. Science (2022) DOI: 10.1126/science.abh3104 (PMID: 35549308)
      3. Xie et al. J Mol Cell Biol (2022) DOI: 10.1093/jmcb/mjac049 (PMID: 35981808)
      4. Riparbelli et al . Dev Cell (2012) DOI: 10.1016/j.devcel.2012.05.024 (PMID: 22898783)
      5. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 10.1002/cm.21755 (PMID: 37036073) We therefore consider this descriptive and analytical foundation to be essential before the development of functional genetic models. Indeed, we are currently generating a conditional genetic model for a ciliopathy in our laboratory. These studies are ongoing and will directly address the type of mechanistic questions raised here, but they extend well beyond the scope and feasible timeframe of the present manuscript.

      We thus maintain that the present work constitutes a necessary and timely contribution, providing a robust reference dataset that will facilitate and guide future functional studies in the field of cilia and meiosis.

      Taking this into account, we would be very pleased to address any additional, concrete suggestions from Ref#2 that could further strengthen the current version of the manuscript

      The high expression of axoneme assembly regulators such as TRiC complex and IFT proteins identified by proteomic analysis is not particularly significant. This time point is precisely the critical period for spermatids to assemble flagella, and TRiC, as a newly discovered component of flagellar axonemes, is reasonably highly expressed at this time. No intrinsic connection with the argument of this paper is observed. In fact, this testicular proteomics has little significance.

      Response:

      We appreciate this comment but respectfully disagree with the reviewer's interpretation of our proteomic data. To our knowledge, this is the first proteomic study explicitly focused on identifying ciliary regulators during testicular development at the precise window (19-21 dpp) when both meiotic cilia and spermatid flagella first emerge.

      While Piprek et al (1) analyzed the expression of primary cilia in developing gonads, proteomic data specifically covering the developmental transition at 19-21 dpp were not previously available. Furthermore, a recent cell-sorting study (2), detected expression of cilia proteins in pachytene spermatocytes compared to round spermatids, but did not explore their functional relevance or integrate these data with developmental timing or histological context.

      In contrast, our dataset integrates histological staging, high-resolution microscopy, and quantitative proteomics, revealing a set of candidate regulators (including DCAF7, DYRK1A, TUBB3, TUBB4B, and TRiC) potentially involved in cilia-flagella coordination. We view this as a hypothesis-generating resource that outlines specific proteins and pathways for future mechanistic studies on both ciliogenesis and flagellogenesis in the testis.

      Although we fully agree that proteomics alone cannot establish causal function, we believe that dismissing these data as having little significance overlooks their value as the first molecular map of the testis at the developmental window when axonemal structures arise. Our dataset provides, for the first time, an integrated view of proteins associated with ciliary and flagellar structures at the developmental stage when both axonemal organelles first appear. We thus believe that our proteomic dataset represents an important and novel contribution to the understanding of testicular development and ciliary biology.

      Considering this, we would again welcome any specific suggestions from Ref#2 on additional analyses or clarifications that could make the relevance of this dataset even clearer to readers.

      1. Piprek et al. Int J Dev Biol. (2019) doi: 10.1387/ijdb.190049rp (PMID: 32149371).
      2. Fang et al. Chromosoma. (1981) doi: 10.1007/BF00285768 (PMID: 7227045). Response to the Referee #3

      In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

      Response: We sincerely thank Ref#3 for their positive assessment of our work and for the thoughtful suggestions that have helped us strengthen the manuscript. We are pleased that the reviewer recognizes both the novelty and the relevance of our study in providing foundational insights into meiotic ciliogenesis during prepubertal testicular development. All specific comments have been carefully considered and addressed as detailed below.

      Major concerns:

      1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

      Response:

      We thank the reviewer for this helpful suggestion. In the revised version, we have strengthened the evidence for cilia identification by including an additional ciliary marker, glutamylated tubulin (GT335), in combination with acetylated tubulin and ARL13B (which were included in the original submission). These data are now presented in the new Supplementary Figure 2, which also includes an example of a non-ciliated spermatocyte showing absence of both ARL13B and AcTub signals.

      Taken together, these markers provide a more comprehensive validation of cilia detection and confirm the absence of ciliary labelling in non-ciliated spermatocytes.

      The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

      Response:

      We appreciate the reviewer's concern and fully agree that antibody specificity is critical when interpreting centrosomal localization. The IFT88 antibody used in our study is commercially available and has been extensively validated in the literature as both a cilia marker (1, 2), and a centrosome marker in somatic cells (3). Labelling of IFT88 in centrosomes has also been previously described using other antibodies (4, 5). In our material, the IFT88 signal consistently appears at one of the duplicated centrosomes and at both spindle poles-patterns identical to those reported in somatic cells. We therefore consider the reported meiotic IFT88 staining as specific and biologically reliable.

      That said, we agree that genetic validation would provide the most definitive confirmation. We would like to inform that we are currently since we are currently generating a conditional genetic model for a ciliopathy in our laboratory that will directly assess both antibody specificity and functional consequences of cilia loss during meiosis. These experiments are in progress and will be reported in a follow-up study.

      1. Wong et al. Science (2015). DOI: 1126/science.aaa5111 (PMID: 25931445)
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      There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

      Response:

      We thank the reviewer for identifying this inconsistency and apologize for the confusion. We confirm that early round spermatids first appear at 19 dpp, as shown in the quantitative data (Figure 1J). This can be detected in squashed spermatocyte preparations, where individual spermatocytes and spermatids can be accurately quantified. The original text contained an imprecise reference to the histological image of 21 dpp (previous line 161), since certain H&E sections did not clearly show all cell types simultaneously. However, we have now revised Figure 1, improving the image quality and adding a zoomed-in panel highlighting early round spermatids. Image for 19 dpp mice in Fig 1D shows early, yet still aflagellated spermatids. The first ciliated spermatocytes and the earliest flagellated spermatids are observed at 20 dpp. This has been clarified in the text.

      In addition, we also thank the reviewer for the suggestion of adding a summary graphic, which we agree greatly facilitates reader comprehension. We have added a new schematic summary (Figure 1K) illustrating the key stages and timing of the first spermatogenic wave.

      In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

      Response:

      We agree with the reviewer that our proteomic analysis was performed on whole testis samples, which contain both germ and somatic cells. Although isolation of pure spermatocyte populations by FACS would provide higher resolution, obtaining sufficient prepubertal material for such analysis would require an extremely large number of animals. To remain compliant with the 3Rs principle for animal experimentation, we therefore used whole-testis samples from three biological replicates per age.

      We acknowledge that our assumption-that the main differences arise from germ cells-is a simplification. However, germ cells constitute the vast majority of testicular cells during this developmental window and are the population undergoing major compositional changes between 15 dpp and adulthood. It is therefore reasonable to expect that a substantial fraction of the observed proteomic changes reflects alterations in germ cells. We have clarified this point in the revised text and have added a statement noting that changes in somatic cells could also contribute to the proteomic profiles.

      The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

      Response:

      We thank the reviewer for this opportunity to clarify our approach. The categorization of protein as being involved in ciliogenesis or flagellogenesis was based on their Gene Ontology (GO) cellular component annotations obtained from the PANTHER database (Version 19.0), using the gene IDs of the Differentially Expressed Proteins (DEPs). Specifically, we used the GO terms cilium (GO:0005929) and motile cilium (GO:0031514). Since motile cilium is a subcategory of cilium, proteins annotated only with the general cilium term, but not included under motile cilium, were considered to be associated with primary cilia or with shared structural components common to different types of cilia. These GO terms are represented in the bottom panel of the Figure 6.

      This information has been added to the Methods section and referenced in the Results for transparency and reproducibility.

      In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

      Response:

      We thank the reviewer for this fair observation and have taken steps to strengthen and refine our interpretation. In the revised version, we now include data from 1-hour and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). The triple immunolabelling with γH2AX, SYCP3, and H1T allows accurate staging of zygotene (H1T⁻), early pachytene (H1T⁻), and late pachytene (H1T⁺) spermatocytes.

      The revised Figure 7 now provides a more complete and statistically supported analysis of DNA damage dynamics, confirming that CH-induced deciliation leads to persistent γH2AX signal at 24 hours, indicative of delayed or defective DNA repair progression. We have also toned down our interpretation in the Discussion, acknowledging that CH could affect other cellular pathways.

      As mentioned before, the conditional genetic model that we are currently generating will allow us to evaluate the role of cilia in meiotic DNA repair in a more direct and specific way.

      Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

      Response:

      We agree that this aspect required clarification. As noted above, we have refined both the Results and Discussion sections to make clear that our assays specifically targeted meiotic spermatocytes.

      We now present data for meiotic stages at zygotene, early pachytene and late pachytene. This is demonstrated with the labelling for SYCP3 and H1T, both specific marker for meiosis that are not detectable in non meiotic cells. We believe that this is indeed a way to assay the meiotic cells, however, we have specified now in the text that we are analysing potential defects in meiosis progression. We are sorry if this was not properly explained in the original manuscript: it is now rephrased in the new version both in the results and discussion section.

      It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

      Response:

      We agree with the reviewer that measuring mRNA levels of Hedgehog pathway target genes, typically GLI1 and PTCH1, is the most common method for measuring pathway activation, and is widely accepted by researchers in the field. However, the methods we use in this manuscript (GLI1 and GLI3 immunoblots) are also quite common and widely accepted:

      Regarding GLI1 immunoblot, many articles have used this method to monitor Hedgehog signaling, since GLI1 protein levels have repeatedly been shown to also go up upon pathway activation, and down upon pathway inhibition, mirroring the behavior of GLI1 mRNA. Here are a few publications that exemplify this point:

      • Banday et al. 2025 Nat Commun. DOI: 10.1038/s41467-025-56632-0 (PMID: 39894896)
      • Shi et al 2022 JCI Insight DOI: 10.1172/jci.insight.149626 (PMID: 35041619)
      • Deng et al. 2019 eLife, DOI: 10.7554/eLife.50208 (PMID: 31482846)
      • Zhu et al. 2019 Nat Commun, DOI: 10.1038/s41467-019-10739-3 (PMID: 31253779)
      • Caparros-Martin et al 2013 Hum Mol Genet, DOI: 10.1093/hmg/dds409 (PMID: 23026747) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

      As for GLI3 immunoblot, Hedgehog pathway activation is well known to inhibit GLI3 proteolytic processing from its full length form (GLI3-FL) to its transcriptional repressor (GLI3-R), and such processing is also commonly used to monitor Hedgehog signal transduction, of which the following are but a few examples:

      • Pedraza et al 2025 eLife, DOI: 10.7554/eLife.100328 (PMID: 40956303)
      • Somatilaka et al 2020 Dev Cell, DOI: 10.1016/j.devcel.2020.06.034 (PMID: 32702291)
      • Infante et al 2018, Nat Commun, DOI: 10.1038/s41467-018-03339-0 (PMID: 29515120)
      • Wang et al 2017 Dev Biol DOI: 10.1016/j.ydbio.2017.08.003 (PMID: 28800946)
      • Singh et al 2015 J Biol Chem DOI: 10.1074/jbc.M115.665810 (PMID: 26451044) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

      In summary, we think that we have used two well established markers to look at Hedgehog signaling (three, if we include the immunofluorescence analysis of SMO, which we could not detect in meiotic cilia).

      These Hh pathway analyses did not provide any convincing evidence that the prepubertal cilia we describe here are actively involved in this pathway, even though Hh signaling is cilia-dependent and is known to be active in the male germline (Sahin et al 2014 Andrology PMID: 24574096; Mäkelä et al 2011 Reproduction PMID: 21893610; Bitgood et al 1996 Curr Biol. PMID: 8805249).

      That said, we fully agree that our current analyses do not allow us to draw definitive conclusions regarding Hedgehog pathway activity in meiotic cilia, and we now state this explicitly in the revised Discussion.

      Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

      Response:

      It is true that, when Hh signaling is inactive (and hence SMO not ciliary), the GLI3FL/GLI3R ratio tends to be low.

      Although our data in prepuberal mouse testes show a strong reduction in total GLI3 protein levels (GLI3FL+GLI3R) as these mice grow older, this downregulation of total GLI3 occurs without any major changes in the GLI3FL/GLI3R ratio, which is only modestly affected (suppl. Figure 6).

      Hence, since it is the ratio that correlates with Hh signaling rather than total levels, we do not think that the GLI3R reduction we see is incompatible with our non-detection of SMO in cilia: it seems more likely that overall GLI3 expression is being downregulated in developing testes via a Hh-independent mechanism.

      Also potentially relevant here is the fact that some cell types depend more on GLI2 than on GLI3 for Hh signaling. For instance, in mouse embryos, Hh-mediated neural tube patterning relies more heavily on GLI2 processing into a transcriptional activator than on the inhibition of GLI3 processing into a repressor. In contrast, the opposite is true during Hh-mediated limb bud patterning (Nieuwenhuis and Hui 2005 Clin Genet. PMID: 15691355). We have not looked at GLI2, but it is conceivable that it could play a bigger role than GLI3 in our model.

      Moreover, several forms of GLI-independent non-canonical Hh signaling have been described, and they could potentially play a role in our model, too (Robbins et al 2012 Sci Signal. PMID: 23074268).

      We have revised the discussion to clarify some of these points.

      All in all, we agree that our findings regarding Hh signaling are not conclusive, but we still think they add important pieces to the puzzle that will help guide future studies.

      There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

      We thank the reviewer for highlighting this important issue. We have now included the sample size (n) for every analysis directly in the figure legends. Although this adds length, it improves transparency and reproducibility.

      Regarding the doubts of Ref#3 about the different sample sizes, the number of spermatocytes quantified in each stage is in agreement with their distribution in meiosis (example, pachytene lasts for 10 days this stage is widely represented in the preparations, while its is much difficult to quantify metaphases I that are less present because the stage itself lasts for less than 24hours). Taking this into account, we ensured that all analyses remain statistically valid and representative, applying the appropriate statistical tests for each dataset. These details are now clearly indicated in the revised figures and legends.

      Minor concerns:

      1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

      Response:

      We thank the reviewer for noticing this terminology error. The expression has been corrected to "pre-weaning males" throughout the manuscript.

      The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

      Response:

      We thank the reviewer for this suggestion. All figures have been relabelled using the standard A-Z panel format, ensuring consistency and easier readability across the manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

      Major concerns:

      1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

      2. The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

      3. There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

      4. In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

      5. The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

      6. In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

      7. Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

      8. It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

      9. Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

      10. There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

      Minor concerns:

      1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

      2. The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

      Significance

      Overall, this is a well-done body of work that deserves recognition for the novel and implicative discoveries it presents. Assuming the conclusions hold true following appropriate statistical analysis and rephrasing, this paper would report the first documented evidence of meiotic cilia in the developing mammalian testis with sufficient rigor to become the foundational work on this topic.

      This paper will be of interest to communities focused on germ cell development, cilia, and Hedgehog signaling. It may prompt a new perspective on Desert Hedgehog signaling as it pertains to spermatogenesis. Further, this work will be of interest to those studying male fertility, as it highlights the potential role of cilia in spermatogenesis.

      Further, the proteomic analysis presented has the potential to invoke hypotheses and experimentation investigating the role of several proteins with previously uncharacterized roles in ciliogenesis, flagellogenesis, and/or spermatogenesis. The finding that the onset of ciliogenesis and flagellogenesis appear to be temporally linked has the potential to prompt research regarding shared molecular mechanisms dictating axonemal formation. We believe this paper has the potential to have an impact in its respective field, underscored by the exquisite microscopy and detailed characterization of meiotic cilia.

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      Referee #2

      Evidence, reproducibility and clarity

      This study focuses on the dynamic changes of ciliogenesis during meiosis in prepubertal mice. It was found that primary cilia are not an intrinsic feature of the first wave of meiosis (initiating at 8 dpp); instead, they begin to polymerize at 20 dpp (after the completion of the first wave of meiosis) and are present in all stages of prophase I. Moreover, prepubertal cilia (with an average length of 21.96 μm) are significantly longer than adult cilia (10 μm). The emergence of cilia coincides temporally with flagellogenesis, suggesting a regulatory association in the formation of axonemes between the two. Functional experiments showed that disruption of cilia by chloral hydrate (CH) delays DNA repair, while the AURKA inhibitor (MLN8237) delays cilia disassembly, and centrosome migration and cilia depolymerization are mutually exclusive events. These findings represent the first detailed description of the spatiotemporal regulation and potential roles of cilia during early testicular maturation in mice. The discovery of this phenomenon is interesting; however, there are certain limitations in functional research.

      Major points:

      1. The prepubertal cilia in spermatocytes discovered by the authors lack specific genetic ablation to block their formation, making it impossible to evaluate whether such cilia truly have functions. Because neither in the first wave of spermatogenesis nor in adult spermatogenesis does this type of cilium seem to be essential. In addition, the authors also imply that the formation of such cilia appears to be synchronized with the formation of sperm flagella. This suggests that the production of such cilia may merely be transient protein expression noise rather than a functionally meaningful cellular structure.

      2. The high expression of axoneme assembly regulators such as TRiC complex and IFT proteins identified by proteomic analysis is not particularly significant. This time point is precisely the critical period for spermatids to assemble flagella, and TRiC, as a newly discovered component of flagellar axonemes, is reasonably highly expressed at this time. No intrinsic connection with the argument of this paper is observed. In fact, this testicular proteomics has little significance.

      Significance

      Strengths: The discovery of a very interesting time window for ciliary growth in spermatocytes.

      Weaknesses: Insufficient analysis of the function of such cilia.

      Readers: Developmental biologists, reproductive biologists

      My expertise: Spermatogenesis, genetics

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

      Few suggestions/comments are listed below:

      Major comments

      1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

      2. The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

      3. How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

      4. The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

      Minor comments

      1. The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

      Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

      In my opinion, these would be interesting points to discuss in the discussion section.

      1. The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

      2. Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

      3. In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

      4. I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

      5. The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

      6. There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

      Significance

      This work provides new information about an important but poorly understood cellular structure present in meiotic cells, the primary cilium. More generally, this work expands on our understanding of testis development in juvenile mice. The microscopy images presented here are beautiful. The work is mostly descriptive but lays the groundwork for future investigations. I believe that this study would of interest to the germ cell, meiosis, and spermatogenesis communities, and with a few modifications, is suitable for publication.

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      Reply to the reviewers

      Reviewer #1 (R1)

      R1 General statement: Here, Escalera-Maurer and colleagues, present an up-to-date distribution of homologues of Hok toxic proteins belonging to the well-annotated, but otherwise functionally obscure, hok/Sok type I toxin-antitoxin system, across the RefSeq database. Although such computational analyses have been done in the past, the authors here find many more hok homologs than described before, and they categorise their distribution based on whether they are encoded on chromosomes, plasmids, or (pro)phages. These computational analyses are in general tricky with T1TAs, as their toxins are quite short (~50 amino acids, as is the case for Hok), which is why the authors here used three separate approaches to expand their search (nucleotide-level BLAST, protein-homology, or both combined with Infernal). The authors cluster the Hok homologues they find based on a 60% sequence identity cut-off (expanding the known clusters in the process), and proceeded to test 31 candidates belonging to 15 sequence-clusters for their toxicity in Salmonella Typhimurium LT2, showing that 30/31 were toxic upon induction. An interesting finding from their endeavours is that hok/Sok homologues are enriched within prophages and large plasmids, but are not enriched near bacterial anti-phage defense systems (in contrast to the SymE/SymR T1TA). The findings suggest that hok/Sok are indeed sometimes linked to phage and plasmid biology, although they might not be antiphage defenses per se (they have been clearly shown in the past to be addiction modules, and this is still clearly true).

      Authors' answer to R1 General statement: __We do not state here that hok/Sok are not anti-phage defense systems, but we simply observe that they do not cluster with anti-phage defense systems. We have also observed (unpublished data) that known defense systems do not systematically cluster together with other defense systems. Therefore, strong association with other defense systems would have been a strong indication of their function in phage defense but the fact that we did not observe any association with defense systems does not exclude they are involved in phage defense. __

      R1_C1: My expertise lies towards the experimental side of the authors' work, I thus cannot comment on the accuracy/robustness of the computational analyses performed here. The authors do a fine job in clearly stating their findings overall; I could follow most of the conclusions, and I deemed that most of them were supported by their work. Additionally, I find that this paper is a missed opportunity to uncover even more novel biology connected to the interesting hok/Sok T1TAs. The paper does not provide a new framework to think about what is the function of the chromosomal/prophage hok/Sok T1TA systems, although I realize that this is very difficult to accomplish, especially when considering that hok/Sok systems have been around in the literature for almost 40 years.

      Authors' answer to R1_C1: We agree with the reviewer, as we indeed performed this analysis having in mind to clarify the role of hok/Sok systems. However, we still believe that our strong survey of Hok loci put in light their enrichment in various mobile genetic elements, such as prophage and large conjugative plasmids, which is indubitably linked to their function. In addition, our study will guide future experimental efforts in uncovering the function of these systems, for example by helping researchers to select relevant homologs to test for a specific function.__ __

      R1_C2: My major comment is in regard to the Hok toxicity assays (Fig. 2). The authors state in the discussion that "Hok peptides originating from chromosomes are as toxic as those from plasmids", but I believe that the way that they tested their constructs might not have allowed them to see toxicity differences between the two groups. Specifically, using the multi-copy plasmid pAZ3 (pBR322 origin of replication; ~15-20 plasmid copies per chromosome) to induce the different Hok toxin homologues in Salmonella Typhimurium LT2 with arabinose might have masked toxicity differences that would otherwise be apparent on the chromosomal expression-level.

      Some of the authors themselves have previously used the FASTBAC-Seq method to study the Hok homologue from plasmid R1, a useful technique during which a toxin is integrated in the chromosome, in order to study their toxicity under natural levels of expression. I believe that an ideal scenario would be to apply FASTBAC-seq to some of the 31 Hok homologues described here (e.g., a subset of plasmidic vs chromosomal Hok homologues) to shed light on potential toxicity differences between the Hok clusters. This would increase the value of the presented study.

      Alternatively, the authors could employ an L-arabinose concentration gradient to titrate the expression levels of the Hok toxins in order to potentially see different toxicity levels from the different homologues. However, this is not going to work in the system as they are using it now for two reasons:

      1. a) the S. Typhimurium LT2 (STm) used here has its arabinose utilization operon intact (araBAD), which means that Salmonella can catabolize arabinose to use it as a carbon source. This catabolization process interferes with the arabinose induction (i.e., Salmonella eats arabinose instead of using it as the Hok inducer). To ameliorate this, the authors could delete the araBAD operon in STm, rendering STm incapable of catabolizing arabinose, and repeat the experiments in that strain. Or use E. coli BW25113 as the expression host, which already has the araBAD operon deleted (it is not clear to me why the different Hok homologues would not be toxic in E. coli, as the different Hok homologues are widely diverse in sequence, as the authors found here).
      2. b) Even with the araBAD operon deleted, the arabinose induction would be bimodally on or off in the population, due to the bimodal expression of the arabinose transporter (AraE; see Khlebnikov et al., 2002). This would again not allow for titratable arabinose-inducible expression from different concentrations of arabinose. The solution for this would be to co-express a separate plasmid with araE, which would render every cell the same in regards to arabinose permeability, and thus the system would be titratable (as explained in Khlebnikov et al., 2002). Therefore, if the authors would be interested to go towards this route, they would have to first delete the araBAD from STm, then transform STm with an araE plasmid, and redo the experiments. In addition, I would propose to the authors to use the drop plate method (agar plate-based), which is more sensitive compared to the liquid assays employed here.

      Having said all that, I understand that all this experimental work would be strenuous and time-consuming, and although I would like to see it happen, this is not my paper. I would be content therefore if the authors toned down the claim that plasmidic vs chromosomal Hok homologues have the same toxicity, and discuss that chromosomal levels of toxicity are an important caveat that has not been explored here.

      __Authors' answer to R1_C2: __ We thank the reviewer for the detailed suggestion on how to better assess toxicity differences by using an araBAD deletion mutant overexpressing araE. We repeated the arabinose induction assays using drop assays and strain BW25223 with plasmid pJAT13araE and our pAZ3 based plasmid carrying Hok CDS homologs. However, we obtained similar data, not being able to distinguish between the toxicity of chromosomal versus plasmidic CDS, even using different concentration of Arabinose. This is probably because low concentration of the Hok protein are sufficient for activity, but here we are bypassing all post-transcriptional silencing by the native Hok mRNAs by expressing directly the protein, and we are using a multicopy plasmid. We now included 0.01% arabinose induction drop assays in the manuscript as the data obtained with other arabinose concentration did not provide new information. In any case, we are still not accessing the native expression levels for the following reasons 1/ chromosomal level of toxicity were not explored here and 2/ only the toxicity of the coding sequence but not the full mRNA was tested. Indeed, we do not know the exact sequence of the hok homolog mRNAs and this is beyond the scope of the study. These remarks were clearly added in the discussion.

      We agree that the sentence "Hok peptides originating from chromosomes are as toxic as those from plasmids" was too strong and we have added the caveats of our experimental design in the discussion. While we indeed did not compare the toxicity of the peptides, we still showed that chromosomal Hok can be toxic upon overexpression, which would not be the case if the sequences were degenerated.

      The reviewer also suggests the use of the FASTBAC-Seq method, that we previously used to study Hok from the R1 plasmid, which is a method to study toxic type I toxins at the native expression level. While FASTBAC-Seq identifies loss-of-function mutants of the systems, it does not allow to determine a difference of toxicity between systems per se. In addition, FASTBAC-Seq was always done in the context of the full mRNA, not only the coding sequence, and these sequences are presently unknown for most homologs.

      Other comments:

      __R1_C3: __a) There is barely any discussion of the Sok component (RNA antitoxin) of the homologues; why is that? Could you please discuss Sok differences across the homologues, or at least explain why this is not discussed at all in the paper (e.g., in the discussion)?

      Authors' answer to R1_C3: __It is not trivial to identify the Sok RNA sequence, this is why it was not done in this study, a paragraph was added in the discussion explaining this. __

      __R1_C4: __b) In the results section, the Hok clusters are referred to as 62 in number ("Because Hok sequences were too short and variable to construct a meaningful phylogenetic tree, we clustered the Hok sequences with a 60% identity threshold and obtained 62 clusters"), but then in the discussion section, the cluster number becomes 74 ("We highlighted the high sequence variability within Hok peptides by obtaining a total of 74 clusters with 60% identity (Fig. S7)."). Which one is the right number, and why is there a discrepancy?

      Authors' answer to R1_C4: We apologize for the discrepancy between the number. The first number corresponded to the Hok hits from the refSeq and we then added the Hok hits from the plasmid and virus databases (performed later in the manuscript). We clarified this information both in the result and discussion texts (61 clusters from RefSeq and 79 in total, 74 was a typo).__ __

      __R1 Significance: __The most well-clarified aspect of the paper presented here is the distribution of Hok homologues, with the novel aspect of the location in which the hok/Sok T1TAs reside (i.e., chromosome, plasmid, or phage). There is room for the molecular genetics part to be developed further, as I discussed earlier, however this study is the most up-to-date characterization of the diversity of Hok homologues, and will be of interest to the T1TA and the general toxin-antitoxin field.

      __Reviewer #2 (R2) __

      R2 General statement: The authors examined how the Hok toxins are spread across bacterial genomes. The manuscript including its figures is hard to read and understand. I commented figure 1 in details, but similar comments apply to the other figures. Overall, the data lack clarity and precision. Finding information about sequences, clusters in the supplementary materials was not easy. The manuscript should be thoroughly revised. In addition, I believe that other aspects should be developed to expand the interest of the study, such as the co-occurrence of multiple systems in chromosomes, on plasmids and whether they are able to crosstalk. This might provide some evolutionary insights into the biology of these toxins.

      __Authors' answer to R2 General statement: __We designed all figures according to established standards for scientific data visualization, although we recognize that different presentations may work better for different audiences. In our detailed response to Figure 1A, we explain how UpSet plots are constructed and interpreted, which we hope clarifies the visualization approach for the full dataset. We are open to discussing specific improvements if the reviewer has suggestions for enhanced clarity. To address concerns about accessibility, we want to clarify that all sequences are compiled in Table S1 with their clus100 identifiers, making them easy to locate. We are open to reorganizing supplementary materials if a different structure would be more user-friendly. Finally, we agree that an extensive analysis of co-occurrences and crosstalks would be valuable. However, predicting crosstalk bioinformatically for all genomes presents challenges, as it would require predicting RNA:RNA interactions between hok mRNA and Sok sequences, which are currently unknown. Given these limitations, this analysis was beyond the scope of the current study.

      R2_C1: The introduction lacks information regarding the Hok protein (size, structure prediction, localization) as well as a bit of explanation about the reason of looking at these toxins. The description of the potential roles should be a bit expanded.

      Authors' answer to R2_C1: Following the comment from the reviewer, we have provided additional information about Hok in the introduction.

      __R2_C2: __When the authors talk about 'loci', they mean genes encoding Hok homologs if I understand correctly. They did not look for the Sok sequences (hok-sok loci).

      __Author's answer to R2_C2: __Indeed, we did not look for the Sok sequences and we are only describing Hok homologs loci, that could either encode or lack a Sok homolog.

      __R2_C3: __It is not clear what the authors did with the sequences for which they could not detect a start codon and a SD (although it is unusual to refer to SD in the context of protein sequence)

      Authors' answer to R2_C3: The peptides were annotated by extending the initial hit until the first start codon. Therefore, all annotated peptides have a start codon. Shine-Dalgarno sequences were annotated when confidently predicted, to provide additional information. Sequences were not excluded based on the presence or absence of the SD.

      __R2_C4: __Figure 1A is not clear. The total of the bars equal 32,532 which is the number of 'loci' detected by the combination of the different methods. However, it is not clear to me how many are redundant. For instance, I suppose that all the 8483 sequences that were retrieved using blastn and Infernal were retrieved using MMseqs2, blastn and Infernal. So, what is the actual number of sequences that were found? When the authors talk about 1264 distinct peptides, what do they mean? What are the numbers on the X axis (18209, 2260, 27728)?

      Author's answer to R2_C4: Figure A1 is a very typical "UpSet" plot, as indicated in the legend (A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot and H. Pfister, "UpSet: Visualization of Intersecting Sets," in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1983-1992, 31 Dec. 2014, doi: 10.1109/TVCG.2014.2346248). Those plots are a data visualization method for showing data with more than two intersecting sets. The Hok sequence hits were obtained by 3 different methods stated on the rows (MMseqs2, blastn and Infernal, therefore the number 18209 is the number of hits by the MMseqs2, 22680 the number of hits by blastn and 27728 the number of hits by Infernal). The columns show the intersections between these three sets. For example, the mentioned 8483 sequences (second column) were only found by blastn and Infernal but not by MMseqs2. The actual total number of sequences found is indeed 32 532. The 1264 distinct peptides are peptides with different sequences. After removing false positives, degenerated sequences and small peptides, we obtained 1264 unique Hok sequences that are found in the 32532 bacterial loci.

      __R2_C5: __About Infernal: first the authors are stating that only 8% of the sequences are lost when not considering the mRNA structure - which they seem to consider as negligeable. Then in the next section, they state that Infernal is the best tool at identifying clusters that are not detected otherwise. Seems a bit contradictory.

      __Authors' answer to R2_C5: __We appreciate the reviewer pointing out this apparent contradiction, we have clarified this part in the revised manuscript. Infernal uses both sequence and structure information simultaneously for homology detection. While only 8% of Infernal's hits are detected uniquely when structural information was considered, these sequences account for 9 additional clusters with notably high sequence diversity, which would otherwise have been undetected. Therefore, we believe that Infernal is the best tool to capture novel cluster diversity.

      __R2_C6: __Cluster determination. The threshold was put at 60% identity. What is the rationale for the 60% identity? Given that the Hok sequences (like toxins and antitoxins from TA systems in general) are highly variable, this leads to a high number of clusters. I'm not sure of the relevance of these clusters. Are there any other criteria to define clusters?

      Authors' answer to R2_C6: We selected 60% identity as a balance between capturing sequence diversity and generating interpretable results. We also tested 70, 80 and 90% and obtained 128, 221, 377 clusters, respectively, which would be too many for a meaningful visualization and interpretation. The best clustering method would be constructing a phylogenetic tree. However, as explained in the discussion, because the high sequence diversity prevented the construction of a reliable phylogenetic tree, clustering was used as an alternative strategy to identify and interpret patterns of sequence variability.

      __R2_C7: __The authors claim that most of the Hok diversity is found on chromosomes. However, the number of chromosomal Hok is higher than that located on plasmids, which might be related to the different sizes of the different replicons ie, chromosomes being larger than plasmids. Is there a way to normalize by determining the density per size?

      Authors' answer to R2_C7: We do not claim that chromosomes contain most of Hok diversity, as this would be indeed influenced by biases in the databases. We are just describing that we found most of the diversity in chromosomes, but we cannot conclude whether this is a true representation of the frequencies in nature.__ __

      R2_C8: '46 of the 62 clusters contained 10 or less distinct sequences and might be in the process of degenerating'. The authors also linked this with SD detection. Please explain. From what was indicated earlier, I understand that sequences with premature stop codons or short sequences (Authors' answer to R2_C8: We did not remove sequences for which we could not predict the SD. Indeed, lacking SD is a sign that the hok mRNA might not be able to play its biological role and would be indicative that the sequences have degenerated. To evaluate this hypothesis, we experimentally tested 5 sequences without a predicted SD and two of those were not toxic (see Table S2). In order to assess if the low abundant clusters contained degenerated sequences we experimentally tested representatives from some of the clusters with only one Hok CDS and found most of them to be toxic.

      R2_C9: 'Only 7.3% of the unique sequences were found on both plasmids and chromosomes'. From this observation, the authors conclude that 'there is little stable transfer from chromosomes to plasmids or vice-versa'. I don't understand what this means. Do they mean identical sequences? The fact that sequences differ from chromosomes to plasmids does not rule out 'stable transfer'. What do they actually mean by stable transfer? Once the gene is horizontally transferred, it is fixed and vertically transmitted? Same comments apply to the inter-genera horizontal transfer by plasmids.

      __Authors' answer to R2_C9: __Due to the impossibility of constructing a reliable phylogenetic tree, we used identity of sequences across different localizations or genera as our marker for recent, stable transfer events. We define stable transfer as the persistence of sequences in an unchanged form following horizontal transfer; long enough to be detected in current databases. Our approach likely underestimates total transfer events, as sequences accumulating mutations after transfer would not be captured. We would expect to observe numerous identical sequences across plasmids and chromosomes if frequent exchange were occurring, unless rapid mutation after the transfer prevented their detection as identical sequences. We have added a sentence to clarify this in the manuscript and removed the term stable transfer.

      __R2_C10: __I don't understand the next section about 'family'. What do the authors mean about 'family'? Genera? The same apply to the next section about the Y to C recoding. Did the authors do point mutations in the conserved amino acids/codons to test whether they are important for toxicity? Some Hok variants lacks some of the conserved amino acids and are toxic (under overexpression conditions in Salmonella). What about T18, C31 and E42?

      Authors' answer to R2_C10: Families (Enterobacteriaceae, Vibrionaceae etc... ) and genera (Escherichia, Salmonella etc...) refer to the taxonomic categories. Following the reviewer comment, we experimentally assessed the toxicity of Hok from R1 plasmid after mutating the conserved amino acids to alanine residues. All the mutants were found to be toxic under our expression conditions.

      __R2_C11: __The prevalence of Hok in chromosomes or on plasmids might depend on various confounding parameters, such as the size, number of sequences available among others. The authors should find methods to correct for all that.

      Authors' answer to R2_C11: Normalization would indeed be needed if we were comparing the prevalence on chromosomes vs the prevalence on plasmids. Here, we do not claim that Hok homologs are more prevalent in plasmid or chromosomes and only describe where we found them.

      __R2_C12: __Link with defense systems. The threshold was set at 20 kb. Why this threshold?

      Authors' answer to R2_C12: The size of defense islands in a previous report was approximately 40 kb, by setting up a 20 kb threshold we searched for defense systems in a region of 40 kb adjacent to each of the homologs (https://doi.org/10.1126/science.aar4120). If the specific homolog was part of a defense island we would expect that it is less than 20 kb apart from any defense system.

      __R2 Significance: __The paper in its current state appears to serve the role of a data repository rather than a thorough and original analysis. It requires extensive revisions before it can be of interest to experts in the toxin-antitoxin field.

      __ ____Reviewer #3 (R3): __

      R3 General statement: In the manuscript, "The Hok bacterial toxin: diversity, toxicity, distribution and genomic localization," by Escalera-Maurer et al., investigate the distribution of Hok type I toxin proteins across bacterial species. The Hok-Sok type I toxin-antitoxin system was first described on plasmids where it serves to maintain the plasmid in a population of bacterial cells: translation of the hok mRNA is prevented via the small antitoxin RNA Sok. Upon plasmid loss, with no new transcription of sok, the highly stable hok mRNA is translated into a small protein, killing the plasmid-less cell. Homologues to the system were identified in the chromosome of E. coli in the 1990s, and subsequent analyses have identified identical systems in other bacterial chromosomes, though they are close relatives to E. coli. Given the increased number of bacterial genomes sequenced, the group examined how widespread Hok may be across bacteria. They used a combination of BLASTn, MMseqs2 (protein) and Infernal (RNA) to identify, as best possible, all possible homologs. They then used sequence identity cut-offs to form Hok "clusters," and identified key features of the cluster as well as tested toxicity of overproduction of 31 homologs in a strain of Salmonella. Overall, though a variety of bioinformatic predictions and analyses, the manuscript identifies an expanded number of Hok members not previously identified and broaden the species it is found in, supported that Hok is not associate with defense systems, and provides additional support that horizontal transfer of hok genes is likely via plasmids (where hok is presumed to have originated).

      Major comments: There are some areas of the text that are a bit too definitive (these can be fixed or better explained in the text) and a few questions raised about the analyses and interpretations.

      Authors' answer to R3 Major Comment: As suggested by the reviewer, we rephrased parts of the manuscript.

      __These are the specific comments: __

      Introduction R3_C1: First paragraph: "Toxin production leads to the death of the cell encoding it" For many chromosomally encoded systems, toxicity has only been observed via artificial overexpression. This is an important point, as for many systems, a true biological function remains unknown. Further, add caveats regarding toxin function (for systems with validated function, they are involved in...). Again, there are still many questions for many t-at systems, in particular the Type I systems.

      __Authors' answer to R3_C1: __Indeed, the function of type 1 TA, in particular chromosomal ones, is still a matter of debate. While for hok/Sok R1, we previously showed death by expression at the chromosomal level, this was not shown for all TA (Le Rhun et al., NAR, 2023). We added that it could lead to the death or growth arrest of the cell instead and added the reviewer changes to for the function part.

      __R3_C2: __Introduction: type I's are more narrow in distribution, but much of this is due to their size and lack of biochemical domains. Again, please clarify more here.

      __Authors' answer to R3_C2: __We added the reviewer suggestion to the text.

      __R3_C3: __Introduction: while Hok's have been found on chromosomes, in E. coli strains, there is clear evidence that many are inactive. This comes up in the discussion, but it is worth including briefly in the introduction.

      Authors' answer to R3_C3: We have now added in the introduction that in the K12 laboratory strain, most chromosomal hok/Sok were found to be inactive.

      __R3_C4: __For the predicted transmembrane domain: it would be worth to include a box/indication as to where that is within the peptide (with the understanding it may not be exact). Is there more/less variation here? I'm assuming all clusters/family have a predicted TM domain?

      __Authors' answer to R3_C4: __When predicting the TM domain using DeepTMHMM - 1.0 prediction (https://services.healthtech.dtu.dk/services/DeepTMHMM-1.0/), 227 out of the 1264 unique Hok sequence are predicted to have a TM (transmembrane), 7 a SP (signal peptide) and a TM and 1025 have a SP. When predicting the TM of the consensus sequence (most abundant amino-acid) shown in Fig. 1D, region A8 to L25 is predicted to be inserted in the membrane, with the Nterm inside and Cterm outside.

      __R3_C5: __What is the cutoff for being a Hok? Did they take the "last hit" and use that in additional searches to see if more appeared? If that was done, and the search was exhaustive, this really important to add for the reader.

      Authors' answer to R3_C5: The MMseqs2 search was performed using 5 iterations as indicated in the M&M, meaning that the hits of the one search were used to search the database again five time in a raw. Importantly, an attempt to increase the number of iterations to 10 did not significantly increase the number of hits. Therefore, at least for the MMseqs2 search in the RefSeq database, we are close to being exhaustive.

      __R3_C6: __Figure S4: the authors state that there was no difference in the degree of toxicity between the clusters. There do appear to be some peptides tested that at the arabinose concentration used did not repress growth as immediately as others. If higher arabinose concentration is used, does that eliminate these differences? OR are many of these suppressors-if diluted back again, do they grow as if they are non-toxic in arabinose?

      Authors' answer to R3_C6: As suggested by Reviewer 1 (R1_C2), we performed titration of arabinose in a system overexpressing araE in a ΔaraBAD but were not able to find difference of toxicity in our conditions, see also our answer to R1_C2.

      __R3_C7: __Discussion: "because non-functional homologs are expected to quickly accumulate mutations..." is a bit problematic. Hok is highly regulated-as are some of the other well-described type I toxins. In MG1655, while the coding sequence may be intact, there are other mutations and/or insertion elements that prevent expression (and be extension, function. Given the lack of consensus data for type Is, it is best to provide more context for this. If the authors wish to argue that they should quickly accumulate mutations, it would be good to provide additional rates/evidence (even for other loci) from the Enterobacteriaceae.

      __Authors' answer to R3_C7: __We agree this statement might need to be supported further. We have removed this sentence to address this concern.

      __Minor comments: __

      __R3_C8: __For the sequences used in the search: please provide the sequence used in addition to the reference to the T1TAdb. Was the full-length hok mRNA, including mok, used? Please provide the nucleic acid sequence (and include description of whether full-length, etc.) in Materials and Methods or in Supplemental.

      __Authors' answer to R3_C8: __Sequences and code were deposited on https://gitub.u-bordeaux.fr/alerhun/Escalera-Maurer_2025. This files named curated_Hok.fasta and hok.fa, corresponding to Hok protein and mRNA sequences respectively are available in the file "T1TAdb input".

      __R3_C9: __60% identity was used for clustering. Did this become a problem-meaning separation of same property amino acid?

      __Authors' answer to R3_C9: __We checked amino acid signatures for each cluster (Fig S2), but could not find anything relevant.

      __R3_C10: __Fig. S2: for the clusters shown, please add in HokB, HokE, etc., to better correspond to Figure 1 in the main text.

      __Authors' answer to R3_C10: __The clusters were annotated according to the suggestion.

      __R3_C11: __Fig S1: this figure is challenging to orient-what are the numbers (8_10_85)?

      Authors' answer to R3_C11: The figure was generated using the CLANS tool, with each unique sequence retrieved by our analysis shown as a dot. Hok homologous sequences are in red and cluster together, the outlier clusters are annotated with the numbers corresponding to their 60% identity cluster. We understand that separating the number using an underscore could lead to confusion, therefore we have now separated the numbers using a coma.

      __R3_C12: __Please make a separate table or sheet for the experimentally tested peptides. Table S1 is quite large and a separate table/sheet would make this easier to find. If possible, please give the files names a more descriptive title (Table S1 in the name for example). This may be an issue with Review Commons but the individual file names were non-descript and the descriptions on the webpage did not indicate what the file contained.

      __Authors' answer to R3_C12: __We named the files Table S1 and File_S1 to S7. We added a table S2 with the experimentally tested peptides. Note that identical peptides can be sometime found in several bacterial loci.

      __R3_C13: __Figure S9: the black arrow for Hok is hard to see-it appears that the long grey bar going through multiple loci is indicative of Hok. Perhaps label this differently to make it easier on the reader (the line initially seemed to be a formatting issue and not indicative of the position of Hok.

      __Authors' answer to R3_C13: __We have now added a new label to indicate where is Hok, and clarified it in the figure legend.

      __R3_C14: __While the authors focused on Hok for this approach, which is fine and appropriate, can they comment at all about where mok is there in these new clusters/sub-families? Sok potential?

      __Authors' answer to R3_C14: __We added a paragraph about Mok in the discussion.

      __R3 Significance: __Overall the paper is a sound bioinformatic exercise and is improved with the testing of numerous "new" Hok proteins. Most of the comments can be done with some clarifications and maybe some additional analyses and/or verification which should take minimal time. The authors are over-emphatic at points as indicated and need to be more careful and precise with their language.

      In terms of advancement, it advances the distribution of these systems and adds to the depth of sub-classes. The audience will be more specialized to those who study these systems.

      Expertise: I have been studying type I toxin-antitoxin systems since the mid-2000s. We published a study examining (and mentioned well by this article!) the distribution in chromosomes of type I toxin-antitoxin systems, identified brand-new systems (that were chromosomally-limited at the time). My lab has continued to study regulation of type I toxins and distribution of chromosomally-only-encoded systems (so not Hok).

    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

      In the manuscript, "The Hok bacterial toxin: diversity, toxicity, distribution and genomic localization," by Escalera-Maurer et al., investigate the distribution of Hok type I toxin proteins across bacterial species. The Hok-Sok type I toxin-antitoxin system was first described on plasmids where it serves to maintain the plasmid in a population of bacterial cells: translation of the hok mRNA is prevented via the small antitoxin RNA Sok. Upon plasmid loss, with no new transcription of sok, the highly stable hok mRNA is translated into a small protein, killing the plasmid-less cell. Homologues to the system were identified in the chromosome of E. coli in the 1990s, and subsequent analyses have identified identical systems in other bacterial chromosomes, though they are close relatives to E. coli. Given the increased number of bacterial genomes sequenced, the group examined how widespread Hok may be across bacteria. They used a combination of BLASTn, MMseqs2 (protein) and Infernal (RNA) to identify, as best possible, all possible homologs. They then used sequence identity cut-offs to form Hok "clusters," and identified key features of the cluster as well as tested toxicity of overproduction of 31 homologs in a strain of Salmonella. Overall, though a variety of bioinformatic predictions and analyses, the manuscript identifies an expanded number of Hok members not previously identified and broaden the species it is found in, supported that Hok is not associate with defense systems, and provides additional support that horizontal transfer of hok genes is likely via plasmids (where hok is presumed to have originated).

      Major comments: There are some areas of the text that are a bit too definitive (these can be fixed or better explained in the text) and a few questions raised about the analyses and interpretations. These are the specific comments:

      Introduction

      First paragraph: "Toxin production leads to the death of the cell encoding it" For many chromosomally encoded systems, toxicity has only been observed via artificial overexpression. This is an important point, as for many systems, a true biological function remains unknown. Further, add caveats regarding toxin function (for systems with validated function, they are involved in...). Again, there are still many questions for many t-at systems, in particular the Type I systems. Introduction: type I's are more narrow in distribution, but much of this is due to their size and lack of biochemical domains. Again, please clarify more here.

      Introduction: while Hok's have been found on chromosomes, in E. coli strains, there is clear evidence that many are inactive. This comes up in the discussion, but it is worth including briefly in the introduction.

      For the predicted transmembrane domain: it would be worth to include a box/indication as to where that is within the peptide (with the understanding it may not be exact). Is there more/less variation here? I'm assuming all clusters/family have a predicted TM domain?

      What is the cutoff for being a Hok? Did they take the "last hit" and use that in additional searches to see if more appeared? If that was done, and the search was exhaustive, this really important to add for the reader.

      Figure S4: the authors state that there was no difference in the degree of toxicity between the clusters. There do appear to be some peptides tested that at the arabinose concentration used did not repress growth as immediately as others. If higher arabinose concentration is used, does that eliminate these differences? OR are many of these suppressors-if diluted back again, do they grow as if they are non-toxic in arabinose?

      Discussion: "because non-functional homologs are expected to quickly accumulate mutations..." is a bit problematic. Hok is highly regulated-as are some of the other well-described type I toxins. In MG1655, while the coding sequence may be intact, there are other mutations and/or insertion elements that prevent expression (and be extension, function. Given the lack of consensus data for type Is, it is best to provide more context for this. If the authors wish to argue that they should quickly accumulate mutations, it would be good to provide additional rates/evidence (even for other loci) from the Enterobacteriaceae.

      Minor comments:

      For the sequences used in the search: please provide the sequence used in addition to the reference to the T1TAdb. Was the full-length hok mRNA, including mok, used? Please provide the nucleic acid sequence (and include description of whether full-length, etc.) in Materials and Methods or in Supplemental.

      60% identity was used for clustering. Did this become a problem-meaning separation of same property amino acid? Fig. S2: for the clusters shown, please add in HokB, HokE, etc., to better correspond to Figure 1 in the main text.

      Fig S1: this figure is challenging to orient-what are the numbers (8_10_85)?

      Please make a separate table or sheet for the experimentally tested peptides. Table S1 is quite large and a separate table/sheet would make this easier to find. If possible, please give the files names a more descriptive title (Table S1 in the name for example). This may be an issue with Review Commons but the individual file names were non-descript and the descriptions on the webpage did not indicate what the file contained.

      Figure S9: the black arrow for Hok is hard to see-it appears that the long grey bar going through multiple loci is indicative of Hok. Perhaps label this differently to make it easier on the reader (the line initially seemed to be a formatting issue and not indicative of the position of Hok.

      While the authors focused on Hok for this approach, which is fine and appropriate, can they comment at all about where mok is there in these new clusters/sub-families? Sok potential?

      Significance

      Overall the paper is a sound bioinformatic exercise and is improved with the testing of numerous "new" Hok proteins. Most of the comments can be done with some clarifications and maybe some additional analyses and/or verification which should take minimal time. The authors are over-emphatic at points as indicated and need to be more careful and precise with their language.

      In terms of advancement, it advances the distribution of these systems and adds to the depth of sub-classes.

      The audience will be more specialized to those who study these systems.

      Expertise: I have been studying type I toxin-antitoxin systems since the mid-2000s. We published a study examining (and mentioned well by this article!) the distribution in chromosomes of type I toxin-antitoxin systems, identified brand-new systems (that were chromosomally-limited at the time). My lab has continued to study regulation of type I toxins and distribution of chromosomally-only-encoded systems (so not Hok).

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      Referee #2

      Evidence, reproducibility and clarity

      The authors examined how the Hok toxins are spread across bacterial genomes. The manuscript including its figures is hard to read and understand. I commented figure 1 in details, but similar comments apply to the other figures. Overall, the data lack clarity and precision. Finding information about sequences, clusters in the supplementary materials was not easy. The manuscript should be thoroughly revised. In addition, I believe that other aspects should be developed to expand the interest of the study, such as the co-occurrence of multiple systems in chromosomes, on plasmids and whether they are able to crosstalk. This might provide some evolutionary insights into the biology of these toxins.

      Introduction:

      The introduction lacks information regarding the Hok protein (size, structure prediction, localization) as well as a bit of explanation about the reason of looking at these toxins. The description of the potential roles should be a bit expanded.

      Results:

      When the authors talk about 'loci', they mean genes encoding Hok homologs if I understand correctly. They did not look for the Sok sequences (hok-sok loci).

      It is not clear what the authors did with the sequences for which they could not detect a start codon and a SD (although it is unusual to refer to SD in the context of protein sequence)

      Figure 1A is not clear. The total of the bars equal 32,532 which is the number of 'loci' detected by the combination of the different methods. However, it is not clear to me how many are redundant. For instance, I suppose that all the 8483 sequences that were retrieved using blastn and Infernal were retrieved using MMseqs2, blastn and Infernal. So, what is the actual number of sequences that were found? When the authors talk about 1264 distinct peptides, what do they mean? What are the numbers on the X axis (18209, 2260, 27728)?

      About Infernal: first the authors are stating that only 8% of the sequences are lost when not considering the mRNA structure - which they seem to consider as negligeable. Then in the next section, they state that Infernal is the best tool at identifying clusters that are not detected otherwise. Seems a bit contradictory.

      Cluster determination. The threshold was put at 60% identity. What is the rationale for the 60% identity? Given that the Hok sequences (like toxins and antitoxins from TA systems in general) are highly variable, this leads to a high number of clusters. I'm not sure of the relevance of these clusters. Are there any other criteria to define clusters?

      The authors claim that most of the Hok diversity is found on chromosomes. However, the number of chromosomal Hok is higher than that located on plasmids, which might be related to the different sizes of the different replicons ie, chromosomes being larger than plasmids. Is there a way to normalize by determining the density per size?

      '46 of the 62 clusters contained 10 or less distinct sequences and might be in the process of degenerating'. The authors also linked this with SD detection. Please explain. From what was indicated earlier, I understand that sequences with premature stop codons or short sequences (<40aa) were removed from the analysis earlier. Lacking an SD is a sign of decay? Were these sequences lacking SD not discarded before starting the analysis? Did the authors experimentally validate some of these sequences?

      'Only 7.3% of the unique sequences were found on both plasmids and chromosomes'. From this observation, the authors conclude that 'there is little stable transfer from chromosomes to plasmids or vice-versa'. I don't understand what this means. Do they mean identical sequences? The fact that sequences differ from chromosomes to plasmids does not rule out 'stable transfer'. What do they actually mean by stable transfer? Once the gene is horizontally transferred, it is fixed and vertically transmitted? Same comments apply to the inter-genera horizontal transfer by plasmids.

      I don't understand the next section about 'family'. What do the authors mean about 'family'? Genera? The same apply to the next section about the Y to C recoding. Did the authors do point mutations in the conserved amino acids/codons to test whether they are important for toxicity? Some Hok variants lacks some of the conserved amino acids and are toxic (under overexpression conditions in Salmonella). What about T18, C31 and E42?

      The prevalence of Hok in chromosomes or on plasmids might depend on various confounding parameters, such as the size, number of sequences available among others. The authors should find methods to correct for all that.

      Link with defense systems. The threshold was set at 20 kb. Why this threshold?

      Significance

      The paper in its current state appears to serve the role of a data repository rather than a thorough and original analysis. It requires extensive revisions before it can be of interest to experts in the toxin-antitoxin field.

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      Referee #1

      Evidence, reproducibility and clarity

      Here, Escalera-Maurer and colleagues, present an up-to-date distribution of homologues of Hok toxic proteins belonging to the well-annotated, but otherwise functionally obscure, hok/Sok type I toxin-antitoxin system, across the RefSeq database. Although such computational analyses have been done in the past, the authors here find many more hok homologs than described before, and they categorise their distribution based on whether they are encoded on chromosomes, plasmids, or (pro)phages. These computational analyses are in general tricky with T1TAs, as their toxins are quite short (~50 amino acids, as is the case for Hok), which is why the authors here used three separate approaches to expand their search (nucleotide-level BLAST, protein-homology, or both combined with Infernal). The authors cluster the Hok homologues they find based on a 60% sequence identity cut-off (expanding the known clusters in the process), and proceeded to test 31 candidates belonging to 15 sequence-clusters for their toxicity in Salmonella Typhimurium LT2, showing that 30/31 were toxic upon induction. An interesting finding from their endeavours is that hok/Sok homologues are enriched within prophages and large plasmids, but are not enriched near bacterial anti-phage defense systems (in contrast to the SymE/SymR T1TA). The findings suggest that hok/Sok are indeed sometimes linked to phage and plasmid biology, although they might not be antiphage defenses per se (they have been clearly shown in the past to be addiction modules, and this is still clearly true).

      My expertise lies towards the experimental side of the authors' work, I thus cannot comment on the accuracy/robustness of the computational analyses performed here. The authors do a fine job in clearly stating their findings overall; I could follow most of the conclusions, and I deemed that most of them were supported by their work. Additionally, I find that this paper is a missed opportunity to uncover even more novel biology connected to the interesting hok/Sok T1TAs. The paper does not provide a new framework to think about what is the function of the chromosomal/prophage hok/Sok T1TA systems, although I realize that this is very difficult to accomplish, especially when considering that hok/Sok systems have been around in the literature for almost 40 years.

      My major comment is in regard to the Hok toxicity assays (Fig. 2). The authors state in the discussion that "Hok peptides originating from chromosomes are as toxic as those from plasmids", but I believe that the way that they tested their constructs might not have allowed them to see toxicity differences between the two groups. Specifically, using the multi-copy plasmid pAZ3 (pBR322 origin of replication; ~15-20 plasmid copies per chromosome) to induce the different Hok toxin homologues in Salmonella Typhimurium LT2 with arabinose might have masked toxicity differences that would otherwise be apparent on the chromosomal expression-level.

      Some of the authors themselves have previously used the FASTBAC-Seq method to study the Hok homologue from plasmid R1, a useful technique during which a toxin is integrated in the chromosome, in order to study their toxicity under natural levels of expression. I believe that an ideal scenario would be to apply FASTBAC-seq to some of the 31 Hok homologues described here (e.g., a subset of plasmidic vs chromosomal Hok homologues) to shed light on potential toxicity differences between the Hok clusters. This would increase the value of the presented study.

      Alternatively, the authors could employ an L-arabinose concentration gradient to titrate the expression levels of the Hok toxins in order to potentially see different toxicity levels from the different homologues. However, this is not going to work in the system as they are using it now for two reasons:

      a) the S. Typhimurium LT2 (STm) used here has its arabinose utilization operon intact (araBAD), which means that Salmonella can catabolize arabinose to use it as a carbon source. This catabolization process interferes with the arabinose induction (i.e., Salmonella eats arabinose instead of using it as the Hok inducer). To ameliorate this, the authors could delete the araBAD operon in STm, rendering STm incapable of catabolizing arabinose, and repeat the experiments in that strain. Or use E. coli BW25113 as the expression host, which already has the araBAD operon deleted (it is not clear to me why the different Hok homologues would not be toxic in E. coli, as the different Hok homologues are widely diverse in sequence, as the authors found here).

      b) Even with the araBAD operon deleted, the arabinose induction would be bimodally on or off in the population, due to the bimodal expression of the arabinose transporter (AraE; see Khlebnikov et al., 2002). This would again not allow for titratable arabinose-inducible expression from different concentrations of arabinose. The solution for this would be to co-express a separate plasmid with araE, which would render every cell the same in regards to arabinose permeability, and thus the system would be titratable (as explained in Khlebnikov et al., 2002).

      Therefore, if the authors would be interested to go towards this route, they would have to first delete the araBAD from STm, then transform STm with an araE plasmid, and redo the experiments. In addition, I would propose to the authors to use the drop plate method (agar plate-based), which is more sensitive compared to the liquid assays employed here.

      Having said all that, I understand that all this experimental work would be strenuous and time-consuming, and although I would like to see it happen, this is not my paper. I would be content therefore if the authors toned down the claim that plasmidic vs chromosomal Hok homologues have the same toxicity, and discuss that chromosomal levels of toxicity are an important caveat that has not been explored here.

      Other comments:

      a) There is barely any discussion of the Sok component (RNA antitoxin) of the homologues; why is that? Could you please discuss Sok differences across the homologues, or at least explain why this is not discussed at all in the paper (e.g., in the discussion)?

      b) In the results section, the Hok clusters are referred to as 62 in number ("Because Hok sequences were too short and variable to construct a meaningful phylogenetic tree, we clustered the Hok sequences with a 60% identity threshold and obtained 62 clusters"), but then in the discussion section, the cluster number becomes 74 ("We highlighted the high sequence variability within Hok peptides by obtaining a total of 74 clusters with 60% identity (Fig. S7)."). Which one is the right number, and why is there a discrepancy?

      Significance

      The most well-clarified aspect of the paper presented here is the distribution of Hok homologues, with the novel aspect of the location in which the hok/Sok T1TAs reside (i.e., chromosome, plasmid, or phage). There is room for the molecular genetics part to be developed further, as I discussed earlier, however this study is the most up-to-date characterization of the diversity of Hok homologues, and will be of interest to the T1TA and the general toxin-antitoxin field.

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      Reply to the reviewers

      __Reply to the Reviewers __

      We thank the Reviewers for their positive assessment and recognition of the paper achievements. The insightful comments will strengthen the data and manuscript.

      Referee #1* *

      Minor comments

      1. Fig 1B - add arrows showing mRNAs being translated or not (the latter mentioned in line 113 is not so easy to see). We have magnified the inset of the colocalisation in the right column; we added arrows and arrowheads to differentiate colocalised and non-colocalised bcd with translating SunTag.

      2. Fig 2A - add a sentence explaining why 1,6HD, 2,5HD and NaCl disrupt P bodies. *

      We have added the information on the use of 1,6HD, 2,5HD, and NaCl to disrupt P-bodies as below. Revised line 158: “To further show that bcd storage in P bodies is required for translational repression, we treated mature eggs with chemicals known to disrupt RNP granule integrity (31, 37, 69-72). Previous work has shown that the physical properties of P bodies in mature Drosophila oocytes can be shifted from an arrested to a more liquid-like state by addition of the aliphatic alcohol hexanediol (HD) (Sankaranarayanan et al., 2021, Ribbeck and Görlich, 2002; Kroschwald et al., 2017). While 1,6 HD has been widely used to probe the physical state of phase-separated condensates both in vivo and in vitro (Alberti et al., 2019; McSwiggen et al., 2019; Gao et al., 2022), in some cells it appears to have unwanted cellular consequences (Ulianov et al., 2021). These include a potentially lethal cellular consequences that may indirectly affect the ability of condensates to form (Kroschwald et al., 2017) and wider cellular implications thought to alter the activity of kinases (Düster et al., 2021). While we did not observe any noticeable cellular issues in mature Drosophila oocytes with 1,6 HD, we also used 2,5 HD, known to be less problematic in most tissues (Ulianov et al., 2021) and the monovalent salt sodium chloride (NaCl), which changes electrostatic interactions (Sankaranarayanan et al., 2021).”

      *Fig 4C - explain in the legend what the white lines drawn over the image represent. And why is there such an obvious distinction in the staining where suddenly the DAPI is much more evident (is the image from tile scans)? *

      Figure 4C is the tile scan image of a n.c.10 embryo and the white line classified the image into four quadrants. We used this image to quantify the extent of bcd (magenta) colocalisation to SunTag (green) in the anterior and posterior domains of the embryo in the bar graph shown in panel C’. There is a formatting error in the image. We will correct this in the revised version. We will also include the details of white lines in the legends. Finally, based on further reviewer comments, in the revised version this data is shifted to the supplementary information.

      • Line 215 - 'We did not see any significant differences in the translation of bcd based on their position, however, there appears an enhanced translation of bcd localised basally to the nuclei (Figure S5).' Since the difference is not significant, I do not think the authors should conclude that translation is enhanced basally. *

      We agree with the reviewer. In this preliminary revision we have changed this statement to: “We did not see any differences in the translation of bcd based on their position with respect to the nuclei position (Figure S5)” (revised line 238-239).

      *Line 218: 'The interphase nuclei and their subsequent mitotic divisions appeared to displace bcd towards the apical surface (Figure S6B).' Greater explanation is needed in the legend to Fig S6B to support this statement as the data just seem to show a nuclear division - I would have thought an apical-basal view is needed to conclude this. *

      We have rearranged this figure and shown in clarity the apical-basal view of the blastoderm nuclei and the displacement of bcd from the surface of the blastoderm in Figure S8.

      New Figure S8: n.c.8 - pre-cortical migration; n.c.12,14- post cortical migration; Mitosis stages of n.c.9-10. The cortical interphase nuclei at n.c. 12,14 displaces bcd. The nuclear area (DAPI, cyan) does not show any bcd particles (magenta) indicated by blue stars. The mitotic nuclei (yellow arrowheads, yellow stars) displace bcd along the plane of nuclear division (doubled headed yellow arrows).

      Fig 5B - the authors compare Bcd protein distribution across developmental time. However, in the early time points cytoplasmic Bcd is measured (presumably as it does not appear nuclear until nc8 onwards) and compare the distribution to nuclear Bcd intensities from nc9 onwards. Is most/all of the Bcd protein nuclear localised form nc9 to validate the nuclear quantitation? Does the distribution look the same if total Bcd protein is measured per volume rather than just the nuclear signal? Are the authors assuming a constant fast rate of nuclear import?

      From n.c.8 onwards, the Bcd signal in interphase nuclei builds up, with the nuclear intensity becoming very high compared to cytoplasmic Bcd. However, we do see significant Bcd signal in the cytoplasm (i.e., above background). In earlier work, gradients of the nuclear Bcd and nuclear-import mutant Bcd overlapped closely (Figure 1B, Grimm et al., 2010). This essentially suggests the nuclear Bcd gradient reflects the corresponding gradient of cytoplasmic Bcd. Further, the nuclear import of Bcd occurs rapidly after photobleaching (Gregor et al., 2007). Based on these observations, and our own measurements, prior to n.c. 9, the cytoplasmic gradient is likely a good approximation of the overall shape, whereas post n.c. 9 the Bcd signal is largely nuclear localised. Further, the overall profile is not dependent on the nuclear volume.

      • Line 259 - 'We then asked if considering the spatiotemporal pattern of bcd translation' - the authors should clarify what new information was included in the model. Similarly in line 286, 'By including more realistic bcd mRNA translation' - what does this actually mean? In line 346, 'We see that the original SDD model .... was too simple.' It would be nice to compare the outputs from the original vs modified SDD models to support the statement that the original model was too simple. *

      We will improve the linking of the results to the model. The important point is that when and where Bcd production occurs is more faithfully used, compared with previous approximations. By including more realistic production domains, we can replicate the observed Bcd gradient within the SDD paradigm without resorting to more complex models.

      Fig S1A - clarify what the difference is between the 2 +HD panels shown.__ __

      The two +HD panels at stage 14 indicate that upon the addition of HD, there are no particles in 70% of the embryos, and 30% show reduced particles. We will add this information to the figure legend.

      • Fig S2E - the graph axis label/legend says it is intensity/molecule. Since intensity/molecule is higher in the anterior for bcd RNAs, is this because there are clumps of mRNAs (in which case it's actually intensity/puncta)? *

      The density of mRNA is very high in the anterior pole; there is a chance that more than one bcd particle is within the imaged puncta (due to optical resolution limitations). We will change the y-axis to average intensity per molecule to average intensity per puncta.


      • Fig S4 - I think this line is included in error: '(B) The line plots of bcd spread on the Dorsal vs. Ventral surfaces.'*

      Yes, we will correct this in the revision.

      • In B, D, E - is the plot depth from the dorsal surface? I would have preferred to see actual mRNA numbers rather than normalised mRNAs. In Fig S4D moderate, from 10um onwards there are virtually no mRNA counts based on the normalised value, but what is the actual number? The equivalent % translated data in Fig S4E look noisy so I wonder if this is due to there being a tiny mRNA number. The same is true for Figs S4D, E 10um+ in the low region.*

      Beyond 10um from the dorsal surface, the number of bcdsun10 counts is very low. It becomes negligible at the moderate and low domains. We will attach the actual counts of mRNA in all these domains as a supplementary table in the revised version.

      General assessment Strengths are: 1) the data are of high quality; 2) the study advances the field by directly visualising Bcd mRNA translation during early Drosophila development; 3) the data showing re-localisation of bcd mRNAs to P bodies nc14 provides new mechanistic insight into its degradation; 4) a new SDD model for Bcd gradient formation is presented. Limitations of the study are: 1) there was already strong evidence (but no direct demonstration) that bcd mRNA translation was associated with release from P bodies at egg activation; 2) it is not totally clear to me how exactly the modified SDD model varies from the original one both in terms of parameters included and model output.

      This is the first direct demonstration of the translation of bcd mRNA released as a single mRNA from P bodies. Previously, we have shown that P bodies disruption releases single bcd from the condensates (31). We have captured a comprehensive understanding of the status of individual bcd translation events, from their release from P bodies at the end of oocyte maturation until the end of blastoderm formation.

      The underlying SDD model – that of localised production, diffusion, and degradation – is still the same (up to spatially varying diffusion). Yet the model as originally formulated did not fit all aspects of the data, especially with regards to the system dynamics. Here, we demonstrate that by including more accurate approximations of when and where Bcd is produced, we can explain the formation of the Bcd morphogen gradient without recourse to any further mechanism.


      Referee #2

      1. Line 114: The authors claim to have validated the SunTag using a fluorescent reporter, but do not show any data. Ref 60 is a general reference to the SunTag, and not the Bcd results in this paper. Perhaps place their data into a supplemental figure or movie? To show the validation of our bcdSun32 line, we have composed a new Figure S1 that shows the translating bcdSun32 (magenta) colocalising to the ScFV-mSGFP2 (green). Yellow arrowheads in the zoom (right panel) points to the translating bcdSun32 (magenta) and red arrowheads points to the untranslated bcdSun32. In addition, we have also shown the validation of bcdSun32 with the anti-GCN4 staining in the main Figure 1B.

      Further, we have dedicated supplementary Figure S3 (previously Figure S2) for the validation of our bcdSun10 construct. Briefly, bcdSun10 is inserted into att40 site of chr.2. We did a rescue experiment, where bcdSun10 rescued the lethality of homozygous bcdE1 null mutant. We then performed a colocalisation experiment using smFISH, where we demonstrated that almost all bcd in the anterior pole are of type bcdSun10. We targeted specific fluorescent FISH probes against 10xSunTag sequence (magenta, Figure S2A) and bcd coding sequence (magenta, Figure S2A). Upon colocalisation, we found ~90% of the mRNA are of bcdSun10 type. The remaining 10% could likely be contributed by the noise level (Figure S2B). We will make sure these points are clear in the revised manuscript.

      Line 128 and Fig. 1E: The claim that bcd becomes dispersed is difficult to verify by looking at the image. The language could also be more precise. What does it mean to lose tight association? Perhaps the authors could quantify the distribution, and summarize it by a length scale parameter? This is one of the main claims of the paper (cf. Line 23 of the abstract) but it is described vaguely and tersely here.

      We have changed the text from, “We also confirmed that bcd becomes dispersed, losing its tight association with the anterior cortex (Figure 1E) (31)” to, “We also confirmed that bcd is released from the anterior cortex at egg activation (Figure 1E) (31, 21).” (Revised line 131).

      The release of bcd mRNA at egg activation was first shown in 2008 (Ref 21, Figure 4, D-E) and again in 2021 (Ref 31, Figure 7 B and E). The main point in line 127-128, “P bodies disassembled and bcd was no longer colocalised with P bodies” and the novel aspect of line 23 is “translation observed”. The distribution of bcd mRNA after egg activation was not the point of this section. We have improved the writing in the revision to make this clearer.

      Line 146, Fig. 1G: This is a really important figure in the paper, but it is confusing because it seems the authors use the word "translation," when they mean "presence of Bcd protein." In other places in the paper, the authors give the impression that "bcd translation" means translation in progress (assayed by the colocalization of GCN4 and bcd mRNA). However, in Fig. 1G, the focus is only on GCN4. Detecting Bcd protein only at the anterior does not mean that translation happens only at the anterior (e.g., diffusion or spatially-restricted degradation could be in play).

      In Figure 1G, we have shown only the “translated” Bcd by staining with a-GCN4. We have changed line 146 from, “Consistent with previous findings, we only observed bcd translation at the anterior of the activated egg and early embryo (Figure 1G-H) (3, 68)” to, “Consistent with previous findings, we only observed the presence of Bcd protein at the anterior of the activated egg and early embryo (Figure 1G-H) (3, 68). (Revised line 151-153). We will use “translating bcd” or “bcd in translation” where we show colocalisation of bcd with BcdSun10 or BcdSun32 elsewhere in the manuscript.

      We did not mean to claim that translation occurred only in the anterior pole. We show that the abundance of bcd is very high in the anterior pole (in agreement with previous work) and that this is where the majority of observed translation events took place. Indeed, we have also shown that posteriorly localised mRNAs have the same BcdSun10 intensity per bcd puncta from the posterior pole (Figure 3B & 4C’ and Figure S2 E), but these are much fewer in number.

      *It would also be helpful to show a plot with quantification of Bcd detection (or translation) on the y-axis and a continuous AP coordinate on the x-axis, instead of just two points (anterior and posterior poles, the latter of which is uninteresting because observing no Bcd at the posterior pole is expected). *

      In Figure 1G,H, our aim was to test whether release from P bodies allowed for bcd mRNA to be translated. We used the presence of Bcd protein at the anterior domain of the oocytes to show this. The posterior pole was included as an internal control. To show the spatial distribution of bcd mRNA and its translation, we used early blastoderm (Figure 3, Figure S4).

      • *

      Another issue with Fig. 1G is that the A and P panels presumably have different brightness and contrast. If not, just from looking at the A and P panels, the conclusion would be that Bcd protein is diffuse (and abundant) in the posterior and concentrated into puncta in the anterior. The authors should either make the brightness and contrast consistent or state that the P panel had a much higher brightness than the A panel.

      We agree with this shortcoming. We have now added the following to Figure 1 legend to clarify this observation. “G: Representative fixed 10 µm Z-stack images (from 10 samples) showing BcdSun32 protein (anti-GCN4) is only present at the anterior of an in vitro activated egg or early embryo 30-minute post fertilization. BcdSun32 protein is not detected in these samples at the posterior pole (image contrast increased to highlight the lack of distinct particles at the posterior). BcdSun32 protein is also not detected at the anterior or posterior of a mature oocyte or an in vitro activated egg incubated with NS8953 (images have the contrast increased to highlight the lack of distinct particles). Scale bar: 20 mm; zoom 2 mm.” (Revised line 623).

      • Line 176: This section is very confusing, because at this point the authors already addressed the spatial localization of translation in Fig. 1G,H (see my above comment). However, here it seems the authors have switched the definition of translation back to "translation in progress." Therefore, the confusion here could be eliminated by addressing the above point.*

      In the revised version, we will use Bcd protein when shown with anti-GCN4 staining. We will use “translating bcd” or “bcd in translation” where we show colocalisation of bcd with a-GCN4 (BcdSun10 or BcdSun32). We will change this in the corresponding text.

      Line 185: The sentence here is seemingly contradictory: "most...within 100 microns" implies that at least some are beyond 100 microns, while the sentence ends with "[none]...more than 100 microns." The language could perhaps be altered to be less vague/contradictory.

      We will clarify this in the revised version. There are few particles visible beyond 100 um. In the lower panel of Figure 3B, the posterior domain shows few particles. However, their actual number compared to bcd counts within the 100 um is negligible (Figure3C). Nonetheless, the few bcd particles we observe do seem to be under translation (quantified in Figure 4C’ and Figure S2E).

      • Line 204: It would be really nice to have quantification of the translation events, such as curves of rate of translation as a function of a continuous AP coordinate, and a curve for each nc.*__ __

      In the revised version we will provide the results quantifying the translation events across the anterior- posterior axis. This will provide a clarity to the presence of bcd and their translation in the posterior domain with time.

      Our colocalisation analysis is semi-automated. It includes an automated counting of the individual bcd particle counts and a manual judgement of the colocalised BcdSun10 protein (distinct spots, above noise) to bcd particles (Figure S3D). The bcd particle counts ran into thousands in each cyan square box (measuring 50um radius and ~ 20um deep from the dorsal surface). We selected three such boxes covering 150um (continuously) from the anterior pole across A-P axis and 20um deep of the flattened embryo mounts across D-V axis (Figure 3A-C, Figure S4). We have also scanned scarce particles in the posterior; however, bcd counts are very low compared to the anterior. Further, in Figure 4 we have repeated the same technique to measure translation of bcd particles in embryos at different nuclear cycles.

      We have also shown continuous intensity measurements of bcd particles with their respective BcdSun10 gradient in Figure 5 across the A-P axis at different nuclear cycles. Here, we know BcdSun10 intensity is not only from the “translating” bcd (colocalised BcdSun10 to bcd particles) but also from the translated BcdSun10 freely diffusing (non-colocalised BcdSun10 to bcd particles). As asked by the reviewer, in the revised version we will add bcd counts and their translation status from anterior to posterior axis for each of the nuclear cycles.

      In our future work, we planned to generate MS2 tagged bcdSun10 to measure the rates of translation in live across all nuclear cycles.

      • *

      *Line 209 and Fig 4C: The authors use the terms "intensity of translation events" or "translation intensity" without clearly defining them. From the figure (specifically from the y-axis label), it looks like the authors are quantifying the intensity per molecule (which is not clearly the same thing as "translation intensity"), but it would be nice if that were stated explicitly. *

      In the relevant result section, we have changed the results text to “the intensity of translation events” for explaining the results of Figure 4C’.

      • In addition, the authors again quantify only two points. This is a continuously frustrating part of the manuscript, which applies to nearly all figures where the authors looked only at two points in space. At a typical sample size of N = 3, it seems well within time constraints to image at multiple points along the AP axis.*__ __

      In addition to the quantification shown at the anterior and posterior locations of the embryo in the Figure 3 and 4, we will show in the revised version, the quantification of translation events across all locations from the anterior to the posterior. We will use three embryos for each nuclear cycle from n.c.1 to 14.

      • Furthermore, it sounds like the authors are saying the "translation intensity" is the same in anterior and the posterior, which is counterintuitive. The expectation is that translation would be undetectable at the posterior end, in part because bcd mRNA would not be present. (Note that this expectation is even acknowledged by the authors on Line 185, which I comment on above, and also on Line 197). There should also be very low levels of Bcd protein (possibly undetectable) at the posterior pole. As such, the authors should explain how they think their claim of the same "translation intensities" in the anterior vs posterior fits into the bigger picture of what we know about Bcd and what they have already stated in the manuscript. They should also explain how they observed enough molecules to quantify at the posterior end. The authors should also disclose how many points are in each box in the boxplot. For example, the sample size is N = 3 embryos. In just three embryos, how many bcd/GCN4 colocalizations did the authors observe at the posterior end of the embryo?*

      In n.c.4 in Figure3, we saw few bcd particles in the posterior. However, at n.c.10 in Figure 4C’ the number of posterior bcd particles are higher than at the early stages. We have quantified them in Figure 4C’. We will clarify this from the new set of quantification we are undertaking now to quantify translation across the A-P axis in the revision.

      Finally, we will also provide the number of bcd particle counts and their colocalisation with a-GCN4 as a supplementary table.

      • Line 215: The sentence that starts on this line seems self-contradictory: I cannot tell whether or not there is a difference in translation based on position. *

      We have not observed any difference in the translation of bcd particles depending on the position along the Z-axis. We will edit this in our revised version.

      • Line 229: Long-ranged is a relative term. From the graph, one could state there is some spatial extent to the mRNA gradient, so it is unclear what the authors mean when they say it is not "long-ranged." Could the mRNA gradient be quantified, such as with a spatial length scale? This would provide more information for readers to make their own conclusions about whether it is long-ranged.*

      We have quantified the bcd mRNA gradient for each n.c. (Figure 5B-C); absolute bcd intensities in Figure 5B, left panel and the normalised intensities in Figure 5C. The length of the mRNA spread appears constant with the half-length maximum of ~75um across all nuclear cycles. Our conclusion of a long ranged Bcd gradient is based on the comparisons of the half-length maximum measurements of bcd particles and BcdSun10 (Figure 5D).

      *Line 230: When the authors claim the Bcd gradient is steeper earlier, a quantification of the spatial extent (exponential decay length scale) would be appropriate. Indeed, lambda as a function of time would be beneficial. It should also be placed in context of earlier papers that claim the spatial length scale is constant. *

      We will show this effectively from the live movies of bcdSun10/nanos-scFv-sGFP2 in the revised version.

      • Lines 235-236: The two sentences that start on these two lines are vague and seemingly contradictory. The first sentence says there is a spatial shift, but the second sentence sounds like it is saying there is no spatial change. The language could be more precise to explain the conclusions. *

      We agree with the reviewer. We will edit this in revision.

      Minor comments

        • Line 81: Probably meant "evolutionarily conserved" * Yes, we have changed, “P bodies are an evolutionarily cytoplasmic RNP granule” to, “P bodies are an evolutionarily conserved cytoplasmic RNP granule.”(Revised line 84-85).

      *Figure 1 legend: part B says "from 15 samples" but also says N = 20. Which is it, or do these numbers refer to different things? *

      We have edited this from, “early embryo (from 15 samples)” to, “early embryo (from 20 samples)”. (Revised line 602).

      • Line 217: migration of what? *

      Edited to “cortical nuclear migration”.

      • Line 228: "early embryo" is vague. The authors should give specific time points or nuclear cycle numbers.*

      Edited to “nuclear cycles 1-8”.

      • Line 301: Other locations in the paper say 75 microns or 100 microns. *

      We will make the changes. It is 100 um.

      • Fig. 5: all images should be oriented such that the dorsal midline is on the upper half of the embryo/image. *

      We will flip the image to match.

      • Fig. 5B: There are light tan and/or light orange curves (behind the bold curves) that are not explained. *

      It is the standard deviation. This will be explained.

      • Fig. 5C: the plot says "normalized" but nowhere do the authors describe what the curves are normalized to. There is also no explanation for what the broad areas of light color correspond to.*__ __

      Normalised to the bcd intensity maxima. This will be explained.

      Significance

      The results, if upheld, are highly significant, as they are foundational measurements addressing a longstanding question of how morphogen gradients are formed, using Bcd (the foundational morphogen gradient) as a model. They also address fundamental questions in genetics and molecular biology: namely, control of mRNA distribution and translation.__ __

      We thank Reviewer 2 for highlighting the importance of our work in the field. We are confident that we address the issues raised by Reviewer 2 with the new set of quantifications we are currently working on.

      Referee #3

        • It is not evident from the main results and methods text that the new SDD model incorporates the phenomenon reported in figure 4B. From my reading, the parameter beta accounts for the Bcd translation rate, which according to figure 7B(ii) effectively switches from off to on around fertilization and thereafter remains constant. Figure 4B shows that the fraction of bcd mRNA engaged in translation decreases beginning around NC12/13, and this is one of the more powerful results that comes from monitoring translation in addition to RNA localization/abundance/stability. My expectation based on figure 4B would be that parameter beta should decrease over time beginning around 90-100 minutes and approach zero by ~150 minutes. This rate could be fit to the experimental data that yields figure 4B. The modeling should be repeated while including this information. This is a good observation. Currently, the reduced rate of bcd translation is modelled by incorporating an increased rate of bcd *mRNA degradation. Of course, this could also be reduced by a change in the rate of translation directly. As stated already, the beta parameter is the least well characterised. In the revision, we will include a model where beta changes but not the mRNA degradation rate. We will improve the discussion to make this point clearer.
      1. The presentation of the SDD model should be expanded to address how well the characteristic decay length fits A) measured Bcd protein distributions, B) measured at different nuclear cycles. This would strengthen the claim that the new SDD model better captures gradient dynamics given the addition of translation and RNA distribution. These experimental data already exist as reported in Figure 5. In the current Figure 7, panels D and D' add little to the story and could be moved to a supplement if the authors want to include it (in any case, please fix the typo on the time axis of fig 7D' to read "hours"). The model per cell cycle and the comparison of experimental and modeled decay lengths could replace current D and D'.*

      Originally, we kept discussion of the SDD model only to core points. It is clear from all Reviewers that expanding this discussion is important. In the revision, we will refocus Figure 7 on describing new results that we can learn. As outlined in the responses above, this paper reveals an important insight: the SDD model – with suitable modifications such as temporally restricted Bcd production – can explain all observed properties of Bcd gradient formation. Other mechanisms – such as bcd mRNA gradients – are not required.

      • The exposition of the manuscript would benefit significantly by including a section either in the introduction or the appropriate section of the results that defines the competing models for gradient formation. In the current version, these models are only cited, and the key details only come out late (e.g., lines 302 onward, in the Discussion). Nevertheless, some of the results are presented as if in dialog with these models, but it reads as a one-sided conversation. For instance: Figure 3. The undercurrent in this figure is the RNA-gradient model. In the context of this model, the results clearly show that translation of bcd is restricted to the anterior. Without this context, Figure 3 could read as a fairly unremarkable observation that translation occurs wherever there is mRNA. Restructuring the manuscript to explicitly name competing models and to address how experimental results support or detract from each competing model would greatly enhance the impact of the exposition.*

      We thank the reviewer for this suggestion. We will add the current models of Bcd gradient formation in the introduction section and will change the narrative of results in the section explaining the models.

      (4A) Related to point 3: The entire results text surrounding Figure 2 should be revised to include more detail about A) what specific hypotheses are being tested; and B) to critically evaluate the limitations of the experimental approaches used to evaluate these hypotheses. Hexanediol and high salt conditions are not named explicitly in the text, but the text touts these as "chemicals" that "disrupt P-body integrity." This implies that the treatments are specific to P-bodies. Neither of these approaches are only disrupting P Body integrity. This does not invalidate this approach, but the manuscript needs to state what hypothesis HD and NaCl treatment addresses, and acknowledge the caveats of the approach (such as the non-specificity and the assumptions about the mechanism of action for HD).

      We have made the following edits to resolve this point. Revised line 158: “To further show that bcd storage in P bodies is required for translational repression, we treated mature eggs with chemicals known to disrupt RNP granule integrity (31, 37, 69-72). Previous work has shown that the physical properties of P bodies in mature Drosophila oocytes can be shifted from an arrested to a more liquid-like state by addition of the aliphatic alcohol hexanediol (HD) (Sankaranarayanan et al., 2021, Ribbeck and Görlich, 2002; Kroschwald et al., 2017). While 1,6 HD has been widely used to probe the physical state of phase-separated condensates both in vivo and in vitro (Alberti et al., 2019; McSwiggen et al., 2019; Gao et al., 2022), in some cells it appears to have unwanted cellular consequences (Ulianov et al., 2021). These include a potentially lethal cellular consequences that may indirectly affect the ability of condensates to form (Kroschwald et al., 2017) and wider cellular implications thought to alter the activity of kinases (Düster et al., 2021). While we did not observe any noticeable cellular issues in mature Drosophila oocytes with 1,6 HD, we also used 2,5 HD, known to be less problematic in most tissues (Ulianov et al., 2021) and the monovalent salt sodium chloride (NaCl), which changes electrostatic interactions (Sankaranarayanan et al., 2021).”

      (4B) Continuing the comment above: it is good that the authors checked that HD and NaCl treatment does not cause egg activation. But no one outside of the field of Drosophila egg activation knows what the 2-minute bleach test is and shouldn't have to delve into the literature to understand this sentence. Please explain in one sentence that "if eggs are activated, then x happens following a short exposure to bleach (citations). We exposed HD and NaCl treated eggs to bleach and observed... ."

      We have made the following edits to resolve this point. Revised line 174: “After treating mature eggs with these solutions, we observed BcdSun32 protein in the oocyte anterior (Figure 2A-B). One caveat to this experiment could be that treating mature eggs with these chemicals results in egg activation which would in turn generate Bcd protein. To eliminate this possibility, we first screened for phenotypic egg activation markers, including swelling and a change in the chorion (73). We also applied the classic approach of bleaching eggs for two minutes which causes lysis of unactivated eggs (74). All chemically treated eggs failed this bleaching test meaning they were not activated (74). While we unable to rule out non-specific actions of these treatments, these experiments corroborate that storage in P bodies that adopt an arrested physical state is crucial to maintain bcd translational repression (31).”

      (4C) Continuing the comment above: The section of the results related to the endos mutation needs additional information. It is not apparent to the average reader how the endos mutation results in changes in RNP granules, nor what the expected outcome of such an effect would "further test the model" set up by the HD and NaCl experiments. The average reader needs more hand-holding throughout this entire section (related to figure 2) to follow the exposition of the results.

      We have made the following edits to resolve this point. Edited line 185: “Finally, we used a genetic manipulation to change the physical state of P bodies in mature oocytes. Mutations in Drosophila Endosulfine (Endos), which is part of the conserved phosphoprotein ⍺-endosulfine (ENSA) family (75), caused a liquid-like P body state after oocyte maturation, similar to that observed with chemical treatment (Figure 2C) (31). This temporal effect matched the known roles of Endos as the master regulator of oocyte maturation (75, 76). endos mutant oocytes lost the colocalisation of bcd mRNA and P bodies, concurrent with P bodies becoming less viscous during oocyte maturation (Figure 2D, Figure S1). Particle size and position analysis showed that bcd mRNA prematurely exhibits an embryo distribution in these mutants (Figure 2E). Due to genetic and antibody constraints, we are unable to test for translation of bcd in the endos mutant. However, it follows that bcd observed in this diffuse distribution outside of P bodies would be translationally active (Figure 2E-F).”

      • (4D) Continuing the comment above: The average reader also needs a better explanation of what hypothesis is being tested in Figure 1 with the pharmacological inhibition of calcium. *

      We have made the following edits to resolve this point. Revised line 138: “We next sought to maintain the relationship between bcd mRNA and P bodies through egg activation. This would act as a control to further test if colocalisation of bcd to P bodies was necessary for its translational repression. Previous work has shown that a calcium wave is required at egg activation for further development (references to add Kaneuchi et al., 2015; York-Anderson et al., 2019; Hu and Wolfner, 2019). Chemical treatment with NS8593 disrupts this calcium wave, while other phenotypic markers of egg activation are still observed (58). Using NS8593 to disrupt the calcium wave in the activated egg, we show P bodies are retained during ex vivo egg activation (Figure 1E). In these treated eggs, bcd mRNA remains colocalised with the retained P bodies (Figure 1F). Based on these results and previous observations (31, 66), we hypothesised that the loss of colocalisation between bcd and P bodies correlates with bcd translation.”

      *It is unclear why Bcd translation could not be measured in the endos mutant background, but it would be necessary to measure Bcd translation in the endos background. If genotypically it is not possible/inconvenient to invoke the suntag reporter in the endos background, would it not be sufficient to immunostain against Bcd itself? Different Bcd antisera have recently been reported and distributed by the Wieschaus and the Zeitlinger groups. *

      We have recently received the Bcd antibody from the Zeitlinger group. This has not been shown to work for immunostaining. It remains unclear if it will be successful in this capacity, but we are currently testing it and will include this experiment in the revision if successful.

      *Figure 4 overall is glorious, but there is a problem with panel C. What are the white lines? Why does the intensity for the green and magenta channel change abruptly in the middle of the embryo? *

      These white lines divide the embryo into 4 compartments. We used this method to quantify the intensity of Bcd translation with respect to the bcd puncta. We will correct this image as there is a problem in formatting.

      *It is noted that neither the methods section or the supplement does not contain any mention of how the modeling was performed. How was parameter beta fit? At least a brief section should be added to the methods describing how beta was fit (pending adjustments suggested in comment 1 above). A platinum-level addition would include a modeling supplement that reports the sensitivity of model outcomes to changes in parameters. *

      We apologise for this omission and will include full methodological details in the revision.

      Minor Comments:

        • Line 28: "Source-Diffusion-Degradation" should be changed to "Synthesis-..."* We will edit in the revised version.

      *Line 39: "blastocyst" should be "blastoderm stage embryo". *

      We will edit in the revised version.

      • Line 81: "P bodies are an evolutionarily cytoplasmic RNP granule." is "conserved" missing here? *

      We will edit in the revised version.

      • Throughout the manuscript, there should be better reporting of the imaged genotypes and whether the suntag is being visualized by indirect immunostaining of fixed tissues or through an encoded nanobody-GFP fusion. *

      We will explain in detail in the revised version.

      • Figure 1G: Why is the background staining so different across conditions? Is this a normalization artifact?*__ __

      We agree with this shortcoming. We have now added the following to the figure legend to clarify this observation. “G: Representative fixed 10 µm Z-stack images (from 10 samples) showing BcdSun32 protein (anti-GCN4) is only present at the anterior of an in vitro activated egg or early embryo 30-minute post fertilization. BcdSun32 protein is not detected in these samples at the posterior pole (image contrast increased to highlight the lack of distinct particles at the posterior). BcdSun32 protein is also not detected at the anterior or posterior of a mature oocyte or an in vitro activated egg incubated with NS8953 (images have the contrast increased to highlight the lack of distinct particles). Scale bar: 20 mm; zoom 2 mm.” (Revised line 623).

      Figure 2 legend: what is +Sch in the x-axis labels of figure 2B? The legend says that 2B is the quantification of the data in 2A, but there is no (presumed control) +Sch image in 2A.__ __

      Thank you for this suggestion we have added the data to Figure 2A.

      • Figure 5A largely repeats information presented in figure 4A. Please consider moving to a supplement. Also, please re-orient embryos to follow the convention that dorsal-most surfaces be presented on the top of the displayed images. *

      Thank you for this suggestion. We will consider moving Figure 5A to the supplementary.

      • The lower-case roman numerals referred to in the text for figure 7B are not included in the corresponding figure panel. *

      We will edit in the revised version.

      • Figure 7C y-axis typo (concentration). *

      We will edit in the revised version.

      • Line 222: "make a long-range functional gradient": more accurate to say, "but also marks mature, Bcd protein which resolves in the expected long-range gradient." *

      We will edit in the revised version.

      • Methods: Please check that all buffers referred to as acronyms are both compositionally defined in the reagents table, and that full names are written out at the time of first mention in the presented order. For instance, Schneider's media is referred to a few times before defining the acronym about midway through the methods section.*__ __

      We have added to Figure 2B: “Quantification of experiments shown in A. The number of oocytes that displayed Bcd protein at the anterior as measured by the presence of BcdSun32 at the anterior of the oocyte, but not the posterior. Schneider’s Insect Medium (+Sch) used as a negative control. N = 30 oocytes for each treatment. Scale bar: 5 um.” (Revised line 646).

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      Referee #3

      Evidence, reproducibility and clarity

      This is a review of "Dynamics of bicoid mRNA localization and translation dictate morphogen gradient formation" by Athilingam et al. In this manuscript, the authors perform quantification of mRNA localization and translation of bicoid, spanning oogenesis through the maternal to zygotic transition, yielding a definitive characterization of Bicoid gradient formation. The experiments, analysis, and interpretation are on the whole performed rigorously. I very much enjoyed this paper, partly for incorporating the aspects of bcd regulation during oogenesis, which compared to embryonic function of bcd is relatively under-studied. Also valuable is improving the characterization of how bcd expression is shut down at NC14. I have several major comments for revision, and a few minor comments. I should stress that none of the major comments are terrible but are intended to improve the impact/readability/flow of this nice manuscript. With the exception of a straightforward immunostaining experiment, all major comments constitute reworking of the model or the text.

      Major Comments:

      1) It is not evident from the main results and methods text that the new SDD model incorporates the phenomenon reported in figure 4B. From my reading, the parameter beta accounts for the Bcd translation rate, which according to figure 7B(ii) effectively switches from off to on around fertilization and thereafter remains constant. Figure 4B shows that the fraction of bcd mRNA engaged in translation decreases beginning around NC12/13, and this is one of the more powerful results that comes from monitoring translation in addition to RNA localization/abundance/stability. My expectation based on figure 4B would be that parameter beta should decrease over time beginning around 90-100 minutes and approach zero by ~150 minutes. This rate could be fit to the experimental data that yields figure 4B. The modeling should be repeated while including this information.

      2) The presentation of the SDD model should be expanded to address how well the characteristic decay length fits A) measured Bcd protein distributions, B) measured at different nuclear cycles. This would strengthen the claim that the new SDD model better captures gradient dynamics given the addition of translation and RNA distribution. These experimental data already exist as reported in Figure 5. In the current Figure 7, panels D and D' add little to the story and could be moved to a supplement if the authors want to include it (in any case, please fix the typo on the time axis of fig 7D' to read "hours"). The model per cell cycle and the comparison of experimental and modeled decay lengths could replace current D and D'.

      3) The exposition of the manuscript would benefit significantly by including a section either in the introduction or the appropriate section of the results that defines the competing models for gradient formation. In the current version, these models are only cited, and the key details only come out late (e.g., lines 302 onward, in the Discussion). Nevertheless, some of the results are presented as if in dialog with these models, but it reads as a one-sided conversation. For instance: Figure 3. The undercurrent in this figure is the RNA-gradient model. In the context of this model, the results clearly show that translation of bcd is restricted to the anterior. Without this context, Figure 3 could read as a fairly unremarkable observation that translation occurs wherever there is mRNA. Restructuring the manuscript to explicitly name competing models and to address how experimental results support or detract from each competing model would greatly enhance the impact of the exposition.

      4A) Related to point 3: The entire results text surrounding Figure 2 should be revised to include more detail about A) what specific hypotheses are being tested; and B) to critically evaluate the limitations of the experimental approaches used to evaluate these hypotheses. Hexanediol and high salt conditions are not named explicitly in the text, but the text touts these as "chemicals" that "disrupt P-body integrity." This implies that the treatments are specific to P-bodies. Neither of these approaches are only disrupting P Body integrity. This does not invalidate this approach, but the manuscript needs to state what hypothesis HD and NaCl treatment addresses, and acknowledge the caveats of the approach (such as the non-specificity and the assumptions about the mechanism of action for HD).

      4B) Continuing the comment above: it is good that the authors checked that HD and NaCl treatment does not cause egg activation. But no one outside of the field of Drosophila egg activation knows what the 2-minute bleach test is and shouldn't have to delve into the literature to understand this sentence. Please explain in one sentence that "if eggs are activated, then x happens following a short exposure to bleach (citations). We exposed HD and NaCl treated eggs to bleach and observed... ."

      4C) Continuing the comment above: The section of the results related to the endos mutation needs additional information. It is not apparent to the average reader how the endos mutation results in changes in RNP granules, nor what the expected outcome of such an effect would "further test the model" set up by the HD and NaCl experiments. The average reader needs more hand-holding throughout this entire section (related to figure 2) to follow the exposition of the results.

      4D) Continuing the comment above: The average reader also needs a better explanation of what hypothesis is being tested in Figure 1 with the pharmacological inhibition of calcium.

      5) It is unclear why Bcd translation could not be measured in the endos mutant background, but it would be necessary to measure Bcd translation in the endos background. If genotypically it is not possible/inconvenient to invoke the suntag reporter in the endos background, would it not be sufficient to immunostain against Bcd itself? Different Bcd antisera have recently been reported and distributed by the Wieschaus and the Zeitlinger groups.

      6) Figure 4 overall is glorious, but there is a problem with panel C. What are the white lines? Why does the intensity for the green and magenta channel change abruptly in the middle of the embryo?

      7) It is noted that neither the methods section or the supplement does not contain any mention of how the modeling was performed. How was parameter beta fit? At least a brief section should be added to the methods describing how beta was fit (pending adjustments suggested in comment 1 above). A platinum-level addition would include a modeling supplement that reports the sensitivity of model outcomes to changes in parameters.

      Minor Comments:

      • Line 28: "Source-Diffusion-Degradation" should be changed to "Synthesis-..."
      • Line 39: "blastocyst" should be "blastoderm stage embryo".
      • Line 81: "P bodies are an evolutionarily cytoplasmic RNP granule." is "conserved" missing here?
      • Throughout the manuscript, there should be better reporting of the imaged genotypes and whether the suntag is being visualized by indirect immunostaining of fixed tissues or through an encoded nanobody-GFP fusion.
      • Figure 1G: Why is the background staining so different across conditions? Is this a normalization artifact?
      • Figure 2 legend: what is +Sch in the x-axis labels of figure 2B? The legend says that 2B is the quantification of the data in 2A, but there is no (presumed control) +Sch image in 2A.
      • Figure 5A largely repeats information presented in figure 4A. Please consider moving to a supplement. Also, please re-orient embryos to follow the convention that dorsal-most surfaces be presented on the top of the displayed images.
      • The lower-case roman numerals referred to in the text for figure 7B are not included in the corresponding figure panel.
      • Figure 7C y-axis typo (concentration).
      • Line 222: "make a long-range functional gradient": more accurate to say, "but also marks mature, Bcd protein which resolves in the expected long-range gradient."
      • Methods: Please check that all buffers referred to as acronyms are both compositionally defined in the reagents table, and that full names are written out at the time of first mention in the presented order. For instance, Schneider's media is referred to a few times before defining the acronym about midway through the methods section.

      Referees cross-commenting

      OK, We've been asked to comment on each others' reviews. I am reviewer 3. We have not been asked, as far as I can tell, to come up with a consensus review.

      Overall, I feel that we are all generally enthusiastic about this manuscript. From most to least enthusiastic, we have reviewer 1, 3, and finally 2. But all three of us are apparently advocating positively and encouraging revision and clarification because, as we all agree, these results are important to publish.

      Consensus Strengths:

      1. The experimental approach is elegant, rigorous, and innovative, especially the real-time visualization of Bcd translation.
      2. The data provide new mechanistic insight into when and where bcd is translated and how this changes over developmental time.
      3. The relocalization of bcd mRNAs to P bodies during nc14 and the implications for RNA degradation are particularly compelling.
      4. The manuscript establishes a path toward refining reaction-diffusion models of morphogen gradients using direct measurements of translation dynamics.

      I agree with all of Reviewer 1's minor points.

      I agree with Reviewer 2's points about:

      • Showing the SunTag validation data using the fluorescent reporter.
      • Clarifying the noted "translation" vs. "protein" issues. This bothered me too, but I wasn't able to articulate the issue as well as done here. This major issue summarizes several of the Reviewer's comments.
      • Generally tightening the precision with which the results are discussed.

      Overall: we have all provided favorable reviews that require mostly tightening of the text, showing some control datasets, maybe quantifying more points across the AP axis, and presenting the SDD model more comprehensively (comparing with old/translation-agnostic model, reporting characteristic decay lengths at different nuclear cycles, incorporating the reported change in translation rate across nuclear cycles (if this survives the clarification of what 'translation' means per Reviewer 2's comments), and perhaps providing more methodological detail on how parameters were fit).

      Significance

      The importance of this study is at several levels. For the developmental biologist, it addresses important mechanisms of translational control and RNA stability over the functional lifetime of a single, critical biological cue that governs embryonic patterning. Not only do the experiments provide quantification of these features, but also point to likely candidates (P-bodies) for gating bcd's translation in the narrow window between egg activation and cellular blastoderm. For the biophysically-inclined, this adds critical quantitative information of translational state that allows for further refining computational models for how this manifestation of a reaction-diffusion system actually comes together in a complex biological context.

      The primary audience for this work will be the two groups above: developmental biologists and scientists interested in the quantitative modeling of biological phenomena.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript by Athilingam et al., the authors are studying the translation of the morphogen Bicoid (Bcd), which is in anterior-posterior patterning of the blastoderm Drosophila embryo. They have used an array of sunTag elements in the 5' UTR of Bcd to detect the localization of translation. They found that, not only is Bcd not translated until egg activation, but it can only be translated at the anterior pole, even though bcd mRNA has a broader spatial distribution.

      In general, the paper uses a cutting-edge methodology to address one of the foundational questions of the best-studied morphogen gradient: namely, what is the spatial distribution of the Bcd source? Together with the dynamics of its spreading (which they addressed in a separate study in 2024) and Bcd degradation, their results point to a modified form of the synthesis/diffusion/degradation (SDD) model of Bcd gradient formation, which they have analyzed in the final subsection of the results. However, there are several major issues that erode the validity and impact of the paper, most of which can be put into the category of vague explanations, missing information, or contradictory statements, making it hard to understand/verify what conclusions can be drawn. This is also coupled with vague figures and captions. We describe these, and a few minor issues, in detail below:

      • Line 114: The authors claim to have validated the SunTag using a fluorescent reporter, but do not show any data. Ref 60 is a general reference to the SunTag, and not the Bcd results in this paper. Perhaps place their data into a supplemental figure or movie?
      • Line 128 and Fig. 1E: The claim that bcd becomes dispersed is difficult to verify by looking at the image. The language could also be more precise. What does it mean to lose tight association? Perhaps the authors could quantify the distribution, and summarize it by a length scale parameter? This is one of the main claims of the paper (cf. Line 23 of the abstract) but it is described vaguely and tersely here.
      • Line 146, Fig. 1G: This is a really important figure in the paper, but it is confusing because it seems the authors use the word "translation," when they mean "presence of Bcd protein." In other places in the paper, the authors give the impression that "bcd translation" means translation in progress (assayed by the colocalization of GCN4 and bcd mRNA). However, in Fig. 1G, the focus is only on GCN4. Detecting Bcd protein only at the anterior does not mean that translation happens only at the anterior (e.g., diffusion or spatially-restricted degradation could be in play).

      It would also be helpful to show a plot with quantification of Bcd detection (or translation) on the y-axis and a continuous AP coordinate on the x-axis, instead of just two points (anterior and posterior poles, the latter of which is uninteresting because observing no Bcd at the posterior pole is expected).

      Another issue with Fig. 1G is that the A and P panels presumably have different brightness and contrast. If not, just from looking at the A and P panels, the conclusion would be that Bcd protein is diffuse (and abundant) in the posterior and concentrated into puncta in the anterior. The authors should either make the brightness and contrast consistent or state that the P panel had a much higher brightness than the A panel.

      • Line 176: This section is very confusing, because at this point the authors already addressed the spatial localization of translation in Fig. 1G,H (see my above comment). However, here it seems the authors have switched the definition of translation back to "translation in progress." Therefore, the confusion here could be eliminated by addressing the above point.
      • Line 185: The sentence here is seemingly contradictory: "most...within 100 microns" implies that at least some are beyond 100 microns, while the sentence ends with "[none]...more than 100 microns." The language could perhaps be altered to be less vague/contradictory.
      • Line 204: It would be really nice to have quantification of the translation events, such as curves of rate of translation as a function of a continuous AP coordinate, and a curve for each nc.
      • Line 209 and Fig 4C: The authors use the terms "intensity of translation events" or "translation intensity" without clearly defining them. From the figure (specifically from the y-axis label), it looks like the authors are quantifying the intensity per molecule (which is not clearly the same thing as "translation intensity"), but it would be nice if that were stated explicitly.

      In addition, the authors again quantify only two points. This is a continuously frustrating part of the manuscript, which applies to nearly all figures where the authors looked only at two points in space. At a typical sample size of N = 3, it seems well within time constraints to image at multiple points along the AP axis.

      Furthermore, it sounds like the authors are saying the "translation intensity" is the same in anterior and the posterior, which is counterintuitive. The expectation is that translation would be undetectable at the posterior end, in part because bcd mRNA would not be present. (Note that this expectation is even acknowledged by the authors on Line 185, which I comment on above, and also on Line 197). There should also be very low levels of Bcd protein (possibly undetectable) at the posterior pole. As such, the authors should explain how they think their claim of the same "translation intensities" in the anterior vs posterior fits into the bigger picture of what we know about Bcd and what they have already stated in the manuscript. They should also explain how they observed enough molecules to quantify at the posterior end. The authors should also disclose how many points are in each box in the boxplot. For example, the sample size is N = 3 embryos. In just three embryos, how many bcd/GCN4 colocalizations did the authors observe at the posterior end of the embryo?

      • Line 215: The sentence that starts on this line seems self-contradictory: I cannot tell whether or not there is a difference in translation based on position.
      • Line 229: Long-ranged is a relative term. From the graph, one could state there is some spatial extent to the mRNA gradient, so it is unclear what the authors mean when they say it is not "long-ranged." Could the mRNA gradient be quantified, such as with a spatial length scale? This would provide more information for readers to make their own conclusions about whether it is long-ranged.
      • Line 230: When the authors claim the Bcd gradient is steeper earlier, a quantification of the spatial extent (exponential decay length scale) would be appropriate. Indeed, lambda as a function of time would be beneficial. It should also be placed in context of earlier papers that claim the spatial length scale is constant.
      • Lines 235-236: The two sentences that start on these two lines are vague and seemingly contradictory. The first sentence says there is a spatial shift, but the second sentence sounds like it is saying there is no spatial change. The language could be more precise to explain the conclusions.

      Minor issues/typos (still must be addressed for content):

      • Line 81: Probably meant "evolutionarily conserved"
      • Figure 1 legend: part B says "from 15 samples" but also says N = 20. Which is it, or do these numbers refer to different things?
      • Line 217: migration of what?
      • Line 228: "early embryo" is vague. The authors should give specific time points or nuclear cycle numbers.
      • Line 301: Other locations in the paper say 75 microns or 100 microns.
      • Fig. 5: all images should be oriented such that the dorsal midline is on the upper half of the embryo/image.
      • Fig. 5B: There are light tan and/or light orange curves (behind the bold curves) that are not explained.
      • Fig. 5C: the plot says "normalized" but nowhere do the authors describe what the curves are normalized to. There is also no explanation for what the broad areas of light color correspond to.

      Referees cross-commenting

      This is Reviewer 2. Yes, I am enthusiastic about the work: it is a much needed set of experiments and it fits well into the overall goal of quantitatively understanding the processes that establish the Bcd gradient. My main concern(s) about this paper is the loose and vague way they described their experiments and the interpretations. My hope is they will use the revision as an opportunity to more precisely explain their work.

      Other than that, I am in agreement with the other reviewers on the need to revise for clarity and publish this important work.

      Significance

      The results, if upheld, are highly significant, as they are foundational measurements addressing a longstanding question of how morphogen gradients are formed, using Bcd (the foundational morphogen gradient) as a model. They also address fundamental questions in genetics and molecular biology: namely, control of mRNA distribution and translation.

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper the authors use the Suntag system to visualise bcd mRNA translation in the Drosophila embryo. They elucidate the relationship between bcd mRNA translation and P body localisation. In the oocyte, bcd mRNAs are localised in P bodies and translationally repressed, but upon egg activation bcd mRNAs are released from P bodies and translated. In addition, during mid-nc14, bcd mRNAs become localised to embryonic P bodies and degraded. The authors use their data to modify the Synthesis, Diffusion, Degradation model of Bcd gradient formation, which recapitulates the Bcd gradient detected experimentally.

      Overall, I think the data are of high quality and support the authors' conclusions. I only have minor comments, as follows:

      Fig 1B - add arrows showing mRNAs being translated or not (the latter mentioned in line 113 is not so easy to see).

      Fig 2A - add a sentence explaining why 1,6HD, 2,5HD and NaCl disrupt P bodies.

      Fig 4C - explain in the legend what the white lines drawn over the image represent. And why is there such an obvious distinction in the staining where suddenly the DAPI is much more evident (is the image from tile scans)?

      Line 215 - 'We did not see any significant differences in the translation of bcd based on their position, however, there appears an enhanced translation of bcd localised basally to the nuclei (Figure S5).' Since the difference is not significant, I do not think the authors should conclude that translation is enhanced basally.

      Line 218: 'The interphase nuclei and their subsequent mitotic divisions appeared to displace bcd towards the apical surface (Figure S6B).' Greater explanation is needed in the legend to Fig S6B to support this statement as the data just seem to show a nuclear division - I would have thought an apical-basal view is needed to conclude this.

      Fig 5B - the authors compare Bcd protein distribution across developmental time. However, in the early time points cytoplasmic Bcd is measured (presumably as it does not appear nuclear until nc8 onwards) and compare the distribution to nuclear Bcd intensities from nc9 onwards. Is most/all of the Bcd protein nuclear localised form nc9 to validate the nuclear quantitation? Does the distribution look the same if total Bcd protein is measured per volume rather than just the nuclear signal? Are the authors assuming a constant fast rate of nuclear import?

      Line 259 - 'We then asked if considering the spatiotemporal pattern of bcd translation' - the authors should clarify what new information was included in the model. Similarly in line 286, 'By including more realistic bcd mRNA translation' - what does this actually mean? In line 346, 'We see that the original SDD model .... was too simple.' It would be nice to compare the outputs from the original vs modified SDD models to support the statement that the original model was too simple.

      Fig S1A - clarify what the difference is between the 2 +HD panels shown.

      Fig S2E - the graph axis label/legend says it is intensity/molecule. Since intensity/molecule is higher in the anterior for bcd RNAs, is this because there are clumps of mRNAs (in which case it's actually intensity/puncta)?

      Fig S4 - I think this line is included in error: '(B) The line plots of bcd spread on the Dorsal vs. Ventral surfaces.' In B, D, E - is the plot depth from the dorsal surface? I would have preferred to see actual mRNA numbers rather than normalised mRNAs. In Fig S4D moderate, from 10um onwards there are virtually no mRNA counts based on the normalised value, but what is the actual number? The equivalent % translated data in Fig S4E look noisy so I wonder if this is due to there being a tiny mRNA number. The same is true for Figs S4D, E 10um+ in the low region.

      Referees cross-commenting

      I think the concerns raised by reviewers 2 and 3 are valid, and that it is feasible for the authors to address all the reviewers' concerns in order to improve the manuscript.

      Significance

      General assessment

      Strengths are: 1) the data are of high quality; 2) the study advances the field by directly visualising Bcd mRNA translation during early Drosophila development; 3) the data showing re-localisation of bcd mRNAs to P bodies nc14 provides new mechanistic insight into its degradation; 4) a new SDD model for Bcd gradient formation is presented. Limitations of the study are: 1) there was already strong evidence (but no direct demonstration) that bcd mRNA translation was associated with release from P bodies at egg activation; 2) it is not totally clear to me how exactly the modified SDD model varies from the original one both in terms of parameters included and model output.

      Advance

      The advance is conceptual, technical and mechanistic.

      Audience

      The results will be important to a broad range of researchers interested in the formation of developmental morphogen gradients and the post-transcriptional regulation of gene expression, particularly the relationship with P bodies.

      My expertise

      Wetlab developmental biologist

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      Reply to the reviewers

      __Reviewer #1 __

      *This study "Interpreting the Effects of DNA Polymerase Variants at the Structural Level" comprises an in-depth analysis of protein sequence variants in two DNA polymerase enzymes with particular emphasis on deducing the mechanistic impact in the context of cancer. The authors identify numerous variants for prioritisation in further studies, and showcase the effectiveness of integrating various data sources for inferring the mechanistic impact of variants. *

      *All the comments below are minor, I think the manuscript is exceptionally well written. *

      *> The main body of the manuscript has almost as much emphasis on usage of the MAVISp tool as analysis of the polymerase variants. I don't think this is an issue, as an illustrated example of proper usage is very handy. I do, however, think that the title and abstract should better reflect this emphasis. E.g. "Interpreting the Effects of DNA Polymerase Variants at the Structural Level with MAVISp". This would make the paper more discoverable to people interested in learning about the tool. *

      We have changed the manuscript title according to the reviewer’s suggestions, and the current title is “Interpreting the Effects of DNA Polymerase Variants at the Structural Level using MAVISp and molecular dynamics simulations.”

      • *

      *> Figure 1. I don't believe there is much value in showing the intersection between the datasets (especially since the in-silico saturation dataset intersects perfectly with all the others). As an alternative, I suggest a flow-chart or similar visual overview of the analysis pipeline. *

      • *

      We moved the former Figure 1 to SI. We decided to keep it at least in SI because it provides guidance on the number of variants relative to the total reported across the different disease-related datasets annotated with the MAVISp toolkit. On the other hand, the suggestion of a visual scheme for the pipeline followed in the analyses is a great idea. We have thus added Figure 1, which illustrates the pipeline workflows for analysis of known pathogenic variants and for discovery of VUS and other unknown variants, as suggested by the reviewer.

      *> Please note in the MAVISp dot-plot figure legends that the second key refers to the colour of the X-axis labels rather than the dots *

      We have revised the code that produces the dotplot so the second key is placed closer to the x-axis and clearer to read.

      Missing figure reference (Figure XXX) at the bottom of page 16

      We apologize for this mistake. Figures, contents, and the order have changed significantly to address all reviewers’ comments; this statement is no longer included. Also, we have carefully proofread the final version of the manuscript before resubmitting it.


      __Reviewer #2 __

      • *

      This manuscript reports a comprehensive study of POLE and POLD1 annotated clinical variants using a recently developed framework, MAVISp, that leverages scores and classifications from evolutionary-based variant effect predictors. The resource can be useful for the community. However, I have a number of major concerns regarding the methodology, the presentation of the results.

      *** On the choice of tools in MAVISp and interpretation of their outputs *

      - Based on the ProteinGym benchmark: https://proteingym.org/benchmarks*, GEMME outperforms EVE for predicting the pathogenicity of ClinVar mutations, with an AUC of 0.919 for GEMME compared to 0.914 for EVE. Thus, it is not clear for me why the authors chose to put more emphasis on EVE for predicting mutation pathogenicity. It seems that GEMME can better predict this property, without any adaptation or training on clinical labels. *

      • *

      We appreciate this comment, but we should not exclude EVE entirely from our data collection or from VEP coverage under MAVISp, based on a difference in AUC of 0.005. It was not our intention to place more emphasis on EVE predictions, and we have revised it accordingly. We would like to clarify the workflow we use for applications of the MAVISp framework in “discovery mode,” i.e., for variants not reported as pathogenic in ClinVar. This relies on AlphaMissense to prioritize the pathogenic variants and then retain further only the ones that also have an impact according to DeMaSk, which provides further indication for loss/gain-of-fitness. DeMaSk nicely fits the MAVISp framework, as it was trained on data from experimental deep mutational scans, which we generally import in the EXPERIMENTAL_DATA module. We have revised the text to make this clearer. GEMME and EVE (or REVEL) can be used for complementary analysis in the discovery workflow. Other users of MAVISp data might want to combine them with a different design, and they have access to all the original scores in the MAVISp database CSV file and the code for downstream analysis to do so. The choice for our MAVISp discovery workflow is mainly dictated by the fact that we have noticed we do not always have full coverage of all variants in many protein instances for EVE, GEMME, and REVEL. In particular, since the reviewer highlights GEMME over EVE, GEMME is currently unavailable for a few cases in the MAVISp database. This is because we need to rely on an external web server to collect the data, which slows down data collection on our end.

      Additionally, we have encountered instances where GEMME was unable to provide an output for inclusion in the MAVISp entries. When we designed the workflow for variant characterization in focused studies, we also made practical considerations. We are also exploring the possibility of using pre-calculated GEMME scores from

      https://datadryad.org/dataset/doi:10.5061/dryad.vdncjsz1s, but we encountered some challenges at the moment that deserve further investigations and considerations. For example, MAVISp annotations rely on the canonical isoform as reported in Uniprot, which can lead to mismatches with the GeMME pre-computed scores. So far, we have identified a couple of entries whose canonical isoforms no longer match the one in the pre-computed GEMME score dataset. Another limitation is the absence of the original MSA files in the dataset, which we would need for a more in-depth comparison with the ones we used for our calculations. We are facing some challenges in reproducing the MSA output from MMseq2-based ColabFold protocol in this context that need to be solved first. Overall, the dataset shows potential for integration into MAVISp, but we need to define the inclusion criteria and compare it with the existing results in more detail.

      Additionally, since the principle behind MAVISp is to provide a framework rooted in protein structure, AlphaMissense was the most reasonable choice for us as the primary indicator among the VEPs for our discovery workflow, and it has performed reasonably well in this case study and others.

      Of course, our discovery design is one of the many applications and designs that could be envisioned using the data provided and collected by MAVISp. We also include all raw scores in the database's final CSV files, allowing other end users to decide how to use them in their own computational design. The design choice we made for the discovery phase of focused studies, using MAVISp to identify variants of interest for further studies, has been applied in other publications (see https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data) in some cases together with experiments. It is also a fair choice for the application, as the ultimate goal is to provide a catalog of variants for further studies that may have a potentially damaging impact, along with a corresponding structural mechanism.

      We have now revised the results section text where Table 1 is cited to clarify this. We also revised the terminology because we are using the VEPs' capability to predict damaging variants, rather than the pathogenic variants themselves. Experiments on disease models should validate our predictions before concluding whether a variant is pathogenic in a disease context, and we want to avoid misunderstandings among readers regarding our stance on this matter.

      - Which of the predictors, among AM, EVE, GEMME, and DeMaSK, provide a classification of variants and which ones provide continuous scores? This should be clarified in the text. If some predictors do not output a classification, then evaluating their performance on a classification task is unfair. The MAVISp framework sets thresholds on the predicted scores to perform the classification and it is unclear from reading the manuscript whether these thresholds are optimal nor whether using universal cutoff values is pertinent. For instance, for GEMME, a recent study shows that fitting a Gaussian mixture to the predicted score distribution yields higher accuracy than setting a universal threshold (https://doi.org/10.1101/2025.02.09.637326*). Along this line, for predictors that do not provide a classification, I am not convinced of the benefit for the users of having access to only binary labels, instead of the continuous scores. The users currently do not have any idea of whether each variant is borderline (close to theshold) or confident (far from threshold). *

      We agree with the reviewer, and this is due to us not being sufficiently clear in the manuscript. We have now revised the first part of the results to clarify this and to explain how we use the MAVISp data for application to focused studies, where the goal is to identify the most interesting variants that are potentially damaging and have a linked structural mechanism. Of course, there are other applications for leveraging the data in the database. We do offer scores to variants instead of just classification labels in the MAVISp csv file. They can be accessed, together with the full dataset, through the MAVISp website and reused for any applications.

      Additionally, we used the scores in the revised manuscript for the VUS variant ranking (Figure 5), applying a strategy recently designed as an addition to the downstream analysis tool kit of MAVISp (​​https://github.com/ELELAB/MAVISp_downstream_analysis), thereby allowing the scores themselves to be taken into account. Also, in the final part of the manuscript, the VEP scores have been used to introduce the ACMG-like classification of the variants in response to reviewer 3 (Figure 9 and Tables S3-S4). We absolutely agree that it is informative to keep the continuous scores, and we have never overlooked this aspect. However, we also need a strategy with a simpler classification to highlight the most interesting variants among thousands or more to start an exploration. This is why we included the support with dotplots and lolliplots, for example. Our purpose here is to identify, among many cases, those with a potentially damaging signature (and thus we need a binary classification for simplicity). Next, we evaluate whether this signature entails a fitness effect (with DeMaSk), and finally, retain only the cases we can identify with a structural mechanism to study further.

      The thresholds we set as the default for data analysis of dotplots in GEMME and DeMaSk are discussed in __Supplementary Text S3 __of the original MAVISp article. In brief, we carried out an ROC analysis against the scores for known pathogenic and benign variants in ClinVar with review status higher than 2. For applicative purposes, one could design other strategies to analyze the MAVISp data too; it is not limited to the workflow we decided to set as the primary one for our focused studies, as already mentioned above.

      We have now also included classification based on the GMM model applied to GEMME scores for POLE and POLD1, so it can be evaluated against other designs for our protein of interest (see Table 1 in the revised version). The method section has been revised to include this part, and the ProteoCast pre-print is cited as a reference. We have not yet officially included this classification in the MAVISp database because we must first follow internal protocols to meet the inclusion criteria for new methods or analyses. We will do so by performing a similar comparison on the entire MAVISp dataset and focusing on high-quality variants, as ClinVar annotations, as we did to set the current thresholds for GEMME in Supplementary Table S3 of the original MAVISp article. We need to allocate time and resources to this pilot, which is scheduled for Q1 2026.

      ** On the presentation and impact of the results

      • While reading the manuscript, it is difficult to grasp the main messages. The text contains abundant discussion about the potential caveats of the framework, the care that should be taken in interpreting the results, and the dependency on the clinical context. Although these aspects are certainly important, this extensive discussion (spread throughout the manuscript) obscures the results. Moreover, the way variants are catalogued throughout the text makes it difficult to grasp key highlights. The reader is left unsure about whether the framework can actually help the clinical practitioners.

      We have revised the text to make it easier to read, including additional MD simulations of three variants of interest and more downstream analyses to clarify the mechanisms of action. We also added a recap of the most interesting variants and their associated mechanisms, along with the ranking of the variants using the different features available in the MAVISp csv file for the VUS. We hope that this makes it more accessible and valuable. In the original publication, Table 2 aimed to provide a summary of the interesting variants, and we have revised it now in light of the ranking results and the additional analyses that allow us to clarify the mechanisms of action further. We have also introduced__ Figure 9 and Tables S3 and S4__, which present data on ACMG-like classification for VUS that can fall into the likely pathogenic or benign categories.

      • In many cases, the authors state that experimental validation is required to validate the results. Could they be more explicit on the experimental design and the expected outcome?

      We have added a section on the point above at pages 21 and 30, where, alongside the summary of mechanisms per variant, we propose the experimental readouts to use based on known MAVE assays or assays that could be designed.

      • AlphaMissense seems to tend to over-predict pathogenicity. Could the authors comment on that?

      We are unsure whether this comment relates to our specific case or to a general feature of AlphaMissense.

      In the latest iteration of our small benchmarking dataset for POLE and POLD1 (as shown in the paper), we achieve a sensitivity of 1 and a balanced specificity of 0.96 for AlphaMissense, which suggests that AlphaMissense does not over-predict pathogenicity very significantly in these proteins, predicting true negatives (i.e., non-pathogenic) mutations quite accurately. As performance was sufficient in our case, we deemed recalibrating the classification threshold for AlphaMissense unnecessary.

      We are aware that this is not necessarily the case for every gene, e.g., it has been shown that AlphaMissense shows lower specificity in some cases (see e.g. 10.3389/fgene.2024.1487608, 10.1038/s41375-023-02116-3). This is also why we found it essential to evaluate its performance with its recommended classification on a gene-specific basis, as done here. In the future, we will keep a critical eye on our predictors to understand whether they are suitable for the specific case of study, or whether they require threshold recalibration or the use of a different predictor.

      ** On specific variants

      • The mention of H1066R, H1068, and D1068Y is very confusing. There seems to be a confusion between residue numbers and amino acid types.

      We have revised the text for typos and errors. This part of the text changed, so these specific variants are no longer mentioned.

      • A major limitation of the 3D modeling is this impossibility to include Zn2+ coordination by cysteine residues. This limitation holds for both POLE and POLD1. Could the authors comment on the implication of this limitation for interpreting the mechanistic impact of variants. In particular, there are several variants reported in the study that consist in gain of cysteines. The authors discuss the potential impact of some of these mutations on the structural stability but not that on Zn coordination or the formation of disulphide bridges.

      This is a great suggestion. We had, for a long time, a plan in the pipeline to include a module to tackle changes in cysteines. We have now used this occasion to include a new module that allows identifying mutations: 1) that are likely to disrupt native disulphide bridges and annotate them as damaging or 2) potential de novo formation of disulphide bridges upon a mutation of a residue to a cysteine, also annotated as damaging with respect to the original functionality. We also included a step that evaluates if the protein target is eligible for the analysis based on the cellular localization, since in specific compartments the redox condition (such as the nucleus) would not favour disulfide bridges. The module has been added to MAVISp, and we are collecting data with the module for the existing entries in the database to be able to release them at one of the following updates. More details are on the website in the Documentation section (https://services.healthtech.dtu.dk/services/MAVISp-1.0/). We could not apply the module to POLE and POLD1 since they are nuclear proteins, and it would not be meaningful to look into this structural aspect either in connection with loss of native cysteines or de novo disulfide bridge formation upon mutations that change a wild-type residue to a cysteine.

      We would like to clarify that the structures we use, as it is a focused study rather than high-throughput data collection for the first inclusion in the MAVISp database, have been modelled with zinc at the correct position. It is just the first layer of high-throughput collection with MAVISp, which uses models without cofactors unless the biocurator attempts to model them or we move to collect further data for research studies (as done here). Prompted by this confusion, we have now added a field to the metadata of a MAVISp entry indicating the cofactor state. Nevertheless, the RaSP stability prediction does not account for the cofactor's presence, even when it is bound in the model. This is discussed in the Method Section. We thus did not further analyze the variants in sites directly coordinating the metal groups due to these limitations.

      • MAVISp does not identify any mechanistic effect for a substantial portion of variants labelled as pathogenic. Could the authors comment on this point?

      We are not sure how to interpret this question. It can be read two ways. Either the reviewer is asking about the known pathogenic ClinVar variants without mechanistic indicators, or more generally, the ones that we label “pathogenic” in discovery (we actually refer to more usually damaging in the dotplots), and for which we cannot associate a mechanism.

      Overall, as a general consideration, it would be challenging to envision a mechanism for each variant predicted to be functionally damaging. For example, in the case of POLE and POLD1, we still lack models of complexes that did not meet the quality-control and inclusion criteria for the binding-free-energy scheme used by the LOCAL INTERACTION module. Also, when it comes to effects on catalysis or to analyzing effects in more detail at the cofactor sites, we could miss effects that would require QM/MM calculations. Other points we have not yet covered include cases related to changes in protein abundance due to degron exposure for degradation, which is one of the mechanistic indicators we are currently developing. Moreover, we used only unbiased molecular simulations of the free protein, and we would need future studies with enhanced sampling approaches and longer timescales to better address conformational changes and changes in the population of different protein conformational states induced by the mutation (including DNA). This can be handled formally by the MAVISp framework using metadynamics approaches, but it would be outside the scope of this work and is a direction for future studies on a subset of variants to investigate in even greater detail.

      Furthermore, modifications related to PTM differ from phosphorylations. Anyway, our scope is to use the platform to provide structure-based characterization of either known pathogenic variants or potentially damaging ones predicted by VEPs, and focus on more detailed analyses of those. As we develop MAVISp further and design new modules, we will also be able to tackle other mechanistic aspects. This discussion, however, is more relevant to the MAVISp method paper itself.

      Moreover, none of the variants discussed are associated with allosteric effect. Is this expected?

      .

      In general, allosteric mutations are rare. Nevertheless, in these case studies, the size of the proteins under investigation also poses some challenges for the underlying coarse-grain model used in the simple mode to generate the allosteric signalling map, as we have found it performs best on protein structures below 1000 residues

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

      The manuscript utilized the MAVISp framework to characterize 64,429 missense variants (43,415 in POLE and 21,014 in POLD1) through computational saturation mutagenesis. The authors integrate protein stability predictions with pathogenicity predictors to provide mechanistic insights into DNA polymerase variants relevant to cancer predisposition and immunotherapy response. There are discussions of known PPAP-associated variants and somatic cancer mutations in the context of known data and some proposed variants of interest (which are not validated).

      Major comments:

      I was unaware of the MAVISp framework. It concerns me that alebit this paper has a lot of technical details about the framework, its not the paper about the framework. I did look into the paper https://www.biorxiv.org/content/10.1101/2022.10.22.513328v5 which keeps benign updated (version five now) for three years, but I do not see a peer reviewed version. It would be unfair of me to peer review the underlying framework of the work but together with the previous comments, I am a bit concerned.

      We have intentionally left the MAVISp resource paper as a living pre-print until we have sufficient data in the database that could be useful to the rest of the community. We have been actively revising the manuscript, thanks to comments from users in previous versions, to ensure it provides a solid resource. We had attempted approximately one and a half years ago a submission to a high-impact journal and even addressed the reviewers’ comments there. Still, we did not receive feedback for a long time, and ultimately, we were not sent to the reviewers again despite more than six months of work on our side. After that, we realized that we would benefit from collecting a larger dataset, and we invested time and effort in that and submitted again for revision, this time through Review Commons in the Summer of 2025. Anyway, the paper has been peer-reviewed by three reviewers through Review Commons. We submitted the revised version and response to reviewers, and it is now under revision with Protein Science. The reviewers’ comments and our responses can be found in the “Latested Referred Preprints” on the Review Commons website with the date of 17th of October 2025.

      We would also like to clarify another point on this. In our experience, it is common practice to keep sofware on BioRxiv even for a long and to bring it to a more complete form in parallel with the community already applying it. This allows feedback from peers in a broad manner. We had similar experiences with MoonlightR, where the first publications with applications within the TCGA-PanCancer papers came before the publication of the tool itself, and the same has been for any of our main workflows, such as MutateX or RosettaDDGPrediction, which are widely used by the community. Finally, it can be considered that the MAVISp framework has already been used in different published peer-review studies (since 2023), attesting to its integrity and potential. Here, the reviewer can read more about the studies that used MAVISp data or modules: https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data

      For example, the authors are using AlphaFold models to predict DDG values. Delgado et al. (2025, Bioinformatics) explicitly tested FoldX on such models and concluded that "AlphaFold2 models are not suitable for point mutation ΔΔG estimation" after observing a correlation of 0.06 between experimental and calculated values. AlphaFold's own documentation states it "has not been validated for predicting the effect of mutations". Pak et al. (2023, PLOS ONE) showed correlation between AlphaFold confidence metrics and experimental ΔΔG of -0.17. Needless to say that these concerns seriously undermine the validity of a major part of the study.

      We appreciate the reviewer’s comments and would like to clarify a point regarding the MAVISp STABILITY module, which we believe may have been misunderstood. Based on the studies cited by the reviewer, which critique the use of AF-generated mutant structures for assessing stability effects, we understand that this assumption may have led to the concern.

      The STABILITY module utilises three in silico tools (FoldX, Rosetta, and RaSP) to assess changes in protein stability resulting from missense mutations. Importantly, the input to these assessments consists of AF models of the WT protein structures, not of AF-generated mutant structures. The mutants are generated using the FoldX and Rosetta protocols, along with estimates of the changes in free energy. For further details and clarification, we kindly refer the reviewer to the MAVISp original publication.

      Also, one should consider the goal of our use of free energy calculations: not to identify the exact ΔΔG values, but to correlate with data from in vitro or biophysical experiments, such as those from cellular experiments like MAVE. We, other researchers, have shown that we have a good agreement in the MAVISp paper (case study on PTEN as an example in the original MAVISp publication and https://pmc.ncbi.nlm.nih.gov/articles/PMC5980760/ https://pubmed.ncbi.nlm.nih.gov/28422960/,10.7554/eLife.49138). Also, we had, before even designing the STABILITY module for MAVISp, verified that we can use WT structures from AlphaFold (upon proper trimming and quality control with Prockech) instead of experimental structure without compromising accuracy in the publications of the two main protocols of the STABILITY module (MutateX and RosettaDDGPrediction and a case study on p53, https://doi.org/10.1093/bib/bbac074,https://doi.org/10.1002/pro.4527). In the focused studies, we also carefully consider whether the prediction is at a site with a low pLDDT score or surrounded by other sites with a low pLDDT score before reaching any conclusions. The pLDDT score is reported in the MAVISp csv file exactly to be used for flagging variants or looking closer at them, as we discuss in this study (see, for example, Figure 2). Additionally, it should be noted that we employ a consensus approach across the two classes of methods in MAVISp to account for their limitations arising from their empirical energy function or backbone stiffness. Furthermore, in the focused studies, we also collected molecular dynamics simulations for the ensemble mode and reassessed the stability on different conformations from the trajectory to compensate for the issues with backbone stiffness of FoldX, RaSP, and Rosetta ΔΔG protocols.

      I have to add that this is also true for the technical choices: Several integrated predictors (DeMaSk, GEMME) are outperformed by newer methods according to benchmarking studies (https://www.embopress.org/doi/full/10.15252/msb.202211474). AlphaMissense, while state-of-the-art, shows substantial overcalling of pathogenic variants. could ensemble meta-predictors (REVEL, BayesDel) improve accuracy?

      The MAVISP framework includes REVEL as one of the VEPs available for data analysis. In this way, we were representing one of the ensemble meta-predictors. This is explained in the MAVISp original paper. We were not aware of BayesDel, which we will consider for one of the next pilot projects to assess new tools for the framework (see more details below on how we generally proceed). Currently, we cannot use REVEL for all variants because we do not necessarily have genomic coordinates for them. We retrieve genomic-level variants corresponding to our protein variants from mutation databases, where available (e.g., ClinVar, COSMIC, or CbioPortal). However, as we strive to cover every possible mutation, several of the variants in MAVISp are not in the database, which means we do not have the corresponding genomic variation for those, limiting our ability to annotate them with VEPs. In the future (see GitHub issue https://github.com/ELELAB/cancermuts/issues/235), we will revise the code to identify the genomic variants that could give rise to each protein mutation of interest, thereby increasing the coverage of VEP annotations.

      We can see from the work cited by the reviewer that ESM-1v, EVE, and DeepSequence are among the top performers, whereas reviewer 2 cited another work in which GEMME outperforms EVE. We have been covering all of them, except ESM-1v, in our framework. We are planning to evaluate for inclusion in MAVISP some of the new top-performing predictors, including ESM-1v, in Q2 2026 (according to the protocol described later in this answer), which is why it is not available yet.

      In our discovery protocol (i.e., when we work on VUS or variants not classified in ClinVar), we generally use AlphaMissense as the first indicator of potentially damaging variants. EVE, REVEL, or GEMME could be used in the case that AlphaMissense data are missing or as a second layer of evidence in the case we want, for example, to select a smaller pool of variants for experimental validation in a protein target with too many uncharacterized variants and too many that pass the evaluation with our discovery workflow. Finally, we rely on DeMaSk, as it also provides information on possible loss- or gain-of-fitness signatures to further filter the variant of interest for the search of mechanistic indicators. Since the MAVISp framework is modular, other users may want to use the data differently and design a different workflow. They have access to them (scores and classifications) through the web portal. The fact that we combine AlphaMissense with DeMaSk could yield final results after further variant filtering and mitigate the issue that AlphaMissense risks over-predicting pathogenicity.

      In general, we work to keep MAVISp up-to-date, and we have developed a protocol for the inclusion of new methodologies in the available module before generating and releasing data with new tools in the database. In particular, we perform comparative studies using data already available in the database to evaluate the performance of new approaches against that of the tools already included. Depending on the module, we use different golden standards that we are also curating in parallel, and it would make sense to apply for that specific module. For example, if the question is to evaluate VEP, we would compare it against ClinVar known variants with good review status. If the VEP performs better than the currently included ones, we can include it as an additional source of annotations and evaluate whether we could change the protocol for the discovery/characterization of variants. We operate similarly for the structural modules. For example, for stability, we are importing experimental data from MAVE assays on protein abundance and use them as a golden standard where we evaluate new approaches against the current FoldX and Rosetta-based consensus for changes in folding free energies. Instead, If we find evidence that suggests switching to a new method or integrating it would be beneficial, we will do so as a result of these investigations. An example of our working mode for evaluating tools for inclusion in the framework is illustrated by how we handled the comparison between RaSP and Rosetta in the MAVISp original article (Supplementary file S2) before officially switching to RaSP for high-throughput data collection. We still maintain Rosetta, especially in focused studies, to validate further variants classified as uncertain.

      *Further, I found the web site of the framework, where I looked for the data on these models, rather user unfriendly. Selecting POLD1, POLD2, or POLE tells me I am viewing entries A2ML1, ABCB11, ABCB6 respectively, when I search for POL and then click: these are the first three entries of the table, bot the what I click on. displaying the whole table and clicking on POLD1, gets me to POLD1. However, when I selected "Damaging mutations on structure" I get "Could not fetch protein structure model from the AlphaFold Protein Structure Database". Many other features are not working (Safari or Chrome, in a Mac). That is a concern for the usability of the dataset. *

      • *

      We have been able to reproduce the bugs identified by the reviewer and have fixed them. The second was connected to recent updates on the AlphaFold Protein Structure Database. We are not really sure how to work and act on the “other features that are not working” due to lack of specificity in this comment. Still, we have worked to make the website more robust: the coauthors of this work and other colleagues in the MAVISp team have extensively tested it across different proteins and with various browsers and operating systems, and we have fixed all identified issues. We also have a GitHub repository where users can open issues to share problems they have been experiencing with the website, which we will fix as promptly as we can (https://www.github.com/ELELAB/MAVISp), as we do for any of the tools we develop and maintain. If the reviewer were to come across other specific problems with the website, we recommend to (anonymously) open issues on the MAVISp repository so that they can be described more in detail and dealt with appropriately.

      This comment seems more related to the MAVISP paper itself than to the POLE and POLD1 entries. We have been doing several revisions to the web app to improve it over time. We are also afraid that the reviewer consulted it during one of these changes, and we hope it will be better now. For POLE and POLD1, the CSV files were, in any case, also available through the MAVISp website itself (https://services.healthtech.dtu.dk/services/MAVISp-1.0/), as well as in the OSF repository connected to this paper (https://osf.io/z8x4j/overview), in case the reader needed to consult them or as a reference for the analyses reported in this paper.

      Albeit this is a thorough analysis with the existing tools, and the authors make some sparse attempts to put the mutants classification in context with examples, the work stays descriptive for know effects in literature, or point out that e.g. "further functional and in vitro assays are required". The examples are not presented in a systematic way, or in an appealing manner. Thus, what this manuscript adds to the web site is unclear. It is a description of content, which could be at least more appealing if examples woudl be more clearly outlined in a conceptual framework, and illustrated more consistently. For exmaple I read in the middle of mage 16 "One such example is the F931S (p.Phe931Ser) variant (Figure 5A)" and then I see "F931 forms contacts with D626, a critical residue for the coordination of Mg2+ which is essential for the correct orientation of the incoming nucleotide (Figure XXX)". Figure 5B is not XXX as this has just many mutations labeled. These issues are very discouraging. I woudl recommend to put much more effort in examples, put them in clearer paragraphs, and decribe results rather than the methodology. Doing both in an intemigled way, clearly does not work for me.

      We have revised the storyline to make it more straightforward for the reader, focusing on the essential messages and avoiding excessive description in the results section, instead conveying the key points directly. We also included new simulation data on three variants and downstream analyses of other variants. We revised the section to focus less on methodologies and more on the actual biological results. We have also added a ranking approach for the VUS and an ACMG-like classification to facilitate the identification of the most important results.

      Additionally, we included a summary Table (Table 2) and Figure 9 that present the main findings on the VUS, and we discussed in the text the possible associated experimental validation.

      We also do not fully understand the reviewer’s comment “the work stays descriptive for know effects in literature”. We agree that we should make a better effort to write the results in a logical and easy-to-follow manner, without risking the reader getting lost in too many details, and with more dedicated subsections. However, the paper does not describe just known effects in the literature. We had, in the previous version, a section aimed at identifying mechanistic indicators for ClinVar-reported variants that are also (in some cases) functionally characterized. This is true, but it is the very first part of the results, and it is still adding structure-based knowledge to these variants. After this, we also reported predicted results with mechanisms for VUS and variants in other databases. We took the opportunity in this revised version to elaborate more on the results of the variants reported in COSMIC and cBioPortal.

      We are afraid that we also do not fully understand the reviewer's comment on the fact that “Thus, what this manuscript adds to the website is unclear.” We have generated POLE and POLD1 data with the MAVISp toolkit in both ensemble and simple mode, and the whole pool of local interactions with other proteins and DNA, specifically for this publication. It should be acknowledged that we have generated new data in ensemble mode, which relies on all-atom microsecond molecular dynamics simulations, and additional modules for the simple mode, including calculations with the flexddg protocol of Rosetta, which is also computationally demanding, to provide a comprehensive overview of the effects of variants in POLE and POLD1. The two proteins were available in the database only in simple mode with the basic default modules, and the remaining data were collected during this research article. This can also be inferred by the references in the csv file of the ensemble mode, which refer only to the DOI of the pre-print of this article. This entails a substantial effort in computing and analysis. The website is the repository for data that researchers collect using the MAVISp protocols or modules; in our opinion, it cannot replace a research project. We designed the database to store the data generated by the framework for others to consult and use for various purposes (e.g., biological studies, preparing datasets for benchmarking approaches against existing ones, or using features for machine learning applications). The entry point in the database is the simple mode, along with some compulsory modules (VEPs, STABILITY, PTM, EFOLDMINE, SASA). After this initial entry point, a biocurator or a team of researchers can decide to expand data coverage by moving into the other modules. Still, at some point, one would need to design focused studies to have a comprehensive overview of the effects on specific targets, as we did here, or, for example, in the publication https://doi.org/10.1016/j.bbadis.2024.167260.

      Furthermore, there are analyses here, especially in the simulations, that are not directly available from consulting the database; in these cases, one needs to use other resources beyond MAVISp to investigate further the mechanisms underlying the predicted mechanistic indicators. We also included simulations of mutant variants to validate the hypothesis further. And another example is the analysis of the effects on the splicing site that is not covered by a structure-based framework, such as MAVISp, but is still an essential aspect in the analysis of the variants' effects.

      Will the community find this analysis useful?

      The analysis provided here will be helpful, especially for researchers interested in experimental studies of these enzymes, because they have throughout the study an extensive portfolio of structural data to consult, including a ranked list of variants by class of effect. We originally started designing MAVISp because we realized it was needed by our experimental collaborators, both in cellular biology and in more clinical research, whenever they needed to predict or simulate variants, and we expanded the concept into a robust, versatile framework for broader use. Especially for those genes where extensive MAVE data are not available (as in this case), having a set of variants to test experimentally is crucial support, as it provides the potential mechanism behind the predicted damaging variant.

      How many ClinVar VUS could be reclassified using MAVISp data under current ACMG/AMP guidelines?

      • *

      The ACMG/AMP variant classification guidelines, to the best of our knowledge, include computational evidence (PP3/BP4) and well-established functional studies (PS3/BS3). Because MAVISp provides multi-level mechanistic predictions derived from structural modelling, these data formally fall within the PP3/BP4 computational category. They cannot be used to reclassify ClinVar VUS independently under ACMG/AMP rules. This is not really the goal of our framework, which is to provide a structure-based framework for investigating potentially damaging variants predicted by VEPs. However, the suggestion of the reviewer is something we wanted to explore too in general with MAVISp data, and we failed because of a lack of time. We checked the requirements for PP3, BP4, and PM1 and developed a classifier for VUS reported in ClinVar, using MAVISp features in accordance with the ACMG/AMP guidelines. Using ClinVar pathogenic and benign variants with at least a review status of 1 for calibration, we obtained thresholds for all MAVISp-supported VEPs (REVEL, AlphaMissense, EVE, GEMME, and DeMaSk). These thresholds were then applied to all ClinVar VUS to determine PP3 (pathogenic-supporting) and BP4 (benign-supporting) evidence. In parallel, we constructed a PM1-like mechanistic evidence category that integrates MAVISp structural stability, protein–protein interactions, DNA interactions, long-range allosteric paths, functional sites, and PTM-mediated regulatory effects. Variants classified as damaging in MAVISp according to such criteria were assigned PM1-like support. These evidence tags provide mechanistic insight to support VUS classification for polymerase proofreading genes. The workflow and complete annotated VUS table are now included in the revised manuscript and in the OSF repository. Although these findings cannot formally reclassify variants under ACMG/AMP criteria, they provide prioritization for PS3/BS3 experimental validation and highlight variants that are likely to be reclassified once supporting functional evidence becomes available.

      How do MAVISp predictions meet calibrated thresholds, as in https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01234-y* for the exonuclease domain of POLE and POLD1? *

      • *

      Mur et al. (Genome Medicine 2023) restricted their ACMG/AMP recommendations to the exonuclease domain (ED) because (i) nearly all known pathogenic germline variants in POLE/POLD1 cluster within the ED, (ii) the ED has a well-characterised structure–function architecture, and (iii) sufficient pathogenic and benign variants exist only within the ED to support empirical calibration. To mirror this approach, we performed the calibration workflow exclusively on ED variants (POLE residues 268–471; POLD1 residues 304–533). For these ED-restricted variants, we recalibrated all MAVISp-derived computational predictors (REVEL, AlphaMissense, EVE, GEMME, DeMaSk) using ClinVar P/LP and B/LB variants. We applied the resulting POLE/POLD1-specific thresholds to all ClinVar VUS within the ED. We also applied our PM1-like structural/functional evidence exclusively to ED variants. The results of this ED-specific analysis are now reported in the revised manuscript (Figure 9 Supplementary Tables S3 and S4), as also explained in the response to the previous question. This ensures that MAVISp predictions are applied in a manner that is consistent with the principles of Mur et al. and ACMG/AMP variant interpretation.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript used the MAVISp framework to characterize 64,429 missense variants (43,415 in POLE, 21,014 in POLD1) through computational saturation mutagenesis. The authors integrate protein stability predictions with pathogenicity predictors to provide mechanistic insights into DNA polymerase variants relevant to cancer predisposition and immunotherapy response. There are discussions of known PPAP-associated variants and somatic cancer mutations in the context of known data and some proposed variants of interest (which are not validated).

      Major comments:

      I was unaware of the MAVISp framework. It concerns me that alebit this paper has a lot of technical details about the framework, its not the paper about the framework. I did look into the paper https://www.biorxiv.org/content/10.1101/2022.10.22.513328v5 which keeps benign updated (version five now) for three years, but I do not see a peer reviewed version. It would be unfair of me to peer review the underlying framework of the work but together with the previous comments, I am a bit concerned. For example, the authors are using AlphaFold models to predict DDG values. Delgado et al. (2025, Bioinformatics) explicitly tested FoldX on such models and concluded that "AlphaFold2 models are not suitable for point mutation ΔΔG estimation" afte observing a correlation of 0.06 between experimental and calculated values. AlphaFold's own documentation states it "has not been validated for predicting the effect of mutations". Pak et al. (2023, PLOS ONE) showed correlation between AlphaFold confidence metrics and experimental ΔΔG of -0.17. Needless to say that these concerns seriously undermine the validity of a major part of the study. I have to add tha this is also true for toher technical choices: Several integrated predictors (DeMaSk, GEMME) are outperformed by newer methods according to benchmarking studies (https://www.embopress.org/doi/full/10.15252/msb.202211474). AlphaMissense, while state-of-the-art, shows substantial overcalling of pathogenic variants. could ensemble meta-predictors (REVEL, BayesDel) improve accuracy?

      Further, I found the web site of the framework, where I looked for the data on these models, rather user unfriendly. Selecting POLD1, POLD2, or POLE tells me I am viewing entries A2ML1, ABCB11, ABCB6 respectively, when I search for POL and then click: these are the first three entries of the table, bot the what I click on. displaying the whole table and clicking on POLD1, gets me to POLD1. However, when I selected "Damaging mutations on structure" I get "Could not fetch protein structure model from the AlphaFold Protein Structure Database". Many other features are not working (Safari or Chrome, in a Mac). That is a concern for the usability of the dataset.

      Albeit this is a thorough analysis with the existing tools, and the authors make some sparse attempts to put the mutants classification in context with examples, the work stays descriptive for know effects in literature, or point out that e.g. "further functional and in vitro assays are required". The examples are not presented in a systematic way, or in an appealing manner. Thus, what this manuscript adds to the web site is unclear. It is a description of content, which could be at least more appealing if examples woudl be more clearly outlined in a conceptual framework, and illustrated more consistently. For exmaple I read in the middle of mage 16 "One such example is the F931S (p.Phe931Ser) variant (Figure 5A)" and then I see "F931 forms contacts with D626, a critical residue for the coordination of Mg2+ which is essential for the correct orientation of the incoming nucleotide (Figure XXX)". Figure 5B is not XXX as this has just many mutations labeled. These issues are very discouraging. I woudl recommend to put much more effort in examples, put them in clearer paragraphs, and decribe results rather than the methodology. Doing both in an intemigled way, clearly does not work for me.

      Will the community find this analysis useful? How many ClinVar VUS could be reclassified using MAVISp data under current ACMG/AMP guidelines? How do MAVISp predictions meet calibrated thresholds as in https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01234-y for the exonuclease domain of POLE and POLD1? Such questions might undermien teh appear of the work and coudl been looked into.

      Referee cross-commenting

      I agree with all the comments raised by reviewer 2; she/he elaborates more on some issues I brought up too briefly (e.g. the choice of GEMME) while other issues that I made more comments about are also mentioned. I only want to note that the statement "A major limitation of the 3D modeling is this impossibility to include Zn2+ coordination by cysteine residues" is not accurate, as there are many 3D structure prediction tools and modeling tools that are capable og handling zinc ions coordinated by cysteines.

      While I respect that Referee 1 is clearly more positive and less concerned by methodological issues, I note that while I agree that "The authors identify numerous variants for prioritisation in further studies" (albeit in a sparse and not well organised manner in my view), I am not convinced by the present manuscript that "the effectiveness of integrating various data sources for inferring the mechanistic impact of variants" is really shown: there are hypotheses generated, but none are tested, so the effectiveness of the approach remains to be proven in my view.

      I still view this as a thorough study and a very brave attempt to be integrative and inclusive, but several methodological limitations and lack of concrete novel insight, seriously dampen my enthusiasm.

      Significance

      Strengths:

      A very comprehensive analysis of POLE and POLD1 missense variants (64,429 total), approximately 600-fold more coverage than the ~100 experimentally characterized variants in the PolED database. The multi-layered MAVISp approach provides mechanistic interpretability beyond simple pathogenic/benign classifications, potentially valuable for understanding variant effects on stability, DNA binding, protein interactions, and allosteric communication. The clinical context is highly relevant given POLE/POLD1 roles in disease.

      Limitations:

      The methodological concerns were outlined above. No solid new insight examples in a validated manner. Examples of how the datasets can be really used are not well-organised as they appear in the context of the approach in perplexed manner.

      Advance:

      The advance is primarily technical and database-driven rather than conceptually novel. Scale, Multi-dimensional assessment, Mechanistic insight and consideration of Clinical framework integration is a clear advance.

      Audience:

      The audience is the POLDPOLE experts; I however doubt if clinical scientists will find the paper useful, especially in the context of the absence of a dedicated resource and the fact that the entried in the MAVISp web-toold are not easily and intuitively accessible and clinical requirements(eg Integration with ACMG/AMP classification frameworks) are not clearly met.

      Reviewer expertise: I am a structural biologist with experience in structure analysis of experimental and predicted models, but no specific expertise or interest in polymerases.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript reports a comprehensive study of POLE and POLD1 annotated clinical variants using a recently developed framework, MAVISp, that leverages scores and classifications from evolutionary-based variant effect predictors. The resource can be useful for the community. However, I have a number of major concerns regarding the methodology, the presentation of the results and the impact of the work.

      On the choice of tools in MAVISp and interpretation of their outputs

      • Based on the ProteinGym benchmark: https://proteingym.org/benchmarks, GEMME outperforms EVE for predicting the pathogenicity of ClinVar mutations, with an AUC of 0.919 for GEMME compared to 0.914 for EVE. Thus, it is not clear for me why the authors chose to put more emphasis on EVE for predicting mutation pathogenicity. It seems that GEMME can better predict this property, without any adaptation or training on clinical labels.
      • Which of the predictors, among AM, EVE, GEMME, and DeMaSK, provide a classification of variants and which ones provide continuous scores? This should be clarified in the text. If some predictors do not output a classification, then evaluating their performance on a classification task is unfair. I would guess that the MAVISp framework sets thresholds on the predicted scores to perform the classification and it is unclear from reading the manuscript whether these thresholds are optimal nor whether using universal cutoff values is pertinent. For instance, for GEMME, a recent study shows that fitting a Gaussian mixture to the predicted score distribution yields higher accuracy than setting a universal threshold (https://doi.org/10.1101/2025.02.09.637326). Along this line, for predictors that do not provide a classification, I am not convinced of the benefit for the users of having access to only binary labels, instead of the continuous scores. The users currently do not have any idea of whether each variant is borderline (close to theshold) or confident (far from threshold).

      On the presentation and impact of the results

      • While reading the manuscript, it is difficult to grasp the main messages. The text contains abundant discussion about the potential caveats of the framework, the care that should be taken in interpreting the results and the dependency on the clinical context. Although these aspects are certainly important, this extensive discussion (spread throughout the manuscript) obscures the results. Moreover, the way variants are catalogued throughout the text makes it difficult to grasp key highlights. The reader is left unsure about whether the framework can actually help the clinical practitionners.
      • In many cases, the authors state that experimental validation is required to validate the results. Could they be more explicit on the experimental design and the expected outcome?
      • AlphaMissense seems to have a tendency to over-predict pathogenicity. Could the authors comment on that?

      On specific variants

      • The mention of H1066R, H1068, and D1068Y is very confusing. There seems to be a confusion between residue numbers and amino acid types.
      • A major limitation of the 3D modeling is this impossibility to include Zn2+ coordination by cysteine residues. This limitation holds for both POLE and POLD1. Could the authors comment on the implication of this limitation for interpreting the mechanistic impact of variants. In particular, there are several variants reported in the study that consist in gains of cysteines. The authors discuss the potential impact of some of these mutations on the structural stability but not that on Zn coordination or the formation of disulphide bridges.
      • MAVISp does not identify any mechanistic effect for a substantial portion of variants labelled as pathogenic. Could the authors comment on this point? Moreover, none of the variant discussed are associated with allosteric effect, is this expected?

      Referee cross-commenting

      I agree with the comments and overall assessment of Reviewer 3. I would like to take this opportunity to clarify that I did not meant 3D modelling of Zinc ion coordination by Cys is impossible in general. I wanted to emphasise that the exclusion some Zinc-binding sites in the present study is a limitation.

      Significance

      The work's strength is its comprehensive analysis. The weaknesses are a methodology that does not seem mature and with output that are still difficult to predict. In addition, it seems that a lot of expertise and manual curation based on metadata (phenotype, functional state...) is needed for the users to benefit from the analysis. The manuscript reads a bit like a catalogue from where it is difficult to understand to what extent the results are significant and impactful.

      I have expertise in computational modelling, protein sequence-structure-function relationship and prediction of variant effects.

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      Referee #1

      Evidence, reproducibility and clarity

      This study "Interpreting the Effects of DNA Polymerase Variants at the Structural Level" comprises an in-depth analysis of protein sequence variants in two DNA polymerase enzymes with particular emphasis on deducing the mechanistic impact in the context of cancer. The authors identify numerous variants for prioritisation in further studies, and showcase the effectiveness of integrating various data sources for inferring the mechanistic impact of variants.

      All the comments below are minor, I think the manuscript is exceptionally well written.

      • The main body of the manuscript has almost as much emphasis on usage of the MAVISp tool as analysis of the polymerase variants. I don't think this is an issue, as an illustrated example of proper usage is very handy. I do however, think that the title and abstract should better reflect this emphasis. E.g. "Interpreting the Effects of DNA Polymerase Variants at the Structural Level with MAVISp". This would make the paper more discoverable to people interested in learning about the tool.
      • Figure 1. I don't believe there is much value in showing the intersection between the datasets (especially since the in-silico saturation dataset intersects perfectly with all the others). As an alternative, I suggest a flow-chart or similar visual overview of the analysis pipeline.
      • Please note in the MAVISp dot-plot figure legends that the second key refers to the colour of the X-axis labels rather than the dots
      • Missing figure reference (Figure XXX) at the bottom of page 16

      Significance

      In addition to identifying a large number of variants in POLE and POLD1 that are good candidates for further investigation, this study acts as a showcase for how evidence from different sources can be combined in a context-dependent manner (in this case, cancer). In terms of limitations, the lack of structures for POLD1 proved a hindrance several times in this study, obscuring some potentially pathogenic variants.

      This manuscript bears some similarity to the MAVISp paper (10.1101/2022.10.22.513328v6), which gives a number of brief examples of analyses that can be conducted with the pipeline. This paper differs in that it shows a full start-to-end analysis and goes into considerable detail about possible mechanisms of pathogenesis. The mechanistic detail and potential clinical relevance set this paper apart.

      This paper would most likely interest those involved in VUS interpretation, both in a clinical and research capacity. Those specialised in DNA polymerase enzymes would also likely find this paper of interest. This paper may also be useful for future research into the impact of the highlighted variants.

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      Referee #3

      Evidence, reproducibility and clarity

      Wiesner et al. use a combination of state-of-the-art imaging techniques to visualize the exocytosis of vesicles labeled with vamp2-phluorin. This work builds on previous findings of the group and aims to quantify vesicle exocytosis along the axon and relate it to the location of actin rings (MPS). Exocytosis indeed occurs in axonal and dendritic regions ; however, at a significantly lower rate than in presynaptic terminals. Exceptionally, the AIS shows a remarkably high exocytosis rate compared to other axonal regions. Perturbation of the MPS with swinholide increases the nonsynaptic release of vamp2-phluorin. The spots supporting exocytosis along the axon lack spectrin but are spatially segregated from regions used for CCP formation.

      This work takes advantage of last-generation optical microscopy approaches to provide a quantitative analysis of exocytosis along the axon in nonsynaptic regions. Findings are solid, and the segregation of spots supporting exocytosis and endocytosis is intriguing. However, it is unclear to me whether the results obtained reflect a general mechanism or if they are biased by the experimental approach. Specifically, I have these major comments:

      1) Use of the term "spontaneous." I understand that the term "spontaneous" refers to exocytosis that "just" occurs. But exocytosis cannot be evaluated without considering electrical activity. Vamp2-phluorin has been extensively used to investigate neurotransmitter release. Since spontaneous neurotransmitter release occurs in the absence of action potentials, it is important to know how the rates of exocytosis are affected after incubation with TTX. These experiments are necessary to show if vesicles are indeed released spontaneously or if they require the presence of action potentials.

      2) The rates of spontaneous exocytosis are expressed in μm² /hour because these events are quite infrequent. According to the methods section, typical recording times are 5 minutes or less (lines 522-533). It would be more appropriate to express values per minute to establish comparisons with other works. The goal is to understand what sort of vesicles are being exocytosed. This is a key question that must be addressed before exploring other aspects such as the relationship to spectrin or endocytosis. If the authors can provide more information about the types of vesicles being exocytosed, this work becomes very relevant. Since I am aware of the technical difficulties associated with this, some suggestions are: use a vamp2-apex2-phluorin construct and confirm vesicle identity by EM, or, use iGluSnFR to confirm neurotransmitter release along axons.

      Minor comments:

      1) Since culture conditions promote synapse formation, could spontaneous exocytosis found along axons related to synapse formation? This aspect could be tested by co-staining with PSD-95 after fixation.

      Significance

      Significance:

      This is state-of-the-art study of the cell biology of neurons. The works demonstrates that vesicles are exocytosed along the axon and describes the molecular characteristics of the cytoskeletal elements involved.

      General assessment:

      The main strength of the study is the quality and diversity of imaging approaches used. The main limitation is defining the type of vesicle being exocytosed. It is important to know if vesicles imaged contain neurotransmitters.

      Advance:

      This paper is technically sound and provides interesting new concepts about how exocytosis occurs in nonsynaptic regions.

      Audience:

      This paper is appropriate for an audience familiarized with cell biology or cellular and molecular neuroscience

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Wiesner et al examines the non-synaptic exocytosis of vesicles in axon initial segment (AIS) as well as proximal and distal axons. Using VAMP2-pHluorin authors convincingly demonstrated that exocytosis occurs in AIS, along the axon shaft, cell body and dendrite. Upon perturbing membrane-associated periodic skeleton (MPS) pharmacologically, exocytic events at the AIS seem to increase, suggesting a potential inhibition of exocytosis at the baseline by MPS. To test whether exocytosis occurs in the area where the MPS is disorganized, authors developed a novel correlative live-cell/super resolution microscopy where exocytic events are identified live by HiLo imaging, followed by fixing the cells and imaging spectrins using SMLM platform and identified the repetitive spectrin map with SReD detector. Using this approach, they have identified that exocytosis in proximal and distal axons occurs at the membrane area with less spectrin. This area is distinct from the clathrin-enriched area where the group has previously identified as the endocytic sites. The strength of the paper lies within the imaging techniques. However, for publication, the following concerns should be addressed.

      Major concerns

      1) The absence of spectrin mesh. Unlike their previous paper using platinum replica EM where filaments are clearly visible, they are using the antibodies against one end of the spectrin, and therefore, they can visualize the periodic distribution of spectrin ends but cannot visualize its meshwork in this study. In addition to this limitation, at thicker processes, molecules below and above the focal plane may or may not be visible, potentially creating the spectrin-less area at the center. Thus, the conclusion regarding the absence of spectrin meshes at the exocytic sites is not well supported.

      2) The rate of exocytosis among controls. The rate of exocytosis at the AIS does not match between Fig. 2C and 3B (1.08 vs 0.64 events/um2/hour). Although the increase in the rate by swinA is relative to the DMSO control, the rate in swinA-treated neurons can be said similar to the control in Fig. 2C. So it is equally likely that DMSO is affecting the rate, rather than swinA. They need an additional control group with no treatment.

      3) Alignment of pHluorin with SMLM images. Since the interpretation depends highly on the perfect alignment of live-cell images with SMLM data and fixation can alter the ultrastructure [PMC7339343], using internal structures like mitochondria as fiducials would be more helpful. However, adding discussion would suffice.

      Minor concerns

      1) The data presented here do not support the claim that actin perturbation favors non synaptic exocytosis (line 205). Please revise the sentence.

      Significance

      General assessment: The strength of the paper is in the imaging techniques. Visualizing the exocytic sites along the axons relative to the MPS is novel. The limitation of the paper is the lack of an approach to fully visualize spectrin networks.

      Advance: If they can provide more convincing data demonstrating that exocytic sites are devoid of the spectrin meshwork, this paper will establish a novel concept regarding how non-synaptic exocytosis occurs along the axon.

      Audience: Researchers working in the neuronal cell biology field will be the main audience of the manuscript.

      Reviewer's expertise: Neuronal cell biology, exocytosis and endocytosis, and imaging

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This manuscript investigates non-synaptic exocytosis along the axon shaft and examines how the submembrane actin-spectrin skeleton shapes the distribution of exocytic sites. Using cultured hippocampal neurons expressing VAMP2-pHluorin, the authors map spontaneous exocytosis along axons and apply a correlative live-cell/super-resolution imaging workflow to visualize the nanoscale organization of spectrin gaps relative to exocytic hotspots. They report that axonal shaft exocytosis is enriched at the AIS, that perturbing the actin-spectrin lattice alters shaft exocytosis, and that exocytic sites generally correspond to spectrin-free regions. The methodological quality and imaging data are excellent and represent a strength of the study.

      Major comments

      The manuscript presents compelling imaging, but several major claims require additional experimental evidence or clarification. The most critical issue concerns the distinction between synaptic and non-synaptic exocytic events. In Figure 1, synaptophysin is used to define synapses, but this is insufficient since synaptophysin is a presynaptic marker and does not confirm the presence of a postsynaptic compartment. The classification of exocytic events as synaptic, therefore, requires co-localization with postsynaptic markers such as PSD95 or Homer. Without this, the paper's main conceptual distinction is not fully supported. Figure 6 requires revision because endocytosis needs to be assessed using a synaptic vesicle-specific assay. A synaptotagmin luminal-domain antibody uptake experiment is recommended, as it would allow precise identification of bona fide SV recycling. This is essential to conclude whether the reported endocytic events reflect synaptic vesicle turnover. The nature of the vesicles undergoing exocytosis along the shaft and at the AIS also remains unresolved. It will be essential to determine whether the authors are observing exocytosis of synaptic vesicles (e.g., VGLUT-positive) or large dense-core vesicles (e.g., BDNF-containing). This can be addressed straightforwardly using available pHluorin-tagged constructs (VGLUT-pHluorin, BDNF-pHluorin). Calcium dependence of the AIS exocytic events should be evaluated. Experiments removing extracellular calcium, blocking voltage-gated calcium channels, or depolarizing neurons (for example, with elevated KCl) would clarify whether these correspond to classical calcium-triggered SV fusion. These requested experiments are realistic in scope and can generally be completed in a few weeks. The imaging, analysis, and methodological descriptions are of high quality, although more information on replication, sample size, and statistical treatment would improve reproducibility.

      Minor comments

      Clarification of the criteria used to classify spectrin gaps versus clathrin clearings would be helpful. Some figure legends require more detailed acquisition parameters. A clearer description of the image registration and alignment steps in the correlative pipeline would improve transparency. Prior literature on non-synaptic axonal exocytosis and on AIS trafficking could be cited more extensively. The figures are generally high quality, and a schematic summarizing the main findings might help readers.

      Referees cross-commenting

      Our reviews are pretty consistent overall, I think. Major requests relate to calcium/depolarization dependency, and I would like to insist on the synaptic vs extra-synaptic and SV vs LDCV issues.

      Significance

      General assessment: This is a technically strong study addressing an interesting and timely question in neuronal cell biology. The imaging quality and methodological innovation are clear strengths. Significant limitations include insufficient distinction between synaptic and non-synaptic release, lack of characterization of vesicle identity, and unclear calcium dependence of AIS exocytosis. Addressing these points will significantly improve the rigor and impact of the study. Advance: The work provides new insights into the spatial organization of axonal exocytosis and its relationship to the actin-spectrin skeleton. The correlative imaging pipeline is valuable. However, the conceptual advance depends on resolving the synaptic/non-synaptic distinction and identifying the vesicle populations involved. Audience: The study will primarily interest a specialized audience in cellular neuroscience, membrane trafficking, and axonal biology. With the recommended revisions, it will also appeal to a broader neurobiology readership interested in nanoscale cytoskeletal organization, synaptic physiology, and axonal signaling.

      Field of expertise: neuronal membrane trafficking, synaptic vesicle cycling, autophagy and endolysosomal pathways, cytoskeletal organization. I do not claim expertise in advanced optical engineering, but feel comfortable evaluating the biological interpretations and trafficking mechanisms.

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      Reply to the reviewers

      We were very pleased to see the very positive evaluation of our work by all 3 reviewers and appreciate their constructive comments and suggestions. We have now addressed all reviewers’ comments by making changes and clarifications to the manuscript.

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

      In the present manuscript, the authors present an in-depth study on the effect of a heat-shock response on the ability of yeast to regain viability after quiescence when their ability to respire is inhibited. They nicely demonstrate that these effects correlate with the measured diffusion coefficients, providing deeper insight into the (at least partially) responsible environmental stress response and the molecular players involved. This work is an important contribution to the growing (or resurging) field of the physical properties of the cell.

      We thank this reviewer for this very positive evaluation.

      My two main comments are the following:

      • The authors determine the diffusion coefficients from the MSD, as well as further analyze them all the way up to the confinement size. As far as I can judge from the manuscript, these analyses are for 2D systems and were initially developed for processes on membranes. How does this change for 3D systems? I understand that for a straightforward qualitative comparison of apparent MSD, this assumption is acceptable, but it may deviate more strongly with the additional analyses the authors present.

      This is indeed an important point, and the reviewer is correct that the trajectories are analyzed in 2D (x,y) while the cytoplasm is a 3D environment. We fully agree that this requires careful interpretation, particularly for metrics beyond the short-lag diffusion coefficient.

      First, for the diffusion coefficient, it is well established that for isotropic 3D motion the movements in all three dimensions are independent of each other and the projected 2D MSD satisfies:

      = 4*D*τ

      Thus, estimating from the short-lag slope of the 2D MSD yields the correct diffusivity of the underlying 3D process (up to standard experimental corrections such as localization error and motion blur). This approach is therefore widely used in cytoplasmic SPT and GEM studies, including in yeast, and is not restricted to membrane diffusion [1, 2].

      Regarding confinement-related metrics derived from longer time lags, we agree that these were originally developed and most rigorously interpreted for 2D systems. In our study, these quantities are intentionally used as effective in-plane (x,y) descriptors of particle motion rather than as a full reconstruction of a 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size depends on assumptions about geometry and isotropy and cannot be done uniquely without full 3D tracking. Nevertheless, MSD-based analyses have been successfully extended to explicitly model and quantify 3D confined diffusion in previous studies, provided that full 3D trajectories or well-defined confinement geometries are available. [2, 3]

      [1] Gómez-García, P.A., Portillo-Ledesma, S., Neguembor, M.V., Pesaresi, M., Oweis, W., Rohrlich, T., Wieser, S., Meshorer, E., Schlick, T., Cosma, M.P., Lakadamyali, M., 2021. Mesoscale Modeling and Single-Nucleosome Tracking Reveal Remodeling of Clutch Folding and Dynamics in Stem Cell Differentiation. Cell Rep. 34. https://doi.org/10.1016/j.celrep.2020.108614

      [2] Delarue, M., Brittingham, G.P., Pfeffer, S., Surovtsev, I. V., Pinglay, S., Kennedy, K.J., Schaffer, M., Gutierrez, J.I., Sang, D., Poterewicz, G., Chung, J.K., Plitzko, J.M., Groves, J.T., Jacobs-Wagner, C., Engel, B.D., Holt, L.J., 2018. mTORC1 Controls Phase Separation and the Biophysical Properties of the Cytoplasm by Tuning Crowding. Cell 174, 338-349.e20.

      [3] Lerner, J., Gómez-García, P.A., McCarthy, R.L., Liu, Z., Lakadamyali, M., Zaret, K.S., 2020. Two-parameter single-molecule analysis for measurement of chromatin mobility. STAR Protoc 1.

      Importantly, we do not assume perfect isotropy of the yeast cytoplasm. Local anisotropies are expected due to organelles, crowding heterogeneity, and cell geometry. However, the system is sufficiently close to isotropic at the length and time scales probed that the extracted confinement radius is highly reproducible across independent biological replicates. In our experiments, we observe consistent radius of confinements across three replicates, indicating that any bias introduced by partial anisotropy or projection into 2D is systematic and small.

      Based on the observed reproducibility and the finite depth of field of our measurements (~100 nm), we estimate that potential errors in the absolute values of confinement-related parameters arising from 2D projection and incomplete isotropy are on the order of We have now clarified this point explicitly in the Methods section, emphasizing that confinement parameters are effective 2D measures, that the cytoplasm is not assumed to be perfectly isotropic, and that the conclusions rely on consistent, comparative measurements obtained under identical imaging and analysis conditions. The updated Methods paragraph is as follows:

      […] Trajectory analysis: Radius of Confinement

      The radius of confinement was obtained only for the subgroup of confined trajectories. It quantifies the degree of confinement by estimating the radius of the 2D area explored by the particle in the imaging plane, which serves as a proxy measurement for the 3D volume that it explores. It was measured by fitting a circle-confined diffusion model to the TE-MSD (ensemble of all trajectories) (Wieser and Schütz, 2008).

      TE-MSD = R^2 * (1 - exp(-4*D*t_lag/R^2)) + O

      where R is the radius of confinement and D is the diffusion coefficient at short timescales. O is an offset value that comes from the localization precision limit inherent to localization-based microscopy methods.

      Trajectories were analyzed in the imaging plane (x,y), and confinement metrics were therefore derived from 2D MSDs. Although particles diffuse in a three-dimensional cytoplasmic environment, projection onto 2D does not bias estimation of the short-lag diffusion coefficient for isotropic motion, since the projected MSD follows ⟨Δr_xy²(τ)⟩ = 4Dτ. However, confinement-related parameters derived from longer lag times should be interpreted as effective in-plane descriptors of mobility rather than as a direct reconstruction of a full 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size would require explicit assumptions about geometry or full 3D tracking. Our conclusions rely on comparative analyses performed under identical imaging and analysis conditions, and the extracted confinement radii were highly reproducible across biological replicates, indicating that any bias introduced by 2D projection or moderate anisotropy is systematic and does not affect the validity of the relative differences reported.

      • The authors show data in the supporting information where the GEMs provide larger foci after stress with longer imaging times. Could the authors provide the images of the shorter imaging times that they use? That seems a more equal comparison than Figure C. It is also unclear to me why fixed cells are used in Figure C, as well as the meaning of the x-axis. In line with this, can the authors exclude that GEMs dimerize/oligomerize after stress, and therefore display a lower diffusion coefficient?

      We are happy to include the images acquired at a shorter time interval and have done so (Fig S2A). We apologize for insufficiently explaining the GEM intensity experiment shown in Figure S2C. The fixation was done to immobilize the GEMs, since they are rapidly diffusing in live cell imaging and the diffusion speed relative to camera exposure time will impact the brightness (any movement of a particle during exposure causes the signal on the detector to become “blurred” and reduces the intensity per pixel). Hence, GEM brightness does not solely reflect the monomer or potential aggregate/multimer state, but is also affected by diffusion speed and exposure time: faster moving GEMs will generally appear dimmer than slower moving ones, since the signal detection during the acquisition time is reduced by the particle movement. Another effect is that, since GEMs are moving in live cell imaging, they have a probability of spatially overlapping, enhancing the signal levels of the single detected spots.

      We have quantified the brightness distribution in the different conditions to detect aggregation or multimerization of GEMs, which we expect to be visible as a shoulder on the Gaussian curve. The x-axis shows the intensity which we have determined for each trajectory. We chose to assess GEM intensity in the frame with the highest intensity, and to take the “Total” intensity, meaning we sum up the intensity of the pixels within the Point Spread Function (PSF) of each localization in that frame.

      To clarify these points, we have extended the description of this experiment in the Results and Methods sections:

      Results:

      [...] Additional evidence for this comes from the observation that imaging GEMs at a lower frame rate (i.e., longer exposure time of 100 ms) showed a uniformly diffuse signal in SCD, whereas distinct foci appeared under starvation conditions (Figures S2A and S2B). This might suggest that GEMs aggregate in starvation. However, imaging GEMs at a faster frame rate (used for SPT, 30 ms exposure time) shows GEMs freely diffusing in all conditions (Figure S2A). Furthermore, analyzing GEM particle intensities in fixed cells, to eliminate motion blur-induced intensity attenuation, showed uniform GEM brightness distributions in all conditions (Figure S2C). Rather than aggregates, the bright foci thus represent immobile, single GEM particles that are confined and appear brighter during long exposure times due to their confinement in low-diffusive compartments. [...]

      Methods:

      [...] Trajectory analysis: Track Total Intensity

      To assess GEM brightness, we determined the intensity of each trajectory in fixed cells. Cell fixation eliminates the motion blur-induced intensity attenuation, which would otherwise confound the GEM brightness depending on the movement speed and confinement. For each individual particle trajectory, the frame with the highest signal intensity of the localized particle was determined and the sum of the pixel intensities of the particle in that frame was calculated as the “Track Total Intensity”. In fixed cells, the GEM intensities were comparable in all conditions (Figure S2C). All GEM intensity histograms show a single, bell-shaped distribution of intensities with no indication of several GEM particles aggregating into brighter foci. [...]

      Other comments: - For the precision of the language, the authors equate ribosome content with macromolecular crowding, with the diffusion of the GEMs throughout, and this becomes more conflated in the discussion, where it is compared to viscosity and macromolecular crowding effects, e.g., translation. Is it macromolecular crowding, mesoscale crowding, nano-rheology, or ribosome crowding? What is measured precisely?

      We agree that careful and consistent nomenclature is important and thank the reviewer for bringing this point to our attention. We believe our manuscript maintains the proper distinctions of the terms diffusion, crowding and viscosity. We refer to what we study with the GEM single-particle tracking consistently as “(cytoplasmic) diffusion”. In Figure 2, we add “crowding” as an additional term since we observe a change in ribosome concentration and we affect the cytoplasmic crowdedness with a hyperosmotic shock. Our in-depth analysis of the confined and unconfined trajectory diffusion suggested that the cytoplasm is not simply globally affected by crowding or viscosity, but contains regions or compartments that trap GEM. Apart from Figure 2, we do not use the term viscosity or crowding, and we only return to “crowding” in the Discussion, either in reference to the aforementioned experiments from Figure 2 (ribosome concentration, hyperosmotic shock) or when discussing studies from cited works.

      However, we did not use the term “macromolecular crowding” consistently and simplified it to “crowding” in a few instances. To be more precise, we now specify “macromolecular crowding” instead of “crowding” wherever applicable; namely in the text referring to Figure 2, where we specifically assess macromolecular crowding.

      • In the EM images, the ribosomes seem smaller after starvation. Is that correct, and how should we interpret this? Is this due to an increased number of monosomes?

      This is an important point, and it indeed appears that in SCD some ribosomes are close together, potentially as polysomes. In SC, the ribosomes appear more distinctly separated from each other, which would be expected due to the polysome collapse that occurs in starvation. However, the apparent size of individual ribosomes is identical in both conditions. Unfortunately, the resolution is not good enough to accurately measure the sizes of the ribosomes and clearly determine their monomer/polysome state.

      • The authors refer to recent work on how biochemical reactions, such as translation, are determined by the cytoplasm. There is some older work on this, see for example in bacteria https://doi.org/10.1073/pnas.1310377110, and also in vitro here DOI: 10.1021/acssynbio.0c00330

      We thank this reviewer for pointing out these publications and have included them in this group of citations.

      • On the section of correlating diffusion and survival outcomes (bottom page 12), it is mentioned that the lowered diffusion could enhance aggregation. However, literature indicates that the opposite is true in buffer; lower diffusion reduces aggregation (also nucleation is inversely proportional to the viscosity).

      This is a valuable point and we have happily expanded on it in the Discussion section. It is true that chemical assays have demonstrated that higher viscosity and slower diffusion decrease nucleation and aggregate formation. However, in vitro studies that alter diffusion through crowding changes have revealed a complex relation between crowding and aggregation propensity. The basic idea is that the excluded volume effect decreases aggregation by stabilization of the more compact, folded state. But the opposite effect, precluded protein folding, has also been ascribed to the excluded volume effect. As of now, studies with different crowders (dextran, ficoll, PEG, etc.) demonstrated increased or reduced protein aggregation upon crowding [1, 2, 3, 4]. The variable effect on aggregation seems to be not only based on the protein that is studied, but also the properties of the crowder (charges, shape, size), the interaction of the crowder with the protein, and the mixture of crowders [5].

      Even though the relationship between crowding and protein aggregation is complex, we speculate that lower diffusion in our more crowded cells could cause protein aggregation, because these starvation conditions are known to induce the formation of protein fibrils and the condensation of mRNA and proteins.

      [1] Uversky, V.N., M. Cooper, E., Bower, K.S., Li, J., Fink, A.L., 2002. Accelerated α-synuclein fibrillation in crowded milieu. FEBS Lett. 515, 99–103. https://doi.org/10.1016/S0014-5793(02)02446-8

      [2] Munishkina, L.A., Cooper, E.M., Uversky, V.N., Fink, A.L., 2004. The effect of macromolecular crowding on protein aggregation and amyloid fibril formation. J. Mol. Recognit. 17, 456–464. https://doi.org/10.1002/jmr.699

      [3] Biswas, S., Bhadra, A., Lakhera, S., Soni, M., Panuganti, V., Jain, S., Roy, I., 2021. Molecular crowding accelerates aggregation of α-synuclein by altering its folding pathway. Eur. Biophys. J. https://doi.org/10.1007/s00249-020-01486-1

      [4] Mittal, S., Singh, L.R., 2014. Macromolecular crowding decelerates aggregation of a β-rich protein, bovine carbonic anhydrase: a case study. J. Biochem. 156, 273–282. https://doi.org/10.1093/jb/mvu039

      [5] Kuznetsova, I.M., Zaslavsky, B.Y., Breydo, L., Turoverov, K.K., Uversky, V.N., 2015. Beyond the excluded volume effects: Mechanistic complexity of the crowded milieu. Molecules 20, 1377–1409. https://doi.org/10.3390/molecules20011377

      To be more precise, we have therefore extended our Discussion section. We believe part of this additional discussion fits better in an earlier section, where we specifically discuss how the cytoplasmic properties, and specifically crowding, have been linked to filament/condensate formation. The updated paragraphs are as follows:

      [...] Additional cytoplasmic rearrangements occur upon energy depletion, including filament formation or the formation of biomolecular condensates (Narayanaswamy et al., 2009; Noree et al., 2010; Petrovska et al., 2014; Prouteau et al., 2017; Riback et al., 2017; Saad et al., 2017; Marini et al., 2020; Stoddard et al., 2020; Cereghetti et al., 2021) highlighting a broader reorganization of the cytoplasm that could further affect the diffusion of macromolecules. In turn, the amount of crowding might also influence the propensity to form condensates and filaments (Heidenreich et al., 2020). Interestingly, in vitro studies have demonstrated a complex, dual effect of crowding on protein fibrillation and aggregation, in suppressing or accelerating it (Uversky et al., 2002; Munishkina et al., 2004; Mittal and Singh, 2014; Biswas et al., 2021). This appears to be dependent not only on the protein of study, but the properties of the crowder (size, charge, shape) and the specific mixture of crowders (Kuznetsova et al., 2015). [...]

      [...] By contrast, extremely low diffusion, as seen in the absence of respiration in glucose starvation, might irreversibly impair cellular functions due to limited movement of proteins and RNA in and out of certain compartments, cellular territories and condensates. Such a model is supported by our analysis of how lower diffusion is the result of confined spaces becoming more prevalent, creating compartments that can trap macromolecules. As previously mentioned, increased crowding and reorganization of the cytoplasm have been linked to condensation and fibril formation of proteins, and, in certain in vitro contexts, accelerated aggregation. This state of crowding-induced low diffusion might therefore enhance protein aggregation or preclude the refolding of damaged proteins, which could disrupt proteostasis and lead to toxic aggregates that are a hallmark of the aging process (López-Otín et al., 2013). Together, these effects on proteins, RNA and other macromolecules likely lead to loss of cell fitness and irreversible arrest of the cells, preventing their reentry into the cell division cycle. [...]

      Reviewer #1 (Significance (Required)):

      General assessment: Strengths: It is a comprehensive study that provides a wealth of information and insight into the intricacies of a field that has received considerable attention, and its views are evolving rapidly. Weaknesses: It may suffer from some overinterpretation of diffusion data. Advance: The significant advance is that the molecular response pathway and precise molecular players are connected to the biophysical response of cells to starvation/quiescence. The dependence of diffusion on starvation has received considerable attention (Jacobs-Wagner, Cell, 2014; the current authors in eLife, 2016; and more recent investigations by Holt, Delarue, and others). Still, the authors take the next step and demonstrate how quiescence, and particularly how the history of a cell affects it, correlates strongly with the diffusion. As far as I can tell, this is new. As mentioned, the molecular insights into the pathways are exceptionally strong from my perspective. From personal experience, this work is also very important for researchers outside of the field from a practical standpoint: Do your measurements change when you stress cells by walking to a microscope? And even if you incubate them there, your measurement outcome will change. In my experience, this is a crucial point, and the cell's history is often overlooked. Audience: Broad -- biophysicists, molecular biologists, cell biologists, biotechnologists. My field of expertise: Biophysics.


      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript addresses an important and longstanding question in the field: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under nutrient limitation and energy stress. The authors combine tools from biophysics, proteomics, stress signaling, and functional genomics to propose that stress-induced cytoplasmic reorganization, rather than ATP availability per se, is critical for long-term survival when respiration is impaired. The topic is timely, the experiments are generally well executed, and the initial phenomenology is compelling. The paper begins with a set of clear and convincing figures that establish an interesting and biologically important phenotype: when cells are shifted into glucose starvation, they can survive long term only if respiration is functional. Blocking respiration with Antimycin A (AntA) severely compromises viability. One straightforward hypothesis is that this defect simply reflects a failure to generate sufficient ATP. The authors, however, show that a 30-minute heat shock (HS) before glucose withdrawal in the presence of AntA largely rescues survival, even though cellular ATP levels remain critically low. In parallel, they use very well-executed GEM single-particle tracking experiments to demonstrate that cytoplasmic particle mobility decreases markedly in glucose-starved, respiration-deficient cells, and that this diffusion defect is also rescued by the pre-HS, again without restoring ATP. Together, these initial figures strongly support the idea that stress-induced remodeling of the cytoplasm, rather than ATP levels per se, is a key determinant of whether cells can enter and maintain a viable quiescent state. The authors then propose that this protective effect of HS is mediated by induction of the environmental stress response (ESR) and by resulting changes in protein expression. To test whether new protein synthesis is required, they pre-treat cells with cycloheximide during the HS and recovery period. This treatment largely, although not completely, abrogates the beneficial effect of HS on survival and diffusion in AntA-treated, glucose-starved cells. This is a strong experiment and supports the idea that HS-induced synthesis of specific proteins is important for protection, while also hinting that some cycloheximide-insensitive or pre-existing components may contribute. To identify the relevant proteins, the authors turn to global proteomic analysis, comparing multiple conditions: glucose starvation (SC), heat shock followed by glucose starvation (HS SC), glucose starvation plus AntA (SC + AntA), and heat shock followed by glucose starvation plus AntA (HS SC + AntA), each at 1 and 20 hours. This is where, in my view, the story becomes significantly harder to follow. The text for Figure 3 relies almost entirely on GO term enrichment, with very little description of individual proteins or even basic quantitative summaries of the dataset. For example, the authors never clearly state how many proteins were robustly quantified, nor what fraction of the proteome that represents. Without this foundational information, it is difficult to evaluate the strength and generality of their conclusions. Related to this, the GO analysis in Figure 3F reports "significant" enrichment for categories such as ribosomes or translation, yet the underlying number of proteins making up these enrichments is not shown. From the volcano plots, it appears that only a very small number of proteins change in some conditions (e.g., SC 20 h), and yet GO terms appear with extremely strong q-values. This is confusing: how can such strong enrichment occur if only a handful of proteins are changing? At minimum, the authors should provide: • the number of significantly up- or down-regulated proteins in each comparison • the number of proteins contributing to each enriched GO category • the magnitude of the changes for these proteins Because the absolute number of significantly changing proteins appears small in several conditions, the current heavy reliance on GO analysis feels unwarranted and potentially misleading. In such cases, it would likely be more informative to list all differentially abundant proteins-either in supplementary materials or in a main-text table-and briefly describe the most relevant ones, rather than relying on broad category labels. Figure 3F, in particular, needs substantially more explanation. A related issue appears in Figure 3G (and the associated text), where the authors emphasize that the proteomic response to HS + AntA and the response to long-term glucose starvation are distinct. While this conclusion is plausible, the analysis also shows a subset of proteins that are upregulated in both conditions. These overlapping proteins may, in fact, represent the core protective module that enables survival in quiescence. The authors do not discuss these proteins at all; instead, they are effectively dismissed in favor of the "distinct responses" narrative. I encourage the authors to identify and discuss these overlapping proteins explicitly. Are they chaperones, proteasome components, antioxidant enzymes, or other classical stress-response factors? Even if the global proteomes differ, the overlapping subset could be highly informative about the minimal set of proteins required to stabilize the cytoplasm and support entry into quiescence. The SATAY screen is a major strength of the paper, as it moves from correlative proteomics to functional genetic analysis. The approach appears well-controlled, but key information is missing: How many unique insertions were obtained? Was the library saturating? What was the read distribution and coverage? The authors also discuss only a small subset of the screen hits. The volcano plots show many additional genes that are not addressed. What categories do these fall into? Are they informative about pathways beyond Ras/PKA and Msn2/4? Presenting a fuller analysis would strengthen the mechanistic interpretation. The parts of the SATAY analysis that are discussed are solid. The screen implicates the Ras/PKA signaling axis and Msn2/4 in survival under HS-preconditioned, respiration-deficient starvation, and the authors validate these hits with targeted survival assays. The correspondence between genetic perturbations and changes in cytoplasmic diffusion is an intriguing connection. However, the analysis stops short of identifying the downstream effector proteins that actually produce the biophysical benefits observed. The manuscript then returns to the idea that improved cytoplasmic diffusion and reduced confinement may be essential for survival. This is an appealing hypothesis, but the evidence remains correlative. It is still unclear whether biophysical rescue is the cause of improved survival or simply a downstream marker of a properly induced stress response. What remains missing is deeper integration of the proteomics and SATAY data to identify which proteins are likely responsible for the adaptive changes in cytoplasmic organization. Overexpression of promising candidates-such as chaperones or proteostasis factors found in the overlap between HS and long-term starvation responses-could help determine whether any single protein or small group of proteins can phenocopy the HS-induced rescue. Importantly, many of the comments above are intentionally broad: the manuscript does not simply require small clarifications but would benefit from substantial expansion and deepening of the analysis. The observations are compelling, but the mechanistic chain connecting ESR activation → proteomic remodeling → cytoplasmic biophysics → survival remains insufficiently developed in the current draft. Clearer quantitative reporting, fuller presentation of the data, and more thoughtful interpretation would significantly strengthen the manuscript.

      We thank reviewer 2 for this very thoughtful evaluation of our manuscript. We agree that expanding the descriptions and analysis of the presented data will improve the manuscript. Importantly, we now provide the proteomics data and the SATAY screen in an accessible format as supplementary materials. We address the individual points below.

      Summary of Major Issues That Need to Be Addressed • Quantitative clarity in the proteomics o State how many proteins were quantified. o Report the numbers of significantly changing proteins in each condition. o Identify the proteins underlying each GO term and provide effect sizes.

      We have now included a supplemental table containing label-free protein abundances for all 3308 reproducibly quantified proteins across all nine conditions (Supplemental Table S4). In addition, we added a sentence to the main text specifying both the number of reproducibly identified proteins and the approximate coverage of the yeast proteome.

      For the comparison of protein abundances between the different stress conditions and logarithmically growing SCD cells, we now indicate the number of significantly changed proteins in the legend of Figure 3E. Furthermore, we include a heatmap of standardized protein abundances for all proteins that were significantly changed in at least one stress condition (Supplemental File S1) and provide all pairwise comparison results in the supplemental table (Supplemental Table S5). This new Supplemental File S1 replaces the previous Supplemental File S1, which had a stricter cutoff, showing all proteins with an abundance change greater than 2 standard deviations.

      The information requested by the reviewer regarding GO term analysis is indeed important and was missing in the original version. We now report, for each GO term, the number of proteins in the top or bottom 10% of differentially abundant proteins and provide the corresponding effect size, calculated as the ratio of the observed to expected hits (Figure 3F).

      • Over-reliance on GO analysis o Provide explicit lists of differentially expressed proteins. o Indicate whether enrichment results are meaningful given the small number of hits.

      We appreciate this reviewer’s comment and agree that the presentation of the proteomic data in Figure 3 relies strongly on GO term enrichment, with limited description of individual proteins. Our primary goal for the proteomic analysis was to characterize the cellular response to stress at a global level rather than to focus on individual proteins or stress-specific details. We therefore intentionally opted for a broader, more coarse-grained analysis to not overcomplicate the manuscript and maintain accessibility for a broad readership.

      That said, we agree that the underlying data should be made fully accessible. We have therefore expanded the supplemental materials to include a heatmap of all proteins that were significantly changed in at least one condition (Supplemental File S1), as well as comprehensive tables reporting protein abundances and pairwise differences across all stress conditions (Supplemental Tables S4 and S5). These additions provide direct access to the protein-level data while preserving the clarity of the main text.

      With respect to GO term analysis, to avoid overinterpretation driven by small protein sets and better comparability across different conditions, we always performed the GO enrichment based on the top and bottom 10% changed proteins. This is already stated in the legend of Figure 3F and in the Methods section. We have now added the key missing parameters of the analysis to Figure 3F (see response above). Given that the analysis identifies multiple GO terms generally associated with the environmental stress response and that these terms exhibit coordinated behavior across conditions (Figure S3A), we are confident that the conclusions drawn from this analysis are robust.

      • Overlooked overlapping proteins o Analyze and discuss the subset of proteins upregulated both by HS and by long-term starvation. o These may represent the core factors enabling survival.

      Indeed, we agree that the overlapping proteins that are observed in our Figure 3G analysis should be presented. Perhaps surprisingly, these proteins (Hxt5, Sps19, Atg8, Aim17, Put1, Fmp45, YNL194C) have diverse functions and have so far not been implemented in the environmental stress response.

      In the Results section, we now mention and briefly discuss the four that are present in both time points of the HS SC +AntA condition. We now mention all of them in the figure legend.

      The modified text from the Results section is as follows:

      [...] Furthermore, the proteins that are enriched in long-term starvation (SC 20 h vs. SCD) and those enriched in pre-HS respiration-deficient starvation (HS SC +AntA 1 h vs. SCD; HS SC +AntA 20 h vs. SCD) are poorly correlated and there is only a small overlap of factors that are significantly upregulated in all conditions (Figure 3G). These proteins are Aim17, Put1, Fmp45 and YNL194C. Aim17 is a mitochondrial protein of unknown function and Put1 is a mitochondrial proline dehydrogenase. Fmp45 and YNL194C are paralogous membrane proteins involved in cell wall organization. Focusing on the broad proteomic adaptation, we looked at the Gene Ontology (GO) terms of the proteomic changes across all conditions, and observed that long-term starvation (SC 20) leads to the upregulation of a few groups of proteins, mostly involved in respiratory activity and rewiring of the metabolism (Figure S3A). [...]

      We greatly appreciate the suggestion to do an overexpression experiment. However, the overlapping proteins are not significant hits in the SATAY, suggesting that they are individually not required for the survival rescue although their overexpression might benefit survival.

      We have therefore chosen to keep a broad perspective on the proteomics results and investigate instead the SATAY results in more detail, since they inherently contain functional relevance to survival. Overall, we feel that the overexpression of those (individually or as a group) would extend beyond the scope of our current manuscript.

      • SATAY analysis needs fuller presentation o Provide insertion numbers, coverage, and basic library statistics. o Discuss additional hits beyond the Ras/PKA/Msn2/4 pathways. o Integrate SATAY results more deeply with proteomics.

      We have added the insertion numbers and genome coverage percentages to the Methods section as follows:

      [...] SATAY Screen: Analysis and Plotting

      Sequencing detected the following total unique transposon numbers: 690’935 (A1), 558’932 (HA1), and 359’935 (HA4d) unique transposons. The transposon insertions in the different genes yielded the following genome coverages: 96.3% (A1), 94.5% (HA1) and 89.3% (HA4). For each gene [...]

      We now also provide the SATAY screen data as Supplemental Table S6.

      In the Results section, we mention some additional hits from the SATAY screen (ribosome biogenesis, mitochondrial respiration) but then shift our focus to the ESR genes. We now add a comment to the ribosome biogenesis genes before going to the ESR:

      [...] The screen revealed several highly significant gene disruptions that promote or impair the HS-mediated rescue of respiration-deficient, glucose-starved cells (Figure 4A, Supplemental Table S6). The most significant gene hits that impair survival in 4 d HS SC +AntA when disrupted are involved in a variety of cellular processes, including ribosome biogenesis (e.g., ARX1, BUD22, RRP6), mitochondrial respiration (e.g., CBR1, COX23, ETR1), and ESR (e.g., MSN2, PSR2, YAP1). Intriguingly, the ribosome biogenesis genes being crucial for survival suggests that new ribosomes might have to be produced to ensure proper translational response during the HS. Notable among the ESR genes are MSN2 and, less significantly scored, MSN4, the master regulators of the ESR. [...]

      To deepen the discussion on the lack of overlap between the SATAY screen and the proteomics, we have added a sentence highlighting that the SATAY screen detected the main regulators of the ESR, and the proteomics revealed its downstream targets involved in proteostasis and other stress proteins, and therefore these two data sets do both point to the ESR as the crucial response behind the HS-induced rescue. The modified Discussion text is as follows:

      [...] Furthermore, the signaling genes that scored highly in the SATAY screen are often regulated through their activity rather than their abundance. Plausibly, their downstream target proteins are differentially expressed, whereas disrupting the regulators themselves leads to strong survival phenotypes. Similar observations have been made in other stress conditions, where fitness-relevant genes showed little overlap with genes with upregulated expression (Birrell et al., 2002; Giaever et al., 2002). Nonetheless, the SATAY screen revealed the principal regulators of the ESR while the proteomic analysis detected many of the ESR downstream targets involved in proteostasis and oxidative stress, demonstrating a functional convergence on the ESR in both data sets. [...]

      • Mechanistic depth remains limited o Clarify whether cytoplasmic biophysical rescue is causal or downstream. o Test whether overexpression of candidate proteins can mimic HS-induced protection. o Expand the discussion of potential mechanisms using insights from both datasets.

      Indeed, the specific mechanism(s) that govern the cytoplasmic properties in our conditions are currently not known, preventing us from manipulating the cytoplasmic properties and confirming a causal relationship. To uncover the mechanisms, extensive follow-up studies on ESR genes and/or proteins would be required, going beyond the scope of this manuscript. Furthermore, our ongoing follow-up studies are pointing towards redundancy of some potential regulation of the cytoplasmic diffusion, further complicating the analysis.

      The suggested overexpression experiment is addressed in a previous comment where the overlapping proteins are mentioned.

      Reviewer #2 (Significance (Required)):

      This manuscript addresses a fundamental and timely question in cell biology: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under conditions of nutrient depletion and compromised energy production. Although quiescence has been studied for decades, the mechanisms that link metabolic state, stress signaling, and the physical properties of the cytoplasm remain incompletely understood. This work brings together biophysical measurements, global proteomics, and unbiased genetic screening in an ambitious effort to illuminate how cells maintain viability when respiration-and thus efficient ATP generation-is disrupted. A key conceptual contribution of this study is the demonstration that ATP levels alone do not dictate survival during starvation. Rather, the ability of cells to mount an appropriate stress response and reorganize the cytoplasm appears to be crucial. The early figures provide compelling evidence that heat shock preconditioning can rescue both viability and cytoplasmic mobility in respiration-deficient cells, even when ATP remains low. This finding is notable because it challenges the widely held assumption that energy charge is the primary determinant of successful entry into quiescence. If strengthened by deeper mechanistic analysis, this insight could reshape how the field views energy stress and cellular dormancy. The identification of the Ras/PKA-Msn2/4 axis as a key regulatory node is also significant, as it connects quiescence survival to well-established nutrient and stress signaling pathways. The integration of a genome-wide SATAY screen adds functional depth and offers the potential to uncover specific downstream effectors that remodel the cytoplasm or stabilize cellular structures during prolonged stress. Finally, the manuscript touches on a concept that is gaining traction across many subfields of biology: that the biophysical state of the cytoplasm is a regulated and physiologically meaningful parameter, not merely a passive consequence of metabolic decline. Understanding how cells tune macromolecular crowding, diffusion, and spatial organization during quiescence could have broad implications beyond yeast, including in stem cell biology, microbial dormancy, cancer cell persistence, and aging. Overall, the questions addressed are important, and the study has the potential to make a meaningful conceptual contribution. However, realizing that impact will require clearer and deeper mechanistic analysis-particularly in the proteomics and SATAY sections-to convincingly identify the specific factors and pathways that mediate the cytoplasmic remodeling underlying survival.


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

      Summary. Yeast haploid cells enter quiescence during nutrient deprivation, undergoing major metabolic, transcriptional and biophysical changes. In particular, quiescent cells remodel their cytoplasm, increasing macromolecular crowding and reducing diffusion. Respiration is known to be essential for entry into quiescence and long-term survival.

      In this study, the authors discovered that respiration is not intrinsically required for yeast to survive glucose-starvation-induced quiescence. In particular, they found that a short heat shock before starvation restores survival even in the absence of respiration (Antimycin A treatment), demonstrating that a stress-induced adaptation can bypass the respiratory requirement. This rescue occurs without ATP recovery and relies on de novo protein synthesis. This stress-induced adaptation also rescues quiescent-like biophysical properties of the cytoplasm (increased crowding) that are normally prevented in non-respiring cells, which are thought to be relevant for cell survival . Proteomics reveals that heat shock induces a distinct stress-response proteome enriched in proteostasis factors. A genetic screen reveals that Ras/PKA inhibition and Msn2/4 activation enable this protective reprogramming. Altogether this highlights the importance and complexity of stress adaptation to quiescence establishment.

      This is an excellent paper in all aspects. I have no major points besides the data accessibility, below.

      We thank this reviewer for this very positive evaluation.

      Main comments. - It would be nice to have the MS data available as Excel files for the community, and uploaded to repositories such as PRIDE. Description of the MS data is a bit expedited to serve the purpose of the paper (clustering to evaluate the similarity of proteomic profiles between conditions, GO term enrichment) so having the full data available might help.

      We agree that the MS data should be accessible. The label-free protein abundances for the reproducibly quantified proteins across all nine conditions (Supplemental Table S4) and the pairwise comparisons shown in Figure 3E (Supplemental Table S5) are now included as supplementary Excel files. The MS data is currently not on PRIDE but we will deposit it there upon publication of our manuscript.

      • Same thing for the SATAY screen. The data is summarized in Fig 4B but I believe that the data should be provided.

      We agree that the SATAY screen results should be accessible as well, and we have now included the data as Supplemental Table S6.

      Minor comments and questions. -I believe that in graphs, the X axis should start at 0 to avoid confusion about the strength of the effect (eg. Fig 2B)

      We thank reviewer 3 for pointing this out, and we have re-evaluated the axis limits of all plots. As suggested, we have adjusted the x-axis in Fig 2B to start at 0 to better highlight the strength of the effect. For our Radius of Confinement and %Confined Trajectories graphs, we believe adjusting the y-axis to start and end at the same values will allow better comparison across figures. However, we chose not to set those y-axes to start at 0, since our measurements lie in a range which is covered by these axes, and these plots would simply include blank space if set to start at 0.

      -I found that using imaging of GEMs at low frequency to reveal cytoplasmic crowding heterogeneity very interesting. Quiescent cells are known to accumulate many "bodies" as discussed in the text, would any of those co-localize with GEM foci?

      Indeed, the imaging at low frequency has revealed that fluorescently-tagged proteins might become trapped in certain regions of the cytoplasm, allowing their detection at conventional imaging frequencies. It is very likely that a similar effect occurs for other cytoplasmic “bodies”, which become visible not only through protein accumulation in a single body but also through low mobility. We have not performed any colocalization experiment with known “bodies” (such as P-bodies or stress granules). Therefore, we do not know if any stress-induced “bodies” are confined to the same spaces as GEMs. However, we would expect at best an incomplete colocalization based on the observation that glucose starvation-induced “bodies” are generally present in a higher percentage of cells than the GEM foci we observe, i.e. it is unlikely that all “bodies” overlap with a GEM focus. It might be interesting to perform such colocalization experiments in follow-up studies, but we feel that such an analysis would go beyond the current scope of this manuscript.

      Reviewer #3 (Significance (Required)):

      General assessment, advances in the field This is an excellent study. The key finding of this paper, ie. that heat shock can compensate for lack of respiration for entry into quiescence, challenges the current views on quiescence establishment. It describes an alternative program that contributes to cell viability upon C source depletion, with details on the proteomic changes occurring in this condition and some of the genetic basis of this pathway. The study is well designed and controlled, the conclusions are in line with the obtained results and very well discussed and placed in perspective. Experimentally, the authors combine several experimental approaches including live-cell single-particle tracking of GEM nanoparticles to quantify cytoplasmic diffusion, FIB-SEM ultrastructural imaging of the cytoplasm to measure macromolecular crowding, proteomics to map stress-induced protein changes and genome-wide SATAY transposon mutagenesis to identify genes required for survival in respiration-deficient cells. The limitations are: -we don't know how this stress program facilitates survival in the absence of restoration of ATP levels. The data suggest that protein homeostasis is involved (chaperones and proteasome up-regulated upon stress, reduced ribosomal and translation-associated proteins down-regulated in the absence of respiration) but the mechanism remains elusive. -the relationships between cytoplasmic crowding and quiescence establishment remain correlative. Yet, the authors provide another pathway to favour viability upon quiescence establishment (with HS) whose activation also displays an increased crowding and reduction of cytoplasmic movement, further consolidating this link. Both of these points are adequately discussed in the manuscript. None of these points should preclude publication of this study, in my opinion.

      Audience. This study would be of interest to researchers in the field of quiescence, biophysics, proteostasis, stress response, nutrient signaling and yeast biology.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary.

      Yeast haploid cells enter quiescence during nutrient deprivation, undergoing major metabolic, transcriptional and biophysical changes. In particular, quiescent cells remodel their cytoplasm, increasing macromolecular crowding and reducing diffusion. Respiration is known to be essential for entry into quiescence and long-term survival.

      In this study, the authors discovered that respiration is not intrinsically required for yeast to survive glucose-starvation-induced quiescence. In particular, they found that a short heat shock before starvation restores survival even in the absence of respiration (Antimycin A treatment), demonstrating that a stress-induced adaptation can bypass the respiratory requirement. This rescue occurs without ATP recovery and relies on de novo protein synthesis. This stress-induced adaptation also rescues quiescent-like biophysical properties of the cytoplasm (increased crowding) that are normally prevented in non-respiring cells, which are thought to be relevant for cell survival . Proteomics reveals that heat shock induces a distinct stress-response proteome enriched in proteostasis factors. A genetic screen reveals that Ras/PKA inhibition and Msn2/4 activation enable this protective reprogramming. Altogether this highlights the importance and complexity of stress adaptation to quiescence establishment.

      This is an excellent paper in all aspects. I have no major points besides the data accessibility, below.

      Main comments.

      • It would be nice to have the MS data available as Excel files for the community, and uploaded to repositories such as PRIDE. Description of the MS data is a bit expedited to serve the purpose of the paper (clustering to evaluate the similarity of proteomic profiles between conditions, GO term enrichment) so having the full data available might help.
      • Same thing for the SATAY screen. The data is summarised in Fig 4B but I believe that the data should be provided.

      Minor comments and questions.

      • I believe that in graphs, the X axis should start at 0 to avoid confusion about the strength of the effect (eg. Fig 2B)
      • I found that using imaging of GEMs at low frequency to reveal cytoplasmic crowding heterogeneity very interesting. Quiescent cells are known to accumulate many "bodies" as discussed in the text, would any of those co-localize with GEM foci?

      Significance

      General assessment, advances in the field

      This is an excellent study. The key finding of this paper, ie. that heat shock can compensate for lack of respiration for entry into quiescence, challenges the current views on quiescence establishment. It describes an alternative program that contributes to cell viability upon C source depletion, with details on the proteomic changes occurring in this condition and some of the genetic basis of this pathway. The study is well designed and controlled, the conclusions are in line with the obtained results and very well discussed and placed in perspective. Experimentally, the authors combine several experimental approaches including live-cell single-particle tracking of GEM nanoparticles to quantify cytoplasmic diffusion, FIB-SEM ultrastructural imaging of the cytoplasm to measure macromolecular crowding, proteomics to map stress-induced protein changes and genome-wide SATAY transposon mutagenesis to identify genes required for survival in respiration-deficient cells.

      The limitations are:

      • we don't know how this stress program facilitates survival in the absence of restoration of ATP levels. The data suggest that protein homeostasis is involved (chaperones and proteasome up-regulated upon stress, reduced ribosomal and translation-associated proteins down-regulated in the absence of respiration) but the mechanism remains elusive.
      • the relationships between cytoplasmic crowding and quiescence establishment remain correlative. Yet, the authors provide another pathway to favour viability upon quiescence establishment (with HS) whose activation also displays an increased crowding and reduction of cytoplasmic movement, further consolidating this link. Both of these points are adequately discussed in the manuscript. None of these points should preclude publication of this study, in my opinion.

      Audience.

      This study would be of interest to researchers in the field of quiescence, biophysics, proteostasis, stress response, nutrient signaling and yeast biology.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript addresses an important and longstanding question in the field: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under nutrient limitation and energy stress. The authors combine tools from biophysics, proteomics, stress signaling, and functional genomics to propose that stress-induced cytoplasmic reorganization, rather than ATP availability per se, is critical for long-term survival when respiration is impaired. The topic is timely, the experiments are generally well executed, and the initial phenomenology is compelling. The paper begins with a set of clear and convincing figures that establish an interesting and biologically important phenotype: when cells are shifted into glucose starvation, they can survive long term only if respiration is functional. Blocking respiration with Antimycin A (AntA) severely compromises viability. One straightforward hypothesis is that this defect simply reflects a failure to generate sufficient ATP. The authors, however, show that a 30-minute heat shock (HS) before glucose withdrawal in the presence of AntA largely rescues survival, even though cellular ATP levels remain critically low. In parallel, they use very well-executed GEM single-particle tracking experiments to demonstrate that cytoplasmic particle mobility decreases markedly in glucose-starved, respiration-deficient cells, and that this diffusion defect is also rescued by the pre-HS, again without restoring ATP. Together, these initial figures strongly support the idea that stress-induced remodeling of the cytoplasm, rather than ATP levels per se, is a key determinant of whether cells can enter and maintain a viable quiescent state. The authors then propose that this protective effect of HS is mediated by induction of the environmental stress response (ESR) and by resulting changes in protein expression. To test whether new protein synthesis is required, they pre-treat cells with cycloheximide during the HS and recovery period. This treatment largely, although not completely, abrogates the beneficial effect of HS on survival and diffusion in AntA-treated, glucose-starved cells. This is a strong experiment and supports the idea that HS-induced synthesis of specific proteins is important for protection, while also hinting that some cycloheximide-insensitive or pre-existing components may contribute. To identify the relevant proteins, the authors turn to global proteomic analysis, comparing multiple conditions: glucose starvation (SC), heat shock followed by glucose starvation (HS SC), glucose starvation plus AntA (SC + AntA), and heat shock followed by glucose starvation plus AntA (HS SC + AntA), each at 1 and 20 hours. This is where, in my view, the story becomes significantly harder to follow. The text for Figure 3 relies almost entirely on GO term enrichment, with very little description of individual proteins or even basic quantitative summaries of the dataset. For example, the authors never clearly state how many proteins were robustly quantified, nor what fraction of the proteome that represents. Without this foundational information, it is difficult to evaluate the strength and generality of their conclusions.

      Related to this, the GO analysis in Figure 3F reports "significant" enrichment for categories such as ribosomes or translation, yet the underlying number of proteins making up these enrichments is not shown. From the volcano plots, it appears that only a very small number of proteins change in some conditions (e.g., SC 20 h), and yet GO terms appear with extremely strong q-values. This is confusing: how can such strong enrichment occur if only a handful of proteins are changing? At minimum, the authors should provide:

      • the number of significantly up- or down-regulated proteins in each comparison
      • the number of proteins contributing to each enriched GO category
      • the magnitude of the changes for these proteins

      Because the absolute number of significantly changing proteins appears small in several conditions, the current heavy reliance on GO analysis feels unwarranted and potentially misleading. In such cases, it would likely be more informative to list all differentially abundant proteins-either in supplementary materials or in a main-text table-and briefly describe the most relevant ones, rather than relying on broad category labels. Figure 3F, in particular, needs substantially more explanation. A related issue appears in Figure 3G (and the associated text), where the authors emphasize that the proteomic response to HS + AntA and the response to long-term glucose starvation are distinct. While this conclusion is plausible, the analysis also shows a subset of proteins that are upregulated in both conditions. These overlapping proteins may, in fact, represent the core protective module that enables survival in quiescence. The authors do not discuss these proteins at all; instead, they are effectively dismissed in favor of the "distinct responses" narrative. I encourage the authors to identify and discuss these overlapping proteins explicitly. Are they chaperones, proteasome components, antioxidant enzymes, or other classical stress-response factors? Even if the global proteomes differ, the overlapping subset could be highly informative about the minimal set of proteins required to stabilize the cytoplasm and support entry into quiescence. The SATAY screen is a major strength of the paper, as it moves from correlative proteomics to functional genetic analysis. The approach appears well-controlled, but key information is missing: How many unique insertions were obtained? Was the library saturating? What was the read distribution and coverage? The authors also discuss only a small subset of the screen hits. The volcano plots show many additional genes that are not addressed. What categories do these fall into? Are they informative about pathways beyond Ras/PKA and Msn2/4? Presenting a fuller analysis would strengthen the mechanistic interpretation. The parts of the SATAY analysis that are discussed are solid. The screen implicates the Ras/PKA signaling axis and Msn2/4 in survival under HS-preconditioned, respiration-deficient starvation, and the authors validate these hits with targeted survival assays. The correspondence between genetic perturbations and changes in cytoplasmic diffusion is an intriguing connection. However, the analysis stops short of identifying the downstream effector proteins that actually produce the biophysical benefits observed. The manuscript then returns to the idea that improved cytoplasmic diffusion and reduced confinement may be essential for survival. This is an appealing hypothesis, but the evidence remains correlative. It is still unclear whether biophysical rescue is the cause of improved survival or simply a downstream marker of a properly induced stress response. What remains missing is deeper integration of the proteomics and SATAY data to identify which proteins are likely responsible for the adaptive changes in cytoplasmic organization. Overexpression of promising candidates-such as chaperones or proteostasis factors found in the overlap between HS and long-term starvation responses-could help determine whether any single protein or small group of proteins can phenocopy the HS-induced rescue. Importantly, many of the comments above are intentionally broad: the manuscript does not simply require small clarifications but would benefit from substantial expansion and deepening of the analysis. The observations are compelling, but the mechanistic chain connecting ESR activation → proteomic remodeling → cytoplasmic biophysics → survival remains insufficiently developed in the current draft. Clearer quantitative reporting, fuller presentation of the data, and more thoughtful interpretation would significantly strengthen the manuscript.

      Summary of Major Issues That Need to Be Addressed

      Quantitative clarity in the proteomics

      • State how many proteins were quantified.
      • Report the numbers of significantly changing proteins in each condition.
      • Identify the proteins underlying each GO term and provide effect sizes.

      Over-reliance on GO analysis

      • Provide explicit lists of differentially expressed proteins.
      • Indicate whether enrichment results are meaningful given the small number of hits.

      Overlooked overlapping proteins

      • Analyze and discuss the subset of proteins upregulated both by HS and by long-term starvation.
      • These may represent the core factors enabling survival.

      SATAY analysis needs fuller presentation

      • Provide insertion numbers, coverage, and basic library statistics.
      • Discuss additional hits beyond the Ras/PKA/Msn2/4 pathways.
      • Integrate SATAY results more deeply with proteomics.

      Mechanistic depth remains limited

      • Clarify whether cytoplasmic biophysical rescue is causal or downstream.
      • Test whether overexpression of candidate proteins can mimic HS-induced protection.
      • Expand the discussion of potential mechanisms using insights from both datasets.

      Significance

      This manuscript addresses a fundamental and timely question in cell biology: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under conditions of nutrient depletion and compromised energy production. Although quiescence has been studied for decades, the mechanisms that link metabolic state, stress signaling, and the physical properties of the cytoplasm remain incompletely understood. This work brings together biophysical measurements, global proteomics, and unbiased genetic screening in an ambitious effort to illuminate how cells maintain viability when respiration-and thus efficient ATP generation-is disrupted. A key conceptual contribution of this study is the demonstration that ATP levels alone do not dictate survival during starvation. Rather, the ability of cells to mount an appropriate stress response and reorganize the cytoplasm appears to be crucial. The early figures provide compelling evidence that heat shock preconditioning can rescue both viability and cytoplasmic mobility in respiration-deficient cells, even when ATP remains low. This finding is notable because it challenges the widely held assumption that energy charge is the primary determinant of successful entry into quiescence. If strengthened by deeper mechanistic analysis, this insight could reshape how the field views energy stress and cellular dormancy.

      The identification of the Ras/PKA-Msn2/4 axis as a key regulatory node is also significant, as it connects quiescence survival to well-established nutrient and stress signaling pathways. The integration of a genome-wide SATAY screen adds functional depth and offers the potential to uncover specific downstream effectors that remodel the cytoplasm or stabilize cellular structures during prolonged stress. Finally, the manuscript touches on a concept that is gaining traction across many subfields of biology: that the biophysical state of the cytoplasm is a regulated and physiologically meaningful parameter, not merely a passive consequence of metabolic decline. Understanding how cells tune macromolecular crowding, diffusion, and spatial organization during quiescence could have broad implications beyond yeast, including in stem cell biology, microbial dormancy, cancer cell persistence, and aging.

      Overall, the questions addressed are important, and the study has the potential to make a meaningful conceptual contribution. However, realizing that impact will require clearer and deeper mechanistic analysis-particularly in the proteomics and SATAY sections-to convincingly identify the specific factors and pathways that mediate the cytoplasmic remodeling underlying survival.

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      Referee #1

      Evidence, reproducibility and clarity

      In the present manuscript, the authors present an in-depth study on the effect of a heat-shock response on the ability of yeast to regain viability after quiescence when their ability to respire is inhibited. They nicely demonstrate that these effects correlate with the measured diffusion coefficients, providing deeper insight into the (at least partially) responsible environmental stress response and the molecular players involved. This work is an important contribution to the growing (or resurging) field of the physical properties of the cell.

      My two main comments are the following:

      • The authors determine the diffusion coefficients from the MSD, as well as further analyze them all the way up to the confinement size. As far as I can judge from the manuscript, these analyses are for 2D systems and were initially developed for processes on membranes. How does this change for 3D systems? I understand that for a straightforward qualitative comparison of apparent MSD, this assumption is acceptable, but it may deviate more strongly with the additional analyses the authors present.
      • The authors show data in the supporting information where the GEMs provide larger foci after stress with longer imaging times. Could the authors provide the images of the shorter imaging times that they use? That seems a more equal comparison than Figure C. It is also unclear to me why fixed cells are used in Figure C, as well as the meaning of the x-axis. In line with this, can the authors exclude that GEMs dimerize/oligomerize after stress, and therefore display a lower diffusion coefficient?

      Other comments:

      • For the precision of the language, the authors equate ribosome content with macromolecular crowding, with the diffusion of the GEMs throughout, and this becomes more conflated in the discussion, where it is compared to viscosity and macromolecular crowding effects, e.g., translation. Is it macromolecular crowding, mesoscale crowding, nano-rheology, or ribosome crowding? What is measured precisely?
      • In the EM images, the ribosomes seem smaller after starvation. Is that correct, and how should we interpret this? Is this due to an increased number of monosomes?
      • The authors refer to recent work on how biochemical reactions, such as translation, are determined by the cytoplasm. There is some older work on this, see for example in bacteria https://doi.org/10.1073/pnas.1310377110, and also in vitro here DOI: 10.1021/acssynbio.0c00330
      • On the section of correlating diffusion and survival outcomes (bottom page 12), it is mentioned that the lowered diffusion could enhance aggregation. However, literature indicates that the opposite is true in buffer; lower diffusion reduces aggregation (also nucleation is inversely proportional to the viscosity).

      Significance

      General assessment:

      Strengths: It is a comprehensive study that provides a wealth of information and insight into the intricacies of a field that has received considerable attention, and its views are evolving rapidly.

      Weaknesses: It may suffer from some overinterpretation of diffusion data.

      Advance:

      The significant advance is that the molecular response pathway and precise molecular players are connected to the biophysical response of cells to starvation/quiescence. The dependence of diffusion on starvation has received considerable attention (Jacobs-Wagner, Cell, 2014; the current authors in eLife, 2016; and more recent investigations by Holt, Delarue, and others). Still, the authors take the next step and demonstrate how quiescence, and particularly how the history of a cell affects it, correlates strongly with the diffusion. As far as I can tell, this is new. As mentioned, the molecular insights into the pathways are exceptionally strong from my perspective. From personal experience, this work is also very important for researchers outside of the field from a practical standpoint: Do your measurements change when you stress cells by walking to a microscope? And even if you incubate them there, your measurement outcome will change. In my experience, this is a crucial point, and the cell's history is often overlooked.

      Audience:

      Broad -- biophysicists, molecular biologists, cell biologists, biotechnologists.

      My field of expertise: Biophysics.

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      Reply to the reviewers

      We thank reviewers for the general positive feedback and insightful suggestions. Reviewers found that our study “provides a rich resource of potential E3-sensor interactions and represents a conceptual and technical advance for the field” and that our “key conclusions are convincing and interesting”. Reviewers suggested both editorial changes to improve the narrative of the manuscript and additional experiments to strengthen the conclusions of the study. We agree with both types of suggestions and decided to modify our manuscript accordingly.

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

      The authors present a rational, AlphaFold-based strategy to systematically identify interactions between human nucleic acid sensors and SPRY-containing proteins. Their findings demonstrate that SPRY domains encode substrate-specific recognition patterns that govern immune responses: TRIM25-ZAP in antiviral defense and restricts LNP-encapsulated RNA, while Riplet-RIG-I for the IFNB1 production and restricts lipofection. They further dissect residue-level contributions to the ZAP-TRIM25 interface by integrating structural predictions with experimental validation. 

      Specific comments.  1. The title of this manuscript appears quite broad given that this study mostly focuses on just TRIM25-ZAP and Riplet-RIG-I pairs. 

      We agree that the original title was broader than the main mechanistic focus of the study. We will therefore revise the title to better reflect that the manuscript primarily dissects SPRY-domain–mediated specificity in the TRIM25-ZAP and Riplet-RIG-I interactions (identified through our AlphaFold-based screening framework), while retaining the broader screening context. Proposed new title: "SPRY domains encode ubiquitin ligase specificity for ZAP and RIG-I"

      In Figure 1b, several predicted interaction scores appear inconsistent with previously reported experimental interactions. For instance, KHNYN has been experimentally validated as a TRIM25-interacting protein, yet its interaction score is notably low in your computational results. Could the authors clarify whether this discrepancy arises because the TRIM25 SPRY domain does not significantly contribute to KHNYN binding? 

      We thank the reviewer for raising this point. To our knowledge, published data only support co-immunoprecipitation of TRIM25 and KHNYN in ZAP-deficient in cells (PMID: 31284899), but this does not by itself demonstrate a direct binary interaction, as the association could be mediated by other factors. Consistent with this, our AlphaFold-based screen predicts a low interaction score between KHNYN and TRIM25, suggesting that this may not be a direct protein-protein interaction. Nevertheless, we concede that our approach may have missed interactions that are governed by a small number of interacting residues. We added the following sentences on the limitation of this approach for such interactions in our discussion:

      • While our screen revealed novel interactions between SPRY domain containing proteins and innate immune sensors, it is plausible that certain interactions were missed. Interactions that rely on a small number of contacting residues or interactions that may be mediated by a third binding partner are likely to score poorly in our approach. Future optimization of our algorithm will improve the detection of such interactions.”*

      In Figure 2c, the authors provide intriguing examples for shared targets by SPRY proteins with quite low homology, and distinct target profiles by nearly identical SPRY domains. However, the underlying mechanisms responsible for these observations are not discussed. 

      This is an important point. At present, we cannot assign a single definitive mechanism for every example, but there are several plausible explanations consistent with our framework. First, our analysis indicates that substrate recognition is often driven by a limited subset of residues at the interaction surface, such that distinct sequences can converge on similar three-dimensional interface chemistry, while small local differences can shift binding preferences. Second, we note that although a large fraction of predicted contacting residues are within SPRY domains, other domains can also contribute to interaction and substrate recognition, which could modulate binding profiles even when SPRY sequences are near-identical. Third, the Pearson’s correlation coefficient was calculated all scores, which may include structures with low confidence scores or low interaction scores

      In Figure 3e and 3f, the authors state that the Riplet-T25 SPRY chimeric protein showed enhanced AlphaFold predicted interaction with ZAP, and validated the interaction experimentally. However, the Alphafold also predicted that an increased interaction for the T25-Riplet chimera, although this mutant failed to be co-precipitated with ZAP. How do the authors reconcile this discrepancy between prediction and experimental outcome? 

      The reviewer noticed an important, nuanced result in Fig. 3e. AlphaFold predicts that the TRIM25 chimera containing the Riplet SPRY domain (T25–Riplet) has a higher interaction score with ZAP than Riplet alone (Fig. 3e), yet this chimera is not recovered in ZAP co-immunoprecipitation (Fig. 3f). We reconcile this by emphasising that our framework uses an empirically benchmarked threshold: known SPRY–sensor interactions typically score >2.5, and we therefore adopted >2.5 as the cutoff for “high-confidence” candidate interactions. While the T25–Riplet chimera shows an increased score relative to Riplet, its score remains below this >2.5 cutoff in Fig. 3e (which reports interaction scores of the chimeras against ZAP). Therefore, the model is consistent with the experimental outcome: AlphaFold suggests some degree of interface compatibility, but not at a level we would classify as a robust/predictive interaction under our validated threshold. We clarified this point in the Results section to explicitly note that sub-threshold “increases” should be interpreted cautiously:

      Using the T25-RipletSPRY instead of the Riplet protein, predicted a higher interaction score despite the lack of specific pull-down between this chimera and ZAP; importantly, this interaction score is below our defined threshold (2.5), highlighting the importance of benchmarking predicted scores against known interactions.”

      It is curious if the authors explain why TRIM25 consistently appears as two bands in many of the presented figures. 

      We have also wondered about this observation as well. Other studies report that the double band pattern in western blots of TRIM25 (PMID: 17392790, 28060952, 21292167) and it is believed to be a product of non-degradative self-ubiquitination of TRIM25, primarily acting on the K117 residue (PMID: 21292167). We will add a brief description of this phenomenon in the figure legend.

      In Figure 4b, the authors show that treatment with a proteasome inhibitor increased RIG-I ligand-induced IFNB1 expression and propose that RIG-I may undergo rapid degradation following its interaction with Riplet. However, the evidence supporting this claim is weak. The authors should demonstrate: (1) that RIG-I is indeed degraded via the proteasome, and (2) whether RIG-I undergoes K48-linked ubiquitination. Mutational analysis of putative ubiquitination sites in RIG-I would help clarify its contribution to the observed IFN responses. 

      This is an important point and we are currently performing experiments addressing these questions. Specifically we will provide evidence of (1) whether RIG-I is degraded after activation using a combination of western blotting and pharmacological inhibition of the proteosome/translation machinery; (2) whether RIG-I goes K48- or K63-mediated ubiquitination by performing coIPs of RIG-I in the presence of HA-Ub wildtype or the commonly used HA-Ub K48 and K63 mutants (PMID: 15728840); and (3) whether lysine-to-arginine substitution of key residues impacts RIG-I ubiquitination/degradation.

      Figure 5 c-g: why do the authors show ZAP-L, but not ZAP-S? 

      Both ZAP-S and ZAP-L isoforms contain identical N-terminal domains, which is the region that interacts with TRIM25. Therefore, we assumed that the interaction between TRIM25 and ZAP-L would be similar to TRIM25-ZAP-S. However, to test this assumption, we will generate equivalent mutations in ZAP-S and perform similar co-immunoprecipitation experiments.

      Reviewer #1 (Significance (Required)): 

      This manuscript starts with the AlphaFold-based screening of interactions between human nucleic acid sensors and SPRY-containing proteins. However, the authors then just focused on TRIM25-ZAP and Riplet-RIG-I, whose interactions have been well demonstrated previously, although other protein interactions were not further explored. Also, the information on the evolutional aspects of TRIM25, ZAP, Riplet and RIG-I did not lead to clear conclusions. The differential contribution of TRIM25-ZAP and Riplet-RIG-I in LNP- and lipofectamine-transduced RNAs is interesting, although data shown in Fig.6 are expected from previous studies, and are not so relevant to other data in this study.  Therefore, the study is not well integrated, although pieces are interesting.  This study may attract researchers in innate 

      My expertise is innate immunity and RNA biology. 

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): 

      The paper describes the discovery of unknown E3-RNA sensor interactions from a large scale in silico prediction screen, as well as the clarification of previously described E3-sensor interactions. These findings extend previous work showing ancient relationships between nucleic acid sensors and RING E3s (e.g. PMID: 33373584), which also described the RIPLET-RIG-I pairing identified in the present screen. 

      The interactions focused on are shown to have functional implications for immune signaling pathways, and stability implications for the bound sensor. The argument for the screen is that E3-target interactions are often too transient to detect biochemically. While possibly true, several of the pairings are confirmed by co-IP, with either WT E3 or a catalytically deficient E3 (known elsewhere as a 'substrate trap'). 

      The key conclusions are convincing and interesting; in particular, the conserved interactions between RIPLET and RIG-I, and TRIM25 and ZAP. The hypothesis that the two E3s arose from a common ancestor is intriguing, and the use of chimeras in functional experiments suggest that the length of the coiled coil domains contributes to substrate targeting. The most interesting observation IMO is that showing that RNA vaccines can be sensed by orthogonal sensor/E3 pathways, depending on transfection method, suggesting that distinct entry routes are surveyed by different sensors. These experiments are well performed as E3 manipulation phenocopies sensor manipulation, supporting that the in silico approach will ID relevant pairings. 

      Including the PAE plots for some of the key interactions would be helpful, as a lot of the interaction confidence metrics are hidden in interaction 'scores'. Fig. 1b heatmap is presented as a row max, so it is difficult to calibrate one E3 against another. The raw data from e.g. fig. 1c would be a valuable addition. This would also help orientate future studies predicting similar protein-protein interactions. 

      We agree with the reviewer and we will provide the raw values for the interaction scores and PAE maps as supplementary data to be included in the final publication.

      Figure 1 appears to just use the isolated SPRY domain for screening - were full-length proteins used?

      The data in Figure 1 was generated using full-length proteins, but it will be interesting to test if a similar screen with SPRY domains alone can replicate the predicted interactions. We will repeat this using SPRY domain sequences.

      How many copies of the FL protein were used. TRIM5 employs a low affinity, high avidity binding method; do binding patterns change when the valency of either component is altered? The Alphafold approach perhaps selects for high affinity binders? I don't expect many more experiments to be done here, but commenting on this would be useful. __ __

      This is a rational consideration that we overlooked. We included in our discussion a comment on the limitation of this approach in the context of multimeric assemblies:

      Similarly, the oligomeric nature of some SPRY-containing proteins [22] is likely to impact the correct placement of these domains and, therefore, impact the predicted interaction score. Future optimization of our algorithm will improve the detection of such interactions.”

      The TRIM25 -Riplet PRYSPRY swap experiments in Figure 3 are very informative and powerful. Some more detail on domain boundaries used would be helpful, including AF predictions of what these chimeras look like with respect to their natural counterparts. 

      We agree with the reviewer about the need to explicitly define domain boundaries. We will include as supplemental information a comparison of the AF prediction of these chimeras in relation to the native proteins.

      While AF can place confidence metrics on domain-domain interactions, SPRY containing proteins are themselves often comprised of regions of high structural confidence (e.g. many available PDBs for RINGs, coils and SPRYs) but their relative arrangement within the molecule is unpredictable. Do isolated SPRYs show any better/worse binding to targets? 

      This is a good point as well, and this can be addressed by repeating the AlphaFold screen using only SPRY domain proteins rather than full-length protein (as described above).

      Technically, fig. 1f does not show that TRIM58 destabilises OAS1, as there is no condition with OAS1 alone. Perhaps alter the text to reflect this or repeat with the necessary control. The direction of the text is fine, as Fig. 1g provides a striking result, but 1f needs attention. 

      The reviewer raises an important consideration. To address this, we will repeat the experiment using a OAS1 alone condition, as suggested.

      Fig. 2c - for clarity, please specify the meaning of the connecting lines between the bait 'hits' in the legend. What does the correlation coefficient relate to exactly? % similarity, is this across the whole molecule, or the PRYSPRY (presumably the latter would be a more useful comparison). And it is well established that single variations in SPRY variable loops can toggle binding, so this could be better referenced in the text. It would also be helpful to see e.g. dissimilar PRYSPRYs binding a common target, as PAE plots in the supplementary. Do any shared motifs occur at the variable loops between dissimilar SPRY molecules? 

      We agree that this figure could be clearer. The connecting lines in Fig. 2c indicate protein-protein predictions with common sensors, i.e. connecting lines between the interaction score of ASH2L-MDA5 and the interaction score of TRIM51-MDA5. We only use % similarity of the SPRY domain alone, not the whole molecule. We have modified the figure legend to clarify this point and we include the PAE maps as supplementary information, as requested.

      Fig. 2i - Bat RIG-I binds both TRIM25 and Riplet? This is in contrast to the predicted directionality in 2h? 

      The reviewer astutely noted that, in Fig.2i, pulling down bat RIG-I co-immunoprecipitated with both bat Riplet and bat TRIM25, while AlphaFold predictions only suggest a Riplet-RIG-I interaction. However, while bat Riplet and bat TRIM25 express at comparable levels in the input sample, bat Riplet was far more enriched in RIG-I pulldowns than bat TRIM25. Our interpretation of this data is that, indeed, bat Riplet-RIG-I interaction is more powerful than TRIM25-Rig-I.

      Fig. 3a-b, Sup Fig. 3c,d - IFNB1 transcript normalised to 3p-hRNA transfection in control NTC cells - the presentation chosen obscures the baseline IFNB1 levels in the different siRNA transfections. What is the fold induction of IFNB1 in the different cell lines? 

      We will include the fold induction in each cell line as supplementary information.

      Fig. 3g - RLUs of EV-A71 is only tested in TRIM25 KO cells transfected with the Riplet T25 chimera. The full panel of cDNAs (parental E3s and the inverse 25-riplet swap) should be tested in parallel to confirm the effect is specific to TRIM25 PRYSPRY. 

      This is a great suggestion that will help clarify the role of different domains of TRIM25 in its antiviral activity. We are currently generating cell-line stably expressing these truncations and will perform the suggested experiment.

      Fig. 4b - time point of 3p-hRNA transfection? Y-axis label suggested normalisation to NTC - incorrect label? What is the effect of bortezomib on IFNB1 mRNA in mock treated cells? 

      We thank the reviewer for spotting this typo, we have known corrected the axis label. We harvested cellular mRNA 8h post-transfection. Bortezomib-treatment slightly reduced the background expression of IFNB1 mRNA, but this signal is very close to the detection limit that it is difficult to draw conclusions. Nevertheless, the addition of bortezomib did not increase IFNB1 mRNA expression in the absence of a stimulus.

      Fig. 4g - these experiments would benefit from an untransfected control cell to clarify how cDNA expression impacts sensor stability. 

      We agree with the reviewer and we will include an untransfected control.

      There seems to be an inverse correlation between sensor degradation and signaling output - is that the summary of Fig. 4? On the one hand, sensor degradation attenuates functional output (Fig. 4b), and the E3 that degrades the sensor is required for sensor function; on the other hand, changing coil-length in the E3 disables sensor degradation (Fig. 4g) but and enhances signaling response (Fig. 3j). Do the chimeras of panel Fig. g, h influence IFNB1 expression in the assay from Fig. 3j - this experiment would marry RIG-I expression with signal output. 

      This is an interesting experiment. We are in the process of generating a TRIM25-/- Riplet-/- cell line, which we will use to reconstitute with the chimeras mentioned above and perform the requested experiment.

      The data is generally clear. To facilitate their interpretation and for clarity, Western blots require size markers and Co-IPs should indicate the flag-/ha-epitope tags. Would make fig. 2 i-j much clearer, particularly given apparent co-migration of IgG (heavy chain?) and riplet, and the lack of control IPs. 

      We agree that contextual markings will improve the interpretation of these results. We will add size markers to the western blots in fig2 in order to improve clarity.

      The figure legends could provide more detail. 

      We will add additional experimental details (such as time points) to the figure legends.

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      Referee #2

      Evidence, reproducibility and clarity

      The paper describes the discovery of unknown E3-RNA sensor interactions from a large scale in silico prediction screen, as well as the clarification of previously described E3-sensor interactions. These findings extend previous work showing ancient relationships between nucleic acid sensors and RING E3s (e.g. PMID: 33373584), which also described the RIPLET-RIG-I pairing identified in the present screen.

      The interactions focused on are shown to have functional implications for immune signaling pathways, and stability implications for the bound sensor. The argument for the screen is that E3-target interactions are often too transient to detect biochemically. While possibly true, several of the pairings are confirmed by co-IP, with either WT E3 or a catalytically deficient E3 (known elsewhere as a 'substrate trap').

      The key conclusions are convincing and interesting; in particular, the conserved interactions between RIPLET and RIG-I, and TRIM25 and ZAP. The hypothesis that the two E3s arose from a common ancestor is intriguing, and the use of chimeras in functional experiments suggest that the length of the coiled coil domains contributes to substrate targeting. The most interesting observation IMO is that showing that RNA vaccines can be sensed by orthogonal sensor/E3 pathways, depending on transfection method, suggesting that distinct entry routes are surveyed by different sensors. These experiments are well performed as E3 manipulation phenocopies sensor manipulation, supporting that the in silico approach will ID relevant pairings.

      Including the PAE plots for some of the key interactions would be helpful, as a lot of the interaction confidence metrics are hidden in interaction 'scores'. Fig. 1b heatmap is presented as a row max, so it is difficult to calibrate one E3 against another. The raw data from e.g. fig. 1c would be a valuable addition. This would also help orientate future studies predicting similar protein-protein interactions.

      Figure 1 appears to just use the isolated SPRY domain for screening - were full-length proteins used? How many copies of the FL protein were used. TRIM5 employs a low affinity, high avidity binding method; do binding patterns change when the valency of either component is altered? The Alphafold approach perhaps selects for high affinity binders? I don't expect many more experiments to be done here, but commenting on this would be useful.

      The TRIM25 -Riplet PRYSPRY swap experiments in Figure 3 are very informative and powerful. Some more detail on domain boundaries used would be helpful, including AF predictions of what these chimeras look like with respect to their natural counterparts.

      While AF can place confidence metrics on domain-domain interactions, SPRY containing proteins are themselves often comprised of regions of high structural confidence (e.g. many available PDBs for RINGs, coils and SPRYs) but their relative arrangement within the molecule is unpredictable. Do isolated SPRYs show any better/worse binding to targets?

      Technically, fig. 1f does not show that TRIM58 destabilises OAS1, as there is no condition with OAS1 alone. Perhaps alter the text to reflect this or repeat with the necessary control. The direction of the text is fine, as Fig. 1g provides a striking result, but 1f needs attention.

      Fig. 2c - for clarity, please specify the meaning of the connecting lines between the bait 'hits' in the legend. What does the correlation coefficient relate to exactly? % similarity, is this across the whole molecule, or the PRYSPRY (presumably the latter would be a more useful comparison). And it is well established that single variations in SPRY variable loops can toggle binding, so this could be better referenced in the text. It would also be helpful to see e.g. dissimilar PRYSPRYs binding a common target, as PAE plots in the supplementary. Do any shared motifs occur at the variable loops between dissimilar SPRY molecules?

      Fig. 2i - Bat RIG-I binds both TRIM25 and Riplet? This is in contrast to the predicted directionality in 2h?

      Fig. 3a-b, Sup Fig. 3c,d - IFNB1 transcript normalised to 3p-hRNA transfection in control NTC cells - the presentation chosen obscures the baseline IFNB1 levels in the different siRNA transfections. What is the fold induction of IFNB1 in the different cell lines?

      Fig. 3g - RLUs of EV-A71 is only tested in TRIM25 KO cells transfected with the Riplet T25 chimera. The full panel of cDNAs (parental E3s and the inverse 25-riplet swap) should be tested in parallel to confirm the effect is specific to TRIM25 PRYSPRY.

      Fig. 4b - time point of 3p-hRNA transfection? Y-axis label suggested normalisation to NTC - incorrect label? What is the effect of bortezomib on IFNB1 mRNA in mock treated cells?

      Fig. 4g - these experiments would benefit from an untransfected control cell to clarify how cDNA expression impacts sensor stability.

      There seems to be an inverse correlation between sensor degradation and signaling output - is that the summary of Fig. 4? On the one hand, sensor degradation attenuates functional output (Fig. 4b), and the E3 that degrades the sensor is required for sensor function; on the other hand, changing coil-length in the E3 disables sensor degradation (Fig. 4g) but and enhances signaling response (Fig. 3j). Do the chimeras of panel Fig. g, h influence IFNB1 expression in the assay from Fig. 3j - this experiment would marry RIG-I expression with signal output.

      The data is generally clear. To facilitate their interpretation and for clarity, Western blots require size markers and Co-IPs should indicate the flag-/ha-epitope tags. Would make fig. 2 i-j much clearer, particularly given apparent co-migration of IgG (heavy chain?) and riplet, and the lack of control IPs.

      The figure legends could provide more detail.

      Significance

      The paper provides a rich resource of potential E3-sensor interactions and represents a conceptual and technical advance for the field. The authors take a novel approach to identify these pairings. Several pairings are validated in CoIPs, and two pairings (T25-ZAP, RIPLET-RIG-I) are studied in detail. Many E3s - including the TRIM proteins which comprise the bulk of E3s studied here - are purported to regulate key nucleic acid sensors in the literature, but the large scale approach taken here provides evidence that the pairings are really quite specific. The findings also supports prior work showing that the PRYSPRY domain (here called the SPRY) is a functionally plastic module that through variable loops can bind a range of different protein substrates.

      The paper will be most interesting to the innate immune field, those working on nucleic acid sensing, and those looking at innate responses to RNA vaccines.

      Regulation of E3 ubiquitin ligases, viral RNA sensing

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      Referee #1

      Evidence, reproducibility and clarity

      The authors present a rational, AlphaFold-based strategy to systematically identify interactions between human nucleic acid sensors and SPRY-containing proteins. Their findings demonstrate that SPRY domains encode substrate-specific recognition patterns that govern immune responses: TRIM25-ZAP in antiviral defense and restricts LNP-encapsulated RNA, while Riplet-RIG-I for the IFNB1 production and restricts lipofection. They further dissect residue-level contributions to the ZAP-TRIM25 interface by integrating structural predictions with experimental validation.

      Specific comments.

      1. The title of this manuscript appears quite broad given that this study mostly focuses on just TRIM25-ZAP and Riplet-RIG-I pairs.
      2. In Figure 1b, several predicted interaction scores appear inconsistent with previously reported experimental interactions. For instance, KHNYN has been experimentally validated as a TRIM25-interacting protein, yet its interaction score is notably low in your computational results. Could the authors clarify whether this discrepancy arises because the TRIM25 SPRY domain does not significantly contribute to KHNYN binding?
      3. In Figure 2c, the authors provide intriguing examples for shared targets by SPRY proteins with quite low homology, and distinct target profiles by nearly identical SPRY domains. However, the underlying mechanisms responsible for these observations are not discussed.
      4. In Figure 3e and 3f, the authors state that the Riplet-T25 SPRY chimeric protein showed enhanced AlphaFold predicted interaction with ZAP, and validated the interaction experimentally. However, the Alphafold also predicted that an increased interaction for the T25-Riplet chimera, although this mutant failed to be co-precipitated with ZAP. How do the authors reconcile this discrepancy between prediction and experimental outcome?
      5. It is curious if the authors explain why TRIM25 consistently appears as two bands in many of the presented figures.
      6. In Figure 4b, the authors show that treatment with a proteasome inhibitor increased RIG-I ligand-induced IFNB1 expression and propose that RIG-I may undergo rapid degradation following its interaction with Riplet. However, the evidence supporting this claim is weak. The authors should demonstrate: (1) that RIG-I is indeed degraded via the proteasome, and (2) whether RIG-I undergoes K48-linked ubiquitination. Mutational analysis of putative ubiquitination sites in RIG-I would help clarify its contribution to the observed IFN responses.
      7. Figure 5 c-g: why do the authors show ZAP-L, but not ZAP-S?

      Significance

      This manuscript starts with the AlphaFold-based screening of interactions between human nucleic acid sensors and SPRY-containing proteins. However, the authors then just focused on TRIM25-ZAP and Riplet-RIG-I, whose interactions have been well demonstrated previously, although other protein interactions were not further explored. Also, the information on the evolutional aspects of TRIM25, ZAP, Riplet and RIG-I did not lead to clear conclusions. The differential contribution of TRIM25-ZAP and Riplet-RIG-I in LNP- and lipofectamine-transduced RNAs is interesting, although data shown in Fig.6 are expected from previous studies, and are not so relevant to other data in this study. Therefore, the study is not well integrated, although pieces are interesting. This study may attract researchers in innate

      My expertise is innate immunity and RNA biology.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): The key conclusions are solid. All the claims are supported by quality data. The content is rich, and no additional experiment is needed. The data and methods are properly presented for reproduction. The experiments are adequately replicated. One comment on statistical analysis is listed below.* *

      __Summary:_ ___ This manuscript investigates how Drosophila immune pathways contribute to defense against a range of filamentous fungi with distinct ecological strategies. The work provides novel insights into Toll pathway activation through pattern recognition receptors and danger signals, relative roles of melanization, phagocytosis, and effects of antimicrobial peptides, and particularly the immune evasion strategy of E. muscae via protoplast formation. These findings are of broad relevance to insect immunology, host-pathogen interactions, and evolutionary biology. * The study is well designed, the experiments are carefully executed, and the manuscript is clearly written. It is novel to demonstrate that E. muscae evades immune recognition via protoplast formation. However, some aspects of clarity and discussion of limitations could be improved before publication.** *

      We thank the reviewer of the positive assessment of our manuscript.We thank the reviewer of the positive assessment of our manuscript.

      Major comments: 1) The Abstract is informative but a bit too long. Consider condensing some sentences and highlighting the novel contributions (e.g., role of protoplasts in immune evasion.).* *

      Good points. We have reduced the abstract. The sentence is 'Our study also reveals that the fly-specific obligate fungus Entomophthora muscae employs a vegetative development strategy, protoplasts, to hide from the host immune response.'

      We believe that the role of protoplasts is already mentioned in the abstract.

      2) The Results may use more mechanistic links. For instance, the section on E. muscae immune evasion could more explicitly connect the morphological findings (protoplasts, lack of cell wall) with specific immune recognition failures.* *

      Our article is a comparison of Drosophila host defense against fungi with various life styles. This obviously complexify the presentation of the results. We have made the maximum of effort to explain our data with clarity. We believe that having two successive sections entitled 'Natural infection with E. muscae barely induces the Toll pathway' followed by ' __Entomophthora muscae hides from the host immune response using a vegetative development strategy'____ __expose well the idea that E. muscae has a specific hiding strategy. We did not change this part.

      3) Please clarify statistical analyses used for survival data (e.g., log-rank tests, multiple testing corrections). * We have clarified the statistical analysis in the method part. The sentence is 'Statistical significance of survival data was calculated with a log-rank test (Mantel-Cox test) comparing each genotype to w*1118 flies'.

      __Minor comments:____ __ Abstract: 1) "The infection outcome depends on the complex interplay between insect immune defenses and fungal adaptive strategies." could be simplified to: "Infection outcomes depend on the interplay between insect immunity and fungal adaptation." 2) Replace "our study uncovers" with "we show" for more concise phrasing. Reduce phrases like "our study reveals" or 'we conclude" in other parts of the manuscript. * Results: p. 5: phrase "survival upon natural infection... reveals the major contribution" → reword to avoid passive tone. p. 10: clarify "vesicles push the membrane outwards" with more precise terminology (e.g., budding, extrusion). * Discussion: p. 20: streamline sentence beginning "These observations provide a mechanistic basis..." (currently too dense).

      We have taken in consideration all these comments. Note that we removed in the revised version the sentence "The infection outcome depends on the complex interplay between insect immune defenses and fungal adaptive strategies." To shorten the abstract, we have removed the sentence 'These observations provide a mechanistic basis for future exploration.'

      **Referee cross-commenting*** *

      I agree with the comments of the other two reviewers.* *

      __Reviewer #1 (Significance (Required)):____ __

      This manuscript investigates how Drosophila immune pathways contribute to defense against a range of filamentous fungi with distinct ecological strategies (generalists, specialists, opportunists). By leveraging a comprehensive panel of genetically defined fly lines and standardized infections, the authors provide a demonstration that the Toll pathway is the predominant systemic antifungal defense, extending classical findings into a comparative framework across fungal lifestyles. The work provides novel insights into Toll pathway activation through GNBP3 and fungal proteases sensed by Psh, while also dissecting the relative contributions of melanization, phagocytosis, and antimicrobial peptides to host protection. Of particular note is the compelling demonstration that the fly specialist E. muscae can evade immune recognition through protoplast-like vegetative forms, minimizing cell-wall exposure and thereby escaping Toll activation.* *

      My expertise and limitations: * Insect biochemistry and molecular biology, with particular focus on innate immunity, serine protease cascades, melanization, and host-pathogen interactions. I also have experience with genetic, biochemical, and functional approaches to dissecting immune signaling pathways in model insects. However, I do not have sufficient expertise to critically evaluate advanced statistical analyses.** *

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)):____ __

      In this work the authors describe the contribution of distinct immune responses in Drosophila melanogaster to systemic and natural infections with 5 fungal species with different lifestyles some being generalists infecting a broad range of insects while others being more specialists or opportunistic. The authors used several well characterized Drosophila mutants of the Toll, Imd, phagocytosis and melanization responses to address this question. They show that Toll pathway is the key player in anti-fungal resistance in both natural and septic infections, whereas melanization plays a minor role mainly during natural infections possibly to limit fungal invasion through the cuticle. The authors show elegantly using different combinations of mutants for antimicrobial peptides genes with antifungal activities that Bomanins and Daisho (1 and 2) are the main Toll effectors mediating resistance to fungi but the authors did not find specific fungus-by-gene interaction, but rather antifungal peptides seem to act in a more general fashion against the fungi tested with significant redundancies between certain classes. Interestingly the authors show that while generalists like Beauveria and Metarhizium strongly activate the Toll pathway, the specialist E. muscae weakly activates the pathway and the opportunistic A. fumigatus does not activate the pathway, indicating that certain fungal species are able to evade sensing by immune pathways. In the context of the Toll activation, the sensor protease Psh and not GNBP3 seem to be the main trigger of the pathway.* *

      __Minor comments____ __ This is an interesting work that compares the contributions of different arms of the fly immune response to 5 fungal species with diverse lifestyles. The use of different lines with different combinations of mutant genes is a strength to highlight the relative contribution of each immune response. Some of the data obtained is intriguing and warrants more future investigations such as the distinct phenotypes of ModSp and GNBP3 mutants in E. muscae infections. The methodology is robust and the conclusions are supported with good experimental evidence. I do not see any major concerns with the work. I just have some minor comments listed below* *

      We thank the reviewer for the positive comments on our manuscript. 1- Statistical significance should be indicated on Figures 1 and 2, although it appears in the legend.

      We have added statistical significance on Figures 1 and 2.

      2- It is not very accurate to use the term resistance of the different mutants to infections with the diverse fungal species in Figures 1 and 2 especially that the authors have reported only survival data in these figures and have not measured fungal proliferation in infected flies (although they did that in later figures). It is more accurate to mention that the mutants flies have different levels of tolerance rather than resistance to fungal infections.* *

      We agree that we cannot use the term 'resistance' in Figures 1 and 2, since this term has now a more restricted meaning in the community. We have replaced the term 'resistance' by 'host defense' or 'surviving' through the text to avoid the confusion, except when the bacterial load was monitored.

      3- The authors show that Toll is over-activated in PPO1/PPO2 double mutant possibly through a negative feedback mechanism. However, there could be another explanation for this observation: For instance, the increased fungal proliferation in the PPO double mutant results in increased protease secretion by fungi enhancing Psh activation! Also, how can fungi manage to proliferate in this double mutant if Toll is overactivated? Could it be that Toll overactivation is triggering a fitness cost?* *

      The reviewer raises a good point. It is difficult to reconcile the susceptibility of PPO1/2 mutants to fungi taking in consideration the higher Toll activation. The higher activation of Toll could be deleterious and We clearly observed higher Toll pathway activation in PPO1/2 flies upon clean injury (Fig. S9C) or injection of dead spores (data not shown). Thus, this higher expression cannot be only explained as a consequence of higher fungal growth.

      4- In Lines 654-655, it is not accurate to say that E. muscae protoplasts are not detected by the immune response since E. muscae natural infections triggers Drs expression at 24 hpi and there is possibly some melanization taking place since PPO1 and PPO2 are required for defense against this fungus. A more accurate explanation is that this fungus is possibly more resistant to the effectors of the host immune response than the other fungi. I think a major point that the authors might have missed to consider in the discussion of their data is that the different fungi used herein may exhibit different levels of resilience to the effector reactions of the host such as AMPs and melanin deposition* *

      *The observation that injection of E. muscae protoplasts do not trigger an immune response above the level of clean injury is a strong argument that support our view that E. muscae protoplasts are not immunogenic. The reviewer is correct by underlying the small but significant induction of Drs at 24h post natural infection. We hypothesize that this could be due to mechanical injury associated with the entry of E. muscae. We have added a sentence to underline the possibility raised by the reviewer: 'Although we cannot rule out that the high pathogenicity of E. muscae may be partly due to the fungus's increased resilience, we favor the interpretation that it is instead mainly driven by its capacity to evade immune detection.'

      __Reviewer #2 (Significance (Required)):____ __

      Although the importance of Toll pathway and melanization in antifungal immunity is not new per se, this work adds to this knowledge by showing that Toll has the upper hand in anti-fungal immunity and that the strength of Toll pathway activation and its effector capacity may vary depending on the type of invading fungus. The work also highlights that certain fungi may employ a delayed switch to hyphal growth to reduce the presence of cell wall sugars as a mechanism to evade immune recognition. Overall, this work significantly adds to the knowledge of Drosophila immunity and raises some interesting questions related to the evolution of host-pathogen interactions and to the complex functions of serine protease cascades regulating Toll and melanization. This work will be of interest to a broad audience in the field of host-pathogen interactions *

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)):____ __

      This is a clearly written manuscript on the immune effector mechanisms regulating Drosophila melanogaster host defense against a broad range of fungal pathogens, including entomopathogenic and saprophytic filamentous fungi. The authors systematically dissect the contribution of major arms of Drosophila immunity, including cellular and humoral responses and melanization and potential mechanisms of cross talk using genetic tools and reporter lines. They also go into detail to characterize the contribution of upstream activators of these responses by fungal PAMPs and the role of antimicrobial effectors (AMPs) in fly susceptibility. * They conclude for no important role of phagocytosis in host defense. Instead, they find important contributions of Toll pathway mainly through the detection of fungal proteases by Persephone rather than b-glucan detection by GNBP3. They also demonstrate that Toll activation is proportional to the virulence of the fungal pathogen, showing little activation of this response by Aspergillus fumigatus. Finally, they identify melanization as another line of host defense that restricts pathogen dissemination and protects fly from invasive fungal disease. A very interesting part of this study is the identification of a virulence strategy of the obligate fungus Entomophthora muscae, which employs a vegetative development strategy, by making protoplast that avoid immune recognition by masking immunostimulatory cell wall molecules to avoid immune recognition by Toll pathway until the very last stage of invasive growth. Overall, this is a very interesting study on host-pathogen interplay in Drosophila, shedding light onto novel pathogenetic mechanism employed by entomopathogenic fungi to adapt to their hosts.** *

      We thank the reviewer for his positive assessment.

      __Major comments for the authors:____ __ 1. The use of reporter fungal strains to capture the dynamic interplay of the pathogen and the different arms of the immune system precludes firm conclusions on the contribution of various immune response to infection. This should be emphasized in the discussion* *

      Unfortunately, we did not fully understand this point. Note that we monitored both survival and when possible fungal load (B. Beauveria, E. muscae and M. anisopliae for Toll; and B. Beauveria, and M. anisopliae for melanization) allowing to state that Toll and Melanization are contributing to host defense by limiting fungal growth.

      2. The route of infection and the method employed to inject fungal spores has an impact on the effector pathways being activated. For example, pricking introduces spores less efficiently in the hemolymph compared to microinjection. The inoculum size in case of microinjection also has profound impact in understanding the role of cellular and humoral immunity during the infection course. For example, the lack of Toll activation in the natural infection with A. fumigatus does not mean that this pathway is not important in host defense against this pathogen.

      We fully agree and expected to clarify this different outcome between septic injury and natural infection. In the case of A. fumigatus, we confirm that Toll is important upon systemic infection but not natural infection because this fungus has a limited ability to penetrate insect by the natural route. We have clarified this in the text by adding the sentence: 'The low Toll pathway activation by A. fumigatus is likely due the weak ability of this fungus to penetrate insect by the natural route.'.

      3. The use of total KO strains does not preclude the cross talk of cellular and humoral immunity and consequently potential defects in cellular immunity upon deletion of a master regulator of the Toll pathway or even its downstream effectors

      The observation that Toll deficient mutants are almost as susceptibility as mutant flies lacking all the four immune modules (△ITPM ) to the five fungal pathogens point to a major role of this pathway. In a previous study (Ryckebusch et al Elife 2025), we have shown that the four immune pathways largely work independently as phagocytosis was still observed in Toll deficient mutant.

      4. Did the authors validate that NimC11; Eater1 flies are not able to phagocytose fungal spores?

      In the first version of this manuscript, we did not validate that NimC1;eater flies are phagocytic deficient also for Fungal spores although our manuscript assumed it. To address the comment of the reviewer, we have extended our study to better characterize the role of the cellular immune response to fungal infection (See new Figure S1).

      Our new results show that NimC1;eater deficient flies have defect in binding to M. anisopliae GFP spores (New Supplement Figure S1E,F). We did not see clear evidence of internalization. Thus, we conclude that the use of NimC1;eater flies is adequate to study the role of the cellular response. We have monitored the survival of hemoless flies that lack nearly all plasmatocytes due to the over-expression of the proapoptotic gene Bax, to natural infection and septic injury with B. bassiana and M. anisopliae. This new piece of data (described in New Supplementary Figure S1A-D) show that hemoless flies display a wild-type survival to B. Bassiana and a mild susceptibility to M. anisopliae consistent with our previous statement that the cellular response is less important than the humoral response. In the revised version, we have added this new piece of data and nuanced our statement on the role of the cellular response to fungal infection.

      5. Is it possible that entomopathogenic fungi bypass phagocytosis as a virulence strategy by inducing large size germinating cells, which are not phagocytosed?

      Indeed, there are several studies have showed that entomopathogenic fungi have evolved sophisticated strategies to evade or survive phagocytosis.

      • Once fungal spores (conidia) germinate, penetrate host tegument and reach the hemocoel, fungi existwithin the hemocoel in the forms of blastospores with thinner cell walls than conidia (M. anisopliae, M. rileyi, B. bassiana), and cell wall-free protoplasts (E. muscae). Wang and St Leger (2006) had demonstrated that host hemocytes can recognize and ingest conidia of M. anisopliae, but this capacity is lost on production of blastospore, because of its ability to avoid detection depending on the cell surface hydrophobic protein gene Mcl1 that is expressed within 20 min of the fungal pathogen contacting hemolymph.
      • Other studieshave shown that blastospores of B. bassiana and M. anisopliae can be phagocytosed at the early stages of infection but manage to emerge from host cells and continue to propagate. Growing hyphal bodies can deform the plasmatocyte cell membrane (Gillespie et al., 2000; Hung and Boucias, 1992; Vilcinskas et al., 1997). Studies have also shown that during the infection process of entomopathogenic fungi in insects, the hemocyte count gradually decreases. For instance, during the infection of Thitarodes xiaojinensis by Ophiocordyceps sinensis, blastospores are the initial cell type present in the host hemocoel and remained for 5 months or more before transformation into hypha, which finally led to host death; and the increase in blastospores quantity coincidence with a decline in hemocyte count (Liu et al., 2019; Li et al., 2020).<br /> In a new set of experiments, we tested the ability of plasmatocytes to phagocytose M. anisopliae-GFP spores. We observed that plasmatocytes bind to the spores, but we did not obtain clear evidence of internalization (New Figure S1E,F). However, this assay was not sufficient to conclusively determine whether plasmatocytes internalize M. anisopliae spores, as GFP fluorescence may be quenched in acidic intracellular compartments. Because entomopathogenic fungi can affect hemocyte abundance, we also monitored the expression level of Hml, a hemocyte-specific marker, in flies following natural infection with B. bassiana, M. anisopliae, M. rileyi, and E. muscae at 2, 3, and 5 days post-infection (see figure below). We did not observe a reduction in hemocyte levels for any of these fungi except M. anisopliae. This suggests that M. anisopliae may reduce hemocyte numbers as a strategy to circumvent the cellular immune response. These results, although promising, were not included in the revised version of the manuscript, as a thorough analysis of the cellular immune response would require a dedicated study on its own.

      Figure: Expression of Hml by RT-qPCR upon natural infection with entomopathogenic fungi (figure not included in the revised manuscript)

      6. Is it possible that fungal toxins kill phagocytes during germination?

      There are indeed evidences that fungal toxins destruxins (DTXs) induce ultrastructural alterations of circulating plasmatocytes and sessile haemocytes of Galleria mellonella larvae. DTXs contribute to the fungal infection process by a true immune-inhibitory effect. This is evidenced by two key findings: first, the germination rate of injected Aspergillus niger spores was slightly but significantly enhanced; second, during incubation, the fungus demonstrated a greater ability to escape from the haemocyte-formed granuloma envelope (Vilcinskas et al., 1997; Vey et al., 2002). But in Drosophila, Destruxin does not appear to affect Drosophila cellular immune responses in vivo. Phagocytosis of E. coli bacterial particles in Destruxin-injected flies appeared to be the same as that seen in PBS-injected flies. The proliferation of bacteria in the Destruxin-injected flies was due to the lower expression of antimicrobial peptide genes suggesting that Destruxin A specifically suppressed the humoral immune response in Drosophila (Pal et al., 2007), which is consistent with major role of antimicrobial peptides in survival to fungi. This point is now discussed in the discussion with a new section on the cellular response to fungal infection.

      __Reviewer #3 (Significance (Required)):____ __

      This is an important work that provide new information on virulence mechanisms of entomopathogenic fungi and the host immune responses that mediate host protection. The authors should address my comments in the discussion and provide some additional evidence by using reporter fungal strains for hemocytes on whether these fungal pathogens completely bypass phagocytosis to invade the host. Therefore, rather than claiming that phagocytosis is not important it should be clarified whether phagocytes are directly involved in host defense or whether the fungus changes its cell wall surface to avoid this line of host defense. My expertise is on phagocyte biology and host-fungal interaction on human fungal pathogens.

      We have added more information showing that plasmatocytes of NimC1;eater larvae fail to bind to spores of M. anisopliae suggesting that this line provides an appropriate tool to assess phagocytosis. We have also analyzed the survival of flies depleted for plasmatocytes via the over-expression of bax, which revealed a mild role for plasmatocyte in defense against M. anisopliae but not B. bassiana. By performing additional experiments, we realized that analyzing the role of cellular immunity in host defense against these five fungi would require much more work and is beyond the scope of this study. We have however added in the revised version a para in the discussion on the the cellular response.

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      Referee #3

      Evidence, reproducibility and clarity

      This is a clearly written manuscript on the immune effector mechanisms regulating Drosophila melanogaster host defense against a broad range of fungal pathogens, including entomopathogenic and saprophytic filamentous fungi. The authors systematically dissect the contribution of major arms of Drosophila immunity, including cellular and humoral responses and melanization and potential mechanisms of cross talk using genetic tools and reporter lines. They also go into detail to characterize the contribution of upstream activators of these responses by fungal PAMPs and the role of antimicrobial effectors (AMPs) in fly susceptibility.

      They conclude for no important role of phagocytosis in host defense. Instead, they find important contributions of Toll pathway mainly through the detection of fungal proteases by Persephone rather than b-glucan detection by GNBP3. They also demonstrate that Toll activation is proportional to the virulence of the fungal pathogen, showing little activation of this response by Aspergillus fumigatus. Finally, they identify melanization as another line of host defense that restricts pathogen dissemination and protects fly from invasive fungal disease. A very interesting part of this study is the identification of a virulence strategy of the obligate fungus Entomophthora muscae, which employs a vegetative development strategy, by making protoplast that avoid immune recognition by masking immunostimulatory cell wall molecules to avoid immune recognition by Toll pathway until the very last stage of invasive growth. Overall, this is a very interesting study on host-pathogen interplay in Drosophila, shedding light onto novel pathogenetic mechanism employed by entomopathogenic fungi to adapt to their hosts.

      Major comments for the authors:

      1. The use of reporter fungal strains to capture the dynamic interplay of the pathogen and the different arms of the immune system precludes firm conclusions on the contribution of various immune response to infection. This should be emphasized in the discussion
      2. The route of infection and the method employed to inject fungal spores has an impact on the effector pathways being activated. For example, pricking introduces spores less efficiently in the hemolymph compared to microinjection. The inoculum size in case of microinjection also has profound impact in understanding the role of cellular and humoral immunity during the infection course. For example, the lack of Toll activation in the natural infection with A. fumigatus does not mean that this pathway is not important in host defense against this pathogen.
      3. The use of total KO strains does not preclude the cross talk of cellular and humoral immunity and consequently potential defects in cellular immunity upon deletion of a master regulator of the Toll pathway or even its downstream effectors
      4. Did the authors validate that NimC11; Eater1 flies are not able to phagocytose fungal spores?
      5. Is it possible that entomopathogenic fungi bypass phagocytosis as a virulence strategy by inducing large size germinating cells, which are not phagocytosed?
      6. Is it possible that fungal toxins kill phagocytes during germination?

      Significance

      This is an important work that provide new information on virulence mechanisms of entomopathogenic fungi and the host immune responses that mediate host protection. The authors should address my comments in the discussion and provide some additional evidence by using reporter fungal strains for hemocytes on whether these fungal pathogens completely bypass phagogytosis to invade the host. Therefore, rather than claiming that phagocytosis is not important it should be clarified whether phagocytes are directly involved in host defense or whether the fungus changes its cell wall surface to avoid this line of host defense. My expertise is on phagocyte biology and host-fungal interaction on human fungal pathogens.

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      Referee #2

      Evidence, reproducibility and clarity

      In this work the authors describe the contribution of distinct immune responses in Drosophila melanogaster to systemic and natural infections with 5 fungal species with different lifestyles some being generalists infecting a broad range of insects while others being more specialists or opportunistic. The authors used several well characterized Drosophila mutants of the Toll, Imd, phagocytosis and melanization responses to address this question. They show that Toll pathway is the key player in anti-fungal resistance in both natural and septic infections, whereas melanization plays a minor role mainly during natural infections possibly to limit fungal invasion through the cuticle. The authors show elegantly using different combinations of mutants for antimicrobial peptides genes with antifungal activities that Bomanins and Daisho (1 and 2) are the main Toll effectors mediating resistance to fungi but the authors did not find specific fungus-by-gene interaction, but rather antifungal peptides seem to act in a more general fashion against the fungi tested with significant redundancies between certain classes. Interestingly the authors show that while generalists like Beauveria and Metarhizium strongly activate the Toll pathway, the specialist E. muscae weakly activates the pathway and the opportunistic A. fumigatus does not activate the pathway, indicating that certain fungal species are able to evade sensing by immune pathways. In the context of the Toll activation, the sensor protease Psh and not GNBP3 seem to be the main trigger of the pathway.

      Minor comments

      This is an interesting work that compares the contributions of different arms of the fly immune response to 5 fungal species with diverse lifestyles. The use of different lines with different combinations of mutant genes is a strength to highlight the relative contribution of each immune response. Some of the data obtained is intriguing and warrants more future investigations such as the distinct phenotypes of ModSp and GNBP3 mutants in E. muscae infections. The methodology is robust and the conclusions are supported with good experimental evidence. I do not see any major concerns with the work. I just have some minor comments listed below

      1. Statistical significance should be indicated on Figures 1 and 2, although it appears in the legend.
      2. It is not very accurate to use the term resistance of the different mutants to infections with the diverse fungal species in Figures 1 and 2 especially that the authors have reported only survival data in these figures and have not measured fungal proliferation in infected flies (although they did that in later figures). It is more accurate to mention that the mutants flies have different levels of tolerance rather than resistance to fungal infections.
      3. The authors show that Toll is over-activated in PPO1/PPO2 double mutant possibly through a negative feedback mechanism. However, there could be another explanation for this observation: For instance, the increased fungal proliferation in the PPO double mutant results in increased protease secretion by fungi enhancing Psh activation! Also, how can fungi manage to proliferate in this double mutant if Toll is overactivated? Could it be that Toll overactivation is triggering a fitness cost?
      4. In Lines 654-655, it is not accurate to say that E. muscae protoplasts are not detected by the immune response since E. muscae natural infections triggers Drs expression at 24 hpi and there is possibly some melanization taking place since PPO1 and PPO2 are required for defense against this fungus. A more accurate explanation is that this fungus is possibly more resistant to the effectors of the host immune response than the other fungi. I think a major point that the authors might have missed to consider in the discussion of their data is that the different fungi used herein may exhibit different levels of resilience to the effector reactions of the host such as AMPs and melanin deposition

      Significance

      Although the importance of Toll pathway and melanization in antifungal immunity is not new per se, this work adds to this knowledge by showing that Toll has the upper hand in anti-fungal immunity and that the strength of Toll pathway activation and its effector capacity may vary depending on the type of invading fungus. The work also highlights that certain fungi may employ a delayed switch to hyphal growth to reduce the presence of cell wall sugars as a mechanism to evade immune recognition. Overall, this work significantly adds to the knowledge of Drosophila immunity and raises some interesting questions related to the evolution of host-pathogen interactions and to the complex functions of serine protease cascades regulating Toll and melanization. This work will be of interest to a broad audience in the field of host-pathogen interactions

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      Referee #1

      Evidence, reproducibility and clarity

      The key conclusions are solid. All the claims are supported by quality data. The content is rich, and no additional experiment is needed. The data and methods are properly presented for reproduction. The experiments are adequately replicated. One comment on statistical analysis is listed below.

      Summary:

      This manuscript investigates how Drosophila immune pathways contribute to defense against a range of filamentous fungi with distinct ecological strategies. The work providesovel insights into Toll pathway activation through pattern recognition receptors and danger signals, relative roles of melanization, phagocytosis, and effects of antimicrobial peptides, and particularly the immune evasion strategy of E. muscae via protoplast formation. These findings are of broad relevance to insect immunology, host-pathogen interactions, and evolutionary biology. The study is well designed, the experiments are carefully executed, and the manuscript is clearly written. It is novel to demonstrate that E. muscae evades immune recognition via protoplast formation. However, some aspects of clarity and discussion of limitations could be improved before publication.

      Major comments:

      1. The Abstract is informative but a bit too long. Consider condensing some sentences and highlighting the novel contributions (e.g., role of protoplasts in immune evasion.).
      2. The Results may use more mechanistic links. For instance, the section on E. muscae immune evasion could more explicitly connect the morphological findings (protoplasts, lack of cell wall) with specific immune recognition failures.
      3. Please clarify statistical analyses used for survival data (e.g., log-rank tests, multiple testing corrections).

      Minor comments:

      Abstract: 1) "The infection outcome depends on the complex interplay between insect immune defenses and fungal adaptive strategies." could be simplified to: "Infection outcomes depend on the interplay between insect immunity and fungal adaptation." 2) Replace "our study uncovers" with "we show" for more concise phrasing. Reduce phrases like "our study reveals" or 'we conclude" in other parts of the manuscript. Results: p. 5: phrase "survival upon natural infection... reveals the major contribution" → reword to avoid passive tone. p. 10: clarify "vesicles push the membrane outwards" with more precise terminology (e.g., budding, extrusion). Discussion: p. 20: streamline sentence beginning "These observations provide a mechanistic basis..." (currently too dense).

      Referee cross-commenting

      I agree with the comments of the other two reviewers.

      Significance

      This manuscript investigates how Drosophila immune pathways contribute to defense against a range of filamentous fungi with distinct ecological strategies (generalists, specialists, opportunists). By leveraging a comprehensive panel of genetically defined fly lines and standardized infections, the authors provide a demonstration that the Toll pathway is the predominant systemic antifungal defense, extending classical findings into a comparative framework across fungal lifestyles. The work provides novel insights into Toll pathway activation through GNBP3 and fungal proteases sensed by Psh, while also dissecting the relative contributions of melanization, phagocytosis, and antimicrobial peptides to host protection. Of particular note is the compelling demonstration that the fly specialist E. muscae can evade immune recognition through protoplast-like vegetative forms, minimizing cell-wall exposure and thereby escaping Toll activation.

      My expertise and limitations:

      Insect biochemistry and molecular biology, with particular focus on innate immunity, serine protease cascades, melanization, and host-pathogen interactions. I also have experience with genetic, biochemical, and functional approaches to dissecting immune signaling pathways in model insects. However, I do not have sufficient expertise to critically evaluate advanced statistical analyses.

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      Reply to the reviewers

      Response to Reviewer 1:

      The authors introduce G2PT, a hierarchical graph transformer model that integrates genetic variants (SNPs), gene annotations, and multigenic systems (Gene Ontology) to predict and interpret complex traits.

      We thank the reviewer for this accurate summary of our approach and contributions.

      Major Comments:

      Comment 1-1. Insufficient Specification of Model Architecture: The description of the "hierarchical graph transformer" lacks technical depth. Key implementation details are missing: how node embeddings are initialized for SNPs, genes, and systems; how graph connectivity is defined at each level (e.g., adjacency matrices used in Equations 5-9, the sparsity); justification for the choice of embedding dimension and number of attention heads, including any sensitivity analysis; and the architecture of the feed-forward neural networks (e.g., number of layers, activation functions, and hidden dimensions).

      __Reply 1-1. __As requested, we have expanded the technical description of the model architecture, including the hierarchical graph transformer (HiGT), in the Materials and Methods section. Details regarding node initialization and hierarchical connectivity are now included in the new paragraph "Model Initialization and Graph Construction." Specifically, all node embeddings corresponding to SNPs, genes, and ontology-defined systems are initialized using uniform Xavier initialization (Glorot and Bengio, 2010).

      We have also clarified our hyperparameter optimization strategy. Learning rate, weight decay, hidden (embedding) dimension, and the number of attention heads were selected via grid search, as summarized in new Supplementary Fig. 8, reproduced below. Based on both performance and computational efficiency, we adopted four attention heads-consistent with the configuration commonly used in academic transformer models (Vaswani et al., 2017) (the original Transformer used eight).

      Regarding the feed-forward neural network, we follow the standard Transformer architecture consisting of two position-wise layers with hidden dimension four times larger than the node embedding size and a GeLU nonlinear activation function (Hendrycks and Gimpel, 2016). This configuration is widely established in the literature and functions as an intermediate processing step following attention; therefore, it is not a focus of hyperparameter tuning. All corresponding updates have been incorporated into the revised Methods section for clarity and completeness.

      Comment 1-2. No Simulation Studies to Validate Epistasis Detection: The ground truth epistasis interaction should use the ones that have been manually validated by literature. The central claim of discovering epistatic interactions relies heavily on the model's attention mechanism and downstream statistical filtering. However, no simulation studies are presented to validate that G2PT can reliably detect epistasis when ground-truth interactions are known. Demonstrating robust detection of non-additive interactions under varying genetic architectures and noise levels in simulated genotype-phenotype datasets is essential to substantiate the method's core capability.

      Reply 1-2. We agree that a simulation of epistasis detection using the G2PT model is a worthy addition to the manuscript. Accordingly, we have now incorporated a new section in the Results titled "Validation of Epistasis through Simulation Studies", which includes two new figures reproduced below (Supplementary Fig. 6 and Fig. 5). We have also added a new Methods section to describe this simulation study under the heading "Epistasis Simulation". These simulation studies show that G2PT recovers epistatic gene pairs with high fidelity when these pairs are coherent with the systems ontology (c.f. 'ontology coherence' in Supplementary Fig. 6, which reflects the probability that both SNPs are assigned to the same leaf system). Furthermore, G2PT outcompetes previous tools, such as PLINK-epistasis, which do not use knowledge of the systems hierarchy in the same way (Supplementary Fig 6b-d). Using simulation parameters consistent with current genome-wide association studies (n = 400,000) and understanding of heritability (h2 = 0.3 to 0.5) (Bloom et al. 2015; Speed and Evans 2023), we find that approximately 10% of all epistatic SNP pairs can be recovered at a precision of 50% (Fig. 5). We have provided the source code for this simulation study in our GitHub repository (https://github.com/idekerlab/G2PT/blob/master/Epistasis_simulation.ipynb)

      Comment 1-3. Lack of Justification for Model Complexity and Missing Ablation Insights: While Supplementary Figure 2 presents ablation studies, the manuscript needs to justify the high computational cost (168 GPU hours using 4×A30 GPUs) of the full model. It remains unclear how much performance gain is specifically due to reverse propagation (Equations 8-9), which is claimed to capture biological context. The benefit of using a full Gene Ontology hierarchy versus a flat system list is not quantified. There is also no comparison between bidirectional versus unidirectional propagation. Overall, the added complexity is not empirically shown to be necessary

      Reply 1-3. We thank the reviewer for prompting a clearer justification of complexity and ablations. We have now revised the Results to (i) quantify the specific value of the ontology and reverse propagation, and (ii) explain why a flat SNP→system model is computationally and biologically sub-optimal. We have added new ablation results to compare bidirectional (forward+reverse) versus forward-only propagation. Reverse propagation has little effect when epistatic pairs are within one system (ontology coherence ρ=1.0) but substantially improves retrieval when interactions span related systems (e.g., ρ≈0.8) (Figure reproduced below) A flat design scores a dense genes×systems map, ignoring known sparsity (sparse SNP→gene assignments; sparse ontology edges) and losing multi-scale context; our hierarchical formulation restricts computation to observed edges (SNP→gene→system) and aggregates signals across levels, yielding better efficiency and biological fidelity.

      Comment 1-4. Non-Equivalent Benchmarking Against PRS Methods: Figure 2 compares G2PT to polygenic risk score (PRS) methods such as LDpred2 and Lassosum, but G2PT is run only on SNPs pre-filtered by marginal association (p-values between 10⁻⁵ and 10⁻⁸), while the PRS methods use genome-wide SNPs. This introduces a strong bias in G2PT's favor by effectively removing noise. A fair comparison would require: (a) running LDpred2 and Lassosum on the same pre-filtered SNP sets as G2PT, or (b) running G2PT on genome-wide or LD-pruned SNP sets. The reported superior performance of G2PT may be driven primarily by this input filtering, not the model architecture.

      Reply 1-4. We appreciate the reviewer's concern regarding benchmarking equivalence. In response, we have extended our analyses to include PRS-CS (Ge et al., 2019) and SBayesRC (Zheng et al., 2024), two state-of-the-art Bayesian shrinkage methods comparable to LDpred2 and Lassosum. Although we initially attempted to run LDpred2 and Lassosum under all SNP-filtering conditions, their computational requirements at UK Biobank scale proved prohibitively time consuming. We therefore focused on PRS-CS and SBayesRC, which offer similar modeling principles with greater computational tractability. These methods have now been run at matched SNP-filtering conditions to our original study. The new results demonstrate that G2PT consistently outperforms PRS-CS and SBayesRC (new Fig. 2, reproduced below), indicating that its performance advantage is not solely attributable to SNP pre-filtering but also to its hierarchical attention-based architecture.

      Comment 1-5: No Details on Hyperparameter Optimization: Although the manuscript mentions grid search for hyperparameter tuning, it provides no information about which parameters were optimized (e.g., learning rate, dropout rate, weight decay, attention dropout, FFNN dimensions), what search space was explored, or what final values were selected. There is also no assessment of how sensitive the model's performance is to these choices. Better transparency would help facilitate reproducibility

      Reply 1-5. We agree with the reviewer and have expanded the manuscript to include full details of hyperparameter optimization. As described in the revised Methods section, we performed a grid search over learning rate {10−3,10−4,10−5} hidden dimension {64,128} and weight decay {0,10−5,10−3}. The results, summarized in Supplementary Fig. 8 (reproduced above), show that model performance is most sensitive to the learning rate, while hidden dimension and weight decay exert more moderate effects. Based on these findings, we selected a learning rate of 10−5, hidden dimension of 64, and weight decay of 10−3 for all subsequent experiments. Although a hidden dimension of 128 slightly improved performance, we adopted 64 to balance predictive accuracy with computational efficiency.

      Comment 1-6. Absence of Control for Key Confounders: In interpreting attention scores as reflecting genetic relevance (e.g., the role of the immunoglobulin system), the model includes only age, sex, and genetic principal components as covariates. Important confounders such as BMI, alcohol use, or medication (e.g., statins) have not been controlled for. Since TG/HDL levels are strongly influenced by environment and lifestyle, it is entirely plausible that some high-attention features reflect environmental tagging, not biological causality.

      Reply 1-6. In the current framework, we included age, sex, and genetic principal components to account for demographic and population-structure effects, focusing on genetic contributions within a controlled baseline. We acknowledge that non-genetic covariates can influence downstream biological states and may indirectly shape attention at the gene or system level. Accurately modeling such effects requires an extended framework where environmental variables directly modulate gene and system embeddings rather than being implicitly absorbed by the attention mechanism. We have clarified these limitations in the Discussion along with plans to incorporate explicit confounder modeling in future extensions of G2PT.

      Comment 1-7. Oversimplified Treatment of SNP-to-Gene Mapping: The SNP-to-gene mapping strategy combines cS2G, eQTL, and nearest-gene annotations, but the limitations of this approach are not adequately addressed. The manuscript does not specify how conflicts between methods are resolved or what fraction of SNPs map ambiguously to multiple genes. Supplementary Figure 2 shows model performance degrades when using only nearest-gene mapping, but there is no systematic analysis of how mapping uncertainties propagate through the hierarchy and affect attention or interpretation.

      Reply 1-7. In the revision (Results), we have clarified how conflicts between cS2G, eQTL, and nearest-gene annotations are resolved, and we have reported the proportion of SNPs that map to multiple genes across these three annotation approaches. We note that the hierarchical attention mechanism enables the model to prioritize among alternative gene mappings in a data-driven manner, and this is a major strength of the approach. As shown in Fig. 3 (Results, reproduced below), SNP-to-gene attention weights reveal dominant linkages, reducing the impact of mapping uncertainty on interpretation. We now explicitly describe this mechanism and acknowledge that further work in probabilistic mapping and fine-mapping approaches is a valuable future direction for improving resolution and interpretability.

      "For SNPs with several potential SNP-to-gene mappings (Methods), we found that G2PT often prioritized one of these genes in particular due to its membership in a high-attention system. For example, the chr11q23.3 locus contains multiple genes including the APOA1/C3/A4/A5 gene cluster (Fig. 3c) which is well-known to govern lipid transport, an important system for G2PT predictions (Fig. 3a). Due to high linkage disequilibrium in the region, all of its associated SNPs had multiple alternative gene mappings available. For example, SNP rs1145189 mapped not only to APOA5 but to the more proximal BUD13, a gene functioning in spliceosomal assembly (a system receiving substantially lower G2PT attention). Here, the relevant information flow learned by G2PT was from rs1145189 to APOA5 to lipid transport and protein-lipid complex remodeling (Fig. 3c; and conversely, deprioritizing BUD13 as an effector gene for TG/HDL). We found that this particular genetic flow was corroborated by exome sequencing, which implicates APOA5 but not BUD13 in regulation of TG/HDL, using data that were not available to G2PT. Similarly, two other SNPs at this locus - rs518547 and rs11216169 - had potential mappings to their closest gene SIK3, where they reside within an intron, but also to regulatory elements for the more distant lipid transport genes APOC3 and APOA4. Here, G2PT preferentially weighted the mappings to APOC3 and APOA4 rather than to SIK3 (Fig. 3c)."

      Comment 1-8. Naive Scoring of System Importance: The method used to quantify the biological relevance of systems (i.e., correlating attention scores with predicted phenotype values) risks circular reasoning. Since the model is trained to optimize prediction, systems that contribute strongly to prediction will naturally show high correlation-even if they are not biologically causal. No comparison is made with established gene set enrichment methods applied to GWAS summary statistics. The approach lacks an independent benchmark to validate that the "important" systems are biologically meaningful.

      Reply 1-8. As requested, we compared G2PT's system-level importance scores with results from MAGMA competitive gene-set analysis, an established enrichment approach. This analysis indeed shows significant correlation between the systems identified by the two approaches (ρ = 0.26, p .01; Supplementary Table. 2), reflecting a shared emphasis on canonical lipid processes. We also observed systems detected by G2PT but not strongly detected by MAGMA's linear enrichment model-for example, the lipopolysaccharide-mediated signaling pathway (Kalita et al. 2022)

      Comment 1-9. No External Validation to Assess Generalizability. All evaluations are performed using cross-validation within the UK Biobank. There is no assessment of generalizability to independent cohorts or diverse ancestries. Given population structure, genotyping platform, and phenotype measurement variability, external validation is essential before claiming the method is suitable for broader use in polygenic risk assessment.

      Reply 1-9. To externally validate the G2PT model requires individual level genotype data with paired TG/HDL measurements, sample size at the scale of the UK Biobank, and GPU access to this data. Thus, we approached the All of Us program, a large and diverse cohort with individual level data and T2D conditions with HbA1C measurements. We first processed the All of Us genotype and phenotype data as we had processed UKBB data (Methods), resulting in 41,849 participants with T2D and 80,491 without T2D across various ethnicities. We then transferred the trained T2D G2PT model to the AoU Workbench and evaluated its performance. The model demonstrated robust discriminative capability with an explained variance of 0.025, as shown in the new Fig. 2d, (reproduced above).

      Comment 1-10. Computational Burden and Scalability Are Not Addressed: The paper notes that training the model requires 168 GPU hours on 4×A30 GPUs for just ~5,000 SNPs. However, there is no discussion of whether G2PT can scale to larger SNP sets (e.g., genome-wide imputed data) or more complex biological hierarchies (e.g., Reactome pathways). Without addressing scalability, the model's applicability to real-world, large-scale genomic datasets remains unclear.

      Reply 1-10. We have addressed scalability with both engineering optimizations and new scalability experiments. First, we refactored the model to use the xFormer memory-efficient attention for the hierarchical graph transformer (Lefaudeux et al., 2022), which also helps full parallelization of training, reducing bottlenecks. Second, we added a scaling study with progressively increasing SNP count. On 4×A30 GPUs, end-to-end training time for the 5k-SNP setting decreased from 4000 to 400 min. (approximately 7 GPU-hours, ×10). These new results are given in Supplementary Fig. 7, reproduced below.

      Minor Comment:

      Comment 1-11. Attention Weights as Mechanistic Insight: The paper equates high attention scores with biological importance, for example in highlighting the immunoglobulin system. There is no causal validation showing that altering the highlighted SNPs, genes, or systems has an actual effect on TG/HDL. Attention weights in transformer models are known to sometimes reflect spurious correlations, especially in high-dimensional settings. The correlation between attention scores and predictions (Supplementary Fig. 3a,b) does not constitute biological evidence. The interpretability claims can be restated without supporting functional or causal validation.

      Reply 1-11. We thank the reviewer for this thoughtful comment. We agree that attention weights are not causal evidence. In the revision, we (1) reframe attention-based findings as hypothesis-generating rather than mechanistic, and (2) add an explicit limitation noting that correlations between attention scores and predictions do not constitute biological validation.

      Response to Reviewer 2:

      This manuscript describes the introduction of the Genotype-to-Phenotype Transformer (G2PT), described by the authors as "a framework for modeling hierarchical information flow among variants, genes, multigenic systems, and phenotypes." The authors used the ratio TG/HDL as a trait for proof of concept of this tool.

      This is a potentially interesting computational tool of interest to bioinformaticians, computational genomicists, and biologists.

      We thank the reviewer for their overall positive assessment of our study.

      Comment 2-1. The rationale for choosing the TG/HDL ratio for this proof of concept analysis is not well justified beyond it being a marker for insulin resistance. Overall the use of a ratio may be problematic (see below). Analyses of TG and HDL separately as individual quantitative traits would be of interest. And an analysis of a dichotomous clinical trait (T2DM or CAD) would also be of great interest.

      Reply 2-1. We thank the reviewer for this suggestion. In the revised manuscript, we have expanded our analyses beyond the TG/HDL ratio to include TG and HDL as individual quantitative traits (Fig. 2, reproduced below). These additional analyses demonstrate that G2PT captures predictive signals robustly across each lipid component, not solely through their ratio. Furthermore, to address the reviewer's interest in clinical outcomes, we incorporated an analysis of type 2 diabetes (T2D) as a dichotomous trait of direct clinical relevance. Collectively, these results strengthen the rationale for our chosen phenotype and show that the G2PT framework generalizes effectively across quantitative and binary traits, consistently outperforming advanced PRS and machine learning benchmarks.

      Comment 2-2. The approach to mapping SNPs to genes does not incorporate the most advanced approaches. This should be described in more detail.

      Reply 2-2. We agree that the choice of SNP-to-gene mapping materially affects both performance and interpretability-indeed, our epistasis simulations suggest that more accurate mappings can improve recovery and localization. In this proof-of-concept work we use a straightforward, modular mapping sufficient to demonstrate the modeling framework, and we have clarified this in the Methods. The architecture is designed to plug-and-play alternative SNP-to-gene maps (e.g., eQTL/colocalization-based assignments, promoter-capture Hi-C). A dedicated follow-up study will systematically compare these alternatives and quantify their impact on attribution and downstream discovery.

      Comment 2-3. The example of gene prioritization at the A1/C3/A4/A5 gene locus is not particularly illuminating, as the prioritized genes are already well-known to influence TG and HDL-C levels and the TG/HDL ratio. Can the authors provide an example where G2PT prioritized a gene at a locus that is not already a well-known regulator of TG and HDL metabolism?

      Reply 2-3. We thank the reviewer for this suggestion. We have revised the manuscript to de-emphasize the well-established APOA1 locus and instead highlight the less expected "Positive regulation of immunoglobulin production" system (Figure 3a,b, Discussion). Here our model prioritizes the gene TNFSF13 based on specific variants that are not previously associated with TG or HDL (e.g., rs5030405, rs1858406, shown in blue). This finding points to an intriguing, non-canonical link between B-cell regulation and lipid metabolism. While full exploration of this finding is beyond the scope of the present methods paper, this example demonstrates G2PT's ability to identify novel, high-priority candidates in atypical systems.

      Comment 2-4. The identification of epistatic interactions is a potentially interesting application of G2PT. However, suppl table 1 shows a very limited number of such interactions with even fewer genes, and most of these are well established biological interactions (such as LPL/apoA5). The TGFB1 and FKBP1A interaction is interesting and should be discussed. What is needed for increasing the number of potential interactions, greater power?

      Reply 2-4. We are glad the reviewer appreciates the use of the G2PT model to identify epistatic interactions. We have now discussed a potential mechanism of epistasis between TGFB1 and FKBP1A in the protein dephosphorylation system (Discussion). In addition, we have addressed the reviewer's question about statistical power through extensive epistasis simulations (Fig. 5 and Supplementary Fig. 6), which show that G2PT's detection ability scales strongly with sample size-1,000 samples are insufficient, performance improves at 5,000, and power becomes reliable at 100,000. Realistic simulations (Fig. 5b-d) further demonstrate that under biologically plausible architectures, G2PT can robustly recover specific interactions even within complex genetic backgrounds

      Comment 2-5. Furthermore, the use of the TG/HDL ratio for the assessment of epistatic interactions may be problematic. For example, if one SNP affected only TG and the other only HDL-C, it would appear to be an epistatic interaction with regard to the ratio, although the biological epistasis may be limited to non-existent.

      Reply 2-5. We have greatly expanded the example phenotypes modeled in our study, Please see our reply 2-1 above.

      Response to Reviewer 3:

      This manuscript by Lee et al provides a sensible and powerful approach to polygenic score prediction. The model aggregates information from SNPs to genes to systems, using a transformer based architecture, which appears to increase predictive performance, produce interpretable outputs of genes and systems that underlie risk, and identify candidates for epistasis tests.

      I think the manuscript is clear and well written, and conducted via state-of-the-art approaches. I don't have any concerns regarding the claims that are made.

      We thank the reviewer for their very positive assessment of our study.

      Major comments:

      Comment 3-1. Specifically, lipid based traits are perhaps the most well-powered and the most biologically coherent; they are also very well-studied biologically and thus overrepresented in the gene ontology. It is unclear whether this approach will work as well for a trait like Schizophrenia for which the underlying pathways are not as well captured in existing ontologies. The authors anticipate this in their limitations section, and I am not expecting them to solve every issue with this, but it would be nice to expand the testing a little bit beyond only this one trait.

      Reply 3-1. We appreciate the reviewer's suggestion to expand beyond a single lipid trait. In the revised manuscript, we have included analyses of additional phenotypes, including low-density lipoprotein (LDL) and T2D (Fig. 2). These additions demonstrate the broader applicability of our framework beyond a single trait class.

      Comment 3-2. It also seems like the authors have not compared their method to the truly latest PRS methods, such as PRS-CSx and SBayesR. I would suggest adding some of the methods shown to be the best from this recent paper: https://www.nature.com/articles/s41598-025-02903-1

      Reply 3-2. We agree these are important comparators. Accordingly, we have extended our comparison to include PRS‑CS (Ge et al., 2019) and SBayesRC (Zheng et al., 2024), following its strong performance demonstrated in recent benchmarking studies (see Figure 2 above). We confirmed that G2PT outperforms advanced PRS methods for all TG/HDL ratio, LDL, and T2D phenotypes.

      Comment 3-3. Another major comment regards whether this method could be applied to traits with just GWAS summary statistics, rather than individual level data. This would not enable identification of specific methods underlying an individual, but it could still learn SNP based weights that could be mapped to genes and systems that could help explain risk when the model is applied to individuals (kind of like a pretraining step?)

      Reply 3-3. We appreciate this suggestion. While SNP weights from GWAS summary statistics could, in principle, serve as informative priors for attention values, incorporating them would require a sophisticated mathematical formulation that is beyond the scope of this study. Our current framework also relies on individual-level genotype and phenotype data to capture multilevel information flow and individual-specific variation.

      Minor comments:

      Comment 3-4. Why the need to constrain to a small number of SNPs? Is it just computational cost? If so, what would happen as power increases and more SNPs exceed the thresholds used?

      Reply 3-4. Yes, it's about computational cost, but we've now modified the code for improved computational efficiency. First, we refactored the model to use the xFormer memory-efficient attention for the hierarchical graph transformer (Lefaudeux et al., 2022), which also helps full parallelization of training, reducing bottleneck effects. Second, we added a scaling study of the impact of varying SNP count. On 4×A30 GPUs, end-to-end training time for the 5k-SNP setting decreased from 65 hours to 7 GPU-hours (×9). We expect performance can potentially increase if more SNPs are provided to the model based on Fig. 2 (reproduced above). With the optimized implementation, users can raise SNP thresholds as power increases; the expected behavior is improved accuracy up to a plateau, while hierarchical sparsity maintains training tractability and ensures well-regularized results.

      Comment 3-5. What type of sample size/power does this method require to work well? If others were to use it, how many SNPs/samples would be needed to obtain good performance?

      Reply 3-5. To address this comment, we quantified performance as a function of training size by subsampling the cohort and retraining G2PT with identical architecture and SNP set. New Supplementary Fig. 3 (reproduced below) shows monotonic gains with sample size across three representative phenotypes. We found that stable performance is reached by ~100k samples. These trends hold for continuous traits (TG/HDL, LDL) and more modestly for a binary trait (T2D), consistent with lower per-sample information for case-control settings.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Lee et al provides a sensible and powerful approach to polygenic score prediction. The model aggregates information from SNPs to genes to systems, using a transformer based architecture, which appears to increase predictive performance, produce interpretable outputs of genes and systems that underlie risk, and identify candidates for epistasis tests.

      I think the manuscript is clear and well written, and conducted via state-of-the-art approaches. I don't have any concerns regarding the claims that are made.

      My two major comments regard a question about how well this will work when compared to other approaches for other traits besides TG:HDL. Specifically, lipid based traits are perhaps the most well-powered and the most biologically coherent; they are also very well-studied biologically and thus overrepresented in the gene ontology. It is unclear whether this approach will work as well for a trait like Schizophrenia for which the underlying pathways are not as well captured in existing ontologies. The authors anticipate this in their limitations section, and I am not expecting them to solve every issue with this, but it would be nice to expand the testing a little bit beyond only this one trait.

      Therefore, I would suggest that the authors test a limited number of additional traits that are not lipid based traits, and ideally not metabolic traits, to see how their model behaves. I would pick well-powered GWAS with a lot of associations but from a different phenotypic category

      It also seems like the authors have not compared their method to the truly latest PRS methods, such as PRS-CSx and SBayesR. I would suggest adding some of the methods shown to be the best from this recent paper: https://www.nature.com/articles/s41598-025-02903-1

      Another major comment regards whether this method could be applied to traits with just GWAS summary statistics, rather than individual level data. This would not enable identification of specific methods underlying an individual, but it could still learn SNP based weights that could be mapped to genes and systems that could help explain risk when the model is applied to individuals (kind of like a pretraining step?)

      Other minor comments:

      Why the need to constrain to a small number of SNPs? Is it just computational cost? If so, what would happen as power increases and more SNPs exceed the thresholds used?

      What type of sample size/power does this method require to work well? If others were to use it, how many SNPs/samples would be needed to obtain good performance?

      Will this work just as well for binary diseases? Is this a straightforward extension of the method or does it require more work?

      Since I think a lot of geneticists will read it, more intuition as to how attention weights map to parameters geneticists think about would be useful, in particular how the graphics in Fig 3 are made (this may be second nature to ML experts but may not be obvious to statistical geneticists)

      The authors claim that G2PT identifies epistatic interactions. Is this true or does it just identify pairs of SNPs that could be subsequently tested for epistasis?

      Significance

      This study does a great job of marrying the latest (interesting) technologies in AI/ML with a specific problem in statistical genetics. The clarity of presentation and interpretability of the model are strong. The main areas for improvement are to clarify how general this approach is -- will it work for other traits, is it truly better than the latest PRS methods, and what are the specifics of the GWAS it requires (sample size, individual-level data, power, type of trait)

      I think the main advance is therefore currently conceptual, but not yet practical, unless more performance comparisons were done.

      It seems like the main audience would be geneticists, since I suspect most AI/ML researchers are familiar with this type of approach. If there are fundamental innovations in applying transformers in this specific way to genetics, that would be good to highlight in more depth.

      My expertise: statistical genetics and computer science, familiar with DNNs but not a practitioner in them.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript describes the introduction of the Genotype-to-Phenotype Transformer (G2PT), described by the authors as "a framework for modeling hierarchical information flow among variants, genes, multigenic systems, and phenotypes." The authors used the ratio TG/HDL as a trait for proof of concept of this tool.

      Specific comments:

      1. The rationale for choosing the TG/HDL ratio for this proof of concept analysis is not well justified beyond it being a marker for insulin resistance. Overall the use of a ratio may be problematic (see below). Analyses of TG and HDL separately as individual quantitative traits would be of interest. And an analysis of a dichotomous clinical trait (T2DM or CAD) would also be of great interest.
      2. The approach to mapping SNPs to genes does not incorporate the most advanced approaches. This should be described in more detail.
      3. The example of gene prioritization at the A1/C3/A4/A5 gene locus is not particularly illuminating, as the prioritized genes are already well-known to influence TG and HDL-C levels and the TG/HDL ratio. Can the authors provide an example where G2PT prioritized a gene at a locus that is not already a well-known regulator of TG and HDL metabolism?
      4. The identification of epistatic interactions is a potentially interesting application of G2PT. However, suppl table 1 shows a very limited number of such interactions with even fewer genes, and most of these are well established biological interactions (such as LPL/apoA5). The TGFB1 and FKBP1A interaction is interesting and should be discussed. What is needed for increasing the number of potential interactions, greater power?
      5. Furthermore, the use of the TG/HDL ratio for the assessment of epistatic interactions may be problematic. For example, if one SNP affected only TG and the other only HDL-C, it would appear to be an epistatic interaction with regard to the ratio, although the biological epistasis may be limited to non-existent.

      Significance

      This is a potentially interesting computational tool of interest to bioinformaticians, computational genomicists, and biologists.

      The proof of concept offered here using a single ratio is not sufficient to conclude its potential utility.

      My expertise is in genetics and molecular mechanisms of lipid metabolism.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors introduce G2PT, a hierarchical graph transformer model that integrates genetic variants (SNPs), gene annotations, and multigenic systems (Gene Ontology) to predict and interpret complex traits.

      Major Comments:

      1. Insufficient Specification of Model Architecture: The description of the "hierarchical graph transformer" lacks technical depth. Key implementation details are missing: how node embeddings are initialized for SNPs, genes, and systems; how graph connectivity is defined at each level (e.g., adjacency matrices used in Equations 5-9, the sparsity); justification for the choice of embedding dimension and number of attention heads, including any sensitivity analysis; and the architecture of the feed-forward neural networks (e.g., number of layers, activation functions, and hidden dimensions).
      2. No Simulation Studies to Validate Epistasis Detection: The ground truth epistasis interaction should use the ones that have been manually validated by literature. The central claim of discovering epistatic interactions relies heavily on the model's attention mechanism and downstream statistical filtering. However, no simulation studies are presented to validate that G2PT can reliably detect epistasis when ground-truth interactions are known. Demonstrating robust detection of non-additive interactions under varying genetic architectures and noise levels in simulated genotype-phenotype datasets is essential to substantiate the method's core capability.
      3. Lack of Justification for Model Complexity and Missing Ablation Insights: While Supplementary Figure 2 presents ablation studies, the manuscript needs to justify the high computational cost (168 GPU hours using 4×A30 GPUs) of the full model. It remains unclear how much performance gain is specifically due to reverse propagation (Equations 8-9), which is claimed to capture biological context. The benefit of using a full Gene Ontology hierarchy versus a flat system list is not quantified. There is also no comparison between bidirectional versus unidirectional propagation. Overall, the added complexity is not empirically shown to be necessary.
      4. Non-Equivalent Benchmarking Against PRS Methods: Figure 2 compares G2PT to polygenic risk score (PRS) methods such as LDpred2 and Lassosum, but G2PT is run only on SNPs pre-filtered by marginal association (p-values between 10⁻⁵ and 10⁻⁸), while the PRS methods use genome-wide SNPs. This introduces a strong bias in G2PT's favor by effectively removing noise. A fair comparison would require: (a) running LDpred2 and Lassosum on the same pre-filtered SNP sets as G2PT, or (b) running G2PT on genome-wide or LD-pruned SNP sets. The reported superior performance of G2PT may be driven primarily by this input filtering, not the model architecture.
      5. No Details on Hyperparameter Optimization: Although the manuscript mentions grid search for hyperparameter tuning, it provides no information about which parameters were optimized (e.g., learning rate, dropout rate, weight decay, attention dropout, FFNN dimensions), what search space was explored, or what final values were selected. There is also no assessment of how sensitive the model's performance is to these choices. Better transparency would help facilitate reproducibility
      6. Absence of Control for Key Confounders: In interpreting attention scores as reflecting genetic relevance (e.g., the role of the immunoglobulin system), the model includes only age, sex, and genetic principal components as covariates. Important confounders such as BMI, alcohol use, or medication (e.g., statins) have not been controlled for. Since TG/HDL levels are strongly influenced by environment and lifestyle, it is entirely plausible that some high-attention features reflect environmental tagging, not biological causality.
      7. Oversimplified Treatment of SNP-to-Gene Mapping: The SNP-to-gene mapping strategy combines cS2G, eQTL, and nearest-gene annotations, but the limitations of this approach are not adequately addressed. The manuscript does not specify how conflicts between methods are resolved or what fraction of SNPs map ambiguously to multiple genes. Supplementary Figure 2 shows model performance degrades when using only nearest-gene mapping, but there is no systematic analysis of how mapping uncertainties propagate through the hierarchy and affect attention or interpretation.
      8. Naive Scoring of System Importance: The method used to quantify the biological relevance of systems (i.e., correlating attention scores with predicted phenotype values) risks circular reasoning. Since the model is trained to optimize prediction, systems that contribute strongly to prediction will naturally show high correlation-even if they are not biologically causal. No comparison is made with established gene set enrichment methods applied to GWAS summary statistics. The approach lacks an independent benchmark to validate that the "important" systems are biologically meaningful.
      9. No External Validation to Assess Generalizability: All evaluations are performed using cross-validation within the UK Biobank. There is no assessment of generalizability to independent cohorts or diverse ancestries. Given population structure, genotyping platform, and phenotype measurement variability, external validation is essential before claiming the method is suitable for broader use in polygenic risk assessment.
      10. Computational Burden and Scalability Are Not Addressed: The paper notes that training the model requires 168 GPU hours on 4×A30 GPUs for just ~5,000 SNPs. However, there is no discussion of whether G2PT can scale to larger SNP sets (e.g., genome-wide imputed data) or more complex biological hierarchies (e.g., Reactome pathways). Without addressing scalability, the model's applicability to real-world, large-scale genomic datasets remains unclear.

      Minor:

      1. Attention Weights as Mechanistic Insight: The paper equates high attention scores with biological importance, for example in highlighting the immunoglobulin system. There is no causal validation showing that altering the highlighted SNPs, genes, or systems has an actual effect on TG/HDL. Attention weights in transformer models are known to sometimes reflect spurious correlations, especially in high-dimensional settings. The correlation between attention scores and predictions (Supplementary Fig. 3a,b) does not constitute biological evidence. The interpretability claims can be restated without supporting functional or causal validation.

      Significance

      Novelty

      This work presents novelty by introducing the first transformer-based model that integrates the GO hierarchy to enable bidirectional mapping between genotype and phenotype. Additionally, the use of attention mechanisms to screen for epistasis offers a novel and computationally efficient alternative to traditional exhaustive SNP-SNP interaction tests.

      Impact

      Target Audience

      • Specialized: Computational biologists working on interpretable machine learning methods in genomics.
      • Broader: Geneticists investigating polygenic traits and drug developers focusing on pathway-level therapeutic targets.

      Limitations vs. Contributions

      While the work presents a clear conceptual advance by incorporating hierarchical biological priors and attention mechanisms, the technical contribution is somewhat limited by its validation on a single trait and the absence of simulation-based benchmarking. Nevertheless, the framework shows potential if extended to other traits and experimentally validated.

      Overall Assessment

      Recommendation: Major Revision

      Strengths:

      • Predictive performance appears strong.
      • The use of biological priors enables interpretability at the pathway level.

      Major Weaknesses:

      • The current validation is limited to a single trait, restricting generalizability.
      • The manuscript lacks a complete and clear description of the model architecture.
      • No simulations are provided to assess the method's ability to recover known epistatic interactions or pathways.

      Reviewer Expertise: Machine learning applications in genomics and genetics.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      __Summary

      Köver et al. examine the genetic and environmental underpinnings of multicellular-like phenotypes (MLPs) in fission yeast, studying 57 natural isolates of Schizosaccharomyces pombe. They uncover that a noteworthy subset of these isolates can develop MLPs, with the extent of these phenotypes varying according to growth media. Among these, two strains demonstrate pronounced MLP across a range of conditions. By genetically manipulating one strain with an MLP phenotype (distinct from the previously mentioned two strains), they provide evidence that genes such as MBX2 and SRB11 play a direct role in MLP formation, strengthening their genetic mapping findings. The study also reveals that while some key genes and their phenotypic effects are strikingly similar between budding and fission yeast, other aspects of MLP formation are not conserved, which is an intriguing finding.

      Overall, the manuscript is well-written, dense yet logically structured, and the figures are well presented. The combination of phenotypic, genetic, and bioinformatics analyses, particularly from wet lab experiments, is commendable. The study addresses a significant gap in our understanding, primarily explored in budding yeast, by providing comprehensive data on MLP diversity in fission yeast and the interplay of genetic and environmental factors.

      In summary, I enjoyed reading the manuscript and have only a few minor suggestions to strengthen the paper:

      Minor revisions:

      1. Although this may seem like a minor revision, but it is a crucial point. Please make sure that all raw data used to generate figures, run stats, sequence data, and scripts used to run data analysis are made publicly available. Provide relevant accession numbers and links to public data repositories. It is important that others can download the various types of data that went into the major conclusions of this paper in order to replicate your analysis or expand upon the scope of this work. I am not sure if the journal has a policy regarding this, but it should be followed to allow for transparency and reproducibility of the research.__

      Reply: We very much agree with the reviewer that sharing raw data and scripts is an essential part of open science. All code and data are deposited to Github (https://github.com/BKover99/S.-Pombe-MLPs) and Figshare (https://figshare.com/articles/software/S_-Pombe-MLPs/25750980), which have now been updated to reflect our revisions. Additionally, the sequenced genomes have been deposited to ENA (PRJEB69522). Where external data was used, it was properly referenced and specifically included in Supplementary Table 3.

      Two out of 57 strains exhibit strong and consistent MLP across multiple environments. Providing more information on these strains (JB914 and JB953), such as their natural habitats and distinct appearances of their MLP phenotypes under varying conditions, would provide valuable insights.

      First, a brief discussion highlighting what differentiates these two strains from the rest would be helpful for readers (e.g. insight into their unique genetic and environmental background that might be linked to the MLP phenotype).

      Additionally, culture tube and microscopy images of these strains, similar to those presented for JB759 in Figure 2A, can be included in the supplementary materials. My reasoning is that these images could help illustrate variation or lack thereof in aggregative group size across different media.

      Reply: We thank the reviewer for highlighting this issue. Our further investigation into these strains has added additional interesting insights. JB914 and JB953 were isolated from molasses in Jamaica and the exudate of Eucalyptus in Australia, respectively, though it remains unclear whether these environments are related or even selective for the ability of these strains to form MLPs. We note that the environment from which a strain is isolated is an incomplete way of assessing its ecology. Indeed, recent research suggests that the primary habitat of S. pombe is honeybee honey and suggests that bees, which may be attracted to a number of sugary substances, may be a vector by which fission yeast are transported (1). Therefore, isolation from a particular nectar or food production environment might not reflect significant ecological differences. We now refer to the location of strain isolation in the manuscript text (lines 208-209).

      However, there is more to learn from the genetic backgrounds of these two strains. We found that JB914 possesses the same variant in srb11 causally related to MLPs as JB759, the MLP-forming parental strain for our QTL analysis. To understand whether the appearance of this variant in these two strains derived from a single mutation event or was a case of convergent evolution, we analysed homology between the genomes of JB759 and JB914, focusing specifically on that variant. We found an approximately 20kb region of homology between JB759 and JB914 surrounding the srb11 truncation variant, in contrast to the majority of the genome, which does not share homology between those two strains (New Supplementary Figure 9A, B)). This result suggests that, while the two strains are largely unrelated, that specific region shares a recent common ancestor and is likely a result of interbreeding across strains.

      Importantly, this analysis further emphasizes the point that the srb11 variant segregates with the MLP-forming phenotype. We conclude this because none of the other strains similar to JB759 (either across the whole genome, or specifically in the region surrounding srb11) exhibit MLPs (New Supplementary Figure 9C). This thereby further complements our QTL analysis on the significance of this variant. We have added this analysis to the manuscript text (lines 337-349).

      Furthermore, we searched other strains which exhibited MLPs in our experiments (e.g. JB953) for frame shifts, insertions or deletions in any other genes in the CKM module or in the genes that were identified in our deletion library screen as adhesive, and did not identify any severe mutations falling into coding regions (other than the srb11 truncation in JB914 and JB759). This indicates that MLPs in these other strains may be caused by differences in regulatory regions surrounding these genes, or variants in other genes that were not identified in our screen. We have added this analysis to our manuscript (lines 424-425) and Supplementary Table 13.

      We agree that microscopy and culture tube images of JB914 and JB953 may give insight into the nature of the MLPs exhibited by those strains. We have included such images of cultures grown in YES, EMM and EMM-Phosphate media in our revision (Lines 207-208, Supplementary Figures 4 and 5). These images are consistent with our adhesion assay screen and show that JB914 and JB953 are adhesive at the microscopic level in the relevant conditions (EMM or EMM-Phosphate).

      The phenotypic outcome of overexpressing MXB2 is striking, as shown in Supplementary Figure 4C. Incorporating at least one of the culture tube images depicting large flocs into the main text, perhaps adjacent to Figure 3 panel D, would improve the visual appeal and highlight this key finding (at the moment those images are only shown in the supplementary materials).

      Reply: We thank the reviewer for this suggestion. In response to Reviewer 2's suggestion to overexpress mbx2 in YES, we created new mbx2 overexpression strains that could overexpress mbx2 in YES, which was not possible in our previous strain in which mbx2 overexpression was triggered by removal of thymine from the media. We have replaced our original data from Figure 3D with data from the new mbx2 overexpression experiment, including flask images.

      I know that the authors discuss the knowledge gap in the intro and results, but the abstract does not mention this critical gap. Please stress this critical gap (i.e., MLPs understudied in fission yeast) with a brief sentence in the abstract. Similarly, please consider writing a brief concluding sentence summarizing the paper's most significant finding referring to the knowledge gap would provide a clearer takeaway message for the reader - the abstract ends abruptly without any conclusion.

      Reply: We agree and have now emphasized the critical gap in our abstract:

      "As MLP formation remains understudied in fission yeast compared to budding yeast, we aimed to narrow this gap." at lines 18-19.

      Additionally, we added the following final sentence to give the reader a clearer takeaway message:

      "Our findings provide a comprehensive genetic survey of MLP formation in fission yeast, and a functional description of a causal mutation that drives MLP formation in nature." at lines 31-32.

      1. The observation that strains with adhesive phenotypes have a lower growth rate compared to non-adhesive strains is a noteworthy point (lines 532-535). This represents yet another example of this classical trade-off. This point could be emphasized in the Discussion or alongside the relevant result, with a brief speculative explanation for this phenomenon.

      Reply: We agree that the nature of the trade-off between MLP formation is an interesting discussion point that could arise from our work. Understanding this trade-off is made more complicated by the fact that growth is always condition-dependent, and measuring growth in strains exhibiting MLPs is non-trivial, as adhesion to labware and thick clumps of cells separated by regions of cell-free media can add variability. Nonetheless, there has been some previous work on this problem. In S. cerevisiae, it was shown that larger group size correlates with slower growth rate (3), and that flocculating cells grow more slowly (4). In S. cerevisiae, cAMP, a signalling molecule heavily involved in regulating growth in response to nutrient availability, also regulates filamentation (5). However, the relationship between flocculation and slow growth is not consistent in the literature. In some settings overexpressing the flocculins FLO8, FLO5, and FLO10 results in slower growth (6), while in others it does not (7). In addition, ethanol production has been shown to improve for biofilms (7).

      Furthermore, in S. cerevisiae, MLP-forming cells grow better in low sucrose concentrations (8) and under various stress conditions (4). Flocculating cells have also shown faster fermentation in media containing common industrial bioproduction inhibitors, despite slower fermentation than non-flocculating cells in non-inhibitory media (9). However, any consequence of this possible advantage on growth has not been characterised.

      In S. pombe, there is less work on this topic; however, it has been shown that deletions of rpl3201 and rpl3202, which code for ribosomal proteins, cause flocculation and slow growth (10). In that case, it is not clear if there is any causal relationship between slow growth and flocculation or if they are both parallel consequences of the ribosomal pathway disruption. We have added some of these points to the portion of the discussion that discusses this tradeoff (Lines 477-499).

      To get a better understanding of this tradeoff in our system, we took several approaches. First, we added a supporting analysis (New Supplementary Figure 12B), using published growth data based on measurements on agar plates for the S. pombe gene deletion library (11). There, the authors defined a set of deletion strains that grow more slowly on EMM than the wild-type lab strain. We found that our MLP hit strains were significantly enriched in this "EMM-slow" category. This information is now included in the manuscript (Lines 409-413, New Supplementary Figure 12B).

      It is, however, possible that for the assays from that work, the appearance of slow growth on solid agar in adhesive cells could be partially artifactual. Indeed, we have observed that adhesive cells tend to stick to flasks and, when grown on agar plates, cells in the same colony can stick to one another rather than to inoculation loops or pin pads. Both of these dynamics can reduce initial inoculation densities. This is less of a concern for our adhesion assay and Figures 2E, 5B, and 5F, because our before-wash intensity was done with a 7x7 pinned square about 10x10 mm2. Nonetheless, as we wanted to make a point about srb10 and srb11 mutants growing faster than other deletion mutants that exhibit MLP-formation, we also conducted growth assays in liquid media (New Figure 5F).

      We observed that srb10Δ and srb11Δ strains (which exhibit MLPs in EMM) show growth curves similar to wild-type cells in minimal (EMM) and rich media (YES). On the other hand, other strains that grow similarly to wild type cells in YES, such as tlg2Δ and rpa12Δ, grow much more slowly in EMM when they clump together. There are also some strains, mus7Δ and kgd2Δ, that grow more slowly in both YES and EMM but are only adhesive in EMM.

      The text mentions two lab strains, JB22 and JB50, displaying strong adhesion under phosphate starvation (lines 525-526), yet the data point for JB22 in Figure 2C is not labeled.

      Reply: We agree that highlighting JB22 on the figure is crucial, given that it was mentioned in the main text. JB22 is now highlighted in green on Fig 2C.

      1. Although I generally avoid commenting on formatting, I found the manuscript to be dense. As mentioned above, I truly enjoyed reading it! But I couldn't help but think of ways to make the manuscript more concise for readers. The Results section spans nine pages (excluding figure captions), and the Discussion is five pages long. The main text contains 6 figures with approximately 27 panels and 32 plots and Venn diagrams, while the supplementary material has 11 figures with 22 panels and about 59 plots. Altogether, the manuscript comprises 17 figures, 49 panels, and roughly 91 plots and Venn diagrams! While I will not request any changes, I encourage the authors to consider streamlining the text/data where possible to focus on the core theme of the study.

      We thank the reviewer for these suggestions and have reorganised some of our figures and text to appear less dense. We have also added several figures and panels in response to reviewer comments. While we endeavor to make our points clear and concise in the main figures, we believe that it is important to retain key supplementary figures so that an interested reader can evaluate the data in more detail:

      A summary of our major changes to the figures is below, and we also provide a manuscript with changes tracked for the reviewers' convenience:

      Fig 2:

      Added Panel E in response to reviewer comments. Fig 3:

      Removed axes for pfl3 and pfl7 from Fig 3C, as the point was made by the other genes displayed (mbx2, pfl8 and gsf2) Replaced Fig 3D with similar data from an improved experiment in response to reviewer comments. Added New Fig 3F from Original Supp Fig 5 Fig 5:

      Moved Original Fig 5A to New Supp Fig 10A. Added New Fig 5F in response to reviewer comments. Original Supp Fig 4 / New Supp Fig 6:

      Removed mbx2 overexpression images from Original Fig 4C, to be replaced by new overexpression data and images in New Fig 3D. Added flask images for srb10 and srb11 deletion mutants from Original Supp Fig 5A to New Supp Fig 6C. Added microscope image for srb11 deletion mutant from Ooriginal Supp Fig 5A to New Supp Fig 6C. Added adhesion assay results from Original Supp Fig 5C to New Supp Fig 6C. Added New Supp Fig 6D in response to review Original Supp Fig 5

      Removed this figure. Original Supp Fig 5A and 5B were moved to New Supp Fig 6. Original Supp Fig 5B was removed to make the manuscript more concise. Original Supp Figs 6, 7 and 8 were combined into New Supp Fig 8.

      Original Supp Fig 6A and 6B are now New Supp Fig 8A and 8B. Original Supp Fig 7 is now New Supp Fig 8C. Original Supp Fig 8A is now New Supp Fig 8D and 8E. Original Supp Fig 8B is now New Supp Fig 8F Original Supp Fig 9/New Supp Fig 10

      Added Original Fig 5A as new Supp Fig 10A. Original Supp Fig 11/New Supp Fig 12

      Removed Original Fig 11B and the relevant text to make the manuscript more concise. Added New Supp Fig 12B in response to reviewer comments. New Supplementary Figures added in response to reviewer comments:

      New Supp Fig 4: Microscopy images of natural isolates. New Supp Fig 5: Flask images of natural isolates New Supp Fig 7: Microscopy and flask images of mbx2 overexpression strains. New Supp Fig 9: Genomic comparisons between JB759 and the MLP-forming wild isolate, JB914. Removed some less relevant points from our discussion, to reduce the length.

      Added new Supplementary Tables:

      Supplementary Table 13: Variants in candidate genes. Added in response to reviewer comments Supplementary Table 14: List of plasmids used in the study.

      **Referees cross-commenting**

      There are many useful recommendations from all the other reviewers that will help improve the final product. Once those points are revised, I think this will be a nice paper of interest to folks interested in natural variation in MLPs and its genetic background.

      Significance

      My expertise: evolutionary genetics, evolution of multicellularity, yeast genetics, experimental evolution

      Overall, the manuscript is well-written, dense yet logically structured, and the figures are well presented. The combination of phenotypic, genetic, and bioinformatics analyses, particularly from wet lab experiments, is commendable. The study addresses a significant gap in our understanding, primarily explored in budding yeast, by providing comprehensive data on MLP diversity in fission yeast and the interplay of genetic and environmental factors.

      In summary, I enjoyed reading the manuscript and have only a few minor suggestions to strengthen the paper.

      Reviewer #2

      Evidence, reproducibility and clarity

      REVIEWER COMMENTS

      Yeast species, including fission yeast and budding yeast, could form multicellular-like phenotypes (MLP). In this work, Kӧvér and colleagues found most proteins involved in MLP formation are not functionally conserved between S. pombe and budding yeast by bioinformatic analysis. The authors analyzed 57 natural S. pombe isolates and found MLP formation to widely vary across different nutrient and drug conditions. The authors demonstrate that MLP formation correlated with expression levels of the transcription factor gene mbx2 and several flocculins. The authors also show that Cdk8 kinase module and srub11 deletions also resulted in MLP formation. The experimental design is logic, the manuscript is well-written and organized. I have a few concerns that should be addressed before the publication.

      Major points:

      1) Line 61-62, how did the authors grow yeast cells in the liquid medium? Shaking or static? If shaking, the nutrient should be even distributed in the medium.

      If static culture, most single yeast cells could precipitate on the bottom, how do you address the advantage of flocculation for increasing the sedimentation? In addition, under static culture, the bottom will have less air than the up medium, how to balance the air and nutrients?

      Reply: In line 61-62 we stated that "Similarly, flocculation could increase sedimentation in liquid media, thereby assisting the search for more nutrient-rich or less stressful environments (4)".

      Our intent was to speculate on the advantages of multicellular-like growth, and cited a review article which has mentioned sedimentation. After further consideration, we decided that this is a minor point and is rather speculative, and removed it altogether from the manuscript.

      In response to the Reviewer's question about how cells were grown in liquid medium, throughout the paper we used shaking cultures for our flocculation assays and for pre-cultures. We have made this more clear in the text where it was ambiguous (e.g. line 189, throughout the methods section, and in the legend of Fig. 2A).

      2) Line 555, it will be interesting to test whether overexpression of mbx2 could cause flocculation in YES medium. In Figure 3D, the authors use two control strains, but only one mbx2 OE strain, mbx2 OE should be tested in both strains. In addition, did the authors transform empty plasmid into the control strains, please indicate in the figure.

      In this experiment, mbx2 was overexpressed using a thiamine-repressible nmt1 promoter, which is a standard construct in fission yeast studies. Assaying MLP formation was not feasible in YES with this strain, because YES is a rich media made up of yeast extract which contains thiamine. Thus, we could not remove thiamine from the media to trigger mbx2 overexpression.

      In order to test the influence of mbx2 overexpression in YES, we constructed strains in which mbx2 was integrated into the genome and expression was driven by the rpl2102 promoter, which has been shown to provide constitutive moderate expression levels (12). We observed strong flocculation in both EMM and YES (Fig 3D, New Supplementary Figure 7) . We did not see strong flocculation in a control in which GFP was expressed under the rpl2102 promoter. The flocculation phenotype was so strong that our original adhesion assay protocol required modification for this experiment, including resuspension in 10 mM EDTA before repinning (Methods). We observed strong adhesion for the mbx2 overexpression strains (Fig 3D), but not for control strains in YES. We could not check adhesion in EMM for those strains because cells pinned on EMM did not survive resuspension in EDTA.

      We performed these experiments in two backgrounds, 968 h90 (JB50), which is one of the parental strains of the segregant library analysed in Figure 3 and 972 h- (JB22), which is an appropriate background for the gene deletion collection.

      We have replaced the data from the original Figure 3D with the new adhesion assay and added New Supplementary Figure 7 to the manuscript (Lines 236-244).

      This result also helped us to further refine our model for the pathway. We can now say that the repression of MLPs in rich media must act via Mbx2, as overexpression of mbx2 is sufficient to abolish it, and is likely to act transcriptionally (if it acted on the protein level, the mild overexpression would likely not have led to the phenotype) (Figure 6, Lines 554-556 in the discussion)

      3) Line 600-601, the authors may do the backcross of srb11Δ::Kan to exclude the possibility caused by other mutations.

      Reply: We thank the reviewer for noticing our concern about suppressor mutations arising in the srb11Δ strain obtained from our deletion library. This initial concern arose following the observation that while qualitatively the srb11Δ::Kan and srb11Δ(CRISPR) strains were both strongly adhesive, there was a minor quantitative difference in their adhesion.

      As we obtained this strain from an h+ deletion library strain backcrossed with a prototrophic h- strain (JB22) in order to restore auxotrophies (13), the chances for a suppressor mutation to arise are very low. We have therefore removed that language from our text. We now suspect that a more likely explanation for this small difference could be the strain background, as our CRISPR engineered strain was made in a JB50 background which has the h90 mating type, while the deletion library strains are h- without auxotrophic markers.

      We would like to emphasize, however, that despite this quantitative difference in the adhesion phenotype between the two srb11Δ strains, they both have a large increase in the adhesion phenotype relative to the respective wild-type strains. To address this point, we have removed the unnecessary statistical comparison of these two deletion strains and focused on their qualitatively high levels of adhesion in the text (lines 267-269) and in our Revised Supplementary Figure 6D.

      Minor points:

      1) Line 506, what are the growth conditions of cells in Figure 2A? Did the authors use the liquid or solid medium? Please mention in the Methods or figure legends.

      Reply: We have updated the manuscript to include the relevant details in the text (line 189), figure caption for Fig. 2A and in the methods section (lines 829-831).

      2) Line 533-535, please explain why the strains exhibiting strong adhesion have a decreased growth rate. Is there any related research? Please add some references.

      Reply: Please see reply to Reviewer 1, comment 5.

      **Referees cross-commenting**

      I agree with most of the comments from other reviewers. This publication may indeed be of interest to a minor area. But the results and the interpretations of the data are interesting and warranted, the findings are scientifically important.

      Significance

      The authors did many large-scale screens and bioinformatic analyses. The experiments in the manuscript are generally logical and sound. This study is useful for deciphering the mechanism of multicellular-like phenotype formation in the fission yeast, with some implications for some other organisms.

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

      Summary: Using a variety of targeted and genome wide analyses, the authors investigate the basis for "multicellular-like phenotypes" in S. pombe. Authors developed several methodologies to detect and quantify "multicellular-like phenotypes" (flocculation, aggregation...) and defined genes involved in these processes in laboratory and wild S. pombe.

      SECTION A - Evidence, reproducibility and clarity

      This is a very solid manuscript that is well-written and supported by convincing data. While one can imagine many additional experiments, the manuscript stands on its own and presents a quite exhaustive analysis of the area. I commend the author for their rigorous work and clear presentation. They are only a few minor points that warrant comments or corrections: - Supplementary Figure 1 is a typical example of the "necessity" to have statistics and P-values everywhere. The data are convincing but what is the evidence that the Filtering assay and the Plate-reader assay values should be linearly related? Lets imagine that Plate-reader assay value is proportional to the square of the Filtering assay value. What would be the Pearson R and P-value in this case? What is most appropriate? Why would one use a linear correlation? What is the "real" significance?

      Reply: We thank the reviewer for pointing out that the data in Supplementary Figure 1 does not appear to be linear and, therefore, reporting the Pearson correlation coefficient may not be the best way to represent the relationship between the two assays. The nonlinear nature of this data could indicate that

      The filtering assay saturates before the plate reader assay, and is less able to distinguish between strains that flocculate strongly and The filtering assay may be more sensitive for strains that show lower levels of flocculation. In general, we observed fewer strains with intermediate phenotypes for both assays, making it difficult to ascertain the true relationship between them; however, we believe that the key result is that the strains with the highest level of flocculation have the highest values in both assays. To capture this aspect of the data, we now report the Spearman correlation which is non-parametric and indicates how similar the ranking of each strain is based on both assays. With the alternative hypothesis being that the correlation is > 0, we report a Spearman correlation coefficient of 0.24 and a P-value of 0.04 (lines 823-826)

      • Minor points: * They are several "personal communications" in the manuscript (page 11, page 18, page 23). It should be checked whether this is accepted in the journal that publishes this manuscript.

      Reply: We thank the reviewer for highlighting this issue. We had three instances of "personal communications" in our original submission.

      The first instance was an acknowledgement for advice on our DNA extraction protocol from Dan Jeffares. We now include this in the Acknowledgements section instead.

      The second communication with Angad Garg described that they observed flocculation while growing cells in phosphate starvation conditions, which was not reported in their publication (14). Though we appreciate their willingness to share unpublished data with us, we have removed this observation from our manuscript and instead rely only on our own observations and arguments based on their published RNA-seq data to make our point.

      The third personal communication with Olivia Hillson supplements a minor hypothesis, namely that deletion of SPNCRNA.781 might cause MLP formation by affecting the promoter of hsr1, for which we had access to unpublished ChIP-seq data, showing its binding to flocculins. Recently published work from a different group (15) also suggests this link between hsr1 and flocculation and is now discussed in our manuscript instead of the result based on unpublished data obtained from personal communication at Lines 397-398.

      * Page 4 check "a few regulators"

      Reply: For clarity, this has now been changed to "several regulatory proteins" at Line 108. The specific proteins we are referring to are highlighted in Figure 1C.

      * Page 19, line 567: "remaining 8 strains" may be confusing as Material and Methods states "remaining 10 strains".

      Reply: Two of the 10 strains were found to be redundant after sequencing as explained in the Methods (Lines 930-934). Therefore, we only added 8 new strains to the analysis. We thank the reviewer for highlighting this as a potential source of misunderstanding, and clarified this point in the text (Lines 247-250 and in the methods).

      **Referees cross-commenting**

      I concur with most comments. Overall, the reviewers agree that this is a solid piece of work that could benefit from minor modifications and should be published. I reiterate that, for me, despite its quality, this publication will only be of interest to specialists.

      Reviewer #3 (Significance (Required)):

      A limited number of studies have investigated "multicellular-like phenotypes" in S. pombe. This manuscript brings therefore new and solid information. Yet, despite an impressive amount of work, our conceptual advance in understanding this process and its phylogenetic conservation remains limited. This is probably best illustrated in the figure 6 that summarize the study and contains 3 question marks and an additional unknown mechanism. (Most of the solid arrows in this figure correspond to interactions within the Mediator complex that were well known before this study.) In addition, while only few studies have been published in this area, the authors' findings are often only bringing additional support to already published observations. Overall, while this manuscript will be of interest to a restricted group of aficionados, it will most likely not attract the attention of a wide readership.

      __ Reviewer #4 (Evidence, reproducibility and clarity (Required)):__

      In this manuscript, the authors explore how multicellular-like phenotypes (MLPs) arise in the fission yeast S. pombe. Although yeasts are characterized as unicellular fungi, diverse species show MLPs, including filamentous growth on agar plates and flocculation in liquid media. MLPs may provide certain advantages in nutritionally poor conditions and protection against external challenges, upon which natural selection can then act. Previous work on MLPs has mostly been carried out in the budding yeasts S. cerevisiae and C. albicans, and little was known about these behaviors in S. pombe. The authors thus set out to investigate both genetic and environmental regulators of MLP formation.

      First, their analysis of published data revealed a limited number of shared regulators of MLP between S. pombe, S. cerevisiae, and C. albicans, although the cell adhesion proteins themselves are largely not conserved. Next, the authors screened a set of non-clonal natural isolates using two high-throughput assays that they developed and found that MLPs vary in strains and depending on nutrient conditions. Focusing on a natural isolate that showed both adhesion on agar plates and flocculation in liquid medium, they then analyzed a segregant library generated from this and a laboratory strain using their assays. Using QTL analysis, they uncovered a frameshift in the srb11 gene, which encodes a subunit of the Mediator complex, as the likely causal inducer of MLP. This was confirmed by additional analyses of strains lacking srb11 or other members of Mediator. Furthermore, the authors showed that loss of srb11 function resulted in the upregulation of the Mbx2 transcription factor, which was both necessary and sufficient for MLP formation in this background. Finally, screening of two additional yeast strain collections (gene and long intergenic non-coding RNA deletion) identified both known and novel regulators representing different pathways that may be involved in MLP formation.

      Altogether, this study provides new perspectives into our understanding of the diverse inputs that regulate multicellular-like phenotypes in yeast.

      Major comments:

      • The methods for screening for adhesion and flocculation are well described, with representative figures that show plates and flasks. However, there are few microscopy images of cells, and it would be interesting and helpful for the reader to have an idea of how cells look when they exhibit MLPs. For instance, are there any differences in cell shape or size when strains present different degrees of adhesion or flocculation? In addition, the authors mention that mutants with strong adhesion generally had lower colony density and are likely to be slower growing. Although their analyses suggest otherwise (page 22), this has a potential for introducing error in their observations, and including images of the adhesion/flocculation phenotypes may provide further support for their conclusions. I suggest that the authors present microscopy images 1) similar to what is shown for JB759 in Figure 2A and 2) of cells growing on agar in the adhesion assay. This could be included for the different Mediator subunit deletions that they tested, where there appear to be varying phenotypes. It could also be informative for a subset of the 31 high-confidence candidates that they identified in their screen.

      Reply: We thank the reviewer for highlighting the need for further microscopic characterisation of MLP forming strains. We therefore now include images of JB914, JB953 (New Supplementary Figures 4, Figure 2E) in liquid media in EMM, EMM-Phosphate, and YES; an srb11 deletion strain (Figure 3F), and mbx2 overexpression strains (New Supplementary Figure 7).

      • Upon identifying a frameshift in srb11 that is responsible for the MLP, the authors assessed whether deletion of other Mediator subunits would result in the same phenotype. They found that srb10 and srb11 deletions both flocculate and show adhesion, while other mutants had milder phenotypes. However, the authors also found that a new deletion of srb11 that they generated had a stronger adhesion phenotype than the srb11 deletion from the prototrophic deletion library, which was attributed this the accumulation of suppressor mutations in the strains of the deletion collection. As the authors make clear distinctions between the phenotypes of different Mediator mutants, I suggest generating and analyzing "clean" deletions of the 6 other subunits that they tested. This would strengthen their conclusion and help to rule out accumulated suppressors as the cause of the differences in the observed phenotypes.

      Reply: We thank the reviewer for noticing our concern about suppressor mutations in the manuscript. As we describe above in response to a similar question from reviewer 2, as the prototrophic deletion library from which we extracted the Mediator deletion strains had been backcrossed during its construction (13), we no longer suspect that small difference between the srb11Δ::Kan strain from the deletion library and the newly created srb11Δ (CRISPR) strains is due to suppressor mutations. Rather, we think they may be a result of the difference in genetic background and possibly mating type between the two strains. We also want to emphasize that this difference is small compared to the difference between the adhesion ratios of the srb11Δ strains and their respective control strains.

      Nevertheless, we made clean, independent Mediator mutants for 5 out of 6 Mediator genes tested (med10Δ, med13Δ, med19Δ, med27Δ, and srb10Δ) as well as an additional mutant that we didn't have in our library, med12Δ (Figure R9). When running the assay on these new strains we got an overall lower dynamic range, possibly due to variations in the water flow rate relative to the first assay. However, we saw a strong phenotype for both library and our own srb10Δ and CRISPR srb11Δ strains. We did not see a significant increase in adhesion for the other Mediator deletion mutants in EMM relative to wild type with the exception of for med10Δ in both the library strain and for our clean mutant, for which we did not observe a phenotype in our previous experiment. We included the experiment for the newly created mutants as New Supplementary Figure S6E and described them in lines 276-281 in our revised manuscript.

      Minor comments:

      • One point that recurs in the manuscript is the idea that mutations that give rise to strong MLPs also generally lead to slower growth, representing a potential trade-off. This idea could be reinforced with measurements of growth rate or generation time by optical density or cell number, for instance, rather than comparisons of colony density. Also, it would be interesting to mention if the slow growth phenotype is only observed in MLP-inducing conditions or also in rich medium.

      Reply: As described above in response to item 5 from Reviewer 1, we have conducted growth assays in liquid media for srb10Δ, srb11Δ, and other mutants from our adhesion screen (tlg2Δ, rpa12Δ, mus7Δ and kgd2Δ) that showed a similar phenotype to those genes in both minimal (EMM) and rich (YES) media. We observe that in rich media, srb10Δ and srb11Δ cells grow similarly to control strains, and they exhibit a lower decrease in growth rate than the other similarly adhesive strains. Both mus7Δ and kgd2Δ cells grow more slowly, even in rich media.

      We have also added data on the tradeoff between growth and adhesion based on growth on solid media from (11) for all mutants identified in our screen (New Supp Fig 12B)).

      Thus, the relationship between slow growth and clumpiness depends on the mutation, and specifically, mutations of the Mediator, including those to srb11 and srb10, seem to decrease the impact of any tradeoff between growth and adhesion.

      • The authors show that the MLPs of the srb10 and srb11 deletions occur through mbx2 upregulation. Do the varying strengths of the phenotypes of the strains lacking different Mediator subunits correlate with mbx2 levels in these backgrounds?

      Reply: There is some evidence from previous work that the relationship between the strength of the MLPs and the expression of mbx2 may not be perfectly proportional. In (16), med12Δ had a higher (though qualitatively comparable) level of mbx2 upregulation than srb10Δ (New Supp Fig 8E), even though that paper reported a milder phenotype for med12Δ than for srb10Δ cells. We did not observe a significant increase in adhesion in our med12Δ strain (New Supp Fig 6D). This suggests that in the case of these mutants, it is not simply the level of mbx2 that controls MLP formation, but that there are likely additional regulatory mechanisms. We have added some discussion on this context in the manuscript (lines 545-547).

      **Referees cross-commenting**

      I agree overall with the comments and suggestions from the other reviewers. The revision would require only minor modifications. The paper is interesting both for the combination of methodologies used and its findings, and I believe that it would benefit a growing community of researchers.

      Reviewer #4 (Significance (Required)):

      This study employed a variety of methods that allowed the authors to uncover previously unknown regulators of MLPs. Taking advantage of the diversity of natural fission yeast isolates as well as the constructed gene and non-coding RNA deletion collections, the authors identified novel genetic determinants that give rise to MLPs, opening new avenues into this exciting area of research. The overall conclusions of the work are solid and supported by the reported results and analyses. This study will be appreciated by a broad audience of readers who are interested in understanding how organisms respond to environmental challenges as well as how MLPs may result in emergent properties that play key roles in these responses. Some of the limitations of the work are described above, with recommendations for addressing these points.

      Keywords for my field of expertise: fission yeast, cell cycle, transcription, replication.

      References for Response to Reviews

      1. Brysch-Herzberg M, Jia GS, Seidel M, Assali I, Du LL. Insights into the ecology of Schizosaccharomyces species in natural and artificial habitats. Antonie Van Leeuwenhoek. 2022 May 1;115(5):661-95.
      2. Jeffares DC, Rallis C, Rieux A, Speed D, Převorovský M, Mourier T, et al. The genomic and phenotypic diversity of Schizosaccharomyces pombe. Nat Genet. 2015 Mar;47(3):235-41.
      3. Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci. 2012 Jan 31;109(5):1595-600.
      4. Smukalla S, Caldara M, Pochet N, Beauvais A, Guadagnini S, Yan C, et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell. 2008 Nov 14;135(4):726-37.
      5. Lorenz MC, Heitman J. Yeast pseudohyphal growth is regulated by GPA2, a G protein alpha homolog. EMBO J. 1997 Dec 1;16(23):7008-18.
      6. Ignacia DGL, Bennis NX, Wheeler C, Tu LCL, Keijzer J, Cardoso CC, et al. Functional analysis of Saccharomyces cerevisiae FLO genes through optogenetic control. FEMS Yeast Res. 2025 Sept 24;25:foaf057.
      7. Wang Z, Xu W, Gao Y, Zha M, Zhang D, Peng X, et al. Engineering Saccharomyces cerevisiae for improved biofilm formation and ethanol production in continuous fermentation. Biotechnol Biofuels Bioprod. 2023 July 31;16(1):119.
      8. Koschwanez JH, Foster KR, Murray AW. Improved use of a public good selects for the evolution of undifferentiated multicellularity. eLife. 2013 Apr 2;2:e00367.
      9. Westman JO, Mapelli V, Taherzadeh MJ, Franzén CJ. Flocculation Causes Inhibitor Tolerance in Saccharomyces cerevisiae for Second-Generation Bioethanol Production. Appl Environ Microbiol. 2014 Nov;80(22):6908-18.
      10. Li R, Li X, Sun L, Chen F, Liu Z, Gu Y, et al. Reduction of Ribosome Level Triggers Flocculation of Fission Yeast Cells. Eukaryot Cell. 2013 Mar;12(3):450-9.
      11. Rodríguez-López M, Bordin N, Lees J, Scholes H, Hassan S, Saintain Q, et al. Broad functional profiling of fission yeast proteins using phenomics and machine learning. Marston AL, James DE, editors. eLife. 2023 Oct 3;12:RP88229.
      12. Hebra T, Smrčková H, Elkatmis B, Převorovský M, Pluskal T. POMBOX: A Fission Yeast Cloning Toolkit for Molecular and Synthetic Biology. ACS Synth Biol. 2024 Feb 16;13(2):558-67.
      13. Malecki M, Bähler J. Identifying genes required for respiratory growth of fission yeast. Wellcome Open Res. 2016 Nov 15;1:12.
      14. Garg A, Sanchez AM, Miele M, Schwer B, Shuman S. Cellular responses to long-term phosphate starvation of fission yeast: Maf1 determines fate choice between quiescence and death associated with aberrant tRNA biogenesis. Nucleic Acids Res. 2023 Feb 16;51(7):3094-115.
      15. Ohsawa S, Schwaiger M, Iesmantavicius V, Hashimoto R, Moriyama H, Matoba H, et al. Nitrogen signaling factor triggers a respiration-like gene expression program in fission yeast. EMBO J. 2024 Oct 15;43(20):4604-24.
      16. Linder T, Rasmussen NN, Samuelsen CO, Chatzidaki E, Baraznenok V, Beve J, et al. Two conserved modules of Schizosaccharomyces pombe Mediator regulate distinct cellular pathways. Nucleic Acids Res. 2008 May;36(8):2489-504.
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      Referee #4

      Evidence, reproducibility and clarity

      In this manuscript, the authors explore how multicellular-like phenotypes (MLPs) arise in the fission yeast S. pombe. Although yeasts are characterized as unicellular fungi, diverse species show MLPs, including filamentous growth on agar plates and flocculation in liquid media. MLPs may provide certain advantages in nutritionally poor conditions and protection against external challenges, upon which natural selection can then act. Previous work on MLPs has mostly been carried out in the budding yeasts S. cerevisiae and C. albicans, and little was known about these behaviors in S. pombe. The authors thus set out to investigate both genetic and environmental regulators of MLP formation.

      First, their analysis of published data revealed a limited number of shared regulators of MLP between S. pombe, S. cerevisiae, and C. albicans, although the cell adhesion proteins themselves are largely not conserved. Next, the authors screened a set of non-clonal natural isolates using two high-throughput assays that they developed and found that MLPs vary in strains and depending on nutrient conditions. Focusing on a natural isolate that showed both adhesion on agar plates and flocculation in liquid medium, they then analyzed a segregant library generated from this and a laboratory strain using their assays. Using QTL analysis, they uncovered a frameshift in the srb11 gene, which encodes a subunit of the Mediator complex, as the likely causal inducer of MLP. This was confirmed by additional analyses of strains lacking srb11 or other members of Mediator. Furthermore, the authors showed that loss of srb11 function resulted in the upregulation of the Mbx2 transcription factor, which was both necessary and sufficient for MLP formation in this background. Finally, screening of two additional yeast strain collections (gene and long intergenic non-coding RNA deletion) identified both known and novel regulators representing different pathways that may be involved in MLP formation.

      Altogether, this study provides new perspectives into our understanding of the diverse inputs that regulate multicellular-like phenotypes in yeast.

      Major comments:

      • The methods for screening for adhesion and flocculation are well described, with representative figures that show plates and flasks. However, there are few microscopy images of cells, and it would be interesting and helpful for the reader to have an idea of how cells look when they exhibit MLPs. For instance, are there any differences in cell shape or size when strains present different degrees of adhesion or flocculation? In addition, the authors mention that mutants with strong adhesion generally had lower colony density and are likely to be slower growing. Although their analyses suggest otherwise (page 22), this has a potential for introducing error in their observations, and including images of the adhesion/flocculation phenotypes may provide further support for their conclusions. I suggest that the authors present microscopy images 1) similar to what is shown for JB759 in Figure 2A and 2) of cells growing on agar in the adhesion assay. This could be included for the different Mediator subunit deletions that they tested, where there appear to be varying phenotypes. It could also be informative for a subset of the 31 high-confidence candidates that they identified in their screen.
      • Upon identifying a frameshift in srb11 that is responsible for the MLP, the authors assessed whether deletion of other Mediator subunits would result in the same phenotype. They found that srb10 and srb11 deletions both flocculate and show adhesion, while other mutants had milder phenotypes. However, the authors also found that a new deletion of srb11 that they generated had a stronger adhesion phenotype than the srb11 deletion from the prototrophic deletion library, which was attributed this the accumulation of suppressor mutations in the strains of the deletion collection. As the authors make clear distinctions between the phenotypes of different Mediator mutants, I suggest generating and analyzing "clean" deletions of the 6 other subunits that they tested. This would strengthen their conclusion and help to rule out accumulated suppressors as the cause of the differences in the observed phenotypes.

      Minor comments:

      • One point that recurs in the manuscript is the idea that mutations that give rise to strong MLPs also generally lead to slower growth, representing a potential trade-off. This idea could be reinforced with measurements of growth rate or generation time by optical density or cell number, for instance, rather than comparisons of colony density. Also, it would be interesting to mention if the slow growth phenotype is only observed in MLP-inducing conditions or also in rich medium.
      • The authors show that the MLPs of the srb10 and srb11 deletions occur through mbx2 upregulation. Do the varying strengths of the phenotypes of the strains lacking different Mediator subunits correlate with mbx2 levels in these backgrounds?

      Referees cross-commenting

      I agree overall with the comments and suggestions from the other reviewers. The revision would require only minor modifications. The paper is interesting both for the combination of methodologies used and its findings, and I believe that it would benefit a growing community of researchers.

      Significance

      This study employed a variety of methods that allowed the authors to uncover previously unknown regulators of MLPs. Taking advantage of the diversity of natural fission yeast isolates as well as the constructed gene and non-coding RNA deletion collections, the authors identified novel genetic determinants that give rise to MLPs, opening new avenues into this exciting area of research. The overall conclusions of the work are solid and supported by the reported results and analyses. This study will be appreciated by a broad audience of readers who are interested in understanding how organisms respond to environmental challenges as well as how MLPs may result in emergent properties that play key roles in these responses. Some of the limitations of the work are described above, with recommendations for addressing these points.

      Keywords for my field of expertise: fission yeast, cell cycle, transcription, replication.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Using a variety of targeted and genome wide analyses, the authors investigate the basis for "multicellular-like phenotypes" in S. pombe. Authors developed several methodologies to detect and quantify "multicellular-like phenotypes" (flocculation, aggregation...) and defined genes involved in these processes in laboratory and wild S. pombe.

      SECTION A - Evidence, reproducibility and clarity

      This is a very solid manuscript that is well-written and supported by convincing data. While one can imagine many additional experiments, the manuscript stands on its own and presents a quite exhaustive analysis of the area. I commend the author for their rigorous work and clear presentation. They are only a few minor points that warrant comments or corrections:

      • Supplementary Figure 1 is a typical example of the "necessity" to have statistics and P-values everywhere. The data are convincing but what is the evidence that the Filtering assay and the Plate-reader assay values should be linearly related? Lets imagine that Plate-reader assay value is proportional to the square of the Filtering assay value. What would be the Pearson R and P-value in this case? What is most appropriate? Why would one use a linear correlation? What is the "real" significance?

      Minor points:

      • They are several "personal communications" in the manuscript (page 11, page 18, page 23). It should be checked whether this is accepted in the journal that publishes this manuscript.
      • Page 4 check "a few regulators"
      • Page 19, line 567: "remaining 8 strains" may be confusing as Material and Methods states "remaining 10 strains".

      Referees cross-commenting

      I concur with most comments. Overall, the reviewers agree that this is a solid piece of work that could benefit from minor modifications and should be published. I reiterate that, for me, despite its quality, this publication will only be of interest to specialists.

      Significance

      A limited number of studies have investigated "multicellular-like phenotypes" in S. pombe. This manuscript brings therefore new and solid information. Yet, despite an impressive amount of work, our conceptual advance in understanding this process and its phylogenetic conservation remains limited. This is probably best illustrated in the figure 6 that summarize the study and contains 3 question marks and an additional unknown mechanism. (Most of the solid arrows in this figure correspond to interactions within the Mediator complex that were well known before this study.) In addition, while only few studies have been published in this area, the authors' findings are often only bringing additional support to already published observations. Overall, while this manuscript will be of interest to a restricted group of aficionados, it will most likely not attract the attention of a wide readership.

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      Referee #2

      Evidence, reproducibility and clarity

      Yeast species, including fission yeast and budding yeast, could form multicellular-like phenotypes (MLP). In this work, Kӧvér and colleagues found most proteins involved in MLP formation are not functionally conserved between S. pombe and budding yeast by bioinformatic analysis. The authors analyzed 57 natural S. pombe isolates and found MLP formation to widely vary across different nutrient and drug conditions. The authors demonstrate that MLP formation correlated with expression levels of the transcription factor gene mbx2 and several flocculins. The authors also show that Cdk8 kinase module and srub11 deletions also resulted in MLP formation. The experimental design is logic, the manuscript is well-written and organized. I have a few concerns that should be addressed before the publication.

      Major points:

      1. Line 61-62, how did the authors grow yeast cells in the liquid medium? Shaking or static? If shaking, the nutrient should be even distributed in the medium. If static culture, most single yeast cells could precipitate on the bottom, how do you address the advantage of flocculation for increasing the sedimentation? In addition, under static culture, the bottom will have less air than the up medium, how to balance the air and nutrients?
      2. Line 555, it will be interesting to test whether overexpression of mbx2 could cause flocculation in YES medium. In Figure 3D, the authors use two control strains, but only one mbx2 OE strain, mbx2 OE should be tested in both strains. In addition, did the authors transform empty plasmid into the control strains, please indicate in the figure.
      3. Line 600-601, the authors may do the backcross of srb11Δ::Kan to exclude the possibility caused by other mutations.

      Minor points:

      1. Line 506, what are the growth conditions of cells in Figure 2A? Did the authors use the liquid or solid medium? Please mention in the Methods or figure legends.
      2. Line 533-535, please explain why the strains exhibiting strong adhesion have a decreased growth rate. Is there any related research? Please add some references.

      Referees cross-commenting

      I agree with most of the comments from other reviewers. This publication may indeed be of interest to a minor area. But the results and the interpretations of the data are interesting and warranted, the findings are scientifically important.

      Significance

      The authors did many large-scale screens and bioinformatic analyses. The experiments in the manuscript are generally logical and sound. This study is useful for deciphering the mechanism of multicellular-like phenotype formation in the fission yeast, with some implications for some other organisms.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      Köver et al. examine the genetic and environmental underpinnings of multicellular-like phenotypes (MLPs) in fission yeast, studying 57 natural isolates of Schizosaccharomyces pombe. They uncover that a noteworthy subset of these isolates can develop MLPs, with the extent of these phenotypes varying according to growth media. Among these, two strains demonstrate pronounced MLP across a range of conditions. By genetically manipulating one strain with an MLP phenotype (distinct from the previously mentioned two strains), they provide evidence that genes such as MBX2 and SRB11 play a direct role in MLP formation, strengthening their genetic mapping findings. The study also reveals that while some key genes and their phenotypic effects are strikingly similar between budding and fission yeast, other aspects of MLP formation are not conserved, which is an intriguing finding.

      Overall, the manuscript is well-written, dense yet logically structured, and the figures are well presented. The combination of phenotypic, genetic, and bioinformatics analyses, particularly from wet lab experiments, is commendable. The study addresses a significant gap in our understanding, primarily explored in budding yeast, by providing comprehensive data on MLP diversity in fission yeast and the interplay of genetic and environmental factors.

      In summary, I enjoyed reading the manuscript and have only a few minor suggestions to strengthen the paper:

      Minor revisions:

      1. Although this may seem like a minor revision, but it is a crucial point. Please make sure that all raw data used to generate figures, run stats, sequence data, and scripts used to run data analysis are made publicly available. Provide relevant accession numbers and links to public data repositories. It is important that others can download the various types of data that went into the major conclusions of this paper in order to replicate your analysis or expand upon the scope of this work. I am not sure if the journal has a policy regarding this, but it should be followed to allow for transparency and reproducibility of the research.
      2. Two out of 57 strains exhibit strong and consistent MLP across multiple environments. Providing more information on these strains (JB914 and JB953), such as their natural habitats and distinct appearances of their MLP phenotypes under varying conditions, would provide valuable insights.

      First, a brief discussion highlighting what differentiates these two strains from the rest would be helpful for readers (e.g. insight into their unique genetic and environmental background that might be linked to the MLP phenotype).

      Additionally, culture tube and microscopy images of these strains, similar to those presented for JB759 in Figure 2A, can be included in the supplementary materials. My reasoning is that these images could help illustrate variation or lack thereof in aggregative group size across different media. 3. The phenotypic outcome of overexpressing MXB2 is striking, as shown in Supplementary Figure 4C. Incorporating at least one of the culture tube images depicting large flocs into the main text, perhaps adjacent to Figure 3 panel D, would improve the visual appeal and highlight this key finding (at the moment those images are only shown in the supplementary materials). 4. I know that the authors discuss the knowledge gap in the intro and results, but the abstract does not mention this critical gap. Please stress this critical gap (i.e., MLPs understudied in fission yeast) with a brief sentence in the abstract. Similarly, please consider writing a brief concluding sentence summarizing the paper's most significant finding referring to the knowledge gap would provide a clearer takeaway message for the reader - the abstract ends abruptly without any conclusion. 5. The observation that strains with adhesive phenotypes have a lower growth rate compared to non-adhesive strains is a noteworthy point (lines 532-535). This represents yet another example of this classical trade-off. This point could be emphasized in the Discussion or alongside the relevant result, with a brief speculative explanation for this phenomenon. 6. The text mentions two lab strains, JB22 and JB50, displaying strong adhesion under phosphate starvation (lines 525-526), yet the data point for JB22 in Figure 2C is not labeled. 7. Although I generally avoid commenting on formatting, I found the manuscript to be dense. As mentioned above, I truly enjoyed reading it! But I couldn't help but think of ways to make the manuscript more concise for readers. The Results section spans nine pages (excluding figure captions), and the Discussion is five pages long. The main text contains 6 figures with approximately 27 panels and 32 plots and Venn diagrams, while the supplementary material has 11 figures with 22 panels and about 59 plots. Altogether, the manuscript comprises 17 figures, 49 panels, and roughly 91 plots and Venn diagrams! While I will not request any changes, I encourage the authors to consider streamlining the text/data where possible to focus on the core theme of the study.

      Referees cross-commenting

      There are many useful recommendations from all the other reviewers that will help improve the final product. Once those points are revised, I think this will be a nice paper of interest to folks interested in natural variation in MLPs and its genetic background.

      Significance

      My expertise: evolutionary genetics, evolution of multicellularity, yeast genetics, experimental evolution

      Overall, the manuscript is well-written, dense yet logically structured, and the figures are well presented. The combination of phenotypic, genetic, and bioinformatics analyses, particularly from wet lab experiments, is commendable. The study addresses a significant gap in our understanding, primarily explored in budding yeast, by providing comprehensive data on MLP diversity in fission yeast and the interplay of genetic and environmental factors.

      In summary, I enjoyed reading the manuscript and have only a few minor suggestions to strengthen the paper.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript by Wu and Griffin describes a mechanism where CHD4 and BRG1, two chromatin remodelling enzymes, have antagonistic functions to regulate extracellular matrix (ECM) plasmin activity and sterile inflammatory phenotype in the endothelial cells of the developing liver. As a follow up from a previous study, the authors investigate the phenotype of embryonic-lethal endothelial-specific CHD4-knockout, leading to liver phenotype and embryo death, and the rescue of this phenotype when subsequently BRG1 is knocked-out also in the endothelium. First, the authors show that the increase in plasmin activator uPAR (which leads to ECM degradation) in CHD4-KO embryos can be rescued by BRG1-KO, and that both CHD4 and BRG1 interact with the uPAR promoter. However, the authors demonstrate that reducing plasminogen by genetic knockout is unable to rescue the CHD4-KO embryos alone, suggesting an additional mechanism. By RNAseq analysis, the authors identify sterile inflammation as another potential contributor to the lethal phenotype of CHD4-KO embryos through increased expression of ICAM-1 in endothelial cells, also showing binding of both chromatin remodellers to ICAM-1 promoter. Finally, the authors use nonsteroidal anti-inflammatory drug carprofen, alone or in combination with plasminogen genetic knockout, and demonstrate CHD4-KO lethal embryonic phenotype rescue with the combination of plasminogen reduction and inflammation reduction, highlighting the synergistic role of both ECM degradation and sterile inflammation in this genetic KO.

      The findings of the manuscript are interesting, experiments well controlled and paper well written. While the work is of potential specialist interest to the field of liver development, there are several issues which authors should address before this paper can be published:

      Major issues:

      1. The authors still see embryonic lethality of some embryos with endothelial BRG1-KO or combined endothelial CHD4/BRG1-KO - could the authors please show or at least comment in the discussion why those animals are dying?

      We observed no dead Brg1-ECko or Brg1/Chd4-ECdko embryos by E14.5. However, at E17.5, there was an 18.8% lethality rate for Brg1-ECko mutants and a 12.5% rate for Brg1/Chd4-ECdko mutants (Fig. 1B). The reasons behind the incomplete rescue of Brg1/Chd4-ECdko embryos and the cause of death in Brg1-ECko mutants remain unknown, as we have mentioned in the revised discussion (see lines 311-316).

      1. In the qRT-PCR results Fig.2c, what is each dot?

      Each dot represents transcripts acquired from a separate embryo. We have modified the figure legend for clarification.

      1. In the same figure, I would expect that in CHD4-KO there is no CHD4 transcript, and in BRG1-KO there is no BRG1 transcript, rather than the reduction shown, which seems quite noisy (though significant) - is it this a result of normalisation? Or is indeed only a certain amount of the transcript reduced?

      The VE-Cadherin Cre mouse line utilized in this study is reported to have progressive Cre expression and activity from E8.5 to E13.5 and only to reach full penetrance across all vasculature at E14.51. The liver sinusoidal ECs (LSECs) analyzed in Fig. 2C were isolated at E12.5, before Cre activity reached its full penetrance. This is likely the primary cause of the variability in gene excision seen in this panel.

      1. In the same figure, is the statistical testing performed before or after normalisation? This can introduce errors if done after normalisation.

      Normalization was performed before statistical analysis to combine relative transcript counts from embryos harvested in multiple litters. This is now clarified in our methods (see lines 486-489).

      1. In some cases, the authors show immunofluorescence images but do not specify how many biological replicates this represents (e.g. Fig.1d, 4c-d). This should be added.

      We have updated the legends for Figs. 1E, 4C-D, and 6E-F, as suggested.

      1. I also encourage the authors to present a supplementary figure with at least one other biological replicate shown for imaging data (optional).

      We appreciated this suggestion but opted not to add additional supplemental figures, which might have been confusing to readers.

      1. The plasminogen reduction by genetic modulation results in drastic changes to the embryos' appearance - is this a whole embryo KO or endothelial-specific KO? Can authors at least comment on the differences?

      The plasminogen-deficient embryos used in this study were global knockouts; this is now clarified on line 177. The Chd4-ECko embryos with varying degrees of plasminogen deficiency that are shown in Fig. 2F were dissected at E17.5, which is ~3 days after the typical time of death for Chd4-ECko embryos. This explains why the dead and partially resorbed mutants in Fig. 2F look so different from their control (Plg-/-) littermate and from the E14.5 Chd4-ECko embryos shown in Fig. 1C.

      1. In Fig.2b, do I understand correctly only 1 sample was analysed with different areas plotted on the graph? If so, this experiment should be repeated on another set of embryos to be robust, and data plotted as a mean of each embryo (rather than areas).

      Each dot represents the mean value obtained after quantifying 4 fluorescent areas within a liver section from a single embryo. The N number indicates the number of embryos used from each genotype. We have updated the figure legend accordingly.

      1. Also in some graphs, authors specify that it was more than n>x embryos, but then - what are the dots on the graph representing? Each embryo? This should be specified (e.g. Fig.2b-c, but please check this in all the figure legends).

      Thank you for this question. We have worked to clarify the legends for all our graphs. Overall, for graphs related to embryos, each dot represents data from a single embryo. Since the sample sizes vary across genotypes, we used the smallest sample size taken from the mutant groups when listing our minimum N.

      1. "we found Plaur was the only gene that was induced in CHD4-ECko LSECs at E12.5 (Figure S3D)." - I am not sure this is correct, as gene Plau is also increased in 2/3 samples?

      Although Plau transcripts were also increased in Chd4-ECko LSECs compared to control samples, our statistical analysis showed a p-value of 0.0564, which was deemed non-significant according to our cutoff criteria of p

      1. I find the title and the running title somewhat misleading and too broad; the authors should specify more detail in the title about the content of the paper - the current statement of the title is somewhat true but shown only for one genetic model and not confirmed for all types of "lethal embryonic liver degeneration".

      We have updated the title to incorporate this suggestion. The revised title is ‘Plasmin activity and sterile inflammation synergize to promote lethal embryonic liver degeneration in endothelial chromatin remodeler mutants.’ The revised running title is ‘Plasmin and inflammation in endothelial mutant livers.’

      Minor issues:

      1. If an animal licence was used, its number should be specified in the ethics or methods section

      We have added this information to the methods (see line 383).

      1. In fig.3g it is very hard to see each of the samples, could authors try to improve this graph for clarity using colours-or split Y axis - or both?

      We have revised Fig. 3G to include a split y-axis, as suggested.

      1. "This indicates that ECs can play a pro-inflammatory role in embryonic livers and highlights the need for tight regulation to ensure normal liver growth." This sentence for me is misleading, EC are producing inflammatory signals only during the CHD4-KO according to the author's data, and authors do not show such data in normal homeostasis condition. Actually, the pro-inflammatory role here seems detrimental, and ECs should not exhibit it for correct development. The authors should rephrase this to be clearer.

      The detrimental inflammation observed when Chd4 was deleted in ECs indicates that endothelial CHD4 normally suppresses inflammation during liver development (Fig. 3F-G, and 4A-B). When endothelial CHD4 functions properly, there is no excessive cytokine activation and inflammation. We have modified the sentence to help clarify this information (see lines 295-297).

      Significance

      General assessment: The study is well controlled and well written. The findings are interesting. The limitation of the findings is only 1 combination genetic model being studied, and it is unclear if the synergistic effect of sterile inflammation and ECM degradation is broadly applicable to other models, where embryo dies because of liver failure.

      Advance: The study makes an incremental advance, following up findings from a previous study. However, it is conceptually interesting.

      Audience: The audience for this manuscript would be a liver development specialist. However, broader concepts could also be applicable to liver disease.

      Expertise: I research in the field of liver regeneration and disease.

      __Reviewer #2 __

      Evidence, reproducibility and clarity

      In essence, Wu et Al find that Chd4 mutant mice exhibit embryonic liver degeneration due to uPA-mediated plasmin hyperactivity and an ICAM-1-driven hyperinflammation and that additional mutation of BRG1 opposes this liver degeneration, possibly via ICAM-1.

      Generally, this is an excellent manuscript with a very logical sequence of experiments, although it has shortcomings such as validating their findings in an independent system, ideally human, and further establishing the translational relevance. Establishing translational relevance through mechanistic experiments that identify specific inflammatory tissue pathways, such as by blocking ICAM-1 and TNF-alpha, could also define developmental aberrations as a model for broader (patho)physiology and thereby enhance the impact on the field.

      Major

      1. The embryonic and postnatal survival data of Chd4-ECko and Brg1/Chd4-Ecdko mice should be included in Fig. 1

      We revised Fig. 1 to add representative photos and lethality rates for control and mutant embryos at E17.5 (see new Fig. 1B). All Chd4-ECko embryos we dissected at E17.5 were dead, which was consistent with our previous report2. Although Brg1/Chd4-ECdko embryos were largely rescued at E17.5, these mutants still die soon after birth due to lung development issues, as we previously reported3.

      1. What is the impact of Chd4-ECko and Brg1/Chd4-ECdko on the multicellular microenvironment? At a minimum, IF or spatial transcriptomics for hepatocyte and biliary markers, pericytes, and other mesenchymal cells would be recommended. Can there be a distinction made on what type of endothelial cell is affected? (sinusoidal lineage, vs. venous vs. lymphatic)

      To assess whether the multicellular microenvironment of Chd4-ECko livers was altered, we performed immunostaining for various cellular markers from E12.5 to E14.5. These markers included LYVE-1 for liver sinusoids; PROX1 and E-cadherin (ECAD) for hepatocytes; CD41 for platelets and megakaryocytes; CD45 for leukocytes; CD68 and F4/80 for macrophages; MPO for neutrophils; TER119 for erythroid cells; and a-smooth muscle actin (SMA) for pericytes and smooth muscle cells (see Fig. 4D and__ Fig. R1*__). Across all the images we examined, no obvious cell-type-specific differences were observed between control and mutant livers.

      Biliary epithelial cells, which begin to differentiate at approximately E15.54, were also assessed using cytokeratin 19 (CK19) immunostaining; however, no CK19-positive cells were detected in control livers at E14.5 (see Fig. R2*). Note that although LYVE-1 is also expressed by lymphatic endothelial cells, lymphatic vessels are not yet established in the liver at E14.52. Therefore, LYVE-1 staining is appropriate for identifying liver sinusoidal ECs at this stage of development. Our data indicate that the affected vasculature in Chd4-ECko livers is predominantly localized to the liver periphery (see Fig. 1D), which LYVE-1 staining shows to be mostly populated by sinusoidal vessels (Fig. R1B and R1F).

      *Please see uploaded Response to Reviewers PDF for Figures R1 and R2

      1. The experiments showing how endothelial Chd4 loss leads to a hyperinflammatory endothelial-and potentially hepatoblast-state are important. However, the relevance of immune cell infiltration in the hematopoietic-developing liver remains unclear. Which immune cells are presumably recruited to inflame the microenvironment then? Bone-marrow-derived? This aspect would benefit from experimental clarification, for example, using migration and/or direct co-culture versus indirect cell co-culture-ideally with or without ICAM-1 blockade-in vitro assays to determine if direct crosstalk with the CD45+ immune cell compartment explains the hyperinflammatory endothelia phenotype.

      In mice, the first hematopoietic cells emerge in the yolk sac at E7.55. Subsequently, embryonic hematopoiesis takes place in the aorta-gonad-mesonephros (AGM) region and the placenta, before immature hematopoietic cells migrate to the fetal liver. After E11.0, the fetal liver becomes the main hematopoietic organ, supporting the expansion and differentiation of hematopoietic stem and progenitor cells into all mature blood cell lineages5-8. Around E16.5, hematopoietic cells migrate to the bone marrow9, so the bone marrow is not a relevant source of infiltrating immune cells in our E12.5-14.5 Chd4-ECko mutants. We therefore examined immune cell populations, including leukocytes, macrophages, and neutrophils, in Chd4-ECko livers. No enrichment of specific immune cell types was observed in Chd4-ECko livers compared with controls at E13.5-14.5 (Fig. R1). Since immune cells develop within fetal livers at this stage, these findings suggest that they are locally activated rather than recruited to Chd4-ECko livers. Moreover, because fetal livers contain a heterogeneous mixture of immature and mature hematopoietic and immune cells, appropriate in vitro cell models to assess immune cell activation in this context are currently lacking. We have added comments to the introduction to address some of these points (see lines 66-68).

      1. Related to the previous comment: Can the authors validate their findings in an independent, ideally human, cell-based system?

      To explore this, we analyzed PLAUR and ICAM1 transcripts following CHD4 and/or BRG1 knockdown in primary human umbilical vein endothelial cells (HUVECs) for 48 hours. No antagonistic regulation of either gene was detected in HUVECs (Fig. R3*). Moreover, while Icam1 transcription was antagonistically regulated by CHD4 and BRG1 in the mouse MS1 EC line (see Fig. 5A), transcriptional regulation of Plaur by these remodelers was observed only in isolated LSECs and not in cultured MS1 cells. Together, these findings demonstrate that BRG1 and CHD4 play context-specific roles when regulating Icam1 and Plaur transcription in different EC types. Furthermore, in vitro versus in vivo EC environments may additionally influence BRG1 and CHD4 activity.

      *Please see uploaded Response to Reviewers PDF for Figure R3

      1. Identifying the specific hematopoietic/immune subset could further increase the paper's impact, as it would more definitively clarify the mechanism in the developing endothelial niche.

      Please see our response to question # 3.

      1. Also, can the authors show experimentally whether, conversely, Chd4 overexpression can limit an endothelial-type of inflammatory liver injury?

      We agree that exploring this suggestion would provide useful insights. However, we currently lack a genetic or inducible endothelial-specific Chd4 overexpression model, which makes it challenging to link our embryonic findings to the context of adult liver injury. For now, our study demonstrates that hepatic ECs regulate sterile inflammation to support embryonic liver development. Future development of appropriate genetic tools will allow us to determine if the role of endothelial CHD4 that is demonstrated in the current study is recapitulated in adult inflammatory liver injury models.

      Minor

      1. A separate figure panel for Chd4fl/fl; Vav-Cre+ appears reasonable, instead of being shown as a table.

      Thank you. Please see our new Fig. S1, which includes representative images (and lethality rates) of control and Chd4fl/fl;Vav-Cre+ embryos at E18.5.

      Significance:

      Generally, this is an excellent manuscript with a strong developmental biology focus, and its translational relevance is not immediately apparent; however, establishing such a link could significantly increase its impact. For example, the significance of these findings in ischemia-reperfusion injury, SOS/VOD, and sepsis could offer therapeutic avenues to stabilize endothelial function.

      The advance is the elegant discovery of a multifactorial endothelial-stabilizing mechanism in development, although its applicability to scenarios beyond developmental mutation remains unknown.

      The strengths are the clear and transparent experimental interrogation. Rightfully, the authors acknowledge that there would be a benefit in finalizing inflammatory blockade, genetic or antibody-mediated, to pin down the mechanistic circuit.

      The reviewer's expertise is: childhood liver diseases, developmental liver organoid generation, stem cells (iPSCs), cell reprogramming

      Reviewer #3

      Evidence, reproducibility and clarity:

      1. Wu et al. report antagonistic roles for chromatin remodelers Chd4 and Brg1, in endothelial cells, during liver development. There is a major flaw in the study which makes it difficult to interpret the conclusions. The genotypes of the mouse models used are flawed. The comparison should be made between two single knockouts (Chd4 single, Brg1 single), double mutants (Chd4/Brg1) and proper controls. For both "single KO", one allele of the other gene is also deleted - Chd4 -Ecko has one allele of Brg1 deleted and vice versa. Also, the proper control should be Chd4 fl/flBrg1fl/fl without the Cre. Since 3 alleles (not just two that belong to the same gene) are deleted in a single knockout, it is impossible to assign the effect to one gene.

      We acknowledge the fact that the single Brg1 and Chd4 EC knockouts in this study each carry a heterozygous deletion allele for the other remodeler (exact genotypes are shown in Fig. 1A). The mating strategy that yielded these mutants was chosen for three reasons. First, we have found that genetic background influences the embryonic phenotypes of these chromatin remodeler mutants3. Moreover, embryonic development at the stages analyzed in this study occurs quickly and requires precise timing for comparative analysis between genotypes. Therefore, it is most rigorous to study littermates when comparing single- and double-mutant embryos for BRG1 and CHD4. To achieve this, we used Brg1fl/fl;Chd4fl/fl females rather than Brg1fl/+;Chd4fl/+ females for timed matings. Although the former females cannot produce single knockout embryos without a compound heterozygous allele of the other remodeler, these females allowed us to generate single- and double-knockouts at a rate of 1/8 embryos. If we had used Brg1fl/+;Chd4fl/+ females for timed matings, we would have been able to generate “clean” single mutants with wildtype alleles of the other remodeler, but the single- and double-knockout generation rate would have been 1/32 embryos. This would have been an impractical mutant generation rate for this study. Second, our prior research demonstrates that heterozygous deletion of Chd4 or Brg1 does not produce the liver phenotypes seen with the respective homozygous deletions2,3. Third, the complete lethality of Chd4-ECko (Brg1fl/+;Chd4fl/fl;VE-cadherin-Cre+) mutants in this study demonstrates that deleting one allele of Brg1 cannot rescue Chd4-related lethality.

      As for controls in this study, we saw no evidence of phenotypes or of any gene deletion in our Cre- embryos (either in this study or in previous ones analyzing similar phenotypes2,3). Therefore, we used Cre- embryos for controls because they were generated at a 1/2 rate by our timed matings, which boosted our output for analyses.

      Specific points

      1. Fig 2c Plaur transcript - no statistical comparison between 2nd and 4th column, Chd4 Ecko vs double mutant. If there is not statistical difference, does not explain the rescue in double mutants

      Thank you for the suggestion. We have included a comparison between Chd4-ECko and Brg1/Chd4-ECdko in our revised Fig 2C. The Kruskal-Wallis test showed a significant difference between the Chd4-Ecko and Brg1/Chd4-ECdkogroups (p=0.016). This indicates that Plaur induction in Chd4-Ecko LSECs is rescued in Brg1/Chd4-ECdko LSECs.

      1. Fig 2e. Comparison should be made between Plg-/- Chd4 fl/fl and Plg-/- Chd4 fl/fl Cre, not other genotypes

      This experiment aims to determine whether different levels of plasminogen (Plg) reduction can rescue the lethality caused by Chd4 deletion. To do this, we set up the mating strategy shown in Fig. 2E to produce appropriate littermate controls and to compare lethality among Plg+/+;Chd4-ECko, Plg+/-;Chd4-ECko, and Plg-/-;Chd4-ECko embryos. This comparison would not have been possible with embryos generated only from mice on a Plg-/- background.

      1. Fig. 4. How does Chd4 or Brg1 activity in endothelial cells lead to Icam1 activation in epithelial cells?

      Since cytokines like IFNg, TNFa, and IL1b can induce ICAM-1 expression in hepatocytes10, we speculate that ICAM-1 expression in hepatoblasts (ECAD+ cells in Fig. 4D) was induced by the elevated TNFa and IL1b produced in Chd4-ECko livers (Fig. 3G).

      1. Mice used in Figure 5 are Cdf4 fl/+ and Cdf4 fl/fl, no Brg1 deletion. The authors improperly compare these to Chd4-Ecko which have one allele of Brg1 deleted. The rescue needs to be done in the same genotype Chd4-Ecko.

      Please note that data from Fig. 5 were generated from cultured ECs (MS1 cells).

      Significance

      Wu et al. report antagonistic roles for chromatin remodelers Chd4 and Brg1, in endothelial cells, during liver development. There is a major flaw in the study which makes it difficult to interpret the conclusions. Genotypes that were chosen for the study make the data not interpretable

      Please see our response to your Question #1


      In summary, we have included the following changes to this revised manuscript:

      • New Figure 1B: Representative images and lethality rates for control, Chd4-ECko, Brg1-ECko, and Brg1/Chd4-ECdko embryos at E17.5.
      • New Figure 2C: qRT-PCR analysis of Chd4, Brg1, and Plaur gene transcripts in E12.5 control and mutant LSECs.
      • Regraphing of Figure 3G: qRT-PCR analysis of Tnf, Il6, and Il1b gene transcripts in E14.5 control and mutant livers.
      • New Figure S1: Representative images and lethality rates for control, Chd4fl/+;Vav-Cre+, and Chd4fl/fl;Vav-Cre+embryos at E18.5. References for this revision:

      Alva JA, Zovein AC, Monvoisin A, Murphy T, Salazar A, Harvey NL, Carmeliet P, Iruela-Arispe ML. VE-Cadherin-Cre-recombinase Transgenic Mouse: A Tool for Lineage Analysis and Gene Deletion in Endothelial Cells. Dev Dyn. 2006;235:759-767. doi: 10.1002/dvdy.20643 Crosswhite PL, Podsiadlowska JJ, Curtis CD, Gao S, Xia L, Srinivasan RS, Griffin CT. CHD4-regulated plasmin activation impacts lymphovenous hemostasis and hepatic vascular integrity. J Clin Invest. 2016;126:2254-2266. doi: 10.1172/JCI84652 Wu ML, Wheeler K, Silasi R, Lupu F, Griffin CT. Endothelial Chromatin-Remodeling Enzymes Regulate the Production of Critical ECM Components During Murine Lung Development. Arterioscler Thromb Vasc Biol. 2024;44:1784-1798. doi: 10.1161/ATVBAHA.124.320881 Shiojiri N, Inujima S, Ishikawa K, Terada K, Mori M. Cell lineage analysis during liver development using the spfash-heterozygous mouse. Lab Invest. 2001;81:17-25. doi: 10.1038/labinvest.3780208 Soares-da-Silva F, Peixoto M, Cumano A, Pinto-do OP. Crosstalk Between the Hepatic and Hematopoietic Systems During Embryonic Development. Front Cell Dev Biol. 2020;8:612. doi: 10.3389/fcell.2020.00612 Ema H, Nakauchi H. Expansion of hematopoietic stem cells in the developing liver of a mouse embryo. Blood. 2000;95:2284-2288. Kieusseian A, Brunet de la Grange P, Burlen-Defranoux O, Godin I, Cumano A. Immature hematopoietic stem cells undergo maturation in the fetal liver. Development. 2012;139:3521-3530. doi: 10.1242/dev.079210 Freitas-Lopes MA, Mafra K, David BA, Carvalho-Gontijo R, Menezes GB. Differential Location and Distribution of Hepatic Immune Cells. Cells. 2017;6. doi: 10.3390/cells6040048 Christensen JL, Wright DE, Wagers AJ, Weissman IL. Circulation and chemotaxis of fetal hematopoietic stem cells. PLoS Biol. 2004;2:E75. doi: 10.1371/journal.pbio.0020075 Satoh S, Nussler AK, Liu ZZ, Thomson AW. Proinflammatory cytokines and endotoxin stimulate ICAM-1 gene expression and secretion by normal human hepatocytes. Immunology. 1994;82:571-576.

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      Referee #3

      Evidence, reproducibility and clarity

      Wu et al. report antagonistic roles for chromatin remodelers Chd4 and Brg1, in endothelial cells, during liver development. There is a major flaw in the study which makes it difficult to interpret the conclusions. The genotypes of the mouse models used are flawed. The comparison should be made between two single knockouts (Chd4 single, Brg1 single), double mutants (Chd4/Brg1) and proper controls. For both "single KO", one allele of the other gene is also deleted - Chd4 -Ecko has one allele of Brg1 deleted and vice versa. Also, the proper control should be Chd4 fl/flBrg1fl/fl without the Cre. Since 3 alleles (not just two that belong to the same gene) are deleted in single knockout it is impossible to assign the effect on one gene.

      Specific points

      1. Fig 2c Plaur transcript - no statistical comparison between 2nd and 4th column, Chd4 Ecko vs double mutant. If there is not statistical difference, does not explain the rescue in double mutants
      2. Fig 2e. Comparison should be made between Plg-/- Chd4 fl/fl and Plg-/- Chd4 fl/fl Cre, not other genotypes
      3. Fig. 4. How does Chd4 or Brg1 activity in endothelial cells lead to Icam1 activation in epithelial cells?
      4. Mice used in Figure 5 are Cdf4 fl/+ and Cdf4 fl/fl, , no Brg1 deletion. The authors improperly compare these to Chd4-Ecko which have one allele of Brg1 deleted. The rescue need s to be done in the same genotype Chd4-Ecko.

      Significance

      Wu et al. report antagonistic roles for chromatin remodelers Chd4 and Brg1, in endothelial cells, during liver development. There is a major flaw in the study which makes it difficult to interpret the conclusions. Genotypes that were chosen for the study make the data not interpretable

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      Referee #2

      Evidence, reproducibility and clarity

      In essence, Wu et Al find that Chd4 mutant mice exhibit embryonic liver degeneration due to uPA-mediated plasmin hyperactivity and an ICAM-1-driven hyperinflammation and that additional mutation of BRG1 opposes this liver degeneration, possibly via ICAM-1.

      Generally, this is an excellent manuscript with a very logical sequence of experiments, although it has shortcomings such as validating their findings in an independent system, ideally human, and further establishing the translational relevance. Establishing translational relevance through mechanistic experiments that identify specific inflammatory tissue pathways, such as by blocking ICAM-1 and TNF-alpha, could also define developmental aberrations as a model for broader (patho)physiology and thereby enhance the impact on the field.

      Major

      • The embryonic and postnatal survival data of Chd4-ECko and Brg1/Chd4-Ecdko mice should be included in Fig. 1
      • What is the impact of Chd4-ECko and Brg1/Chd4-ECdko on the multicellular microenvironment? At a minimum, IF or spatial transcriptomics for hepatocyte and biliary markers, pericytes, and other mesenchymal cells would be recommended. Can there be a distinction made on what type of endothelial cell is affected? (sinusoidal lineage, vs. venous vs. lymphatic)
      • The experiments showing how endothelial Chd4 loss leads to a hyperinflammatory endothelial-and potentially hepatoblast-state are important. However, the relevance of immune cell infiltration in the hematopoietic-developing liver remains unclear. Which immune cells are presumably recruited to inflame the microenvironment then? Bone-marrow-derived? This aspect would benefit from experimental clarification, for example, using migration and/or direct co-culture versus indirect cell co-culture-ideally with or without ICAM-1 blockade-in vitro assays to determine if direct crosstalk with the CD45+ immune cell compartment explains the hyperinflammatory endothelia phenotype.
      • Related to the previous comment: Can the authors validate their findings in an independent, ideally human, cell-based system?
      • Identifying the specific hematopoietic/immune subset could further increase the paper's impact, as it would more definitively clarify the mechanism in the developing endothelial niche.
      • Also, can the authors show experimentally whether, conversely, Chd4 overexpression can limit an endothelial-type of inflammatory liver injury?

      Minor

      • A separate figure panel for Chd4fl/fl; Vav-Cre+ appears reasonable, instead of being shown as a table.

      Significance

      Generally, this is an excellent manuscript with a strong developmental biology focus, and its translational relevance is not immediately apparent; however, establishing such a link could significantly increase its impact. For example, the significance of these findings in ischemia-reperfusion injury, SOS/VOD, and sepsis could offer therapeutic avenues to stabilize endothelial function.

      The advance is the elegant discovery of a multifactorial endothelial-stabilizing mechanism in development, although its applicability to scenarios beyond developmental mutation remains unknown.

      The strengths are the clear and transparent experimental interrogation. Rightfully, the authors acknowledge that there would be a benefit in finalizing inflammatory blockade, genetic or antibody-mediated, to pin down the mechanistic circuit.

      The reviewer's expertise is: childhood liver diseases, developmental liver organoid generation, stem cells (iPSCs), cell reprogramming

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Wu and Griffin describes a mechanism where CHD4 and BRG1, two chromatin remodelling enzymes, have antagonistic functions to regulate extracellular matrix (ECM) plasmin activity and sterile inflammatory phenotype in the endothelial cells of the developing liver. As a follow up from a previous study, the authors investigate the phenotype of embryonic-lethal endothelial-specific CHD4-knockout, leading to liver phenotype and embryo death, and the rescue of this phenotype when subsequently BRG1 is knocked-out also in the endothelium. First, the authors show that the increase in plasmin activator uPAR (which leads to ECM degradation) in CHD4-KO embryos can be rescued by BRG1-KO, and that both CHD4 and BRG1 interact with the uPAR promoter. However, the authors demonstrate that reducing plasminogen by genetic knockout is unable to rescue the CHD4-KO embryos alone, suggesting an additional mechanism. By RNAseq analysis, the authors identify sterile inflammation as another potential contributor to the lethal phenotype of CHD4-KO embryos through increased expression of ICAM-1 in endothelial cells, also showing binding of both chromatin remodellers to ICAM-1 promoter. Finally, the authors use nonsteroidal anti-inflammatory drug carprofen, alone or in combination with plasminogen genetic knockout, and demonstrate CHD4-KO lethal embryonic phenotype rescue with the combination of plasminogen reduction and inflammation reduction, highlighting the synergistic role of both ECM degradation and sterile inflammation in this genetic KO.

      The findings of the manuscript are interesting, experiments well controlled and paper well written. While the work is of potential specialist interest to the field of liver development, there are several issues which authors should address before this paper can be published:

      Major issues:

      • The authors still see embryonic lethality of some embryos with endothelial BRG1-KO or combined endothelial CHD4/BRG1-KO - could the authors please show or at least comment in the discussion why those animals are dying?
      • In the qRT-PCR results Fig.2c, what is each dot?
      • In the same figure, I would expect that in CHD4-KO there is no CHD4 transcript, and in BRG1-KO there is no BRG1 transcript, rather than the reduction shown, which seems quite noisy (though significant) - is it this a result of normalisation? Or is indeed only a certain amount of the transcript reduced?
      • In the same figure, is the statistical testing performed before or after normalisation? This can introduce errors if done after normalisation.
      • In some cases, the authors show immunofluorescence images but do not specify how many biological replicates this represents (e.g. Fig.1d, 4c-d). This should be added.
      • I also encourage the authors to present a supplementary figure with at least one other biological replicate shown for imaging data (optional).
      • The plasminogen reduction by genetic modulation results in drastic changes to the embryos' appearance - is this a whole embryo KO or endothelial-specific KO? Can authors at least comment on the differences?
      • In Fig.2b, do I understand correctly only 1 sample was analysed with different areas plotted on the graph? If so, this experiment should be repeated on another set of embryos to be robust, and data plotted as a mean of each embryo (rather than areas).
      • Also in some graphs, authors specify that it was more than n>x embryos, but then - what are the dots on the graph representing? Each embryo? This should be specified (e.g. Fig.2b-c, but please check this in all the figure legends).
      • "we found Plaur was the only gene that was induced in CHD4-ECko LSECs at E12.5 (Figure S3D)." - I am not sure this is correct, as gene Plau is also increased in 2/3 samples?
      • I find the title and the running title somewhat misleading and too broad; the authors should specify more detail in the title about the content of the paper - the current statement of the title is somewhat true but shown only for one genetic model and not confirmed for all types of "lethal embryonic liver degeneration".

      Minor issues:

      • If an animal licence was used, its number should be specified in the ethics or methods section
      • In fig.3g it is very hard to see each of the samples, could authors try to improve this graph for clarity using colours-or split Y axis - or both?
      • "This indicates that ECs can play a pro-inflammatory role in embryonic livers and highlights the need for tight regulation to ensure normal liver growth." This sentence for me is misleading, EC are producing inflammatory signals only during the CHD4-KO according to the author's data, and authors do not show such data in normal homeostasis condition. Actually, the pro-inflammatory role here seems detrimental, and ECs should not exhibit it for correct development. The authors should rephrase this to be clearer.

      Significance

      General assessment: The study is well controlled and well written. The findings are interesting. The limitation of the findings is only 1 combination genetic model being studied, and it is unclear if the synergistic effect of sterile inflammation and ECM degradation is broadly applicable to other models, where embryo dies because of liver failure.

      Advance: The study makes an incremental advance, following up findings from a previous study. However, it is conceptually interesting.

      Audience: The audience for this manuscript would be a liver development specialist. However, broader concepts could also be applicable to liver disease.

      Expertise: I research in the field of liver regeneration and disease.

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      Reply to the reviewers

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

      SECTION A - Evidence, Reproducibility, and Clarity Summary The study investigates the neurodevelopmental impact of trisomy 21 on human cortical excitatory neurons derived from induced pluripotent stem cells (hiPSCs). Key findings include a modest reduction in spontaneous firing, a marked deficit in synchronized bursting, decreased neuronal connectivity, and altered ion channel expression-particularly a downregulation of voltage‐gated potassium channels and HCN1. These conclusions are supported by a combination of in vitro calcium imaging, electrophysiological recordings, viral monosynaptic tracing, RNA sequencing, and in vivo transplantation with two‐photon imaging.

      Major Comments • Convincing Nature of Key Conclusions: The study's conclusions are generally well supported by a diverse set of experimental approaches. However, certain claims regarding the intrinsic properties of the excitatory network would benefit from further qualification. In particular, the assertion that reduced synchronization is solely attributable to altered ion channel expression might be considered somewhat preliminary without additional corroborative experiments.

      1.1) We agree with the reviewer and now write in the abstract: 'Together, these findings demonstrate long-lasting impairments in human cortical excitatory neuron network function associated with Trisomy 21 .' And in the Introduction: 'Collectively, the observed changes in ion channel expression, neuronal connectivity, and network activity synchronization may contribute to functional differences relevant to the cognitive and intellectual features associated with Down syndrome.'

      • One major limitation of the current experimental design is the reliance on predominantly excitatory neuronal cultures derived from hiPSCs. Although the authors convincingly demonstrate differences in network synchronization and connectivity between trisomic (TS21) and control neurons, the almost exclusive focus on excitatory cells limits the physiological relevance of the in vitro network. In the developing cortex, interneurons and astrocytes play crucial roles in modulating network excitability, synaptogenesis, and plasticity. Therefore, incorporating these cell types-either through co-culture systems or through directed differentiation protocols that yield a more heterogeneous neuronal population-could help to determine whether the observed deficits are intrinsic to excitatory neurons or are compounded by a lack of proper inhibitory regulation and glial support. 1.2) Thank you for this thoughtful comment. We agree that interneurons and astrocytes are crucial for network function. To clarify, astrocytes are generated in this culture system, as we previously reported in our characterisation of the timecourse of network development using this approach (Kirwan et al., Development 2025). However, our primary goal was to first isolate and define the cell-autonomous defects intrinsic to TS21 excitatory neurons, minimizing the complexity introduced by additional neuronal types. This focused approach was chosen also because engineering a stable co-culture system with reproducible excitatory/inhibitory (E/I) proportions is a significant undertaking that extends beyond the scope of this initial investigation, and has proven challenging to date for the field. By establishing this foundational phenotype, our work complements prior studies on interneuron and glial contributions. Future studies building on this work will be essential to dissect the more complex, non-cell-autonomous effects within a heterogeneous network. Importantly, since our initial submission, two highly relevant preprints have emerged-including a notable study from the Geschwind laboratory at UCLA (Vuong et al., bioRxiv, 2025; Risgaard et al., bioRxiv, 2025), as well as our own complementary study Lattke et al, under revision, that highlight widespread transcriptional changes in excitatory cells of the human fetal DS cortex, providing strong validation for our central findings. This convergence of results from multiple groups underscores the timeliness and importance of our work.

      • Furthermore, the assessment of neuronal connectivity via pseudotyped rabies virus tracing, while innovative, has inherent limitations. The quantification of connectivity as a ratio of red-to-green fluorescence pixels may be influenced by differential viral infection efficiencies, variations in the expression levels of the TVA receptor, or even by the lower basal activity levels observed in TS21 cultures. Complementary approaches-such as electron microscopy for synaptic density analysis or functional connectivity measurements using multi-electrode arrays (MEAs)-could provide additional structural and functional insights that would validate the rabies tracing data. 1.3) Thank you for this constructive feedback. While we cannot formally exclude that TS21 cells might express the TVA receptor at lower levels due to generalized gene dysregulation, we infected all WT and TS21 cultures in parallel using identical virus preparations and titers to minimize technical variability. Crucially, we also addressed the potential confound of differential basal activity by performing the rabies tracing under TTX incubation (see Suppl. Fig. 7), which blocks network activity and ensures that viral spread reflects structural connectivity alone.

      While complementary methods like EM or MEA could provide additional insight, they fall outside the scope of the current study. We are confident that our rigorous controls validate our use of the rabies tracing method to assess structural connectivity.

      • Qualification of Claims: Some conclusions, particularly those linking specific ion channel dysregulation (e.g., HCN1 loss) directly to network deficits, might be better presented as preliminary. The authors could temper their language to indicate that while the evidence is suggestive, the mechanistic link remains to be fully established. 1.4) We have revised the text to more clearly indicate that the link between HCN1 dysregulation and network deficits is correlative and remains to be fully established. While our ex vivo recordings suggest altered Ih-like currents consistent with reduced HCN1 expression, we now present these findings as preliminary and hypothesis-generating, pending further functional validation. We write in the discussion: However, further targeted functional validation will be needed to confirm a causal link.

      • Need for Additional Experiments: Additional experiments that could further consolidate the current findings include: o Inclusion of Inhibitory Neurons or Co-culture Systems: Incorporating interneurons or astrocytes would help determine whether the observed deficits are solely intrinsic to excitatory neurons. See 1.2 o Alternative Connectivity Assessments: Complementing the rabies virus tracing with electron microscopy or multi-electrode array (MEA) recordings would add structural and functional validation of the connectivity differences. See 1.3 o Extended Temporal Profiling: Monitoring network activity over a longer developmental window would clarify whether the observed deficits represent a delay or a permanent alteration in network maturation. 1.5) In vivo we were able to track the cells for up to five months post-transplantation supporting the interpretation of a permanent alteration.

      • Reproducibility and Statistical Rigor: The methods and data presentation are largely clear, with adequate replication and appropriate statistical analyses. Nonetheless, a more detailed description of the experimental replicates, particularly regarding the viral tracing and in vivo transplantation studies, would enhance reproducibility. The availability of raw data and scripts for calcium imaging analysis would also further support independent verification. We thank the reviewer for these suggestions and we now provide a more detailed description of replicates. We also add the raw data.

      Minor Comments • Experimental Details: Minor revisions could include clarifying the infection efficiency and expression levels of the viral constructs used in connectivity assays to rule out technical variability.

      See 1.3

      • Literature Context: The authors reference prior studies appropriately; however, integrating a brief discussion comparing their findings with alternative DS models (e.g., organoids or other hiPSC-derived systems) would improve contextual clarity. We thank the reviewer for this helpful suggestion. We have now added a brief discussion comparing our findings with those reported in alternative Down syndrome models, including brain organoids and other hiPSC-derived systems. This addition helps to contextualize our results within the broader field and highlights the unique strengths and limitations of our in vitro and in vivo xenograft approach. We write: 'Our findings align with and extend previous studies using alternative Down syndrome models, such as brain organoids and other hiPSC-derived systems. Organoid models have provided valuable insights into early neurodevelopmental phenotypes in DS, including altered interneuron proportions (Xu et al Cell Stem Cell 2019) but also suggest that variability across isogenic lines can overshadow subtle trisomy 21 neurodevelopmental phenotypes (Czerminski et al Front in Neurosci 2023). However, these systems often lack the structural complexity, vascularization, and long-term maturation achievable in vivo. By using a xenotransplantation model, we were able to assess the maturation and functional properties of human neurons within a physiologically relevant environment over extended time frames, offering complementary insights into DS-associated circuit dysfunction (Huo et al Stem Cell Reports 2018; Real et al., 2018).

      • Presentation and Clarity: Figures are generally clear,.But the manuscript contains a minor labeling error. On page 13, the figure is erroneously labeled as "Fig6A", whereas, based on the context and corresponding data, it should be "Fig5A". I recommend that the authors correct this mistake to ensure consistency and avoid potential confusion for readers. Thank you for pointing this out. This has been corrected in the revised manuscript.

      Reviewer #1 (Significance (Required)):

      SECTION B - Significance • Nature and Significance of the Advance: The work offers a substantial conceptual advance by providing a mechanistic link between trisomy 21 and impaired neuronal network synchronization. Technically, the study integrates state-of-the-art imaging, electrophysiology, and transcriptomic profiling, thereby offering a multifaceted view of DS-related neural dysfunction. Clinically, the findings have the potential to inform future therapeutic strategies targeting network connectivity and ion channel function in Down syndrome.

      We thank the reviewer for this very supportive comment.

      • Context in the Existing Literature: The study builds on previous observations of altered network activity in DS patients and DS mouse models (e.g., altered EEG synchronization and reduced synaptic connectivity). It extends these findings to human-derived neuronal models, thus bridging a gap between clinical observations and molecular/cellular mechanisms. Relevant literature includes studies on DS neurodevelopment and the role of ion channels in synaptic maturation. • Target Audience: The reported findings will be of interest to researchers in neurodevelopmental disorders, Down syndrome, and ion channel physiology. Additionally, the study may attract the attention of those working on hiPSC-derived models of neurological diseases, as well as clinicians interested in the pathophysiology of DS. • Keywords and Field Contextualization: Keywords: Down syndrome, trisomy 21, neuronal connectivity, synchronized network activity, hiPSC-derived cortical neurons, ion channel dysregulation.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary The manuscript by Peter et al., reports on the neuronal activity and connectivity of iPSC-derived human cortical neurons from Down syndrome (DS) that is caused by caused by trisomy of the human chromosome 21 (TS21). Major points: Although the manuscript is potentially interesting, the results appear somehow preliminary and need to be corroborated by control experiments and quantifications of effects to fully sustain the conclusions. (1) The authors have not assessed the percentage of WT and TS21 cells that acquire a neuronal or glia identity in their cultures. Indeed, the origin of alterations in network activity and connectivity observed in TS21 neurons could simply derive from reduced number of neurons arising from TS21 iPSC. Alternatively, the same alteration in network activity and connectivity could derive from a multitude of other factors including deficits in neuronal development, neurite extension, or intrinsic electrophysiological properties. In the current version of the manuscript, none of these has been investigated. 2.1) We thank the reviewer for this thoughtful comment. In response, we included an in vivo characterization of cell-type proportions at the same time points where we observed network activity defects using in vivo calcium imaging (see Supplementary Fig. 6).

      Previous work has identified several cellular and molecular phenotypes in human cells, postmortem tissue, and mouse models-including those mentioned by the reviewer. In this study, our focus was on investigating neural network activity, intrinsic electrophysiological properties both in vitro and in vivo, and preliminary bulk RNA sequencing. We have also independently measured cell proportions in the human fetal cortex and conducted a more extensive transcriptomic analysis of Ts21 versus control cells in a separate study (Lattke et al., under revision). We observed a reduction of RORB/FOXP1-expressing Layer 4 neurons in the human fetal cortex at midgestation, as well as increased GFAP+ cells, reduced progenitors and a non significant reduction of Cux2+ cells in late stage DS human cell transplants, along with a gene network dysregulation specifically affecting excitatory neurons (Lattke et al., under revision). Here, we provide complementary findings, demonstrating reduced excitatory neuron network connectivity in vitro and decreased neural network synchronised activity in both in vitro and in vivo models (see also 2.8). We agree with the reviewer that this could be for a number of reasons, both cell autonomous (channel expression and/or function) or non-autonomous (connectivity and/or network composition - as reflected in differences in proportions of SATB2+ neurons generated in TS21 cortical differentiations).

      (2) Electrophysiological properties of TS21 and WT neurons at day 53/54 in vitro indicate an extremely immature stage of development (i.e. RMP between -36 and -27 mV with most of the cells firing a single action potential after current injection) in the utilized culture conditions: This is far from ideal for in vitro neuronal-network studies. Finally, reduced activity of HCN1 channels should be confirmed by specific recordings isolating or blocking the related current.

      2.2) Thank you for this thoughtful comment. We have also conducted ex vivo electrophysiological recordings and found that the neurons exhibit relatively immature properties, consistent with the known slow developmental trajectory of human neuron cultures. In light of this and the absence of direct confirmatory evidence, we now refer to the observed reduction in HCN1 as preliminary.

      Main points highlighting the preliminary character of the study. 1) In Figure 1 immunofluorescence images of the neuronal differentiation markers (Tbr1, Ctip2 and Tuj1) are showed. However, no quantification of the percentage of cells expressing these markers for WT and TS21 neurons is reported. On the other hand, simple inspection of the representative images clearly seams to indicate a difference between the two genotypes, with TS21 cultures showing lower number of cells expressing neuronal markers. This quantification should be corroborated by a similar staining for an astrocyte marker (GFAP, but not S100b since is triplicated in DS). This is an extremely important point since it is obvious that any change in the percentage of neurons (or the neuron/astrocyte ratio) in the cultures will strongly affect the resulting network activity (shown in Figure 2) and the connectivity (showed in Figure 4). Possibly, the quantification should be done at the same time points of the calcium imaging experiments.

      2.3) See 2.1. We included an in vivo characterization of cell-type proportions at the same time points where we observed network activity defects using in vivo calcium imaging. (see Supplementary Fig. 6).

      2) In Figure 2 the authors show some calcium imaging traces of WT and TS21 cultures at different time points. However, they again do not show any quantification of neuronal activity. A power spectra analysis is shown in Supplementary Figure 2, but only for WT cultures, while in Supplementary Figure 3 a comparison between WT and Ts21 power spectra is done, but only at the 50 day time point, while difference in synchrony are assessed at 60 days. At minimum, the author should include in main Figure 2 the quantification of the mean calcium event rate and mean event amplitude at the different time points and the power spectra analysis for both WT and TS21 cultures at the same timepoints.

      2.4) We thank the reviewer for this comment. We now add the power spectra analysis in the main Figure 2 and quantification of the mean calcium burst rate and mean event amplitude in SuppFig. 4.

      Of note, the synchronized neuronal activity is present in WT cultures at day 60, but totally lost at subsequent time-points (70 and 80 days). The results of this later time points are different from previous data from the same lab (Kirwan et al., 2015). How might these data be explained? It would be important to rule out any potential issues with the health of the culture that could explain the loss of neuronal activity.It would be beneficial to check cell viability at the different time points to exclude possible confounding factors ? A propidium staining or a MTT assay would strongly improve the soundness of the calcium data.

      2.5) We thank the reviewer for this important observation. The difference from the findings reported in Kirwan et al., 2015 is due to the use of a different neuronal differentiation medium in the current study (BrainPhys versus N2B27). BrainPhys medium supports robust early network activity compared to N2B27 (onset before day 60 in BrainPhys, post-day 60 in N2B27), resulting in an earlier decline in synchrony at later stages (day 70-80 in BrainPhys, compared with day 90-100 in N2B27). Importantly, in our in vivo xenograft model, burst activity is sustained up to at least 5 months post-transplantation (mpt), indicating that the neurons retain the capacity for network activity over extended periods in a more physiological environment. We adapted the text accordingly.

      3) In Figure 3 there is no quantification of the number and/or density of transplanted neurons for WT and TS21, but only representative images. As above, inspection of the representative images seems to show a decrease in cells labeled by the Tbr1 neuronal marker for TS21 cells. Moreover, the in vivo calcium imaging of transplanted WT and TS21 cells lacks most of the quantification normally done in calcium imaging experiments. Are the event rate and event amplitude different between WT and TS21 neurons ? The measure of neuronal synchrony by mean pixel correlation is not well explained, but it looks somehow simplistic. Neuronal synchrony can be more precisely measured by cross-correlation analysis or spike time tiling coefficients on the traces from single-neuron ROI rather than on all pixels in the field of view, as apparently was done here.

      2.6) We thank the reviewer for these valuable points. We now include quantification of the number and density of transplanted neurons for both WT and Ts21 grafts in Extended Data Figure 5 (see 2.1).

      Regarding the in vivo calcium imaging, we appreciate the reviewer's suggestion to include additional standard metrics. We have quantified the event rate in Real et al 2018. These analyses reveal that Ts21 neurons show a reduction in event rate.

      We agree that our initial description of the synchrony analysis using mean pixel correlation was not sufficiently detailed. We have now clarified this in the Methods and Results, and we acknowledge its limitations. Importantly, we note that the reduced synchronisation is a highly consistent phenotype, observed across at least six independent donor pairs, different differentiation protocols, and both in vitro (and in two independent labs) and in vivo settings. As suggested, future studies using ROI-based approaches-such as cross-correlation or spike-time tiling coefficients-would provide a more refined characterization of synchrony at the single-neuron level (Sintes et al, in preparation). We now include this point in the discussion.

      4) The results on reduced neuronal connectivity in Figure 3 look very striking. However, these results should be accompanied by control experiments to verify the number of neuronal cells and neurite extension in WT and Ts21 cultures. These two parameters could indeed strongly influence the results. As the cultures appear to grow in clusters, bright-field images and TuJ1 staining of the cultures will also greatly help to understand the degree of morphological interconnection between the clusters.

      We now add Tuj1 staining in Supplementary figure 10.

      5) The authors performed RNA-seq experiments on day 50 cultures. Why the authors do not show the complete differential gene expression analysis, but only a small subset of genes? A comprehensive volcano plot and the complete list of identified genes with logFC and FDR values would be helpful. If possible, comparison of the present data (particularly on KCN and HCN expression changes) with published and publicly available expression datasets of other human or human Down syndrome iPSC-derived neurons or human Down syndrome brains will greatly increase the soundness of the present findings. In addition, the gene ontology (GO) results are mentioned in the text, but are not presented. Showing the complete GO analysis for both up and downregulated genes will help the reader to better understand the RNA-seq results. Notably, the results shown in Supplementary Figure on GRIN2A and GRIN2B expression (with values of 300-700 counts versus 2000-4000 counts, respectively) clearly indicate that in both WT and TS21 cultures the NMDA developmental switch has not occurred yet at the 50 days timepoint.

      We now show volcano plots in Supplementary Fig. 11.

      6) The measure of hyperpolarization-activated currents shown in Figure 5 lack proper control experiments. First, the hyperpolarizing current in TS21 cells do not reach a steady-state as the controls. The two curves are therefore hard to compare. To exclude possible difference in kinetic activation, the authors should have prolonged the current injection period (1-2 seconds). Second, to ultimately prove that such currents are mediated by HCN channels in WT cells the authors should perform some control experiments with a specific HCN blocker. A good example of a suitable protocol, with also current blockers to exclude all other possible current contributions, is the one reported in Matt et al Cell. Mol. Life Sci. 68, 125-137 (2011).

      2.7) We thank the reviewer for this detailed and helpful comment. We agree that to definitively identify the recorded currents as Ih, it would be necessary to isolate them pharmacologically using specific HCN channel blockers and appropriate controls, such as those described in Matt et al., Cell. Mol. Life Sci. Unfortunately, due to current constraints, we no longer have access to the animals used in this study and cannot allocate the necessary time or resources, we are unable to perform the additional experiments at this stage.

      However, our goal here was to use electrophysiological recordings as an indication of altered HCN channel activity, which we then support with molecular evidence. We now emphasize this point more clearly in the revised manuscript.

      7) The manuscript lacks information on the statistical analysis used. Also, the numerosity of samples is not clear. Were the dots shown in some graph technical replicates from a single neuronal induction or were all independent neuronal inductions or a mix of the two ? Please clarify.

      We now clarify the numbers in the Figure legend.

      8) The method section lacks important information to guarantee reproducibility. Just a few examples: • Only electrophysiology methods for slice are reported, but not for in vitro culture.

      We now clarify these details in the methods.

      • Details on Laminin coating is lacking. What concentration was used ? Was poly-ornithine or poly-lysine used before Laminin coating ? We now clarify these details in the methods.

      • How long cells were switched to BrainPhys medium before calcium imaging ? We now clarify these details in the methods.

      Minor point/typos etc.

      Introduction • Page 4 line 6: in the line "Trisomy 21 in humans commonly results in a range in developmental and morphological changes in the forebrain ..." "in" could be replaced by "of". We have fixed this. • Page 5 line 2: please remove "an" before the word "another". We have fixed this. • Page 5 line 2: please replace "ecitatory" with "excitatory". We have fixed this typo.

      Results • Page 10 line 25: The concept of "pixel-wise" appears for the first time in this section and could be better introduced to facilitate the understanding of the experiment. • In the "results" section, page 11 line 1 and 4, references are made to "Figure 4D" and "4F," but these figures do not appear to be present in the figure section. Upon reviewing the rest of the section, the data seem to refer to "Figure 3D" and "3E." We have fixed this. Discussion • Page 15 line 20: please replace "synchronised" with "synchronized". We have fixed this typo. • Page 16 line 11: please replace "T21" with "TS21". We have fixed this typo. Methods • Page 19 line 12: "Pens/Strep" has to be replaced by Pen/Strep. We have fixed this typo. • Page 20 line 20: "Tocris Biocience" has to be replaced by "Tocris Bioscience". We have fixed this typo. • Page 21 line 2: "Addegene" has to be replaced by "Addgene". We have fixed this typo. Figures • Figure 3: the schematic experimental design (Fig. 3A) could be enlarged to match the width of the images/graphs below. We have fixed this. • Figure 5: the reviewer suggests resizing/repositioning the graphs in Fig. 1A so that they match the width of those below. We have fixed this. • Figure S1D: In all the figures of the paper, the respective controls for the TS21 1 and TS21 2 lines are labelled as "WT1/WT2," while in these graphs, they are called "Ctrl1" and "Ctrl2." To ensure consistency throughout the paper, it is suggested to change the names in these graphs. We have fixed this. • Figure S4L: The graph is not very clear, especially regarding the significance reported at -50 pA, please modify the graphical visualization and/or add a legend in the caption. We have fixed this.

      Reviewer #2 (Significance (Required)):

      Nature and significance of the advance for the field. The results presented in the manuscript are potentially interesting and useful, but not completely novel (currents deregulation has already been highlighted in mouse models of Down Syndrome).

      2.8) We thank the reviewer for this comment. While we agree that current deregulation has been observed in mouse models of Down syndrome, the novelty and significance of our study lie in demonstrating these alterations directly in human neurons using both in vitro and in vivo xenograft models.

      This is a critical advance because the human cortex has distinct developmental and functional properties not fully recapitulated in mice. In fact, three recent studies have already highlighted significant defects mainly in excitatory neurons within the fetal human DS cortex (Vuong et al., bioRxiv, 2025; Risgaard et al., bioRxiv, 2025; Lattke et al, under revision). Our work builds directly on these observations by providing, for the first time, an electrophysiological and network-level characterization of these human-specific deficits.

      Our findings thus provide translationally relevant insight that is not merely confirmatory but extends previous work by grounding it in a human cellular context.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The manuscript by Peter et al., reports on the neuronal activity and connectivity of iPSC-derived human cortical neurons from Down syndrome (DS) that is caused by caused by trisomy of the human chromosome 21 (TS21).

      Major points:

      Although the manuscript is potentially interesting, the results appear somehow preliminary and need to be corroborated by control experiments and quantifications of effects to fully sustain the conclusions.

      (1) The authors have not assessed the percentage of WT and TS21 cells that acquire a neuronal or glia identity in their cultures. Indeed, the origin of alterations in network activity and connectivity observed in TS21 neurons could simply derive from reduced number of neurons arising from TS21 iPSC. Alternatively, the same alteration in network activity and connectivity could derive from a multitude of other factors including deficits in neuronal development, neurite extension, or intrinsic electrophysiological properties. In the current version of the manuscript, none of these has been investigated.

      (2) Electrophysiological properties of TS21 and WT neurons at day 53/54 in vitro indicate an extremely immature stage of development (i.e. RMP between -36 and -27 mV with most of the cells firing a single action potential after current injection) in the utilized culture conditions: This is far from ideal for in vitro neuronal-network studies. Finally, reduced activity of HCN1 channels should be confirmed by specific recordings isolating or blocking the related current.

      Main points highlighting the preliminary character of the study.

      1) In Figure 1 immunofluorescence images of the neuronal differentiation markers (Tbr1, Ctip2 and Tuj1) are showed. However, no quantification of the percentage of cells expressing these markers for WT and TS21 neurons is reported. On the other hand, simple inspection of the representative images clearly seams to indicate a difference between the two genotypes, with TS21 cultures showing lower number of cells expressing neuronal markers. This quantification should be corroborated by a similar staining for an astrocyte marker (GFAP, but not S100b since is triplicated in DS). This is an extremely important point since it is obvious that any change in the percentage of neurons (or the neuron/astrocyte ratio) in the cultures will strongly affect the resulting network activity (shown in Figure 2) and the connectivity (showed in Figure 4). Possibly, the quantification should be done at the same time points of the calcium imaging experiments.

      2) In Figure 2 the authors show some calcium imaging traces of WT and TS21 cultures at different time points. However, they again do not show any quantification of neuronal activity. A power spectra analysis is shown in Supplementary Figure 2, but only for WT cultures, while in Supplementary Figure 3 a comparison between WT and Ts21 power spectra is done, but only at the 50 day time point, while difference in synchrony are assessed at 60 days. At minimum, the author should include in main Figure 2 the quantification of the mean calcium event rate and mean event amplitude at the different time points and the power spectra analysis for both WT and TS21 cultures at the same timepoints.

      Of note, the synchronized neuronal activity is present in WT cultures at day 60, but totally lost at subsequent time-points (70 and 80 days). The results of this later time points are different from previous data from the same lab (Kirwan et al., 2015). How might these data be explained? It would be important to rule out any potential issues with the health of the culture that could explain the loss of neuronal activity.It would be beneficial to check cell viability at the different time points to exclude possible confounding factors ? A propidium staining or a MTT assay would strongly improve the soundness of the calcium data.

      3) In Figure 3 there is no quantification of the number and/or density of transplanted neurons for WT and TS21, but only representative images. As above, inspection of the representative images seems to show a decrease in cells labeled by the Tbr1 neuronal marker for TS21 cells. Moreover, the in vivo calcium imaging of transplanted WT and TS21 cells lacks most of the quantification normally done in calcium imaging experiments. Are the event rate and event amplitude different between WT and TS21 neurons ? The measure of neuronal synchrony by mean pixel correlation is not well explained, but it looks somehow simplistic. Neuronal synchrony can be more precisely measured by cross-correlation analysis or spike time tiling coefficients on the traces from single-neuron ROI rather than on all pixels in the field of view, as apparently was done here.

      4) The results on reduced neuronal connectivity in Figure 3 look very striking. However, these results should be accompanied by control experiments to verify the number of neuronal cells and neurite extension in WT and Ts21 cultures. These two parameters could indeed strongly influence the results. As the cultures appear to grow in clusters, bright-field images and TuJ1 staining of the cultures will also greatly help to understand the degree of morphological interconnection between the clusters.

      5) The authors performed RNA-seq experiments on day 50 cultures. Why the authors do not show the complete differential gene expression analysis, but only a small subset of genes? A comprehensive volcano plot and the complete list of identified genes with logFC and FDR values would be helpful. If possible, comparison of the present data (particularly on KCN and HCN expression changes) with published and publicly available expression datasets of other human or human Down syndrome iPSC-derived neurons or human Down syndrome brains will greatly increase the soundness of the present findings. In addition, the gene ontology (GO) results are mentioned in the text, but are not presented. Showing the complete GO analysis for both up and downregulated genes will help the reader to better understand the RNA-seq results. Notably, the results shown in Supplementary Figure on GRIN2A and GRIN2B expression (with values of 300-700 counts versus 2000-4000 counts, respectively) clearly indicate that in both WT and TS21 cultures the NMDA developmental switch has not occurred yet at the 50 days timepoint.

      6) The measure of hyperpolarization-activated currents shown in Figure 5 lack proper control experiments. First, the hyperpolarizing current in TS21 cells do not reach a steady-state as the controls. The two curves are therefore hard to compare. To exclude possible difference in kinetic activation, the authors should have prolonged the current injection period (1-2 seconds). Second, to ultimately prove that such currents are mediated by HCN channels in WT cells the authors should perform some control experiments with a specific HCN blocker. A good example of a suitable protocol, with also current blockers to exclude all other possible current contributions, is the one reported in Matt et al Cell. Mol. Life Sci. 68, 125-137 (2011).

      7) The manuscript lacks information on the statistical analysis used. Also, the numerosity of samples is not clear. Were the dots shown in some graph technical replicates from a single neuronal induction or were all independent neuronal inductions or a mix of the two ? Please clarify.

      8) The method section lacks important information to guarantee reproducibility. Just a few examples: - Only electrophysiology methods for slice are reported, but not for in vitro culture. - Details on Laminin coating is lacking. What concentration was used ? Was poly-ornithine or poly-lysine used before Laminin coating ? - How long cells were switched to BrainPhys medium before calcium imaging ?

      Minor point/typos etc.

      Introduction

      • Page 4 line 6: in the line "Trisomy 21 in humans commonly results in a range in developmental and morphological changes in the forebrain ..." "in" could be replaced by "of".
      • Page 5 line 2: please remove "an" before the word "another".
      • Page 5 line 2: please replace "ecitatory" with "excitatory"

      Results

      • Page 10 line 25: The concept of "pixel-wise" appears for the first time in this section and could be better introduced to facilitate the understanding of the experiment.
      • In the "results" section, page 11 line 1 and 4, references are made to "Figure 4D" and "4F," but these figures do not appear to be present in the figure section. Upon reviewing the rest of the section, the data seem to refer to "Figure 3D" and "3E."

      Discussion

      • Page 15 line 20: please replace "synchronised" with "synchronized".
      • Page 16 line 11: please replace "T21" with "TS21".

      Methods

      • Page 19 line 12: "Pens/Strep" has to be replaced by Pen/Strep.
      • Page 20 line 20: "Tocris Biocience" has to be replaced by "Tocris Bioscience".
      • Page 21 line 2: "Addegene" has to be replaced by "Addgene".

      Figures

      • Figure 3: the schematic experimental design (Fig. 3A) could be enlarged to match the width of the images/graphs below.
      • Figure 5: the reviewer suggests resizing/repositioning the graphs in Fig. 1A so that they match the width of those below.
      • Figure S1D: In all the figures of the paper, the respective controls for the TS21 1 and TS21 2 lines are labelled as "WT1/WT2," while in these graphs, they are called "Ctrl1" and "Ctrl2." To ensure consistency throughout the paper, it is suggested to change the names in these graphs.
      • Figure S4L: The graph is not very clear, especially regarding the significance reported at -50 pA, please modify the graphical visualization and/or add a legend in the caption.

      Significance

      Nature and significance of the advance for the field. The results presented in the manuscript are potentially interesting and useful, but not completely novel (currents deregulation has already been highlighted in mouse models of Down Syndrome).

      Work in the context of the existing literature. This work follows the line of evidence that characterizes Down Syndrome in human neurons (Huo, H.-Q. et al. Stem Cell Rep. 10, 1251-1266 (2018); Briggs, J. A. et al. Etiology. Stem Cells 31, 467-478 (2013)), both in vitro and in xenotransplanted mice, by corrborating some important findings already found in animal models (Stern, S., Segal, M. & Moses, E. EBioMedicine 2, 1048-1062 (2015); Cramer, N. P., Xu, X., F. Haydar, T. & Galdzicki, Z. Physiol. Rep. 3, e12655 (2015); Stern, S., Keren, R., Kim, Y. & Moses, E. http://biorxiv.org/lookup/doi/10.1101/467522 (2018) doi:10.1101/467522.

      Audience. Scientists in the field of pre-clinical biomedical research, especially those working on neurodevelopmental disorders and iPSC-based non-animal models.

      Field of expertise. In vitro electrophysiology, Neurodevelopmental disorders, Down Syndrome, ips cells.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The study investigates the neurodevelopmental impact of trisomy 21 on human cortical excitatory neurons derived from induced pluripotent stem cells (hiPSCs). Key findings include a modest reduction in spontaneous firing, a marked deficit in synchronized bursting, decreased neuronal connectivity, and altered ion channel expression-particularly a downregulation of voltage‐gated potassium channels and HCN1. These conclusions are supported by a combination of in vitro calcium imaging, electrophysiological recordings, viral monosynaptic tracing, RNA sequencing, and in vivo transplantation with two‐photon imaging.

      Major Comments

      • Convincing Nature of Key Conclusions: The study's conclusions are generally well supported by a diverse set of experimental approaches. However, certain claims regarding the intrinsic properties of the excitatory network would benefit from further qualification. In particular, the assertion that reduced synchronization is solely attributable to altered ion channel expression might be considered somewhat preliminary without additional corroborative experiments.
      • One major limitation of the current experimental design is the reliance on predominantly excitatory neuronal cultures derived from hiPSCs. Although the authors convincingly demonstrate differences in network synchronization and connectivity between trisomic (TS21) and control neurons, the almost exclusive focus on excitatory cells limits the physiological relevance of the in vitro network. In the developing cortex, interneurons and astrocytes play crucial roles in modulating network excitability, synaptogenesis, and plasticity. Therefore, incorporating these cell types-either through co-culture systems or through directed differentiation protocols that yield a more heterogeneous neuronal population-could help to determine whether the observed deficits are intrinsic to excitatory neurons or are compounded by a lack of proper inhibitory regulation and glial support.
      • Furthermore, the assessment of neuronal connectivity via pseudotyped rabies virus tracing, while innovative, has inherent limitations. The quantification of connectivity as a ratio of red-to-green fluorescence pixels may be influenced by differential viral infection efficiencies, variations in the expression levels of the TVA receptor, or even by the lower basal activity levels observed in TS21 cultures. Complementary approaches-such as electron microscopy for synaptic density analysis or functional connectivity measurements using multi-electrode arrays (MEAs)-could provide additional structural and functional insights that would validate the rabies tracing data.
      • Qualification of Claims: Some conclusions, particularly those linking specific ion channel dysregulation (e.g., HCN1 loss) directly to network deficits, might be better presented as preliminary. The authors could temper their language to indicate that while the evidence is suggestive, the mechanistic link remains to be fully established.
      • Need for Additional Experiments: Additional experiments that could further consolidate the current findings include:
        • Inclusion of Inhibitory Neurons or Co-culture Systems: Incorporating interneurons or astrocytes would help determine whether the observed deficits are solely intrinsic to excitatory neurons.
        • Alternative Connectivity Assessments: Complementing the rabies virus tracing with electron microscopy or multi-electrode array (MEA) recordings would add structural and functional validation of the connectivity differences.
        • Extended Temporal Profiling: Monitoring network activity over a longer developmental window would clarify whether the observed deficits represent a delay or a permanent alteration in network maturation.
      • Reproducibility and Statistical Rigor: The methods and data presentation are largely clear, with adequate replication and appropriate statistical analyses. Nonetheless, a more detailed description of the experimental replicates, particularly regarding the viral tracing and in vivo transplantation studies, would enhance reproducibility. The availability of raw data and scripts for calcium imaging analysis would also further support independent verification.

      Minor Comments

      • Experimental Details:

      Minor revisions could include clarifying the infection efficiency and expression levels of the viral constructs used in connectivity assays to rule out technical variability. - Literature Context:

      The authors reference prior studies appropriately; however, integrating a brief discussion comparing their findings with alternative DS models (e.g., organoids or other hiPSC-derived systems) would improve contextual clarity. - Presentation and Clarity:

      Figures are generally clear,.But the manuscript contains a minor labeling error. On page 13, the figure is erroneously labeled as "Fig6A", whereas, based on the context and corresponding data, it should be "Fig5A". I recommend that the authors correct this mistake to ensure consistency and avoid potential confusion for readers.

      Significance

      • Nature and Significance of the Advance:

      The work offers a substantial conceptual advance by providing a mechanistic link between trisomy 21 and impaired neuronal network synchronization. Technically, the study integrates state-of-the-art imaging, electrophysiology, and transcriptomic profiling, thereby offering a multifaceted view of DS-related neural dysfunction. Clinically, the findings have the potential to inform future therapeutic strategies targeting network connectivity and ion channel function in Down syndrome. - Context in the Existing Literature:

      The study builds on previous observations of altered network activity in DS patients and DS mouse models (e.g., altered EEG synchronization and reduced synaptic connectivity). It extends these findings to human-derived neuronal models, thus bridging a gap between clinical observations and molecular/cellular mechanisms. Relevant literature includes studies on DS neurodevelopment and the role of ion channels in synaptic maturation. - Target Audience:

      The reported findings will be of interest to researchers in neurodevelopmental disorders, Down syndrome, and ion channel physiology. Additionally, the study may attract the attention of those working on hiPSC-derived models of neurological diseases, as well as clinicians interested in the pathophysiology of DS. - Keywords and Field Contextualization:

      Keywords: Down syndrome, trisomy 21, neuronal connectivity, synchronized network activity, hiPSC-derived cortical neurons, ion channel dysregulation.

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      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...

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      Referee #3

      Evidence, reproducibility and clarity

      In this work Neupane et al used large-scale robust CRISPR-based gene activation and ablation screens to identify novel regulators of α-synuclein pathology in synucleinopathies using as read-out p-αSyn129 signals by high-throughput fluorescence microscopy. The authors reveal that mitochondrial protein OXR1 promotes Ser129-phosphorylated αSyn aggregation, while ER-associated EMC4 suppresses it via enhanced autophagic clearance, highlighting new possible mechanistic pathways in disease progression of alpha-synucleinopathies.

      Major comments:

      1. As correctly pointed out by the authors in Introduction p-Syn is associated with aggregates, but its functional role is far to be clear and both neuroprotective or pro-aggregations effects have been proposed. Further it has been shown that, physiological neuronal activity augments Ser129-phospho αSyn, which is a trigger for protein-protein interactions, which in turn is necessary for mediating αSyn function at the synapse (https://doi.org/10.1016/j.neuron.2023.11.020). As a consequence modulation of p- p-αSyn as possible therapeutical target for PD and synucleinopathies is quite a complicate matter. The assumption, on which the whole paper is based, that increase in p-αSyn equates to αSyn aggregation and disease progression is rather weak to this reviewer, unless further validation of it is provided. Indeed while the authors performed experiments on human iPSC-derived cortical and dopaminergic neurons on p-αSyn analysis, any measurement of αSyn aggregates/oligomers, and neuronal degeneration is provided. It is recommended to provid this experiments ideally using different tecnique like αSyn-Proximity Ligation Assay for measurements of oligomers, as it has been largely validated in autoptic brains of PD, MSA and DLB patients (doi: 10.1007/s00401-025-02871-w.), as well as cell viability/apoptosis and neurites degeneration measurements upon OXR1 and EMC4 modulation in iPSC derived cortical and dopaminergic neurons.
      2. The authors claims in Results page 5: "The absence of cytoplasmic pSyn129 signal in HEK293 cells lacking α-Syn overexpression demonstrates that elevated α-Syn levels are essential to drive robust and rapid aggregation. Moreover, it indicates that the 81A antibody selectively recognizes de novo aggregates rather than the recombinant seeds". The fact that ab81A recognize deNovo aggregates and not rec seeds is quite speculative, not supported by data, and might rather indicate that ab 81A does not recognize aggregates. Thus this further implays that other technology like for example Seeding amplification assays are being employed by the authors in addition to p-αSyn129 signals in validation experiments for example in genetic PD (ideally GBA1 or LRRK2) IPSC-derived dopaminergic neurons.

      Minor comments:

      1. The strain-specific effects especially from patients-derived fibrils of OXR1 activation and EMC4 depletion on pSyn levels is rather weak in comparison with RAB3 and PIKFYVE (fig 3F-G) and therefore the expected relevance of these results especially in vivo in patients should be better clarified and modulated in discussion
      2. In discussion authors write "We observed that OXR1 activation preferentially increases α-Syn aggregates phosphorylation (EP1536Y) in neuronal somata, suggesting that mitochondrial dysfunction exacerbates α-Syn phosphorylation in later-stage aggregates." This is quite a surprising result since distal axonal endings are particularly susceptible to mitochondrial impairments for anatomical and physiologically reasons and if p-αSyn129 accumulation is driven by mithocondrial disfunction as suggested by this paper, this should be detected in neurites as well. Please clarify.
      3. Authors say that they targeted mitochondrial, trafficking, and motility (MTM) genes in human cellular models. While mitochondrial and trafficking is clear in the context of Parkinson and neurodegnerative disease, less clear is the motility genes. Please expand on this.

      Significance

      This is a well written, comprehensive study with a well characterized, robust CRISPR-based gene activation and ablation screening pipeline to identify novel regulators of α-synuclein pathology. Methodology is rigorous and clearly described and results are well presented. The major limitation relays in the validation experiments where only one main read-out that is p-αSyn129 fluorescence signal is employed, limiting the significance and impact of the presented results. I believe that the basic science community might benefit principally of the proposed methodology of a large high-throughput screening to modulate a large set of genes, a platform that in principle might be used also for other scientific questions.

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      Referee #2

      Evidence, reproducibility and clarity

      In the present study, Neupane et al. performed arrayed CRISPR activation and ablation screens, targeting genes related to mitochondria, trafficking and motility, to identify genes that modulate the presence of Ser 129 phosphorylated alpha-synuclein aggregates (pSyn129) upon administration of exogenous preformed alpha-synuclein fibrils. The screens have been performed in HEK cells stably overexpressing alpha-synuclein in two independent replicates, and hits have been further validated in induced pluripotent stem cell derived forebrain and dopaminergic neurons. Following functional validations, the authors conclude that enhancing the expression of OXR1 results in a modest increase in the number of pSyn129 puncta within cells, and their size, while partial loss of EMC4 expression reduces these puncta. To date some pre-print studies have used genome-wide CRISPR screening to identify modifiers of the accumulation of alpha-synuclein preformed fibrils in cells, suggesting the importance of uptake and endolysosomal trafficking for the propagation of alpha-synuclein aggregates in recipient cells. Although the topic is of interest in the field of Parkinson's disease and synucleinopathies in general, the readout of the present screen (presence of pSyn129) is not very sensitive and without investigating endogenous alpha-synuclein or cell homeostasis in neuronal models limits the stated conclusions.

      Major comments:

      • Please clarify whether the positive control genes RAB13 and PIKFYVE were nominated hits within the CRISPR screens. Specifically, the authors state that the positive control of the CRISPRa screen was RAB13, expected to reduce pSyn129 upon overexpression, nevertheless this gene does not appear as a hit in the CRISPRa volcano plot (although present in table S1 but not making the cutoff). In figure 2D, activation of RAB13 does not seem to impact the main readout phenotype. Moreover, in the CRISPRo screen, PIKFYVE was used, but this gene is also not presented as a hit linked to reducing pSyn129 in the CRISPRo plot. If these control genes do not come up as hits, it is difficult to support the conclusions of the screen.
      • The effect size for screen hits presented in figure 2A/B is rather small. It is difficult to interpret the power of these findings in the absence of uptake efficiency controls, such as dextrans of appropriate molecular weights.
      • The readout of the screen is not very sensitive, and it is unclear what it represents. Specifically, in Figure 2F, G the authors validate the hits OXR1 and EMC4, showing a small effect, albeit statistically significant. The authors should strengthen this data by adding more experiments addressing, for instance, what the pSyn spot area and spot intensity signify for the cell. Some experiments in a neuronal context are important, including SNCA KO as a negative control.
      • It is unclear why the authors chose to follow up on the OXR1 and EMC4 hits. Please explain the rationale for follow-up studies.
      • Generally, the notable difference in the number of pSyn129+ cells in the non-targeting across various experiments (including Fig.1G/I compared to Fig.2G/I or Fig. 3F/G or flow cytometry experiment) suggests the readout is not very sensitive.

      For instance, in figure S3 it would be important to add an experiment controlling for cell number as opposed to LDH release, as the micrographs show some differences in cells number, e.g. in the ntg vs. EMC4 condition. - The data is not sufficient to suggest that OXR1 and EMC4 are strong modulators of alpha-synuclein aggregation, as the authors suggest based on figures 2 and 3 that show statistically significant difference and a rather small effect size. It is important to provide more insight into how these genes may affect endogenous alpha-synuclein and cellular homeostasis in more detail, especially in neuronal models. Further investigating the hits in this direction in additional genetic backgrounds would also increase the relevance of the findings, e.g. in SNCA triplication or GBA-PD neurons.<br /> In Fig. S8B the immunoblot analysis shows there may be an effect of EMC4 and OXR1 CRISPRa on α-synuclein levels; please quantify for both iPSC-derived cortical neurons and dopaminergic neurons. - The pattern of tyrosine hydroxylase staining in Figure 5F does not seem specific or as expected for iPSC-derived dopaminergic neurons. Furthermore, since endogenous SNCA expression is expected to be analogous to the expression of TH (with TH+ cells expressing higher SNCA), it would be important to compare pSyn129 between TH+ cells and/or relative to the TH+ area.

      Minor comments:

      • The authors report that RAB13 overexpression reduces pSyn129⁺ prevalence, whereas RAB13 ablation (CRISPRo screen) enhances pSyn129⁺ levels (Figures 2D-2E). Please revise as these specific figures show no effects for this gene.
      • Please specify how many individual cells (approximately) were quantified in each figure legend.
      • Figure 3F/G may be better as a supplemental figure since it does not add to the conclusions of the study.
      • It would be good to clarify for the reader some of the genes that serve as positive controls for the screen's readout (as shown in Fig. S2D/G).
      • It would be helpful to further clarify which cell type was used in each figure legend.

      Significance

      Important topic but their experimental design limits the significance of their findings. Hard to improve the work in a reasonable amount of time. Also many technical issues.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This study by Neupane et al. investigates modulators of α-synuclein aggregation, focusing on Ser129-phosphorylated α-synuclein (pSyn129), a pathological hallmark of Parkinson's disease (PD). The authors performed high-content image-based, arrayed CRISPR activation (CRISPRa) and knockout (CRISPRo) screens targeting > 2300 genes related to mitochondrial function, intracellular trafficking, and cytoskeletal reorganization. Using α-Syn overexpressing HEK293 cells, they identified OXR1 and EMC4 as novel modulators of pSyn129 abundance. Key findings were that activation of the mitochondrial protein OXR1 increased pSyn129 by decreasing ATP levels, while ablation of the ER-associated protein EMC4 reduced pSyn129 by enhancing autophagic flux and lysosomal clearance. These findings were validated in human iPSC-derived cortical and dopaminergic neurons.

      My major comments have to do with statistical methods and with significance of their findings.

      Major comments:

      Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

      The claims and conclusions are generally well-supported by the presented data. The dual CRISPRa/CRISPRo screen provides a robust initial discovery platform, and the validation in iPSC-derived neurons strengthens the findings and their translational relevance. The mechanistic insights into OXR1 (ATP levels) and EMC4 (autophagic flux, lysosomal clearance) are supported by the described experiments. The use of two antibodies (81A and EP1536Y) for pSyn129 also enhances confidence in the measurements. I had a few questions about the statistical methods. The main concern I have about methodology for the screen is whether the authors have corrected for multiple hypotheses in their discovery screen. This is not clear from the text, methods, or legends (for Figures 2A/2B/2C).

      • Figure 1B suggests a very large range of activation (multiple orders of magnitude) in the initial screen. What is the relationship between level of expression change and functional effect across the screen? How upregulated/downregulated are OXR1 and EMC4 at the mRNA and protein levels?
      • Supplemental Figure S2D: Why do the non-targeting controls differ from the majority of the CRISPRa genes? If I am reading the figure correctly, it seems strange that the vast majority of the CRISPRa gene targets reduces pSyn pathology relative to the non-targeting controls (which is why I am wondering whether the level of increased expression correlates with the level of functional effect).
      • In Figure 2A/B/C, is the p-value adjusted in any way for multiple comparisons? If so, this should be indicated in the legend. If not, why not? (The potential for false positives in a screen is very large and requires correction for multiple comparisons.)
      • Figure 3: It's interesting that different seeding materials have different effects. However, it's quite surprising that the authors find less seeding with MSA-derived material in both the CRISPRa and CRISPRo context. This contradicts the work of Peng and coauthors (PMID 29743672) who find that MSA-derived material is much more potent in seeding aggregates in a number of different cell types. Do the authors have any thoughts about why this is the case?
      • Figure 7A: pSyn129 image in the non-targeting control is poor - the very bright dots look like artifact. Not clear why the authors don't corroborate with EP1536Y antibody as they do in Figure 5.
      • Overall methodology: Are the pSyn inclusions soluble? This could be easily determined by performing 1% TritonX extraction, for example, and it helps us understand how "pathological" the inclusions are.
      • OPTIONAL: The authors perform some interesting experiments looking at genes affected downstream by, for example, OXR1 over-expression. It would be useful to understand whether the upstream effect is dependent on downstream effect. This could be tested by performing double perturbations (e.g. OXR1 overexpression and CCL8 knockout or ALDOC upregulation).
      • OPTIONAL: The link between EMC4 ablation and enhanced ER-driven autophagic flux/lysosomal clearance could be corroborated with additional experiments. E.g.: Does EMC4 normally inhibit this pathway? Or only in the context of aSyn fibril seeding?

      Are the suggested experiments realistic in terms of time and resources?

      The OPTIONAL experiments are generally feasible as they employ methods that the lab is already using in this paper.

      Are the experiments adequately replicated and statistical analysis adequate?

      See comment about multiple hypothesis testing above.

      Significance

      This is a well-designed, difficult-to-accomplish study that expands the landscape of pS129Syn modulators. The validation of the primary hits identified in HEK293 cells in iPSC-derived neurons gives the findings greater relevance.

      Strengths:

      • Novelty: Using an unbiased and high-throughput approach, the study identifies two novel regulators of α-Syn aggregation, namely OXR1 and EMC4.
      • Methodological Rigor: The use of arrayed CRISPRa/CRISPRo screens with high-content imaging is powerful and difficult to accomplish. Methodologically, this is a tour de force.
      • Orthogonal Validation: The use of multiple α-Syn fibril polymorphs/strains and different antibodies (81A, EP1536Y) strengthens the robustness of the findings.

      Limitations:

      • It's not clear to me that pSyn129 is the ultimate readout. At a minimum, we should know something about the solubility of the inclusions. Some panels (e.g. Figure 7A) are not very informative in terms of what the authors are calling pSyn129+.
      • The study relies on in vitro cellular models. While iPSC-derived neurons are relevant, the complexity of the brain environment, including glial cell interactions is not fully captured. This is fine for an initial report, but it does limit the significance.
      • OXR1 and EMC4 seem to be very generic modulators. It's not clear to me that their effects are specific to aSyn or to PD in any way - they might just be effects on very basic cellular functions that would be applicable to a number of stressors or proteinopathies. Maybe that is fine (we probably need to get rid of tau aggregates, too!), but I don't think the authors can claim that they have identified "organelle-specific genetic nodes of aSyn pathology" since they biased their screen towards mitochondria and they don't test any other pathological aggregates. Moreover, from a translational perspective, it's not clear to me that implicating the antioxidant pathway or lysosomal/autophagosomal pathways in the pathogenesis of PD is new, and it's not clear that the specific genes identified would make good therapeutic targets.
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      Referee #3

      Evidence, reproducibility and clarity

      Sheidaei et al., report how chromosomes are brought to positions that facilitate kinetochore-microtubule interactions during mitosis. The study focusses on an important early step of the highly orchestrated chromosome segregation process. Studying kinetochore capture during early prophase is extremely difficult due to kinetochore crowding but the team has taken up the challenge by classifying the types of kinetochore movements, carefully marking kinetochore positions in early mitosis and linking these to map their fate/next-positions over time. The work is an excellent addition to the field as most of the literature has thus far focussed on tracking kinetochore in slightly later stages of mitosis. The authors show that the PANEM facilitates chromosome positioning towards the interior of the newly forming spindle, which in turn facilitates chromosome congression - in the absence of PANEM chromosomes end up in unfavourable locations, and they fail to form proper kinetochore-microtubule interactions. The work highlights the perinuclear actomyosin network in early mitosis (PANEM) as a key spatial and temporal element of chromosome congression which precedes the segregation process.

      Major points

      1) The complexity of tracking has been managed by classifying kinetochore movements into 4 categories, considering motions towards or away from the spindle mid-plane. While this is a very creative solution in most cases, there may be some difficult phases that involve movement in both directions or no dominant direction (eg Phase3-like). It is unclear if all kinetochores go through phase1, 2, 3 and 4 in a sequential or a few deviate from this pattern. A comment on this would be helpful. Also, it may be interesting to compare those that deviate from the sequence, and ask how they recover in the presence and absence of azBB.

      2) Would peripheral kinetochore close to poles behave differently compared to peripheral kinetochore close to the midplane (figure S4) ?In figure 3D, are they separated? If not, would it look different?

      3) Uncongressed polar chromosomes (eg., CENPE inhibited cells) are known to promote tumbling of the spindle. In figure 5B with polar chromosomes, it will be helpful to indicate how the authors decouple spindle pole movements from individual kinetochore movements.

      4) The work has high quality manual tracking of objects in early mitosis- if this would be made available to the field, it can help build AI models for tracking. The authors could consider depositing the tracking data and increasing the impact of their work.

      Minor points

      1. It will be helpful for readers to see how many kinetochores/cell were considered in the tracking studies. Figure legends show kinetochore numbers but not cell numbers.
      2. Discussion point: If cells had not separated their centrosomes before NEBD, would PANEM still be effective? Perhaps the cancer cell lines or examples as shown in Figure 6A have some clues here.
      3. Figure 7 cartoon shows misalignment leading to missegregation. It may be useful to consider this in the context of the centrosome directed kinetochore movements via pivoting microtubules. Is this process blocked in azBB treated cells?
      4. Are all the N-CIN- lines with PANEM highly sensitive to azBB? In other words, is PANEM essential for normal congression in some of these lines.
      5. Are congression times delayed in lines that naturally lack PANEM?
      6. Page 23 "we first identified the end of congression" how does this relate to kinetochore oscillations that move kinetochores away from the metaphase plate?
      7. Are spindle pole distances (spindle sizes) different in early and late mitotic cells (4min vs 6min after NEBD) in control vs azBB treated cells? Please comment on Figure S2E (mean distance) in the context of when phase 4 is completed. Does spindle size return to normal after congression?

      Significance

      The current work builds upon their previous work, in which the authors demonstrated that an actomyosin network forms on the cytoplasmic side of the nuclear envelope during prophase. This work explains how the network facilitates chromosome capture and congression by tracking motions of individual kinetochores during early mitosis. The findings can be broadly useful for cell division and the cytoskeletal fields.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Sheidaei et al. reported on their study of chromosome congression during the early stages of mitotic spindle assembly. Building on their previous study (ref. #15, Booth et al., Elife, 2019), they focused on the exact role of the actin-myosin-based contraction of the nuclear envelope. First, they addressed a technical issue from their previous study, finding a way to specifically impair the actomyosin contraction of the nuclear membrane without affecting the contraction of the plasma membrane. This allowed them to study the former more specifically. They then tracked individual kinetochores to reveal which were affected by nuclear membrane contraction and at what stage of displacement towards the metaphase plate. The investigation is rigorous, with all the necessary controls performed. The images are of high quality. The analyses are accurate and supported by convincing quantifications. In summary, they found that peripheral chromosomes, which are close to the nuclear membrane, are more influenced by nuclear membrane contraction than internal chromosomes. They discovered that nuclear membrane contraction primarily contributes to the initial displacement of peripheral chromosomes by moving them towards the microtubules. The microtubules then become the sole contributors to their motion towards the pole and subsequently the midplane. This step is particularly critical for the outermost chromosomes, which are located behind the spindle pole and are most likely to be missegregated.

      Significance

      While the conclusions are somewhat intuitive and could be considered incremental with regard to previous works, they are solid and improve our understanding of mitotic fidelity. The authors had already reported the overall role of nuclear membrane contraction in reducing chromosome missegregation in their previous study, as mentioned fairly and transparently in the text. However, the reason for this is now described in more detail with solid quantification. Overall, this is good-quality work which does not drastically change our understanding of chromosome congression, but contributes to improving it. Personally, I am surprised by the impact of such a small contraction (of around one micron) on the proper capture of chromosomes and wonder whether the signalling associated with the contraction has a local impact on microtubule dynamics. However, investigating this point is clearly beyond the scope of this study, which can be published as it is.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      Sheidaei and colleagues report a novel and potentially important role for an early mitotic actomyosin-based mechanism, PANEM contraction, in promoting timely congression of chromosomes located at the nuclear periphery, particularly those in polar positions. The manuscript will interest researchers studying cell division, cytoskeletal dynamics, and motor proteins. Although some data overlap with the group's prior work, the authors extend those findings by optimizing key perturbations and performing more detailed analyses of chromosome movements, which together provide a clearer mechanistic explanation. The study also builds naturally on recent ideas from other groups about how chromosome positioning influences both early and later mitotic movements.

      In its current form, however, the manuscript is not acceptable for publication. It suffers from major organizational problems, an overcrowded and confusing Results section and figures, and a lack of essential experimental controls and contextual discussion. These deficiencies make it difficult to evaluate the data and the authors' conclusions. A substantial structural revision is required to improve clarity and persuasiveness. In addition, several key control experiments and more conceptual context are needed to establish the specificity and relevance of PANEM relative to other microtubule- and actin-based mitotic mechanisms. Testing PANEM in additional cell lines or contexts would also strengthen the claim. I therefore recommend Major Revision, addressing the structural, conceptual, and experimental issues detailed below.

      Major Comments

      1. Structural overhaul and figure reorganization

      The Results section is overly dense, lacks clear structure, and includes descriptive content that belongs in the Methods. Many figure panels should be moved to Supplementary Materials. A substantial reorganization is required to transform the manuscript into a focused, "Reports"-type article. - Move methodological and descriptive details (e.g., especially from the second Results subheading and Figure 2) to the Methods or Supplementary Materials. - Remove repetitive statements that simply restate that later phenotypes arise as consequences of delayed Phase 1 (applicable to subheadings 3 onward). - Figure 4I: This panel is currently unclear and should be drastically simplified. I recommend to reorganize figures as follows: - Figure I: Keep as single figure but simplify. Figure 1D and 1E could be combined, move unnormalized SCV to supplementary materials. Same goes for 1F. - New Figure 2: Combine current Figures 2A, 3A, 3C, 3D, 4C, 4F, and 4H to illustrate how PANEM contraction facilitates initial interactions of peripheral chromosomes with spindle microtubules which increases speed of congression initiation. - New Figure 3: Combine current Figures 5A, 5C, 5D, 5F, 6B, 6C, and lower panels of 4H to show how PANEM contraction repositions polar chromosomes and reduces chromosome volume in early mitosis to enable rapid initiation of congression. - New Figure 4: Combine Figures 7A, 7B, 7D, 7E, 7F, expanded Supplementary Figure S7, and new data to demonstrate that PANEM actively pushes peripheral chromosomes inward which is important for efficient chromosome congression in diverse cellular contexts. 2. Specificity and redundancy of actin perturbation

      To establish the specificity and relevance of PANEM, the authors should include or discuss appropriate controls:

      - Apply global actin inhibitors (e.g., cytochalasin D, latrunculin A) to disrupt the entire actin cytoskeleton. These perturbations strongly affect mitotic rounding and cytokinesis but only modestly influence early chromosome movements, as reported previously (Lancaster et al., 2013; Dewey et al., 2017; Koprivec et al., 2025). The minimal effect of global inhibition must be addressed when proposing a localized actomyosin mechanism. Comment if the apparent differences in this approach and one that the authors were using arises due to different cell types.
      - Clarify why spindle-associated actin, especially near centrosomes, as reported in prior studies using human cultured cells (Kita et al., 2019; Plessner et al., 2019; Aquino-Perez et al., 2024), was not observed in this study. The Myosin-10 and actin were also observed close to centrosomes during mitosis in X.laevis mitotic spindles (Woolner et al., 2008). Possible explanations include differences in fixation, probe selection, imaging methods, or cell type. Note that some actin probes (e.g., phalloidin) poorly penetrate internal actin, and certain antibodies require harsh extraction protocols. Comment on possibility that interference with a pool of Myo10 at the centrosomes is important for effects on congression.
      
      1. Expansion of PANEM functional analysis

      To strengthen the conclusions and broaden the study beyond the group's previous work, PANEM function should be tested in additional contexts (some may be considered optional but important for broader impact): - Test PANEM function in at least one additional cell line that displays PANEM to rule out cell-line-specific effects. - Examine higher-ploidy or binucleated cells to determine whether multiple PANEM contractions are coordinated and if PANEM contraction contributes more in cells of higher ploidies or specific nuclear morphologies. - Investigate dependency on nuclear shape or lamina stiffness; test whether PANEM force transmission requires a rigid nuclear remnant. - Analyze PANEM's contribution under mild microtubule perturbations that are known to induce congression problems (e.g., low-dose nocodazole). - Evaluate PANEM contraction role in unsynchronized U2OS cells, where centrosome separation can occur before NEBD in a subset of cells (Koprivec et al., 2025), and in other cell types with variable spindle elongation timing. - Quantify not only the percentage of affected cells after azBB but also the number of chromosomes per cell with congression defects in the current and future experiments. 4. Conceptual integration in Introduction and Discussion The manuscript should better situate its findings within the context of early mitotic chromosome movements: - Clearly state in the Introduction and elaborate in the Discussion that initiation of congression is coupled to biorientation (Vukušić & Tolić, 2025). This provides essential context for how PANEM-mediated nuclear volume reduction supports efficient congression of polar chromosomes. - Explain that PANEM is most critical for polar chromosomes because their peripheral positions are unfavorable for rapid biorientation (Barišić et al., 2014; Vukušić & Tolić, 2025). - Discuss how cell lines lacking PANEM (e.g., HeLa and others) nonetheless achieve efficient congression, and what alternative mechanisms compensate in the absence of PANEM. For example, it is well established that cells congress chromosomes after monastrol or nocodazole washout, which essentially bypasses the contribution of PANEM contraction.

      Minor Comments

      These issues are more easily addressable but will significantly improve clarity and presentation.

      Introduction

      • Remove the reference to Figure 1A in the Introduction. The portion of Figure 1 and related text that recapitulates the authors' previous work should be incorporated into the Introduction, not the Results.

      Results (by subheading)

      • First subheading: When introducing the ~8-minute early mitotic interval, cite additional studies that have characterized this period: Magidson et al., 2011 (Cell); Renda et al., 2022 (Cell Reports); Koprivec et al., 2025 (bioRxiv); Vukušić & Tolić, 2025 (Nat Commun); Barišić et al., 2013 (Nat Cell Biol).
      • Second subheading: Cite key reviews and foundational research on kinetochore architecture and sequential chromosome movement during early mitosis: Mussachio & Desai, 2017 (Biology); Itoh et al., 2018 (Sci Rep); Magidson et al., 2011 (Cell); Vukušić & Tolić, 2025 (Nat Commun); Koprivec et al., 2025 (boRxiv); Rieder & Alexander, 1990 (J Cell Biol); Skibbens et al., 1993 (J Cell Biol); Kapoor et al., 2006 (Science); Armond et al., 2015 (PLoS Comput Biol); Jaqaman et al., 2010 (J Cell Biol).
      • Third subheading: Clarify why some kinetochores on Figure 3A appear outside the white boundaries if these boundaries are intended to represent the nuclear envelope.
      • Fourth subheading: Note that congression speed is lower for centrally located kinetochores because they achieve biorientation more rapidly (Barišić et al., 2013, Nat Cell Biol; Vukušić & Tolić, 2025, Nat Commun).
      • Fifth subheading: Cite studies on polar chromosome movements: Klaasen et al., 2022 (Nature); Koprivec et al., 2025 (bioRxiv). Clarify that Figure 5F displays only those kinetochores that initiated directed congression movements.
      • Sixth subheading (currently in Discussion): Move the final paragraph of the Discussion into the Results and expand it with preliminary analyses linking PANEM contraction to congression efficiency across untreated cell types or under mild nocodazole treatment.

      Discussion

      • When discussing cortical actin, cite key reviews on its presence and function during mitosis: Kunda & Baum, 2009 (Trends Cell Biol); Pollard & O'Shaughnessy, 2019 (Annu Rev Biochem); Di Pietro et al., 2016 (EMBO Rep).

      Significance

      Advance

      This study's main strength is its novel and potentially important demonstration that contraction of PANEM, a peripheral actomyosin network that operates contracts early mitosis, contributes to the timely initiation of chromosome congression, especially for polar chromosomes. While PANEM itself was previously described by this group, this manuscript provides new mechanistic evidence, improved perturbations, and detailed chromosome tracking. To my knowledge, no prior studies have mechanistically connected this contraction to polar chromosome congression in this level of detail. The work complements dominant microtubule-centric models of chromosome congression and introduces actomyosin-based forces as a cooperating system during very early mitosis. However, the impact of the study is currently limited by major organizational issues, insufficient controls, and incomplete contextualization within existing literature. Addressing these issues will substantially improve clarity and credibility.

      Audience

      Primary audience of this study will be researchers working in cell division, mitosis, cytoskeleton dynamics, and motor proteins. The findings may interest also the wider cell biology community, particularly those studying chromosome segregation fidelity, spindle mechanics, and cytoskeletal crosstalk. If validated and clarified, the concept of PANEM could be integrated into textbooks and models of chromosome congression and could inform studies on mitotic errors and cancer cell mechanics.

      Expertise

      My expertise lies in kinetochore-microtubule interactions, spindle mechanics, chromosome congression, and mitotic signaling pathways.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript investigates the role of DOT1L and its H3K79 methyltransferase activity in dendritic cell (DC) differentiation. The authors employ a combination of in vitro FLT3L/SCF bone marrow culture systems, in vivo inducible knockout models, and genome-wide H3K79me2 ChIP-seq and RNA-seq analyses to demonstrate that DOT1L influences the balance between pDC and cDC2 differentiation, while leaving cDC1 development largely unaffected. The study further identifies transcriptional and epigenetic programs associated with these changes, linking DOT1L deficiency to altered antigen presentation pathways and loss of pDC-associated transcription factors. The paper provides valuable insights into DC biology. However, some of the key conclusions rely heavily on in vitro systems and short-term tamoxifen deletion models, which limit the interpretation of the in vivo data. Strengthening or clearly defining these limitations would substantially improve the paper's impact and clarity.

      Major Comments

      1. To strengthen the paper, the authors could follow one of two alternative strategies:

      (1) Validate their in vitro observations through in vivo experiments, or

      (2) Focus on deepening and refining their in vitro findings, moving the limited in vivo data to the supplementary material and explicitly acknowledging the limitations of the tamoxifen-inducible system.

      Strategy 1 - Strengthen in vivo validation

      -   The experiments presented in Figures 3 and 5 could be repeated in a competitive bone marrow chimera setting (e.g. CD45.1/CD45.2 irradiated hosts reconstituted with a 1:1 mix of WT CD45.1⁺ and Dot1l-KO CD45.2⁺ cells).
      -   This design would allow dissection of direct (cell-intrinsic) versus indirect effects of DOT1L deficiency and could mitigate confounding effects of incomplete or asynchronous deletion.
      -   After reconstitution, mice could be maintained on tamoxifen-supplemented chow for a longer period to ensure efficient recombination and adequate time for observing phenotypic consequences.
      -   Flow cytometric analysis of spleen and bone marrow should use more refined panels to explore DC precursor and subset deficiencies. Suggested reference panels: Rodrigues et al., Immunity 2024; Minutti et al., Nat. Immunol. 2024; Zhu et al., Nat. Immunol. 2015.
      

      Strategy 2 - Refine in vitro system and reposition in vivo data - The authors could replicate their differentiation assays under conditions that emulate the chimera approach by co-culturing WT (CD45.1⁺) and Dot1l-KO (CD45.2⁺) bone marrow cells. - This would reveal potential competition or cross-talk between WT and mutant cells and provide clearer mechanistic insight into cell-intrinsic versus extrinsic effects. - The authors should examine how tamoxifen itself affects differentiation and measure the kinetics of deletion and H3K79me loss to better contextualize the dynamic response. - It would also be valuable to assess which cDC2 subtypes (A vs. B) are preferentially affected by Dot1l deficiency, again using more sophisticated flow cytometry panels (see references above). If this in vitro-focused strategy is adopted, the in vivo data could be moved to the supplementary material, with explicit acknowledgment that the inducible deletion model and the gradual nature of H3K79me dilution limit the interpretation of the in vivo findings. 2. In Figures 2 and 3, the efficiency of H3K79me2 depletion following Dot1l excision should be assessed directly. Although DOT1L is the sole H3K79 methyltransferase, the dilution kinetics of H3K79me2 can vary depending on the proliferation rate. Quantifying the H3K79me2 signal in bone marrow-derived cell culture samples would clarify whether the deletion window allowed complete loss of the methylation mark. 3. Several observations are not discussed in sufficient depth: - The finding that Dot1l deletion increases antigen-presentation signatures might reflect stress or activation rather than lineage fate change. - The authors could also acknowledge that DOT1L's effect might be indirect, acting through cytokine feedback loops or altered progenitor proliferation, especially given the co-expression of Kit, Flt3, and Irf8 in early DC progenitors. - Moreover, because H3K79 methylation is primarily associated with transcriptional elongation rather than initiation, the observed transcriptional changes could result from broader alterations in chromatin accessibility or polymerase processivity, rather than direct promoter regulation. Discussing this mechanistic aspect would help clarify whether DOT1L's role in DC differentiation reflects a direct control of lineage-defining gene expression or a secondary consequence of disrupted transcriptional elongation dynamics.

      Minor Comments

      1. Terminology: The manuscript repeatedly refers to "mature" DCs-please clarify whether this means activated or fully differentiated cells.
      2. Ontogeny statements: <br /> The assertion that DCs of lymphoid origin are well established should be softened; the lymphoid contribution to some DC lineages remains under discussion.
      3. Transitional DCs (tDCs): <br /> The equivalence between tDCs and pre-cDC2As remains controversial. This should be acknowledged.
      4. Cytokine supplementation: <br /> The inclusion of SCF in the FLT3L-based differentiation assays should be justified, it is not a standard procedure.
      5. Macrophage contamination: <br /> The presence of C1qa, C1qb, and C1qc transcripts in some datasets suggests possible macrophage contamination. Please discuss how this was controlled for or how it might affect interpretation.

      Significance

      This study provides important insights into the epigenetic regulation of DC differentiation by DOT1L. The conclusions would be more compelling if supported by in vivo validation or, alternatively, if the limitations of the current in vivo data were transparently acknowledged and the focus shifted toward mechanistic in vitro depth.

      With these revisions, the manuscript would represent a valuable contribution to understanding how chromatin modification integrates with transcriptional control in shaping dendritic cell fate.

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      Referee #2

      Evidence, reproducibility and clarity

      Bouma et al. present a comprehensive analysis of DOT1L-mediated histone H3K79 methylation across canonical DC subsets. By mapping the methylation landscape, the authors demonstrate that DOT1L regulates both shared and subset-specific gene programs. They show that in vitro or in vivo deletion of Dot1l, followed by in vitro differentiation, results in reduced myeloid progenitors and pDCs alongside an increase in cDC2s, while cDC1 numbers remain largely unaffected. Functionally, Dot1l-deficient DCs fail to produce IFNα upon stimulation. Transcriptomic profiling reveals enrichment of antigen presentation pathways in Dot1l-KO subsets, with upregulated MHC class II surface expression in pDCs. Mechanistically, pharmacological inhibition of DOT1L links these effects to its methyltransferase activity. Collectively, the data suggest that DOT1L differentially regulates canonical DC subset development and represses antigen presentation pathways.

      The manuscript is well-written and technically sound. However, several conclusions would benefit from deeper discussion or additional experimental validation.

      Major Comments

      1. Interpretation of DC balance changes and cell-cycle effects

      The authors propose that DOT1L loss skews DC differentiation toward a pDC-like phenotype. However, DOT1L deletion or inhibition, and the consequent global loss of H3K79 methylation, is well known to downregulate key cell-cycle genes (e.g., Cyclin D1, Cyclin E, CDK4/6, MCM family) while upregulating cell-cycle inhibitors (e.g., Cdkn1a and b). These transcriptional changes are associated with slower proliferation, G1 arrest or delayed S-phase entry, and reduced DNA replication fork progression. Importantly, blocking DNA synthesis (e.g., with aphidicolin or mitomycin C) during early culture inhibits DC emergence, underscoring that proliferation is essential for differentiation. The authors should discuss how their findings align with this established literature. Could the observed DC subset shifts result from impaired cell-cycle progression rather than lineage-specific transcriptional reprogramming? A more detailed consideration of this point is needed. 2. Discrepancy between in vitro and in vivo pDC phenotypes

      The in vitro data show a marked reduction in pDCs, yet in vivo pDC numbers appear unchanged. Although the discussion briefly mentions proliferation differences, this discrepancy deserves a clearer explanation or experimental follow-up.

      Minor Comments

      • Clarify statistical methods, specify biological replicate numbers, and indicate whether corrections for multiple comparisons were applied to transcriptomic analyses.
      • The introduction is somewhat lengthy and repetitive; condensing it would improve focus.
      • In the discussion sometimes it is not clear the distinction between findings and speculation.
      • Ensure consistent gene name formatting throughout (e.g., Dot1l, Dot1L).

      Significance

      The current manuscript fills a gap in knowledge, and this is its major strength. Other strengths are clarity and technical appropriateness.

      The major weakness is that the work is mainly descriptive. Mechanistic insights into DOT1L-dependent transcriptional regulation are still weak. The proposed mechanism -that DOT1L maintains pDC identity through H3K79 methylation at key transcription factors (Tcf4, SpiB, Irf8)- is intriguing but currently lacks functional evidence. The authors should consider validating this model experimentally, by modulating the expression of these genes without affecting DOT1L activity. Also the model suggesting that DOT1L indirectly represses antigen presentation via the Fbxo11-Ciita pathway is interesting but remains speculative. Additional mechanistic data would help support this claim.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this study, Bouma et al. investigate the epigenetic mechanisms involved in dendritic cell (DC) development, focusing on the role of the lysine methyltransferase DOT1L, which mediates histone H3 lysine 79 (H3K79) methylation. The authors first show that Dot1l is expressed across most DC subsets and their progenitors. Consistently, DOT1L activity was detected in these subsets, as ChIP-seq analysis revealed an enrichment of H3K79 methylation marks around the transcription start sites of numerous genes that regulate DC fate. These marks were associated with active transcription, as confirmed by RNA sequencing. To assess the functional role of Dot1l in DC development, the authors used Rosa26Cre-ERT2 × Dot1l^flox/flox mice. Bone marrow (BM) cells from these mice were treated in vitro with tamoxifen and cultured with FLT3L and SCF to induce DC differentiation. Dot1l deletion impaired the development of plasmacytoid DCs (pDCs) and enhanced the generation of conventional DC2 (cDC2), while leaving cDC1 development unaffected. Similarly, in vivo tamoxifen treatment of Rosa26Cre-ERT2 × Dot1l^flox/flox mice for three days led to a comparable impairment of DC development upon in vitro culture of BM cells. Beyond mature DCs, Dot1l deletion also disrupted the ability of BM cells to generate common myeloid progenitors (CMPs), monocyte-dendritic cell progenitors (MDPs), and common DC progenitors (CDPs). These effects were attributed to the methyltransferase activity of DOT1L, as pharmacological inhibition of DOT1L produced similar outcomes. Interestingly, while in vivo tamoxifen treatment altered the frequencies of progenitor populations (MDP, CDP, CMP) in the BM, it did not significantly change the frequency of pDCs in the BM or spleen. Moreover, an increase in the cDC2 population was observed only in the BM, with no effect detected in the spleen. With these findings the authors claim that epigenetic regulation of gene expression by DOT1L is important for proper dendritic cell development.

      Major comments.

      While this study demonstrates that DOT1L regulates DC development in vitro, its inducible deletion in vivo using tamoxifen does not appear to significantly affect the overall distribution or function of DCs. Therefore, further investigation is needed to clarify the role of DOT1L in regulating DC fate under physiological conditions. The authors analyzed DC populations at only two time points (3 and 12 days) following tamoxifen-induced Dot1l deletion. As noted in the discussion, these time points are relatively early considering the lifespan of DCs, which often extends beyond this period. It would thus be important to assess the effects of Dot1l deletion over a longer duration (e.g., at least one month) to fully evaluate its impact on DC development. In addition to the BM, an extensive analysis of DCs population should be carried in the spleen as well as lymph nodes. Given the broad activity of the Rosa26-Cre system, prolonged deletion may affect overall mouse health and/or the function of other cell types that contribute to DC development; therefore, using a DC-specific Cre driver (e.g., CD11c-Cre) would provide a more targeted approach. Alternatively, competitive BM chimera experiments could be performed by reconstituting irradiated control mice with a 1:1 mixture of BM cells from Rosa26Cre-ERT2 × Dot1l^flox/flox and Rosa26Cre-ERT2 × Dot1l^wt/flox mice, both pre-treated with tamoxifen in vitro. Such experiments would offer more definitive evidence for the role of DOT1L in DC development in vivo. Aside from this point, the data and methods are clearly presented, and the figures are largely self-explanatory. All experiments were adequately replicated three times. Statistical analyses were primarily performed using t-tests, and ANOVA with multiple comparisons when appropriate. Since these are parametric tests that assume a normal distribution, it would be important to confirm whether the analyzed samples meet this assumption. If not, non-parametric tests should be used instead.

      Minor comments.

      It would be informative to show how specific Dot1l expression is in DCs and their progenitors compared with other immune lineages (e.g., lymphocytes) and their precursors. The data suggest that DOT1L regulates H3K79 methylation of both shared and subset-specific genes among DC populations. The authors could elaborate on how this regulation achieves cell-type specificity-perhaps through differential Dot1l expression levels across DC subsets.

      Interestingly, Dot1l deletion both in vitro and in vivo markedly reduces the frequency of common DC progenitors (CDPs), which give rise to cDC1 and cDC2. The authors should discuss how such a substantial loss of progenitors does not proportionally affect downstream cDC populations. Although in vivo tamoxifen-induced deletion of Dot1l in Rosa26Cre-ERT2 × Dot1l^flox/flox mice does not significantly alter the overall distribution of DC subsets (pDCs and cDCs), it appears to modify their phenotype. It would therefore be valuable to examine how Dot1l loss impacts the functional properties of individual DC subsets. While pDC responsiveness to CpG stimulation seems preserved in the absence of Dot1l, assessing how cDCs respond to TLR3 and TLR4 stimulation and their capacity to activate T cells would provide important additional insights.

      Significance

      General assessment: Bouma et al. present compelling evidence that DOT1L is an important regulator of DC differentiation in vitro from bone marrow-derived cells. They further demonstrate that DOT1L regulates DC development through its lysine methyltransferase activity, mediating histone H3K79 methylation. While these in vitro findings are robust and well supported, the physiological relevance of DOT1L function in vivo remains less clearly established. Additional experiments would help to strengthen the conclusions regarding its role under physiological conditions.

      Advance: While numerous transcription factors have been described as key regulators of DC subset development and fate, the role of epigenetic regulation in this process remains relatively understudied and poorly understood. This study addresses this important gap in the literature and provides novel insights into the role of H3K79 methylation mediated by DOT1L in controlling DC development.

      Audience: This paper will be of interest for a specialized audience in the field of the regulation of dendritic cell ontogeny. This work could influence additional research to investigate the epigenitc regulation of DCs development.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      In this study, Weethington et al investigate how the abundance/activity of signaling proteins change over time following stimulation of NK cells and if the dynamics of these changes are coupled to cell cycle progression. Using CyTOF to measure these proteins in single cells and using several NK cell models, the investigators categorize proteins by the dynamics of these changes as cells progress through G1, S, and G2/M. The investigators indicate that the majority of proteins increase monotonically or semi-monotonically during cell cycle progression, while others exhibit non-monotonic changes - increasing from G1 -> S and then decreasing form S -> G2/M or vice-versa. The authors then use these data to inform mathematical models to identify the cellular processes that may give rise to these non-monotonic changes, identifying protein synthesis, degradation, or signaling kinetics as potential mechanisms.

      Major comments

      I do not understand the rationale for comparing time points (post-stimulation) between progressive cell cycle phases. Although there is a fixed temporal ordering to cell cycle phases (G1 -> S -> G2/M), there is no temporal relationship between protein abundance measurements at a post-stimulation time point in different cell cycle phases. For example, take CD69 in Fig 2E,G: the authors cite non-monotonous changes occurring at the 32, 64, and 256 min timepoints and semi-monotonic changes at all other time points. The abundance of CD69 at 32 min post-stimulation in G1 has no temporal relationship to the 32 min time point in S or G2 phase, so it is not clear how a statement about monotonicity can be made in this context? I believe the appropriate analysis strategy to interrogate the question posed by the authors in this paper is to compare the entire time-course of protein abundance between phases (i.e. the shape/magnitude of change in protein abundance in G1 vs S vs G2). Through this lens, the CD69 data in Fig 2G would suggest that the decrease in protein abundance at later time points (relative to untreated within the same phase) is larger in S phase than in G1 or G2. It should also be noted that the CD69 dynamics following stimulation is completely different in primary cells (Fig 2) vs the NK cell line (Fig S3), making interpretation and generalization very difficult. It is also difficult to assess the magnitude of differences in protein abundance given that there are often no measures of variance indicated in the bar plots visualizing these changes (e.g. Fig 2G, Fig S2B). I am aware that the authors use a pair of one-sided t tests to make statements of statistic significance for these comparisons. However, in single-cell assays of this scale with hundred to thousands of data points per condition, t tests are prone to Type I error and often overpowered to identify truly meaningful differences. Is a >5% decrease in mean abundance from G1 to S phase in a single experiment (independent replicates do not appear to have been performed) and no follow-up validation experiments sufficient to make the statement that this decrease is biologically meaningful? And then stratify proteins into classes based on these relatively small changes?

      Significance

      Our current knowledge of the mammalian cell cycle comes mostly studies in epithelial and fibroblast cells. A better understanding of the cell cycles of other cell types, how it is regulated, and how it influences other cell biological events would be a significant benefit to the field

      General assessment: I believe that this study has fundamental concerns (described above) that must be addressed before this manuscript should advance to publication

      Audience: Basic research, cell cycle and immunology audiences.

      My background is in experimental and computational cell cycle biology

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Wethington, Nayak, Jensen et al. investigated changes within protein abundances in distinct NK cell cycle stages after NKG2D stimulation of primary human NK cells and the NK cell line NKL. In addition the authors use mathematical models to define distinct patterns of signaling protein abundances across different cell cycle stages.

      Overall, the manuscript is well written and of interest for the scientific community. However, the manuscript could benefit from additional improvements.

      Major comments

      1. It remains unclear how many replicates were used within the manuscript throughout. Please state the number of replicates clearly. Since there is considerable variation between different human donors an n=3-5 would be preferable for the NKG2D stimulation of primary human data to draw valuable conclusions.
      2. Did the authors compare non-reactive vs reactive NK cells after NKG2D stimulation and if yes, how does the pattern look for the signaling molecules between distinct cell cycle phases when comparing those? It would be interesting to see the distribuition of CD107a negative and positive NK cells within the different cell cycle stages upon stimulation. This would potentially also provide an internal negative control as the signaling proteins within the CD107a negative population are expected to go through less changes.
      3. The link between the first part (NKG2D stimulation) and second part (mathematical modeling) remains a bit unclear. Was any of the NKG2D stimulation data used to train the mathematical modeling? If not a potential way to improve the link would be to describe the mathematical modeling first and subsequently validate certain patterns in the NKG2D modeling or to compare cytokine only induced changes (only IL-2) to receptor signaling changes (NKG2D stimulation).

      Minor comments:

      1. The level of NKG2D is not shown within manuscript and could be added as an additional supplementary figure.
      2. The authors mention CDKs influencing cell signaling. Did the authors track the abundance of CDK molecules upon NK cell stimulation?
      3. Figure 2E shows a lot of information and is a bit crowded. Potentially it would be easier to split the information up? Show a heatmap of the expression of the significant proteins at all different timepoints and then show the abundance changes in detail for a few proteins for specific timepoints.

      Significance

      General assessment:

      The manuscript provides an interesting mathematical modeling as well as CyTOF data from NK cell stimulations about differences in protein abundances throughout different cell cycle stages of NK cells. The data of the NK cell stimulation could be better linked to the mathematical modeling to make a stronger case for the robustness of the model and for more mechanistic conclusions. The manuscript contains a lot of data which is sometimes presented to condensed (Figure 2), the manuscript could benefit from a clearer red line throughout/focus on key molecules.

      Audience:

      The data presented is of interest for the specialized NK cell community but the discussion section could be improved by making a stronger case of how the herein presented data/model will benefit further studies within the NK cell or general immunology field.

      My field of expertise: NK cell biology, tissue-resident NK cells.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: The paper reports the results of a study that examines how cell cycle stages influence NK cell receptor signaling. The authors find that while most signaling proteins increase monotonically with cell cycle progression, a subset shows non-monotonic variations. Simple computational models are used to explore mechanisms to qualitatively explain the observations.

      Major comments:

      I am not convinced that the use of models here substantially contributes to the understanding of the observations. The reason is that the results are fairly intuitive and actually to a good extent already well known to those who construct models of reaction kinetics including cell-cycle dynamics. So for example the observation that protein numbers increase mononotically with cell-cycle progression is the obvious thing to expect because since most proteins have a lifetime that exceeds the mean cell-cycle duration then it follows that naturally the protein numbers have to increase during the cell-cycle. This already explains the bulk of the observations. For those proteins where there is non-monotic behaviour, indeed there is something more complex going on but here there are many possibilities. As they say, if we have a 2nd order reaction then the firing rate of bimolecular reactions could increase or decrease with cell-cycle progression because it decreases with cell volume and increases with abundance, both of which factors vary with cell-cycle progression. A model is not quite needed to see that this may lead to non-monotic behaviour. If the model was fitted to the data, i.e. the experimental distributions of protein abundance with cell-cycle progression were fitted to the model and then these are used to constrain possible mechanisms, then yes I would agree that the model brings in some added benefit. Another criticism is the modelling approach itself involves strong simplifications that may not be entirely realistic." : (i) the volume does not seem to change within one cell-cycle stage, e.g. it is 1.3 for all times within the S phase.. "This assumption may be questionable, particularly for cell-cycle stages that occupy a large portion of the cycle." The cell volume generally should vary continuously with time within the cell-cycle and because the propensities are time-dependent then the SSA is not anymore exact and hence one needs to use modifications of it which account for such phenomena. (ii) the doubling of gene copy number due to DNA replication seems to have been omitted from the model. This is expected to lead to a considerable change in the protein numbers at the point in the cell-cycle where DNA replication occurs and hence appears to be an important factor for this study. (iii) how do we reconcile protein concentration homeostasis with the models described in this paper? This is a well known phenomenon, see for e.g. Nature communications 9.1 (2018): 4496 and references therein. (iv) cell-size control mechanisms are not included in the model (adder, sizer, timer); the choice is known to crucially alter protein dynamics across the cell-cycle so difficult to see how one can ignore the inclusion of these. See for e.g. PLoS computational biology 18.10 (2022): e1010574.

      Minor comments:

      The literature on models of gene expression (mRNA and protein dynamics) including cell-cycle dynamics is extensive and the discussion of this paper would benefit from including more of this. Some of these papers include Biophysical Journal, 107 (2014), 301-313; Journal of theoretical biology 348 (2014): 1-11.; PLoS computational biology 12.8 (2016): e1004972; Plos one 15.1 (2020): e0226016; J. Chem. Phys. 159, 224102 (2023).

      Significance

      This is an interesting paper with both data and modelling. However, presently, the connection between them does not appear strong enough to fully support the conclusions drawn.

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      Reply to the reviewers

      We thank the Reviewers for their positive assessment of the quality and significance of our work, as well as for their insightful comments, which have helped us to further improve the manuscript. We have addressed the majority of the comments in the revised version and, for those that require additional time, we outline below a detailed plan of the experiments we intend to perform.

      We agree with Reviewer #2 that a more detailed mechanistic understanding of the drug effects would further strengthen the study, and we are grateful to both reviewers for the constructive experimental suggestions provided to address this point. In particular, we are highly motivated to better define the causal role of C18 sphingolipid alterations in mediating the effects of the drugs, as suggested by Reviewer #2, as well as to investigate the involvement of the retromer complex in the lysosome-to-Golgi connection, as suggested by Reviewer #1.

      Below, we provide a point-by-point description of the revisions already incorporated into the manuscript, along with the planned experiments that will address the remaining comments

      REVIEWER #1:

      VPS13B is a bridge-like lipid transfer protein, the loss or mutation of which is associated with Cohen syndrome (CS) involving Golgi fragmentation. In this study, the authors performed image-based chemical screens to identify compounds capable of rescuing the Golgi morphology in VPS13B-KO HeLa cells. They identified 50 compounds, the majority of which are lysosomotropic compounds or cationic amphiphilic drugs (CADs). Treatment of cells with several of these compounds causes lysosomal lipid storage, as assessed by BMP/LBPA staining, filipin staining, or LipidTOX staining. Interestingly, most LipidTOX puncta colocalized with transferrin receptor-positive compartments but not lysosomes. Similar to lysosomotropic compounds, knocking down NPC1 or SMPD1, mimicking lysosomal storage disease, also substantially rescued Golgi morphology. The authors show that VPS13B-KO cells have reduced C18 sphingolipids, which is reversed by treatment with CADs. Finally, the authors show that two CADs partially rescue neurite outgrowth in neuronal cultures. However, these drugs do not rescue the size of VPS13B KO organoids.

      Overall, this is an impressive study identifying CADs as potential therapeutics for CS and suggesting sphingolipid upregulation as a general strategy for CS treatment. The morphological and lipidomics analyses unravel important molecular basis of CS pathology. This study will be of high interest to the field of lipid biology and organelle homeostasis. I have a few comments to help improve the quality of this study.

      1. The reverse of lipid changes in VPS13B-KO cells by CADs is intriguing. Are CAD-mediated benefits such as Golgi morphology recovery permanent or only transient within 24 hours of treatment? How do the CADs affect the Golgi morphology in WT HeLa cells?

      RESPONSE:

      We thank the reviewer for this insightful question Indeed, the effects of CADs on Golgi organization are most evident in VPS13B KO cells, where the Golgi apparatus is severely fragmented and becomes more compact upon drug treatment, whereas the effect is much less apparent in wild-type cells. Nevertheless, a careful quantitative analysis of the images (now presented in the new Fig. S7) demonstrates that the impact of these compounds on Golgi morphology is not restricted to KO cells but is likely more general, supporting a link between lysosomal storage and Golgi organization. Although this observation indicates an indirect effect (consistent with the proposed mechanism of action), rather than a direct correction of VPS13B loss, it does not compromise in our opinion their potential beneficial effect for KO cells as shown also from the results obtained in organoid-derived neurons.

      Under continuous treatment, azelastine keeps the Golgi in a compact state for 72 hours without any noticeable deleterious effect on the cells (see new Fig. S10) Raloxifene, on the contrary proved to be toxic over the same time period. We believe this difference reflects the mechanism of action of CADs, which progressively accumulate within acidic organelles and may eventually reach a toxic threshold upon prolonged exposure. For this reason, lower drug concentrations administered over longer treatment periods may represent a viable alternative strategy. In this regard, we also refer the reviewer to our response to the comment on brain organoids below.

      1. Is it surprising that Azelastine-induced lipid storage in transferrin receptor compartments (early and recycling endosomes)? I suggest more controls to examine LipidTOX overlap with Golgi markers or other late endosome/lysosome markers such as LBPA and CD63.

      RESPONSE:

      We agree with the reviewer that this observation is somewhat unexpected. However, we would like to clarify that we do not intend to suggest that lipid storage occurs primarily in early or recycling endosomes, which would indeed contradict a substantial body of existing evidence. Rather, our data indicate that this particular dye (LipidTOX) labels recycling endosomes, at least in HeLa cells. This finding is consistent with the widely accepted view that lysosomal lipid storage exerts broader effects on intracellular trafficking, not limited to late endosomes/lysosomes. We corrected the text in order to clarify this concept.

      LipidTOX was specifically developed to detect drug-induced phospholipidosis, and based on our data, it appears suitable for this purpose. To our knowledge, there is no published information detailing its intracellular localization, which motivated us to perform these control experiments. Unfortunately, the proprietary formulation of this product does not allow informed speculations to explain the observed localization or whether this could refer to the intact molecule or to a catabolite.

      As suggested by the reviewer, we plan to perform co-staining with additional markers to further clarify this this point.

      1. Does the LipidTOX/TFRC overlap suggest potential roles of retrograde transport in supplying sphingolipids to the Golgi? The authors can quickly test if the knockdown of a retromer subunit (VPS35) blocks Azelastine-induced recovery of Golgi morphology.

      RESPONSE:

      We thank the reviewer for this insightful suggestion. Indeed, the retromer complex represents one of the best-characterized trafficking pathways from the endosomal system to the Golgi, and this relatively straightforward experiment could help to mechanistically clarify our observations. We plan to test whether VPS35 knockdown interferes with the effects of the drugs.

      What is the rationale to use 500 nM to 1 uM azelastine and raloxifene for neuronal cultures and organoids? At such concentrations, no obvious changes in Golgi morphology or lipid storage were observed (Fig 4). Also, the lipidomics analysis was performed after 10 uM compound treatment. It might be worth trying dose-response experiments in organoid tests.

      RESPONSE:

      We thank the reviewer for this question. The rationale about this choice was indeed missing from our previous version of the manuscript. The reason of lowering the concentrations comes indeed from toxicity tests, preliminarily performed over long-term treatment of both WT and VPS13B KO organoids. This information has now been explicitly included in the Results section of the revised manuscript, and the broader implications are also discussed in the Discussion section.

      MINOR COMMENTS:

      It is important to know whether the authors used TGN or cis-Golgi markers for Golgi morphology analysis. Please label the two channels in Fig. 2C and throughout all figures. In many cases, it is not clear what is stained in the green channel to show the Golgi morphology. It was not even stated in the legend.

      RESPONSE:

      We now included the antibody staining in all figure legends where it was previously missing.

      The authors stated that Recovery of Golgi morphology is dependent on lysosomal lipid storage. However, while the data show positive correlation between the two, no causal relationship is established by the data. It seems true that in all conditions (CADs or genetic knockdown) where lysosomal lipid storage was observed, the authors detect the Recovery of Golgi morphology. However, budesonide did not depend on lysosomal lipid storage to recover the Golgi morphology. Thus, the recovery of Golgi morphology is NOT dependent on lysosomal lipid storage, but inducing lysosomal lipid storage appears sufficient to recover Golgi morphology in VPS13B-KO HeLa cells.

      RESPONSE:

      We thank the reviewer for this comment and we agree that the previous title of the paragraph could have been misleading. This has been now changed in: “Lysosomal lipid storage mediates the recovery of Golgi morphology” which is probably less prone to ambiguous interpretations.

      Obviously, in the previous version of the title we wanted to mean that Golgi recovery is dependent on lipid storage “in the context of CAD treatment” and not as a general statement.

      With respect to the cause–effect relationship, we believe that the strongest evidence supporting this link is the observation that genetically induced lipid storage phenocopies the effects of drug treatment. We hope that this conclusion is now sufficiently clear from the revised text.

      Each figure needs a title before the detailed legends for specific panels.

      RESPONSE:

      Titles have now been included to all figure legends.

      Fig 8. Y axis labeling is missing.

      RESPONSE:

      Axes labels have now been included

      Does U18666A rescues Golgi morphology in VPS13B-KO cells?

      RESPONSE:

      We thank the reviewer for this comment. U18666A indeed also corrects Golgi morphology. The result is now included in the new figure S5.

      Please do not repeat the result section in discussion. Focus on the most important points.

      RESPONSE:

      We thank the reviewer for this comment. We shortened the descriptive part of the discussion trying as much as possible to avoid repetitions with the result session and keeping only the more essential information for the flow of the discussion.

      Reviewer #1 (Significance (Required)):

      This is an impressive study that identifies Cationic Amphiphilic Drugs (CADs) as potential therapeutics for Cohen syndrome (CS) and suggests sphingolipid upregulation as a general strategy for diseases driven by VPS13B loss-of-function. The unbiased approaches, notably the chemical screen and lipidomics, provide novel mechanistic insights into the underlying pathology of CS. This study will be of high interest to researchers in the fields of lipid biology and organelle homeostasis. It will also be highly valuable for clinical pediatricians managing CS patients.

      REVIEWER #2:

      This manuscript describes a compound screening aimed at identifying molecules that can restore Golgi organization in VPS13B knockout (KO) cells. The authors identify several compounds, most of which are lysosomotropic, and analyze their effects on Golgi morphology and lipid composition using multiple approaches. They report that VPS13B KO cells exhibit a reduction in C18-N-acyl sphingolipids, which can be restored by several of the identified compounds. Furthermore, two of these compounds, azelastine and raloxifene, promote neurite outgrowth in VPS13B KO cortical organoids. These findings are interesting and could potentially contribute to a better understanding of the pathophysiology of Cohen syndrome and the development of therapeutic strategies. However, despite the large number of analyses presented, the study remains largely descriptive, and there is no coherent mechanistic explanation for how these compounds restore Golgi structure in VPS13B KO cells. In addition to the reduction in C18-N-acyl sphingolipids, the KO cells display alterations in several other lipid species (LPC, LPE, PC40:1, PE42:1, TG, etc.), and treatment with the selected compounds induces further lipid accumulations, including cholesterol and BMP/LBPA. The relationship between these diverse lipid changes and the observed Golgi recovery lacks clarity and mechanistic consistency.

      MAJOR COMMENTS:

      The finding that compounds cannot prevent Golgi fragmentation caused by brefeldin A or nocodazole but can suppress statin-induced fragmentation is intriguing, but the underlying mechanism is not addressed. It is not evident whether this difference results from changes in membrane lipid composition or restoration of Rab/SNARE trafficking. The authors should examine Rab prenylation and SNARE localization by immunofluorescence or Western blotting to support their interpretation.

      RESPONSE:

      We thank the reviewer for this suggestion and agree that the ability of these compounds to counteract statin-induced Golgi fragmentation is indeed intriguing. The primary reason we did not further explore this aspect is that we evaluated the effects of statins not to be a central focus of the present study. Nevertheless, we fully agree that this observation represents a valuable opportunity to gain additional insight into the mechanism underlying drug-induced Golgi recovery.

      To address this point, we plan to analyze Rab prenylation by Western blot and Rab localization by microscopy, focusing on a Golgi-associated Rab protein such as Rab6. In addition, we will employ downstream inhibitors of Rab prenylation, such as 3-PEHPC (an inhibitor of type II protein geranylgeranyltransferase (GGTase-II)), which should allow us to formally distinguish effects related to impaired Rab prenylation from those arising from inhibition of cholesterol biosynthesis.

      Although restoration of C18 sphingolipids (SM 36:1, CER 36:1) is observed upon compound treatment, its causal role in Golgi recovery or neurite outgrowth is not established. The authors should test whether blocking the increase of C18 SM/CER prevents the rescue of Golgi or neuronal phenotypes.

      RESPONSE:

      We sincerely thank the reviewer for this comment. We agree that, based on the current data, a definitive cause–effect relationship between Golgi recovery and the increase in C18 sphingolipids cannot be firmly established, and we acknowledge that a deeper understanding of this issue will require further investigation. Furthermore, we believe that addressing this would not only provide a better mechanistic understanding of the biological processes behind the effect of the drugs but provide a potential avenue for therapeutic intervention. For these reasons, we are strongly motivated to pursue this aspect further.

      With respect to the reviewer’s specific suggestion, we agree that preventing the increase in C18 sphingolipids would be an ideal experimental approach. However, the limited understanding of the regulatory mechanisms controlling C18 sphingolipid homeostasis currently precludes a fully informed strategy. In principle, if the observed increase were due to enhanced synthesis, one could envisage blocking it by silencing ceramide synthases with C18 selectivity, such as CERS1. The experiment shown in Fig. 7E (azelastine treatment in the presence of sphingolipid synthesis inhibitors) was designed with this rationale in mind. However, these results suggest that azelastine-induced C18 sphingolipid accumulation is unlikely to result from increased synthesis, and is instead more consistent with reduced degradation, in line with the proposed mechanism of action of CADs.

      Based on these considerations, we propose to invert the experimental approach and test whether cellular re-complementation with C18 sphingolipids is sufficient to recapitulate the drug-induced Golgi recovery. We are aware of the technical challenges associated with the targeted delivery of exogenously supplied lipids, particularly given the likelihood that effective rescue would require lipid access to the Golgi apparatus. Based on current knowledge, we anticipate that externally supplied lipids would primarily traffic either to the ER via non-vesicular routes or to endosomes/lysosomes through endocytic uptake. From both locations they could eventually reach to some extent the Golgi. The route from endosomes to Golgi in particular as been intensively studied in the past with the use of fluorescent sphingolipid analogs1,2 and may well work also with native lipids.

      Since we are not able to predict in advance which lipid species would be more effective or the optimal delivery strategy, we plan to test re-complementation using C18 sphingomyelin and some of its potential precursors, including C18 ceramide as well as using alternative delivery strategies such as incorporation in liposomes of different formulations and delivery at the plasma membrane with bovine serum albumin or cyclodextrins as carriers.

      1. Puri et al., (2001). J Cell Biol.154:535-47 (doi: 10.1083/jcb.200102084)
      2. Koivusalo et al.,(2007). Mol Biol Cell. 18:5113-23 (doi: 10.1091/mbc.e07-04-0330)

        In Figure 7D, comparisons should include the LM and HM fractions isolated from WT cells.

      RESPONSE:

      Wild-type control were included in the figure as requested.

      The subcellular fractionation experiment should be repeated using AZL and RAL, the compounds used in organoid experiments, rather than TFPZ, to assess whether similar results are obtained. The compounds used differ across experiments, making it difficult to draw consistent conclusions.

      RESPONSE:

      We thank the reviewer for this comment and apology for some inconsistencies in the selection of the compounds to highlight in the figures which are mostly remnants of the drug prioritization history over the progression of the project. We tried to make it more consistent in the current version.

      In the new version of figure 7D, AZL is substituting TFPZ, while TFPZ data were moved to supplementary figure S19.

      Golgi morphology in VPS13B KO cells is reported to recover in NPC1 KD and SMPD1 KD cells, but it is not shown whether SM 36:1, CER 36:1, or other lipid levels also increase or change in these conditions. If Golgi morphology recovery occurs via the same mechanism as with compound treatment, a similar lipid pattern should be observed.

      RESPONSE:

      We thank the reviewer for this question that allowed us to expand our study including new interesting findings. We agree that this is an important point to strengthen the link between CAD and genetic perturbation effects. Given the availability of several published lipidomic datasets modelling LDS in HeLa and in other cell lines, we decided to perform a re-analysis of those to specifically focus on C18 sphingolipids. We found a relative increase of 36:1 upon depletion of LSD genes in all analyzed datasets for NPC1 and SMPD1, but also for more than 15 other LSD genes including NPC2, recapitulating what we find with all the CAD molecules tested in our study. These changes, were not noticed or at least not discussed by most of the authors. This is not surprising since those studies are focused on different biological questions. We believe that these findings, besides reinforcing our hypothesis of a common mechanism between CAD and NPC1/SMPD1 KO, have of general interest for the regulation of C18 sphingolipids, which are among the relative few lipid species with a bona fide specific protein binding partner and proposed to play a crucial role in Golgi traffic.

      MINOR POINTS:

      The manuscript lacks sufficient information about the compound library used for screening (number and source of compounds, compound type).

      RESPONSE:

      We apologize if this information was not sufficiently visible in the original version of the manuscript. The data about source, catalog number, formulation and several additional identifiers is included in the File S1. This is now clearly indicated in the methods so that I can be more easily visible to the readers

      Fig. 3A: a WT control image is required.

      RESPONSE:

      A WT control image is now included in the new version of Figure 3.

      Fig. 4: include representative images at concentrations higher than 1.25 µM.

      RESPONSE:

      Representative images are now included for all concentrations higher than 1.25 µM, as requested.

      Abbreviations such as BMP/LBPA should be defined when first mentioned.

      RESPONSE:

      The abbreviation of BMP/LBPA was already defined when first mentioned in the original version of the manuscript

      The abbreviation for raloxifene is inconsistent (RLX vs RAL) and should be unified.

      RESPONSE:

      Raloxifene is now abbreviated as RLX all over the manuscript.

      Fig. 5C: the meaning of the green and magenta bars is not explained.

      RESPONSE:

      Color code for figure 5C has been included.

      The definitions and centrifugation parameters for light and heavy membrane fractions should be clearly stated in the Methods.

      RESPONSE:

      The centrifugation parameters were already defined in the original manuscript. It is not clear to us, which parameter the Referee is referring to. Below is the sentence in the methods section:

      “Gradients were centrifuged at 165,000 g for 1.5 h at 4°C with a SW40Ti Swinging-Bucket rotor (Beckman-Coulter). The LM and HM fractions were collected at the 35%-HB and 35%-40.6% interfaces, respectively”

      The concentration and incubation times for BFA and nocodazole should be included in the main text or figure legends.

      RESPONSE:

      Concentrations and incubation times of BFA and nocodazole were already present in the legend of figure 5.

      Fig. 8C, D, G, H: y-axes lack labels and must be defined.

      RESPONSE:

      Axes labels have now been included

      There are multiple typographical errors, including "VPS12" instead of "VPS13B", that should be corrected.

      RESPONSE:

      We corrected this specific mistake as well as others that we could identify after careful reading of the manuscript.

      Reviewer #2 (Significance (Required)):

      While the dataset is extensive and technically detailed, the manuscript lacks a clear mechanistic explanation connecting lipid changes to Golgi restoration. The choice and comparison of compounds are inconsistent across experiments, and the interpretation remains speculative. Substantial revision and additional experiments are required before the study can be considered for publication.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript describes a compound screening aimed at identifying molecules that can restore Golgi organization in VPS13B knockout (KO) cells. The authors identify several compounds, most of which are lysosomotropic, and analyze their effects on Golgi morphology and lipid composition using multiple approaches. They report that VPS13B KO cells exhibit a reduction in C18-N-acyl sphingolipids, which can be restored by several of the identified compounds. Furthermore, two of these compounds, azelastine and raloxifene, promote neurite outgrowth in VPS13B KO cortical organoids. These findings are interesting and could potentially contribute to a better understanding of the pathophysiology of Cohen syndrome and the development of therapeutic strategies. However, despite the large number of analyses presented, the study remains largely descriptive, and there is no coherent mechanistic explanation for how these compounds restore Golgi structure in VPS13B KO cells. In addition to the reduction in C18-N-acyl sphingolipids, the KO cells display alterations in several other lipid species (LPC, LPE, PC40:1, PE42:1, TG, etc.), and treatment with the selected compounds induces further lipid accumulations, including cholesterol and BMP/LBPA. The relationship between these diverse lipid changes and the observed Golgi recovery lacks clarity and mechanistic consistency.

      Major comments

      The finding that compounds cannot prevent Golgi fragmentation caused by brefeldin A or nocodazole but can suppress statin-induced fragmentation is intriguing, but the underlying mechanism is not addressed. It is not evident whether this difference results from changes in membrane lipid composition or restoration of Rab/SNARE trafficking. The authors should examine Rab prenylation and SNARE localization by immunofluorescence or Western blotting to support their interpretation.

      Although restoration of C18 sphingolipids (SM 36:1, CER 36:1) is observed upon compound treatment, its causal role in Golgi recovery or neurite outgrowth is not established. The authors should test whether blocking the increase of C18 SM/CER prevents the rescue of Golgi or neuronal phenotypes.

      In Figure 7D, comparisons should include the LM and HM fractions isolated from WT cells.

      The subcellular fractionation experiment should be repeated using AZL and RAL, the compounds used in organoid experiments, rather than TFPZ, to assess whether similar results are obtained. The compounds used differ across experiments, making it difficult to draw consistent conclusions.

      Golgi morphology in VPS13B KO cells is reported to recover in NPC1 KD and SMPD1 KD cells, but it is not shown whether SM 36:1, CER 36:1, or other lipid levels also increase or change in these conditions. If Golgi morphology recovery occurs via the same mechanism as with compound treatment, a similar lipid pattern should be observed.

      Minor points

      The manuscript lacks sufficient information about the compound library used for screening (number and source of compounds, compound type).

      Fig. 3A: a WT control image is required. Fig. 4: include representative images at concentrations higher than 1.25 µM. Abbreviations such as BMP/LBPA should be defined when first mentioned. The abbreviation for raloxifene is inconsistent (RLX vs RAL) and should be unified. Fig. 5C: the meaning of the green and magenta bars is not explained. The definitions and centrifugation parameters for light and heavy membrane fractions should be clearly stated in the Methods. The concentration and incubation times for BFA and nocodazole should be included in the main text or figure legends. Fig. 8C, D, G, H: y-axes lack labels and must be defined. There are multiple typographical errors, including "VPS12" instead of "VPS13B", that should be corrected.

      Significance

      While the dataset is extensive and technically detailed, the manuscript lacks a clear mechanistic explanation connecting lipid changes to Golgi restoration. The choice and comparison of compounds are inconsistent across experiments, and the interpretation remains speculative. Substantial revision and additional experiments are required before the study can be considered for publication.

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      Referee #1

      Evidence, reproducibility and clarity

      VPS13B is a bridge-like lipid transfer protein, the loss or mutation of which is associated with Cohen syndrome (CS) involving Golgi fragmentation. In this study, the authors performed image-based chemical screens to identify compounds capable of rescuing the Golgi morphology in VPS13B-KO HeLa cells. They identified 50 compounds, the majority of which are lysosomotropic compounds or cationic amphiphilic drugs (CADs). Treatment of cells with several of these compounds causes lysosomal lipid storage, as assessed by BMP/LBPA staining, filipin staining, or LipidTOX staining. Interestingly, most LipidTOX puncta colocalized with transferrin receptor-positive compartments but not lysosomes. Similar to lysosomotropic compounds, knocking down NPC1 or SMPD1, mimicking lysosomal storage disease, also substantially rescued Golgi morphology. The authors show that VPS13B-KO cells have reduced C18 sphingolipids, which is reversed by treatment with CADs. Finally, the authors show that two CADs partially rescue neurite outgrowth in neuronal cultures. However, these drugs do not rescue the size of VPS13B KO organoids.

      Overall, this is an impressive study identifying CADs as potential therapeutics for CS and suggesting sphingolipid upregulation as a general strategy for CS treatment. The morphological and lipidomics analyses unravel important molecular basis of CS pathology. This study will be of high interest to the field of lipid biology and organelle homeostasis. I have a few comments to help improve the quality of this study.

      1. The reverse of lipid changes in VPS13B-KO cells by CADs is intriguing. Are CAD-mediated benefits such as Golgi morphology recovery permanent or only transient within 24 hours of treatment? How do the CADs affect the Golgi morphology in WT HeLa cells?
      2. Is it surprising that Azelastine-induced lipid storage in transferrin receptor compartments (early and recycling endosomes)? I suggest more controls to examine LipidTOX overlap with Golgi markers or other late endosome/lysosome markers such as LBPA and CD63.
      3. Does the LipidTOX/TFRC overlap suggest potential roles of retrograde transport in supplying sphingolipids to the Golgi? The authors can quickly test if the knockdown of a retromer subunit (VPS35) blocks Azelastine-induced recovery of Golgi morphology.
      4. What is the rationale to use 500 nM to 1 uM azelastine and raloxifene for neuronal cultures and organoids? At such concentrations, no obvious changes in Golgi morphology or lipid storage were observed (Fig 4). Also, the lipidomics analysis was performed after 10 uM compound treatment. It might be worth trying dose-response experiments in organoid tests.

      Minor:

      1. It is important to know whether the authors used TGN or cis-Golgi markers for Golgi morphology analysis. Please label the two channels in Fig. 2C and throughout all figures. In many cases, it is not clear what is stained in the green channel to show the Golgi morphology. It was not even stated in the legend.
      2. The authors stated that Recovery of Golgi morphology is dependent on lysosomal lipid storage. However, while the data show positive correlation between the two, no causal relationship is established by the data. It seems true that in all conditions (CADs or genetic knockdown) where lysosomal lipid storage was observed, the authors detect the Recovery of Golgi morphology. However, budesonide did not depend on lysosomal lipid storage to recover the Golgi morphology. Thus, the recovery of Golgi morphology is NOT dependent on lysosomal lipid storage, but inducing lysosomal lipid storage appears sufficient to recover Golgi morphology in VPS13B-KO HeLa cells.
      3. Each figure needs a title before the detailed legends for specific panels.
      4. Fig 8. Y axis labeling is missing.
      5. Does U18666A rescues Golgi morphology in VPS13B-KO cells?
      6. Please do not repeat the result section in discussion. Focus on the most important points.

      Significance

      This is an impressive study that identifies Cationic Amphiphilic Drugs (CADs) as potential therapeutics for Cohen syndrome (CS) and suggests sphingolipid upregulation as a general strategy for diseases driven by VPS13B loss-of-function. The unbiased approaches, notably the chemical screen and lipidomics, provide novel mechanistic insights into the underlying pathology of CS. This study will be of high interest to researchers in the fields of lipid biology and organelle homeostasis. It will also be highly valuable for clinical pediatricians managing CS patients.

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      Reply to the reviewers

      Manuscript number: RC-2025-02932

      Corresponding author(s): Amit Tzur

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

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

      1. General Statements

      We thank all Referees for their insightful comments and thoughtful review of our manuscript.

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      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. *

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      __! Original comments by Reviewers #1-3 are in gray. __


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

      The study highlights a dephosphorylation switch mediated by PP2A as a critical mechanism for coupling E2F7/8 degradation to mitotic exit and G1 phase. The study is clear and experiments are well conducted with appropriate controls

      I have some concerns highlighted below:

      Point 1. In this sentence: This intricate network of feedback mechanisms ensures the orderly progression of the cell cycle. What feedback mechanism are the authors referring to?

      Thank you for pointing this out. We aimed for a general comment. The original line was replaced with: “The intricate network of (de)phosphorylation and (de)ubiquitination events in cycling cells establishes feedback mechanisms that ensure orderly cell cycle progression.

      Point 2. Characterization of disorder in the N-terminal segments of E2F7 and E2F8

      What does it mean disorder in this title?

      “Disorder” is a structural biology term for describing an unstructured (floppy) region in a protein. We suggest the following title in hope to improve clarity: “The N-terminal segments of E2F7 and E2F8 are intrinsically unstructured”

      Point 3. In the paragraph on the untimely degradation of E2F8 the authors keep referring to APC/C Cdc20, however the degradation is triggered by the Ken box which is specifically recognised by APC/C Cdh1. Can it be due to another ligase not APC/C?

      In our anaphase-like system, Cdh1 cannot associate with the APC/C due to persistently high Cdk1 activity, maintained by the presence of non-degradable Cyclin B1. While the KEN-box is classically recognized as a Cdh1-specific motif, previous studies have also clearly demonstrated that APC/C-Cdc20 can mediate the degradation of KEN-box substrates. For example, BubR1 interacts with Cdc20 via two KEN-box motifs (PMIDs: 25383541, 27939943 and 17406666). Nek2A is targeted for degradation by the APC/C in mitotic egg extracts lacking Cdh1, in a manner that depends on both D-box and KEN-box motifs (PMID: 11742988). CENP-F degradation in Cdh1-null cells has been shown to be dependent on both Cdc20 and a KEN-motif (PMID: 20053638). Thus, the most simple explanation for our results is that degradation is KEN box dependent and controlled by Cdc20.

      Regarding alternative E3 ligases, KEN-box mutant variants of non-phosphorylatable E2F8 remained stable in APC/CCdc20-active extracts, suggesting that this degradation is indeed APC/C-specific.

      Please also see our response to Reviewer #3, Point 3.

      Point 4. The assays to detect dephosphorylation are rather indirect so it is difficult to establish whether phosphorylation of CDK1 and dephosphorylation by PP2A on the fragments is direct.

      First, the phosphorylation sites analyzed in this study conform to the full and most canonical Cdk1 consensus motif: S/TPxK/R. While recognizing that other kinases are proline directed as well, the cell cycle dependent manner of this control, and presence of a similar CDK-dependent mechanism for Cdc6, points us towards considering the role of CDKs.

      Second, consistent with the direct role of CDK1 in this regulation, NMR experiments demonstrate conformational shifts of recombinant E2F8 following incubation with Cdk1–Cyclin B1 (not included in manuscript, but shown here for reviewer consideration); see Figure below. We have not yet established equivalent biochemical systems for PP2A.

      Figure legend: NMR-based monitoring of E2F7 (a-c) and E2F8 (d-f) phosphorylation by Cdk1.

      a(d). 15N,1H-HSQC spectrum of E2F7(E2F8) prior to addition of Cdk1. Threonine residues of interest, T45 (T20) conforming to the consensus sequence (followed by a proline), and T84 (T60) lacking the signature sequence are annotated. b(e). Strips from the 3D-HNCACB spectrum used for assigning E2F7(E2F8) residues. Black (green) peaks indicate a correlation with the 13Cα (13Cβ) of the same and previous residues. The chemical shifts assigned to T45 (T20) and T84 (T60) match the expected values for K44(K19) and P83(P59), thereby confirming the assignment. c(f). Top, overlay of subspectra before adding Cdk1 (black) and after 16 h of activity (red) at 298 K. Bottom, change in intensities of the T45/T84 in E2F7 and T20/T60 in E2F8 showing how NMR monitors phosphorylation and distinguishes between various threonine residues.


      Third, PP2A is likely the principal phosphatase counteracting Cdk1-mediated phosphorylation during mitotic exit, targeting numerous APC/C substrates (PMID: 31494926). In light of our findings and the extensive literature, it is therefore reasonable to propose that E2F7 and E2F8 may also be direct PP2A targets.

      Fourth, we cannot fully exclude the possibility that dephosphorylation of E2F7 and E2F8 by PP2A occurs indirectly. Nevertheless, indirect studies of PP2A substrate identification in the literature often rely on similar genetic perturbations, chemical inhibition, cell-free systems (coupled with immunodepletion, inhibitory peptides/proteins, and small-molecule inhibitors), and phosphoproteomics. Moreover, more direct assays are not without caveats, as they lack the cellular stoichiometric context, an important limitation for relatively promiscuous enzymes such as phosphatases.

      Importantly, repeated attempts (conventional [Co-IP] and less conventional [affinity microfluidics]) to detect interactions between PP2A and E2F7 and E2F8 were unsuccessful. This result was unfortunate but not surprising, given that transient substrate–phosphatase interactions are often challenging to capture experimentally.

      Given our evidence showing the regulation of E2F7 and E2F8 degradation in a manner that depends on Cdk1 and PP2A, the title of the manuscript remains appropriate: "Cdk1 and PP2A constitute a molecular switch controlling orderly degradation of atypical E2Fs.”

      Please also see our response to Reviewer #3 Point 1.

      Point 5. Although there seems to be a control by phosphorylation and dephosphorylation (which could be indirect), it is difficult to establish the functional consequences of this observation. The authors propose a feedback mechanism which regulates the temporal activation inactivation of E2F7/8 however, there are no evidence in support of this.

      The components being studied here have been extensively characterized, as have the direct and indirect interactions that connect them and ensure orderly cell cycle progression. For example: i) The E2F1–E2F7/8 transcriptional circuitry functions as a negative feedback loop; ii) Cdk1 and PP2A counteract one another’s activity; iii) E2F1 promotes the disassembly of APC/CCdh1; iv) E2F7 and E2F8 are APC/C substrates with cell cycle-relevant degradation patterns; and v) Loss of Cdh1 leads to premature S-phase entry.

      Our study brings these components together into a coherent regulatory module operating in cycling cells, revealed through cell-free biochemistry and newly developed methodologies with broad applicability to signaling research. We believe that advancing mechanistic understanding at this level of central regulators is impactful. And notably, this is a model, which we expect others in the field to test. We stand behind the result of each individual experiment and based on those findings are proposing a feedback circuit.

      To address your suggestion, we incorporated phenotypic analyses (see Figure on the next page). Although modest and variable due to transient overexpression, these data align with the mechanistic model proposed in our study.

      In Panel a, overexpression of E2F7 or E2F8 reduces E2F1 and its target Plk1, consistent with the established negative feedback within the E2F1–E2F7/8 transcriptional circuitry. A broader impact on cell cycle progression was also evident: G1-phase cells increased and S-phase cells decreased (Panel b), hinting at a delayed G1–S transition when E2F1, an essential driver of S-phase and mitotic entry, is downregulated by excess E2F7 or E2F8.

      We next examined the effects of hyper- vs. hypo-phosphorylation–mimicking mutants of E2F7 and E2F8 on E2F1 and Plk1 (Panels c and d). Both raw data (top) and quantification (bottom) are shown. Despite ectopic overexpression, our experimental conditions highlighted the diffenrential outcome of the two phospho-mutant variants. Speificially, E2F1 and Plk1 levels were consistently higher upon expression of non-phosphorylatable variants of E2F7 (T45A/T68A) and E2F8 (T45D/T68D) relative to their phophomimetic counterparts (T45D/T68D; T20D/T44D). These findings suggest that E2F1 downregulation is more pronounced when E2F7/E2F8 are hyper-phosphorylated at Cdk1-regulated sites that control their half-lives. Furthermore, the proportion of S-phase cells was consistently lower for the phospho-mimicking mutants compared with the non-phosphorylatable variants, with complementary, though less pronounced, shifts in G1-phase cells (Panel e).

      Figure legend: Evidence for cell cycle control linked to Cdk1–PP2A regulation of the E2F1–E2F7/E2F8 axis.

      a) Immunoblot analysis showing reduced E2F1 and its target protein Plk1 upon E2F7/E2F8 overexpression. Antibodies used for immunoblotting (IB) are indicated. b) Cell cycle phase distribution after E2F7/E2F8 overexpression, based on DNA content. Left: representative histograms. Right: quantification of G1- and S-phase cells. Means (x) with individual biological replicates (color-coded; N = 4) are shown. c,d) Top: E2F1 and Plk1 protein levels in cells expressing phosphomimetic (TT-DD) or non-phosphorylatable (TT-AA) E2F7 (c) or E2F8 (d) variants. Antibodies used are indicated (*distorted signal excluded). Bottom: quantification relative to loading controls. Means (x) with individual values (N = 3/4) are shown. e) Cell cycle phase distribution following expression of E2F7/E2F8 phospho-mutant variants. Means (x) with individual values (N = 4) are shown. All experiments were performed in HEK293T cells. Cells were fixed 40–44 h post-transfection. DNA content was assessed using propidium iodide (PI). Mutation sites: T45/T68 (E2F7); T20/T44 (E2F8. Statistical significance was determined by two-tailed Student’s t-test; P-values are indicated.


      Taken together, these results support a model in which Cdk1-site (de)phosphorylation modulates the stability of E2F7 and E2F8, thereby shaping E2F1 output and influencing cell cycle preogresion.

      Point 6. Reviewer #1 (Significance (Required)):

      The study is a good and well conducted work to understand the mechanisms regulating degradation of E2F7/8 by APC/C. This is crucial to establish coordinated cell cycle progression. While the hypothesis that disruption of this mechanism is likely responsible for altered cell cycle progression, there are no evidence this is just a back up pathway, whose functional significance could be limited to lack of APC/C Cdh1 activity. These experiments are rather difficult but the authors could comment on the limitation of the study and emphasise the hypothetical alterations which could result from the alterations of the described feedback loop

      We thank Reviewer #1 for this comment. Accordingly, we have expanded the discussion to further elaborate on the potential molecular outcomes and limitations of our study.

      Reviewer #2 (Evidence, reproducibility, and clarity (Required)):

      Summary: The authors provide strong biochemical evidence that the regulation of E2F7 and E2F8 by APC is affected by CDK1 phosphorylation and potentially by PP2A dependent dephosphorylation. The authors use both full length and N-terminal fragments of E2F8 in cell-free systems to monitor protein stability during mitotic exit. The detailed investigation of the critical residues in the N-terminal domain of E2F8 (T20/T44) is well supported by the combination of biochemical and cell biology approaches.

      We thank Reviewer #2 for their encouraging feedback.

      Point 1. Major: It is unclear how critical the APC-dependent destruction of E2F7 and E2F8 is for cell cycle progression or other cellular processes. Prior studies have reported that Cyclin F regulation of E2F7 is critical for DNA repair and G2-phase progression. This study would be improved if the authors could provide a cellular phenotype caused by the lack of APC dependent regulation of E2F8 and/or E2F7.

      We thank Reviewers #2 and #1 for this comment, which prompted substantial revisions. Below, we reiterate our response to Reviewer #1.

      The molecular components examined in this study are well established in the literature. Key principles include: (i) the reciprocal regulation between E2F1 and its repressors, E2F7 and E2F8, which forms a transcriptional feedback loop; (ii) the opposing activities of Cdk1 and PP2A; (iii) the capacity of E2F1 to attenuate APC/CCdh1 activity; (iv) the fact that E2F7 and E2F8 are APC/C substrates with defined cell cycle–dependent degradation patterns; and (v) the requirement for Cdh1 to prevent premature S-phase entry.

      Our study integrates these elements into a unified framework operating in proliferating cells. This framework is supported by biochemical reconstitution experiments and newly developed methodological tools, which we anticipate will be broadly applicable for dissecting signaling pathways. We view this type of mechanistic synthesis as valuable for the field. Importantly, we do not present this as a definitive model, but rather as a testable regulatory circuit constructed from robust individual findings.

      In response to your request, we incorporated additional phenotypic analyses (see Figure, next page). Although modest and variable due to transient overexpression, the results are consistent with the regulatory architecture we propose.

      In panel a, elevating E2F7 or E2F8 levels reduces E2F1 and its downstream target Plk1, consistent with the established inhibitory feedback exerted by E2F7 and E2F8 on E2F1. Additionally, we observed an increase in G1-phase cells and a decrease in S-phase cells (Panel b), hinting at a delayed G1–S transition when E2F1, a key transcriptional engine of S- and M-phase entry, is downregulated by excess E2F7 or E2F8.

      Figure legend: Evidence for cell cycle control linked to Cdk1–PP2A regulation of the E2F1–E2F7/E2F8 axis.

      a) Immunoblot analysis showing reduced E2F1 and its target protein Plk1 upon E2F7/E2F8 overexpression. Antibodies used for immunoblotting (IB) are indicated. b) Cell cycle phase distribution after E2F7/E2F8 overexpression, based on DNA content. Left: representative histograms. Right: quantification of G1- and S-phase cells. Means (x) with individual biological replicates (color-coded; N = 4) are shown. c,d) Top: E2F1 and Plk1 protein levels in cells expressing phosphomimetic (TT-DD) or non-phosphorylatable (TT-AA) E2F7 (c) or E2F8 (d) variants. Antibodies used are indicated (*distorted signal excluded). Bottom: quantification relative to loading controls. Means (x) with individual values (N = 3/4) are shown. e) Cell cycle phase distribution following expression of E2F7/E2F8 phospho-mutant variants. Means (x) with individual values (N = 4) are shown. All experiments were performed in HEK293T cells. Cells were fixed 40–44 h post-transfection. DNA content was assessed using propidium iodide (PI). Mutation sites: T45/T68 (E2F7); T20/T44 (E2F8. Statistical significance was determined by two-tailed Student’s t-test; P-values are indicated.


      We next examined how phospho-regulation of E2F7 and E2F8 influences cell cycle control by comparing the effects of phospho-mimetic and non-phosphorylatable variants on E2F1 levels and cell cycle distribution (panels c and d). Both the raw data and the corresponding quantitative analyses are presented. Despite exogenous overexpression, we identified conditions that distinguish the behaviors of the two mutant classes. Cells expressing the phospho-mimetic variants consistently exhibited lower E2F1 and Plk1 levels than those expressing the non-phosphorylatable forms. This pattern supports a model in which phosphorylation of key Cdk1 sites in E2F7 and E2F8 elevates their stability, thereby enhancing their ability to suppress E2F1. Panel e extends these observations to cell cycle behavior: compared with the non-phosphorylatable variants, The phospho-mimetic forms of E2F7 and E2F8 consistently lower the proportion of S-phase cells, accompanied by corresponding shifts in the G1 population.

      The central aim of this manuscript is to define how the Cdk1–PP2A axis is integrated into the APC/C–E2F1 regulatory network controlling cell cycle progression. Collectively, our findings support a model in which Cdk1/PP2A-dependent (de)phosphorylation modulates the stability of E2F7 and E2F8, thereby fine-tuning E2F1 activity and cell cycle progression.

      Point 2. Minor: All optional: It would have been interesting to see the T20A/T44A/KM in the live cell experiment (Figure 3F).

      This is an excellent point. Following Reviewer #2’s request, we generated a stable cell line expressing a KEN-box mutant variant of E2F8-T20A/T44A (N80 fragment). The figure below demonstrates the impact of the KEN-box mutation on the dynamics of N80-E2F8-T20A/T44A in HeLa cells. Together, our data from both cellular and cell-free systems show that the temporal dynamics of both wild-type and non-phosphorylatable variants of E2F8 depends on the KEN degron. Please note that due to differences in the flow cytometer settings used for acquiring the original measurements and those newly generated at the Reviewer’s request, the numeric data for N80-E2F8-T20A/T44A-KEN mutant will not be integrated into the original plots shown in the original Figure 3c–e in the manuscript.

      Figure legend: Dynamics of mutant variants of N80-E2F8-EGFP in HeLa cells.

      Top: Bivariate plots showing DNA content (DAPI) vs. EGFP fluorescence, with G1/G1-S phases and G2/M phases highlighted (black and gray frames, respectively). Bottom: Histograms showing EGFP signal distributions within these cell cycle phases. Blue arrows highlight subpopulations of G2/M cells with relatively low EGFP levels. The data was generated by flow cytometry.


      Point 3. Figure 4C-D - include the corresponding blots for the WT E2F7.

      This is a good point, which we previously overlooked. The requested data will be integrated in the revised manuscript.

      Point 4. It is unclear how selective or potent the PP2A inhibitors are that are used in Figure 5. Is it possible to include known targets of PP2A (positive controls for PP2A inhibition) in the analysis performed in Figure 5?

      Thank you for this helpful suggestion. Following Reviewer #2’s comment, we performed gel-shift assays of Cdc20 and C-terminal fragment of KIF4 (Residues: 732-1232), both known targets of PP2A (PMIDs: 26811472; 27453045). See data below.

      __Figure legend: PP2A inhibitor LB-100 block protein dephosphorylation in G1-like extracts. __

      Time-dependent gel shifts of mitotically phosphorylated Cdc20 and the C-terminal fragment of KIF4 (residues 732–1232) following incubation in G1 extracts supplemented with LB-100 or okadaic acid (OA; positive control). Substrates (IVT, 35S-labeled) were resolved by PhosTag SDS–PAGE and autoradiography.


      Point 5. Is the APC still active in LB-100 or OA treated conditions? Is it possible to demonstrate the APC is active using known substrates in this assay (e.g., Securin (Cdc20) and Geminin (Cdh1) or similar).

      This is an excellent point and we should have clarified this previously. Importantly, treatment with 250 µM LB-100 does not abolish APC/C-mediated degradation (otherwise, the assay would not be viable), but it does attenuate degradation kinetics. This is reflected by the prolonged half-lives of Securin and Geminin relative to mock-treated extracts (see below). Consistently, we noted in the manuscript: “Although APC/C-mediated degradation is also affected, it remains efficient, allowing us to measure relative half-lives of APC/C targets that cannot undergo PP2A-mediated dephosphorylation.” Following this comment, and one by Reviewer #3, these data will be included in the revised manuscript.


      __Figure legend: APC/C-specific activity in cell extracts treated with LB-100. __

      Time-dependent degradation of EGFP–Geminin (N-terminal fragment of 110 amino acids) and Securin in extracts supplemented with LB-100 and/or UbcH10 (recombinant). A control reaction contained dominant-negative (DN) UbcH10. Proteins (IVT, 35S-labeled) were resolved by SDS-PAGE and autoradiography.


      Reviewer #2 (Significance (Required)): Advance: A detailed analysis is provided for the critical N-terminal residues in E2F7 and E2F8 that when phosphorylated are capable of restricting APC destruction. The work builds on prior work that had identified the APC regulation of E2F7 and E2F8.

      Point 6. Audience: The manuscript would certainly appeal to a broad basic research audience that is interested in the regulation of APC substrates and/or E2F axis control via E2F7 & E2F8. The study could have a broader interest if the destruction of E2F7 or E2F8 could be shown to be biologically relevant (e.g., critical for cell fate decision G1 vs G0, G1 length, timely S-phase onset, or expression of E2F1 target genes in the subsequent cell cycle).

      To clarify, we subdivided Reviewers’ comments into separate points. Reviewer #2’s Points 1 and 6 address essentially the same issue; our detailed response is therefore provided under Point 1. We again thank Reviewer #2 for raising this concern, which led to substantial revisions to both the manuscript text and the supporting data.

      We thank Reviewer #2 for their constructive comments and criticism.

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

      This manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

      Point 1. However, several points in this paper require further clarification for it to have a meaningful impact on the research community. The characterization of the phosphatase is unclear to me. The use of OA is necessary to guide the research, but it is not precise enough to rule out PP1 and then identify which PP2A is involved - PP2A-B55 or PP2A-B56. To clarify this, the regulatory subunits should either be eliminated or inhibited using the inhibitors developed by Jakob Nilsson's team.

      We are grateful for this comment, which prompted an extensive series of experiments that have undoubtedly strengthened our manuscript.

      First, we wish to clarify that LB-100, unlike okadaic acid (OA), is not considered a PP1 inhibitor.

      Second, we have conducted a large set of experiments to address this important question of the strict identity of the phosphatase involved in the dephosphorylation of atypical E2Fs.

      I. We initially attempted to immunodeplete the catalytic subunit of PP2A (α) from G1 extracts as a means to validate PP2A-dependent dephosphorylation. In retrospect, this was a naïve approach given the protein’s high abundance; although immunoprecipitation was successful, immunodepletion was inefficient, preventing us from using this strategy (see Panel a in the figure below). As an alternative, we incubated immunopurified PP2A-Cα with mitotic phosphorylated E2F7 and E2F8 fragments (illustrated in Panel b). A time-dependent gel-shift assay demonstrated enhanced dephosphorylation in the presence of immunopurified PP2A-Cα (Panel c) compared to immunopurified Plk1 (control reaction), suggesting that mitotically phosphorylated E2F7 and E2F8 are targeted by PP2A.

      Figure legend: Immunopurified PP2A-Cα facilitates dephosphorylation of E2F7 and E2F8 in cell extracts. a) Inefficient immunodepletion (ID) of the catalytic subunit α of PP2A (PP2A-Cα) from cell extracts despite three rounds of immunopurification, as detected by immunoblotting (IB) with anti-PP2A-Cα and anti-BIP (loading control; LC) antibodies (BD bioscience, Cat#: 610555; Cell Signaling Technology, Cat#: 3177). Briefly, G1 cell extracts were diluted to ~10 mg/mL in a final volume of 65 μL. Anti-PP2A-Cα antibodies (3 μg) were coupled to protein G magnetic DynabeadsTM (15 μL; Novex, Cat#: 10004D) for 20 min at 20 °C. For each depletion round, antibody-coupled beads were incubated with cell extracts for 15 min at 20 °C. Cell extracts and beads were sampled after each step to assess immunodepletion and immunopurification (IP) efficiency. Equivalent immunopurification steps are shown for Plk1 (bottom). b) Schematic of the dephosphorylation assay using mitotically phosphorylated in vitro translated (IVT) targets and immuno-purified PP2A-Cα/Plk1. c) Dephosphorylation of mitotically phosphorylated E2F7 and E2F8 fragments, detected by electrophoretic mobility shifts in Phos-Tag SDS-PAGE. Immunopurified Plk1 was used for control reactions (antibodies: Santa Cruz Biotechnology: Cat#: SC-17783). *Image was altered to improve visualization of mobility shifts.


      II. Next, we used pan-B55-specific antibodies for immunodepletion of all B55-type subunits. This approach was unsuccessful despite five rounds of immunopurification (see Panel a in the figure below). Both suboptimal binding and the high abundance of endogenous B55 subunits likely contributed to this outcome. Thus, dephosphorylation in B55-depleted extracts could not be tested.

      Figure legend: PP2A-B55 facilitates dephosphorylation of E2F7 and E2F8 fragments.

      a) __Immunodepletion (ID) of B55 subunits in G1 extracts is inefficient despite five rounds of immunopurification; assessed by immunoblotting (IB) using anti-pan-B55 and anti-Cdk1 (loading control; LC) antibodies (see previous figure for more details). Cell extracts and beads were sampled after each round to monitor immunodepletion and immunopurification efficiency. b) Schematic of a dephospho-rylation assay using immuno-purified B55 subunits. __c) __Dephosphorylation of mitotically phosphorylated E2F7 and E2F8 fragments by immuno-purified B55. Control reactions performed with immuno-purified Plk1. d) __Schematic of a dephosphorylation assay performed in G1 cell extracts supplemented with B55-interacting (B55i) or control peptides (see peptide sequence on next page). RO-3306 was added to limit Cdk1 activity. __e) __Dephosphorylation of E2F7 and E2F8 fragments (mitotically phosphorylated) in G1 extracts supplemented with B55-interacting/control peptides. __f) __Schematic of the dephosphorylation assay using in vitro–translated B55/B56 subunits (unlabeled). __g) __Dephosphorylation of mitotically phosphorylated E2F7 (top) and E2F8 (bottom) fragments in reticulocyte lysate containing B55/B56 subunits. Dephosphorylation was assessed by electrophoretic mobility shifts in Phos-Tag SDS-PAGE. Panels marked with an asterisk were adjusted to improve visualization of gel-shifts. Arrowheads denote distinct, time-dependent mobility-shifted forms of E2F7 and E2F8 fragments. Antibodies used: anti-pan-B55 (ProteinTech, Cat#: 13123-1-AP); anti-Plk1 (Santa Cruz Biotechnology, Cat#: SC-17783); anti-Cdk1 (Santa Cruz Biotechnology, Cat#: SC-53217). Dynabeads™ (Novex, Cat#: 10004D) were used for immunopurification.


      As with PP2A-Cα, we incubated immunoprecipitated B55 subunits with mitotically phosphorylated E2F7 and E2F8 fragments (illustrated in Panel b). The results were less definitive compared to PP2A-Cα; nevertheless, they demonstrated accelerated dephosphorylation in the presence of immunopurified B55 subunits (Panel c) relative to Plk1 (control). These results hint at B55-mediated dephosphorylation of E2F7 and E2F8.

      III. Given that PP2A-B55 could be immunodepleted satisfactorily, despite successful immunoprecipitation, we ordered the B55-specific peptide and corresponding control peptide reported recently by Jakob Nilsson’s team as PP2A-B55 inhibitors (see below).

      Figure legend: Adapted from Kruse, T., et al., 2024; ____Science Advances. Figure 3, Panel B. ____PMID: 39356758.


      Despite our long-anticipated wait for these peptides to arrive, this line of experimentation proved disappointing. We wish to elaborate:

      The study by Kruse et al. (PMID: 39356758) is an elegant integration of classical enzymology, performed at the highest level, with structural insight into the conserved PP2A-B55 binding pocket that governs substrate specificity. Their work identified a consensus peptide that binds PP2A-B55 specifically with nanomolar affinity.

      Kruse et al. provide compelling evidence for a direct and specific interaction between their reported B55 inhibitor (B55i) and PP2A-B55. Their data show that the engineered inhibitor disrupts the binding of helical elements that underlie substrate recognition by PP2A-B55.

      However, we could not find direct evidence of PP2A-B55 enzymatic inhibition by the B55i peptide; for example, a B55-specific in vitro dephosphorylation assay demonstrating sensitivity to B55i in a dose-dependent manner. To the best of our understanding, the sole functional consequence described by Kruse et al. was the delay in mitotic exit observed upon expression of YFP-tagged B55i peptides in cells. However, this approach is indirect, given the long interval between cell manipulation and analysis and the complexity of mitotic exit. Furthermore, we assumed that the requested reagents had been validated in cell-free extracts; however, Kruse et al. do not report any experiments performed in these systems. We, in fact, became uncertain whether we had correctly understood Reviewer #3’s request to use these reagents and therefore sought clarification from the Editor.

      In vitro, Kruse et al. reported nanomolar binding affinities for B55i (Figure S14). In our cell extracts, however, we required concentrations of approximately 250 μM to detect an effect on dephosphorylation, evident as altered electrophoretic mobility of both E2F7 and E2F8 (Panel e). At this concentration, the peptide also caused nonspecific effects, rendering the extracts highly viscous (‘gooey’), at times preventing part of the reaction mixture from passing through a 10 μL pipette tip.

      The gel-shift assays shown in Panel e (Page 16) do demonstrate delayed dephosphorylation in extracts treated with the B55i peptide relative to the control peptide. Nevertheless, we prefer to exclude these data because the peptide concentrations required for the assay compromised extract integrity. Moreover, we believe that the PP2A-B55–specific peptide described by Nilsson et al. requires additional validation before it can be considered a reliable functional inhibitor in cell-free systems or in vivo. Accordingly, we are unable to directly address the experiments as suggested.

      IV. In the final set of experiments (Page 16, Panels f and g), we supplemented dephosphorylation reactions with in vitro–translated B55/B56 subunits (illustrated in Panel f). Although the expected concentration of in vitro–translated proteins in reticulocyte lysate is relatively low (100–400 nM), we reasoned that supplementing the reactions with excess of regulatory B subunits (non-radioactive) could still promote dephosphorylation in a differential manner that reflects the B55/B56 preference of E2F7 and E2F8.

      We cloned and in vitro expressed all nine B55/B56 regulatory subunits. While the exact amount of each subunit introduced into the reaction cannot be precisely determined, their expression levels were reasonably uniform (see figure below).

      __Figure legend: Expression of B55/B56 subunits in reticulocyte lysate. __B55/B56 subunits were cloned into the pCS2 vector and expressed in reticulocyte lysate supplemented with ³⁵S-Methionin. Proteins were resolved by SDS–PAGE and autoradiography.


      Returning to Panel g (Page 16), B55 subunits facilitated the accumulation of lower–electrophoretic mobility forms of both E2F7 and E2F8 fragments to the greatest extent. This is evident from the distinct lower–mobility species that emerge over time (marked by arrowheads) and the smear intensity corresponding to the buildup of dephosphorylated forms. Among the tested subunits, B55β exerted the strongest effect on both substrates, suggesting that mitotically phosphorylated E2F7 and E2F8 display a heightened preference for the PP2A-B55β holoenzyme. Control reactions with reticulocyte lysate are also shown.

      Taken together, our original and newly added data indicate that PP2A, specifically PP2A-B55, counteracts Cdk1-dependent phosphorylation during mitotic exit. Importantly, cell cycle regulators such as Cdc20 can be targeted by both PP2A-B55 and PP2A-B56 holoenzymes. Thus, while we are confident in concluding that mitotically phosphorylated E2F7 and E2F8 are targeted by PP2A-B55, we cannot rule out the possibility of functional interactions between E2F7/E2F8 and PP2A-B56.

      V. Last, but certainly not least, we used AlphaFold 3 to model interactions between the N-terminal fragments of E2F7 and E2F8 and the PP2A regulatory subunits. To clarify: for us, AlphaFold 3 remains very much a computational “black box,” and although this may sound like an overstatement, we did not anticipate obtaining meaningful or interpretable output.

      According to the AlphaFold 3 developer guidelines, the Interface Predicted Template Modeling (IPTM) score is the primary confidence metric for protein–protein interaction predictions. IPTM values above 0.8 indicate high-confidence predictions, whereas values below 0.6 likely reflect failed interaction predictions. In our models, none of the predicted interactions exceeded 0.6 (see figure below). Nevertheless, for both E2F7 and E2F8 fragments, IPTM scores were consistently higher for B55 subunits than for B56 subunits, with B55β yielding the highest scores (each interaction was modeled five times).

      __Figure legend: AlphaFold 3 predicts preferential interactions between E2F7 and E2F8 and PP2A-B55β. __Protein–protein interaction predictions between N-terminal fragments of E2F7 and E2F8 and B55/B56 regulatory subunits of PP2A were generated using AlphaFold 3 (AF3). The plot shows IPTM scores from five models per protein pair.


      Even if one assumes a scenario in which AlphaFold 3 scores are inaccurate or effectively random, such non-specific behavior would not be expected to produce: (i) a reproducible preference of two distinct substrates for B55β and B55γ, in that order (the modeled fragments of E2F7 and E2F8 share The ability of AlphaFold 3, and specifically the IPTM metric, to predict bona fide PP2A B55/B56–substrate interactions remains unvalidated. Accordingly, we do not rely on these predictions as experimental evidence. Nonetheless, in retrospect, the IPTM scores for the E2F7 and E2F8 fragments proved, unexpectedly, to be highly informative. While we are not the first to explore AlphaFold in the context of PP2A phosphatases (e.g., Kruse et al.), at this early stage of AlphaFold 3 these observations are compelling and may ultimately have implications for PP2A-mediated signaling that extend well beyond the cell-cycle field.

      Point 2. It would also be valuable for this study to investigate the mechanisms underlying this regulation. In particular, is it exclusive to E2F7-8 or could other substrates contribute to the generalisation of this regulatory process?

      Assuming Reviewer #3 is referring to the cell cycle mechanism regulating E2F7 and E2F8 half-life via conditional degrons, we wish to clarify that the temporal dynamics of APC/C targets regulated by dephosphorylation has been demonstrated previously. Examples include KIFC1, CDC6, and Aurora A (PMIDs: 24510915; 16153703; 12208850, respectively).

      Point 3. The observation that Cdc20 may target E2F8 is interesting but needs to be further clarified to ensure that weak Cdh1 activity does not contribute to this degradation. Elimination of Cdc20 would be necessary to support the authors' conclusion.

      We gratefully acknowledge this input. The newly implemented experiment and corresponding findings are presented on the next page. The immunodepletion (ID) procedure (Panel a) achieved >60% reduction of Cdc20 and Plk1 in mitotic extracts (Panel b), as confirmed by immunoblotting (IB). Plk1-depleted extracts were used to validate extract-specific activity after successive rounds of immunodepletion at 20°C. Bead-bound Cdc20 and Plk1 were also analyzed by IB for validation (Panel b, right).

      As expected, the phospho-mimetic E2F8 fragment (T20D/T44D) remained stable in Plk1- and Cdc20-depleted mitotic extracts, serving as negative control (Panel c). In contrast, degradation of the non-phosphorylatable variant (T20A/T44A), as well as the APC/CCdc20 substrate Securin (positive control), was strongly hampered in Cdc20-depleted extracts compared to Plk1-depleted extracts. These results confirm that the untimely degradation of the non-phosphorylatable E2F8 in mitotic extracts is Cdc20-dependent.

      Figure legend: Untimely degradation of the non-phosphorylatable E2F8 in mitotic extracts is Cdc20-dependent.____a) Schematic of the immunodepletion (ID) protocol; additional technical details are provided below. b) Plk1 (top) and Cdc20 (bottom) levels in NDB mitotic extracts before and after three rounds of immunodepletion, as detected by immunoblotting (IB). Plk1 and Cdc20 levels were normalized to Tubulin and Cdk1, respectively. Both normalized and raw values are presented as percentages. Immunoprecipitation (IP) efficiency is shown on the right. c) Degradation profiles of phospho-mutant E2F8 variants and Securin (positive control) in NDB mitotic extracts depleted of Plk1 (control) or Cdc20.

      __ ---__

      Point 4. This study focuses on two proteins of the E2F family. These two proteins share similar domains, phosphorylation sites and a KEN box. However, their sensitivity to APC is different. What might explain this difference? Are there any inhibitory sequences for E2F7? Or why is the KEN box functional in E2F8 but not in E2F7?

      This is an excellent question. Here are our thoughts: The processivity of polyubiquitination by the APC/C varies between substrates in ways that influence degradation rate and timing (PMID: 16413484). Although E2F7 and E2F8 are related, their sequence identity is below

      50%, and their C-terminal domains differ substantially (see below) [FIGURE]. These structural differences likely contribute to differences in APC/C-mediated processivity and, consequently, to variations in protein half-lives. Additionally, E2F8 contains two functional KEN-boxes involved in its degradation, whereas E2F7 has only one. This may increase the kon rate of E2F8 for the APC/C, further enhancing its recognition and ubiquitination. Furthermore, re-examining the study by de Bruin and Westendorp (PMID: 26882548, Figure 2f; copied below), we note that the dynamic of inducibly expressed EGFP-tagged E2F7 in cells exiting mitosis is milder compared to E2F8 (see the black lines in both charts). This, as well as the oversensitivity of E2F7 degradation to Cdh1 downregulation accord with E2F7 being less potent substrate of APC/CCdh1.

      Figure legend: Adapted from Boekhout et al., 2016; ____EMBO Reports. Figure 2, Panel F. ____PMID: 26882548.


      The stability of the E2F7 fragment in cells and extracts was unexpected. We initially hypothesized that the unique N-terminal tail of E2F7 masks the KEN-box, functioning as an inhibitory sequence. However, removal of this region did not restore degradation (original manuscript; Figure 1e). Furthermore, extending the fragment by 20 additional residues failed to confer degradation (original manuscript; Figure S2). These observations suggest that E2F7 may require a distal or modular docking site for APC/C recognition. We did not pursue this question further.

      Point 5. An additional element that could strengthen this work would be referencing the study by Catherine Lindon: J Cell Biol, 2004 Jan 19;164(2):233-241. doi: 10.1083/jcb.200309035. In Figure 1 of this article, there is a degradation kinetics analysis of APC/C complex substrates such as Aurora-A/B, Plk1, cyclin B1, and Cdc20. This could help position the degradation of E2F7/8 relative to known APC/C targets. This can be achieved by synchronizing cells with nocodazole and then removing the drug to allow cells to progress and complete mitosis.

      This is an interesting point and one we should have clarified better previously. The temporal dynamics of E2F8 in synchronized HeLa S3 cells, relative to three known APC/C substrates, were reported in our previous study (PMID: 31995441; Figure 1a, copied on the right). Specifically, protein levels were measured for Cyclin B1, Securin, and Kifc1. Unlike Cyclin B1 and Securin, which are targeted by both APC/CCdc20 and APC/CCdh1, Kifc1 is degraded exclusively by APC/CCdh1. Cells were released from a thymidine–nocodazole block.

      Following Reviewer #3’s comment, we re-blotted the original HeLa S3 synchronous extracts. The new data [FIGURE] can be incorporated into the revised manuscript if requested.

      Point 6. Minor points: Does phosphorylation of E2F7-8 proteins alter their NMR profile? This could help understand how phosphorylation/dephosphorylation affects their sensitivity to the APC/C complex.

      Excellent suggestion. Indeed, we had originally aimed to include a more extensive set of NMR data in this manuscript. Our goal was to monitor E2F7 and E2F8 fragments in cell extracts and assess structural changes induced by phosphorylation and dephosphorylation during mitosis and mitotic exit. However, purifying the E2F7 fragment proved more challenging than anticipated. In addition, the extract-to-substrate ratio requires further optimization: Substrate concentrations must be high enough for reliable NMR detection, but below levels that would saturate the enzymatic activity in the extracts.

      That said, the short answer to the reviewer’s question is Yes: NMR profiles of E2F7 and E2F8 fragment do change following incubation with recombinant Cdk1–Cyclin B1 (see next page). If possible, we wish to exclude these NMR data from the manuscript.

      Point 7. Do these substrates bind to the APC/C complex before degradation? Does E2F7 bind better than E2F8?

      We were unable to detect interactions between endogenous E2F7 and E2F8 and the APC/C complex. In general, detecting endogenous E2F8, and especially E2F7, by immunoblotting proved challenging, making co-immunoprecipitation (Co-IP) even more difficult.

      Figure legend: NMR-based monitoring of E2F7 (a-c) and E2F8 (d-f) phosphorylation by Cdk1.

      a(d). 15N,1H-HSQC spectrum of E2F7(E2F8) prior to addition of Cdk1. Threonine residues of interest, T45 (T20) conforming to the consensus sequence (followed by a proline), and T84 (T60) lacking the signature sequence are annotated. b(e). Strips from the 3D-HNCACB spectrum used for assigning E2F7(E2F8) residues. Black (green) peaks indicate a correlation with the 13Cα (13Cβ) of the same and previous residues. The chemical shifts assigned to T45 (T20) and T84 (T60) match the expected values for K44(K19) and P83(P59), thereby confirming the assignment. c(f). Top, overlay of subspectra before adding Cdk1 (black) and after 16 h of activity (red) at 298 K. Bottom, change in intensities of the T45/T84 in E2F7 and T20/T60 in E2F8 showing how NMR monitors phosphorylation and distinguishes between various threonine residues.


      However, interactions between EGFP-tagged E2F7 snd E2F8 and Cdh1 have been demonstrated previously (PMID: 26882548, Figure 2e). In contrast, only the N-terminal fragment of E2F8, but not the corresponding fragment of E2F7, was found to bind Cdh1 (see figure on the right). This observation is consistent with the stability of the E2F7 fragment in APC/C-active extracts.

      __Figure legend: N-terminal fragment of E2F8 but not E2F7 binds Cdh1. __

      Co-Immunoprecipitation (IP) was performed in HEK293 cells transfected with EGFP-tagged E2F7/E2F8 fragments, using GFP-Trap® (Chromotek, Cat#: GTMA-20). Antibodies used for immunoblotting: ant-GFP (Santa Cruz Biotechnology: Cat#: SC-9996); anti-Cdh1 (Sigma-Aldrich, Cat#: MABT1323).


      Point 8. Why do the authors state that 250 µM of LB-100 has little effect on APC/C activity?

      We thank Reviewers #2 and 3 for raising this point. As shown in the manuscript, treatment with 250 µM LB-100 does not abolish APC/C-mediated degradation (otherwise, the assay would not be viable). However, it does attenuate degradation kinetics, as reflected by the prolonged half-lives of Securin and Geminin (see figure below).

      __Figure legend: APC/C-specific activity in cell extracts treated with LB-100. __

      Time-dependent degradation of EGFP–Geminin (N-terminal fragment of 110 amino acids) and Securin in extracts supplemented with LB-100 and/or UbcH10 (recombinant). A control reaction contained dominant-negative (DN) UbcH10. Proteins (IVT, 35S-labeled) were resolved by SDS-PAGE and autoradiography.


      Point 9. How can E2F8 be a substrate for both the SCF and APC/C complexes? (If I understood correctly.)

      This can happen because they are degraded by different E3 at different times during the cell cycle. To clarify further, certain proteins can be targeted by both the APC/C and SCF complexes, reflecting distinct regulatory needs. A classic example is CDC25A, as shown by M. Pagano and A. Hershko in 2002 (PMID: 12234927). Additional examples include the APC/C inhibitor EMI1 (PMIDs: 12791267 [SCF] and 29875408 [APC/C]).

      Reviewer #3 (Significance (Required)): This manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

      We wish to thank Reviewer #3 for their positive and encouraging view of our work.

    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 manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

      • However, several points in this paper require further clarification for it to have a meaningful impact on the research community. The characterization of the phosphatase is unclear to me. The use of OA is necessary to guide the research, but it is not precise enough to rule out PP1 and then identify which PP2A is involved - PP2A-B55 or PP2A-B56. To clarify this, the regulatory subunits should either be eliminated or inhibited using the inhibitors developed by Jakob Nilsson's team. It would also be valuable for this study to investigate the mechanisms underlying this regulation. In particular, is it exclusive to E2F7-8 or could other substrates contribute to the generalisation of this regulatory process?

      • The observation that Cdc20 may target E2F8 is interesting, but needs to be further clarified to ensure that weak Cdh1 activity does not contribute to this degradation. Elimination of Cdc20 would be necessary to support the authors' conclusion.

      • This study focuses on two proteins of the E2F family. These two proteins share similar domains, phosphorylation sites and a KEN box. However, their sensitivity to APC is different. What might explain this difference? Are there any inhibitory sequences for E2F7? Or why is the KEN box functional in E2F8 but not in E2F7?

      • An additional element that could strengthen this work would be referencing the study by Catherine Lindon: J Cell Biol, 2004 Jan 19;164(2):233-241. doi: 10.1083/jcb.200309035. In Figure 1 of this article, there is a degradation kinetics analysis of APC/C complex substrates such as Aurora-A/B, Plk1, cyclin B1, and Cdc20. This could help position the degradation of E2F7/8 relative to known APC/C targets. This can be achieved by synchronizing cells with nocodazole and then removing the drug to allow cells to progress and complete mitosis.

      Minor points:

      • Does phosphorylation of E2F7-8 proteins alter their NMR profile? This could help understand how phosphorylation/dephosphorylation affects their sensitivity to the APC/C complex.

      • Do these substrates bind to the APC/C complex before degradation? Does E2F7 bind better than E2F8?

      • Why do the authors state that 250 µM of LB-100 has little effect on APC/C activity?

      • How can E2F8 be a substrate for both the SCF and APC/C complexes? (If I understood correctly.)

      Significance

      This manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors provide strong biochemical evidence that the regulation of E2F7 and E2F8 by APC is affected by CDK1 phosphorylation and potentially by PP2A dependent dephosphorylation. The authors use both full length and N-terminal fragments of E2F8 in cell-free systems to monitor protein stability during mitotic exit. The detailed investigation of the critical residues in the N-terminal domain of E2F8 (T20/T44) is well supported by the combination of biochemical and cell biology approaches.

      Major:

      It is unclear how critical the APC-dependent destruction of E2F7 and E2F8 is for cell cycle progression or other cellular processes. Prior studies have reported that Cyclin F regulation of E2F7 is critical for DNA repair and G2-phase progression. This study would be improved if the authors could provide a cellular phenotype caused by the lack of APC dependent regulation of E2F8 and/or E2F7.

      Minor:

      All optional: It would have been interesting to see the T20A/T44A/KM in the live cell experiment (Figure 3F). Figure 4C-D - include the corresponding blots for the WT E2F7. It is unclear how selective or potent the PP2A inhibitors are that are used in Figure 5. Is it possible to include known targets of PP2A (positive controls for PP2A inhibition) in the analysis performed in Figure 5? Is the APC still active in LB-100 or OA treated conditions? Is it possible to demonstrate the APC is active using known substrates in this assay (e.g., Securin (Cdc20) and Geminin (Cdh1) or similar).

      Significance

      Advance: A detailed analysis is provided for the critical N-terminal residues in E2F7 and E2F8 that when phosphorylated are capable of restricting APC destruction. The work builds on prior work that had identified the APC regulation of E2F7 and E2F8.

      Audience: The manuscript would certainly appeal to a broad basic research audience that is interested in the regulation of APC substrates and/or E2F axis control via E2F7 & E2F8. The study could have a broader interest if the destruction of E2F7 or E2F8 could be shown to be biologically relevant (e.g., critical for cell fate decision G1 vs G0, G1 length, timely S-phase onset, or expression of E2F1 target genes in the subsequent cell cycle).

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      Referee #1

      Evidence, reproducibility and clarity

      The study highlights a dephosphorylation switch mediated by PP2A as a critical mechanism for coupling E2F7/8 degradation to mitotic exit and G1 phase. The study is clear and experiments are well conducted with appropriate controls

      I have some concerns highlighted below :

      1. In this sentence : This intricate network of feedback mechanisms ensures the orderly progression of the cell cycle. What feedback mechanism are the authors referring to?

      2. Characterization of disorder in the N-terminal segments of E2F7 and E2F8

      What does it mean disorder in this title?

      1. In the paragraph on the untimely degradation of E2F8 the authors keep referring to APC/C Cdc20, however the degradation is triggered by the Ken box which is specifically recognised by APC/C Cdh1. Can it be due to another ligase not APC/C?

      2. The assays to detect dephosphorylation are rather indirect so it is difficult to establish whether phosphorylation of CDK1 and dephosphorylation by PP2A on the fragments is direct.

      3. Although there seems to be a control by phosphorylation and dephosphorylation (which could be indirect), it is difficult to establish the functional consequences of this observation. The authors propose a feedback mechanism which regulates the temporal activation inactivation of E2F7/8 however, there are no evidence in support of this.

      Significance

      The study is a good and well conducted work to understand the mechanisms regulating degradation of E2F7/8 by APC/C. This is crucial to establish coordinated celll cycle progression. While the hypothesis that disruption of this mechanism is likely responsible for altered cell cycle progression, there are no evidence this is just a back up pathway, whose functional significance could be limited to lack of APC/C Cdh1 activity. These experiments are rather difficult but the authors could comment on the limitation of the study and emphasise the hypothetical alterations which could result from the alterations of the described feedback loop

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      Reply to the reviewers

      We have submitted a revision plan to Review Commons to address the criticisms of the reviewers. We will post the revised manuscript after completing the experiments.

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      Referee #2

      Evidence, reproducibility and clarity

      In the well-written manuscript by Tarafder et al., the authors follow up on their previous investigations of the filamentous bacteriophage Pf4, which self-assembles into a crystalline droplet surrounding Pseudomonas aeruginosa cells within a biofilm. Using theoretical coarse-grained molecular dynamics (MD) simulations, they predict that binding a small molecule or protein to the surface of bacteriophage Pf4 should disrupt the attraction-in this case depletion attraction-between individual phage particles. To test this hypothesis, nanobodies were raised against Pf4, and two promising candidates, Nb43 and Nb-D11, were identified. These nanobodies were characterized using biochemical assays, and binding of Nb43 to CoaB, the major coat protein, was visualized using cryo-EM. Using fluorescence microscopy and cryo-ET, the authors convincingly demonstrate that nanobodies can disrupt Pf4 crystalline droplet formation. Strikingly, nanobody-mediated disruption of Pf4 droplets also increases antibiotic susceptibility of P. aeruginosa both in vitro and in biofilm settings.

      Major comments

      1) Theoretical modelling: The MD simulations, as currently presented, do not add conceptual depth to the study. The idea that blocking an interaction site between phages (whether through active-site interference, obstruction of a protein-protein interface, or simple steric hindrance) would prevent alignment is straightforward and does not necessarily require MD simulations to justify. As such, this section feels superfluous and is currently the weakest point in an otherwise strong manuscript. Unless the simulations can meaningfully address at least some of the questions listed below, the authors should consider removing this part:

      The MD simulation is very simplistic, and filamentous phages are clearly not hard rods, as seen in the cryo-EM images. Would a certain degree of Pf4 flexibility allow to stabilize droplets even in the presence of low concentrations of Pf4 binders?

      How do the MD simulations explain that already pre-formed crystalline droplets can be penetrated and disassembled by small Pf4 binders?

      The authors state that Pf4 binders must be large relative to the depletant particles. Can this be demonstrated experimentally? Is there a sweet spot, as large molecules potentially cannot penetrate preformed droplets?

      2) Nanobody penetration into crystalline droplets (Extended Data Fig. 6a-d) vs. antibiotic penetration (Fig. 4) The authors show that Nb43 penetrates Pf4 droplets even at concentrations that do not disrupt droplet stability. How do the authors explain that a relatively large nanobody penetrates the crystalline droplet, whereas a much smaller antibiotic does not diffuse trough the droplet?

      In the experiments shown in Figure 4, the authors assess antibiotic activity against P. aeruginosa in the presence of Pf4 crystalline droplets. If I understand correctly, the additionally added Pf4 droplets do not physically encompass the bacteria, yet they still reduce antibiotic tolerance. If so, this appears to contradict the conclusion that Pf4 droplets act primarily as a diffusion barrier (as stated in the section title). Instead, this would suggest that Pf4 may reduce antibiotic potency through another mechanism (e.g., direct binding or sequestration). Would it be possible to test the addition of Pf4 alone, without the biopolymer alginate, to determine whether Pf4 itself is sufficient to increase antibiotic tolerance?

      Minor comments:

      • Title: The title is overstated. Please consider changing it to something similar to: "Targeted disruption of phage liquid crystalline droplets abolishes antibiotic tolerance in Pseudomonas aeruginosa biofilms."
      • Introduction sentence: "...where filamentous phage particles align along their axis in the presence of biopolymer,..." Please introduce what biopolymers are and specify which types are relevant here.
      • Amorphous Pf4 aggregates after Nb43 treatment (Fig 3b,e): The authors should discuss the nature of these aggregates. It appears that smaller spindles are both broken up and impeded in their formation after Nb43 treatment, whereas larger aggregates seem to persist.
      • Fig. 3c and 3f: Please describe how liquid crystalline structures were defined in the fluorescence images. Were thresholds for size, intensity, or morphology applied?
      • Use of P. aeruginosa ΔPAO728: For clarity, please explain why the strain lacking the Pf4 integrase is included in the in-vitro assays.

      Discussion:

      Neisseria meningitidis and Vibrio cholerae use filamentous phages to increase virulence. Do these phages also form liquid crystalline droplets? If not, how do the authors envision that the nanobody strategy described here could be applied to prevent infection? In general, the findings are hard to generalize to other biofilms matrices, which are highly heterogenous.

      Significance

      Bacterial biofilms and their associated antibiotic tolerance represent a major clinical burden, and new strategies to overcome these defenses are urgently needed. The strategy presented here-targeting and disrupting the protective extracellular matrix formed by liquid crystalline Pf4 phage droplets-is an exciting and innovative approach with clear translational potential for combating P. aeruginosa biofilms. The study is experimentally rigorous, well written, and carefully analyzed, and it represents a logical and impactful next step following the group's previous work. This manuscript will have significant impact on the field of P. aeruginosa biofilm research by providing a mechanistically grounded method to disrupt the protective biofilm architecture. However, it is important to note that the extracellular matrix architecture of biofilms formed by other bacterial species differs substantially, and thus the current findings cannot be directly generalized beyond P. aeruginosa without further investigation.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Tarafder et al. describes an interdisciplinary approach, combining biophysical modeling and microbiology, to target antibiotic tolerance in P. aeruginosa biofilms. A key conceptual contribution is the strategy of inhibiting a biophysical mechanism instead of a biochemical interaction. The study is logically organized, advancing from a theoretical model to the design of effective nanobody inhibitors, which are then validated across a series of experimental systems, from in vitro assays to complex static and flow-cell biofilms. The data robustly support the authors' conclusions, suggesting a potentially valuable approach for managing biofilm-based infection. Overall, this is a very interesting and robust study. The conclusions are well-supported by the evidence provided, and the manuscript is well-written, with figures that effectively illustrate the key results.

      Major comments:

      1. The fundamental characteristics of Nb43 and Nb-D11 (e.g., affinity, stability) should be provided. To solidify the central claim, the direct interaction between CoaB and Nb43 should be confirmed using an orthogonal biochemical method. urthermore, it is important to test whether Nb43 binds to the CoaB proteins from Pf1/Pf5/Pf6 to assess its specificity and broad application in other PA hosts such as MPAO1 and PA14
      2. In the static biofilm assay (Fig. 5a-b), the use of crystal violet staining only reports total biomass. To clarify the mechanism of action, experiments should distinguish whether Nb43 primarily prevents biofilm attachment/formation or actively eradicates an established biofilm. This is particularly relevant for the pre-incubation condition.
      3. The discussion should address the limitations of this therapeutic approach. A key concern is the potential for Pf4 reinfection and subsequent relapse of chronic infection, which is a major challenge in the field. Additionally, the manuscript would be strengthened by a more critical and direct comparison of this Nb-based strategy against existing anti-virulence or anti-biofilm alternatives, highlighting its potential advantages and drawbacks.

      Minor comments

      1. The prevention of Pf activation in P. aeruginosa biofilms is an important aspect that should be addressed in the Introduction and Discussion.
      2. In the Methods section for the biophysical model, the choice of specific parameters (e.g., phage length a=80 nm, depletant diameter σ=2.4 nm) is justified by referencing the system being modeled. However, a brief sentence explicitly stating that these values were chosen based on the known dimensions of Pf4 and alginate would be helpful for readers that are not familiar with the system.

      Significance

      This study provides a mechanistic insight into the advance and offers a complementary approach to treating biofilm-related infections, which remains an unexplored area in the field. The reported findings are likely to be of interest and significance to microbiologists and clinicians concerned with biofilm infections.

      My own expertise lies in the genetic and biochemical aspects of prophage induction and biofilm formation. Therefore, the details of nanobodies and their potential side effects fall outside the scope of my evaluation.

  4. Dec 2025
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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Liu et al. provided evidence of the interaction between endocytosis and VAMP8-mediated endocytic recycling of clathrin-mediated endocytosis (CME) cargo through a knockdown approach combined with total internal reflection fluorescence (TIRF) microscopy, western blotting, and functional assays in a mammalian cell line system. They demonstrated that VAMP8 impairs the initial stages of CME, such as the initiation, stabilization, and invagination of clathrin-coated pits (CCPs). VAMP8 indirectly regulates CME by facilitating endocytic recycling. The depletion of VAMP8 alters endosomal recycling, as shown here by the transferrin receptor, towards lysosomal degradation, thereby inhibiting clathrin-coated vesicle (CCV) formation. Overall, I found this study to be highly engaging because of its elucidation of the unexpected role of R-Snare in influencing the levels of cargo proteins within the context of clathrin-mediated endocytosis (CME). This MS will be helpful for researchers in endocytosis and protein trafficking fields. It appears to me that VAMP8 interacts with multiple targets within the endo-lysosomal pathway, collectively influencing the clathrin-mediated endocytosis (CME). Therefore, the contribution of lysosomes in this context should be evaluated. This matter should be addressed experimentally and discussed in the MS before considering publication.

      Major comments:

      1. Figure 4D demonstrates that the knockdown of VAMP8 leads to an increase in lysosome numbers and lysosomal perinuclear clustering, as evidenced by LAMP1 staining (Figure 5A). Additionally, the knockdown of VAMP8 results in the downregulation of most surface receptors, as illustrated in Figure 3A, which typically follows the lysosomal degradation pathway. The observed reduction in TfR cargo could be attributable to the decreased presence of the Tfn Receptor in siVAMP8-treated cells compared to that in control cells. How do the authors explain this phenomenon? Upon reviewing these observations, I suggest that the mechanism outlined in the manuscript-specifically, "Depletion of VAMP8 skews endosomal recycling of CME cargo, exemplified here by transferrin receptor, toward lysosomal degradation, thereby inhibiting CCV formation"-may serve as a secondary rather than a primary cause. This can be ruled out by the following experiments:
        • Assessment of lysosomal biogenesis markers through RT-PCR or Western blotting following VAMP8 knockdown.
        • Assessment of transferrin receptor stability under VAMP8 knockdown conditions using cycloheximide.
        • Previous studies have indicated that perinuclear clustering of lysosomes is correlated with increased degradative activity. Therefore, assessing the lysosomal perinuclear index in the images presented in Figure 5A (LAMP1) effectively determines the presence or absence of this phenomenon.
      2. Given that VAMP8 is implicated in lysosomal fusion events, I hypothesized that VAMP8 undergoes degradation via the lysosomal pathway. However, Figure 4F indicates that there was no restoration of VAMP8 following leupeptin treatment. Could you please provide an explanation for this discrepancy or is it trafficked to proteasomal degradation pathway?
      3. Figure 5A and 5C demonstrate that the restoration of TfnR in siVAMP8 under leupeptin conditions was similar to the levels observed in the sicontrol without leupeptin. However, no enhancement in TfnR uptake (Figure 5F) was detected in cells treated with siVAMP8 under leupeptin treatment conditions. How can these observations be reconciled with each other?

      Minor comments:

      1. The manuscript does not provide details of the western blotting method and quantification criteria.
      2. Fig1A &B) - The siVAMP8 #1 blot indicates a reduction exceeding 90%, whereas the bar graph depicts a reduction of 70-80%. It is advisable to elucidate the quantification criteria in the Methods section to prevent potential confusion. Were the protein levels normalized to the loading control?
      3. Enhancing the readability of the graph could be achieved by labeling the Y-axis as either 'All CCP' or 'Bonafide CCP' of CME analysis graphs.
      4. The legends of panels 1M and N do not correlate with the corresponding figures. Need corrections.
      5. Fig 4D- Is the technique employed for electron immunogold staining utilizing a lysosome-specific antibody? How do the authors substantiate their assertion that the darkly stained structures are lysosomes and not other cellular compartments?
      6. Electron micrographs of siVAMP8 cells revealed the presence of dark-stained bodies near the plasma membrane. The implications of this observation should be explained in the discussion section.
      7. Fig5A- Provide the color code for the merged images.
      8. Fig5G- schematic needs to be improved to demonstrate the contribution of increased lysosomal content.

      Significance

      VAMP8 is an R-SNARE critical for late endosome/lysosome fusion and regulates exocytosis, especially in immune and secretory cells. It pairs with Q-SNAREs to mediate vesicle fusion, and its dysfunction alters immunity, inflammation, and secretory processes. This study revealed that the SNARE protein VAMP8 influences clathrin-mediated endocytosis (CME) by managing the recycling of endocytic cargo rather than being directly recruited to clathrin-coated vesicles. This study advances our understanding of cellular trafficking mechanisms and underscores the essential role of recycling pathways in maintaining membrane dynamics. This is an excellent piece of work, and the experiments were designed meticulously; however, the mechanism is not convincing enough at this point. This MS will surely benefit the general audience, specifically the membrane and protein trafficking and cell biology community.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors investigate the role of the SNARE protein VAMP8 in endocytic recycling and clathrin-mediated endocytosis (CME). Using siRNA knockdown, live-cell imaging, and recycling assays, they report that VAMP8 depletion impairs clathrin-coated pit (CCP) initiation, stabilisation, and invagination, thereby inhibiting CME. Furthermore, they suggest that VAMP8 knockdown promotes transferrin receptor (TfR) degradation and slows its recycling. Consistent with previous studies, knockdown of CALM expression inhibits CME, whereas overexpression of wild-type or L219S/M244K mutant CALM rescues CME.

      Major concerns:

      1. The authors claim their work "reshape our understanding" of CME by proposing that VAMP8 regulates CME through cargo recycling rather than by direct recruitment to clathrin-coated vesicles (CCVs). However, the concept that cargo recycling influences CME efficiency is not new. Prior work has established that cargo clustering stabilises CCPs and that cargo availability strongly impacts pit dynamics. Similarly, studies of CALM, Hrb, and SNAREs have implicated recycling and SNARE interactions in CME. The observation that reduced CME cargo expression (e.g. TfnR) in VAMP8-depleted cells impairs CME is therefore consistent with earlier findings, not a new paradigm. Moreover, the manuscript raises a conceptual paradox: if VAMP8 recruitment is dispensable for CME, why is VAMP8 recruited to CCPs, and why does its depletion produce such a striking phenotype?
      2. The authors note that VAMP8 knockdown reduces TfnR expression, which in turn reduces its surface levels (Figure 1N). Nevertheless, they report that VAMP8 knockdown also diminishes the endocytic efficiency of these TfRs already delivered to the plasma membrane (Figure 1M). Without rescue experiments - for example, re-expression of VAMP8 or TfnR - the specific roles of VAMP8 or cargo availability cannot be confirmed.
      3. The authors argue that overexpression of WT and L219S/M244K mutant CALM rescues CME, supporting the view that abolishing VAMP8 recruitment to CCVs does not impair CME. Yet previous studies have demonstrated that CALM is essential for CME through recruitment of multiple proteins, including the R-SNAREs VAMP8, VAMP3, and VAMP2. Miller et al. have shown a conserved interaction mechanism between CALM and these SNAREs. Thus, the finding that mutant CALM rescues CME does not sufficiently demonstrate that VAMP8 recruitment is unimportant. Furthermore, Sorkin's group showed that high levels of CALM overexpression inhibit transferrin and EGF receptor endocytosis and disrupt clathrin localisation in the trans-Golgi network (PMID: 10436022). In Figure S2, the authors clearly express CALM at levels far exceeding endogenous amounts. Such overexpression may itself perturb membrane trafficking, complicating interpretation of the rescue data.
      4. Most conclusions rely solely on TfR. Without examining additional receptors (e.g. EGFR, LDLR), the general claim regarding "cargo availability" remains unsubstantiated. The authors should quantify surface TfR levels following VAMP8 knockdown and/or leupeptin treatment. It also remains unclear why leupeptin treatment fails to induce TfR accumulation in lysosomes of control siRNA-treated cells.
      5. The manuscript presents several kymographs, but the appearance and disappearance of CCPs are difficult to discern. While this reviewer is not an expert in quantitative imaging analysis, it appears that in both siControl and siVAMP8 cells the tracks are either unusually persistent or very short-lived, with the only obvious differences being the brightness of the spots and tracks. Although some quantitative analyses are provided, the quality and representativeness of the imaging data remain unconvincing.
      6. Terms such as "productive" and "abortive" CCPs are used inconsistently and without clear definition in figure legends. In addition, the manuscript's claims of novelty, both in the Significance Statement and the main text, are overstated relative to prior literature.

      Significance

      General assessment: While the study shows that VAMP8 depletion negatively affects CME and TfR trafficking, the manuscript suffers from limited novelty, logical inconsistencies, and experimental shortcomings.

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      Referee #1

      Evidence, reproducibility and clarity

      Liu and colleagues show that the knockdown of VAMP8 impairs CME by downregulating various cargo receptors, including TfnR, by rerouting theses receptors to lysosomes for degradation instead of recycling them to the plasma membrane. The results also imply that the lack of sufficient receptors (CME-cargo and associated endocytic machinery) in turn impairs the initiation/stabilization of nascent CCPs and their subsequent invagination. As shown by specific mutations in CALM, VAMP8 is apparently not directly required for CME. The emloyed cmeAnalysis DASC assay appears to be state of the art. The data are overall convincing. Nevertheless, the authors should address/clarify the following points:

      Major comments:

      • A rescue experiment of the VAMP8 knockdown using VAMP8 and RFP-VAMP8 should be included to exclude off-target effects and demonstrate the functionality of the RFP-VAMP8 construct.
      • Please confirm that the RFP-VAMP8 expression levels in the CALM(WT) and CALM(SNARE) cells are comparable (compare first panels in Fig. 2A and 2B) and provide information about the RFP-VAMP8 expression levels compared to endogenous VAMP8. Figure 2D shows that RFP-VAMP8 is not enriched in CCPs in CALM(SNARE) cells. This raises the questions whether endocytic vesicles in the CALM(SNARE*) background indeed lack VAMP8 or still contain some residual VAMP8 levels. A complete lack of VAMP8 would imply that VAMP8 does not play a major role in determining the fate (fusion partner) of the endocytic vesicles (in the pathways analyzed by the authors). If possible, provide experimental data to solve this issue or discuss this point.

      Minor comments:

      • Fig. 1 N and M: The figure panels should be switched to fit to the legend, or vice versa.
      • In contrast to Table S1, which show a reduction of TfnR by a factor of 1.8, the Western blot analysis (Fig. 3C) shows a 4-fold reduction. Please explain the divergence.
      • It is surprising that the TfnR knockdown phenocopies the VAMP8 knockdown. Why does the knockdown of a single receptor affect endocytosis, measured by the eGFP-CLCa recruitment? Compared to other plasma membrane receptors, how abundant is TnfR? If available, please provide references demonstrating that the knockdown of other receptors has similar effects on endocytosis?
      • The authors should briefly discuss to which degree the knockdown of VAMP8 may also affect receptor exocytosis, thereby contributing to a reduction of cargo receptors at the plasma membrane and impaired CME.
      • VAMP8 has an established role in autophagosome - lysosome flux, favoring the fusion with the lysosomes. In the present study, VAMP8 knockdown seems to reroute receptors for lysosomal degradation in the absence of VAMP8. Please discuss.
      • For clarity, the authors may consider to restructure their abstract, directly starting with their finding that "Depletion of VAMP8 skews endosomal recycling of CME cargo, exemplified by ..........

      Significance

      Overall, this study provides significant insights into the role of VAMP8 in the recycling of receptors to the plasma membrane. The lack of VAMP8 results in rerouting of plasma membrane receptors to lysosomes and thereby indirectly reduces endocytosis. The results will be of broad interest in the field of membrane trafficking. The reviewers field of expertise is membrane trafficking, in particular molecular mechanisms of exocytosis.

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      Reply to the reviewers

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

      Summary: In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

      Major comments: - Are the key conclusions convincing? The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary. 1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment. *

      Authors: We will stain cells for endogenous MKRN2 and quantify nuc/cyto ratio of MKRN2 without heat stress, without heat stress + TAK243, with HS and with HS + TAK243. We will do the same in the MKRN2-GFP overexpressing line while also staining for FK2.

      *2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity. *

      Authors: We have acknowledged the limitation of using only TAK243 in our study by rephrasing statements about dependency on “ubiquitination” to “UBA1-dependent associations”.

      * 3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. *

      Authors: We have acknowledged the fact that our experiments do not include DRiP and SG dynamics assays using ligase-dead mutants of MKRN2 by altering our statement regarding MKRN2-mediated ubiquitination of DRiPs in the text (as proposed by reviewer 1).

      *- 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. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation. 1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms. *

      Authors: We thank the reviewer for bringing this important control. We will address it in revisions.

      We will quantify the nuclear/cytoplasmic distribution of endogenous and GFP-MKRN2 under control, TAK243, heat shock, and combined conditions, and assess MKRN2–ubiquitin colocalization by FK2 staining in unstressed cells.

      * 2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance. *

      Authors: We thank the reviewer for this suggestion and have altered the phrasing of some of our statements in the text accordingly.


      * 3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. *

      Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot for G3BP1.

      *

      • 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. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. • Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. • Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. • Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

      Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning.

      • Are the data and the methods presented in such a way that they can be reproduced? Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions.

      • Are the experiments adequately replicated and statistical analysis adequate? Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential. *

      Authors: We thank the reviewer for noting this accidental omission. We now clarify in the legend of Figure 5 that the experiments with DRiPs were replicated three times.

      Minor comments: - Specific experimental issues that are easily addressable. • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used

      Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot and RT-qPCR.

      • * In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers. * Authors: We have added a mention in the legend of Figure S2 that these are total cell lysates before pulldown. The apparent differences in biotin staining are small and not sufficient to question the results of our APEX-proteomics.

      • * In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates. * Authors: Based on first principles in regulation of gene expression, it is unlikely that total MKRN2 expression levels would decrease appreciably through transcriptional or translational regulation within the short timescale of these experiments (1 h TAK243 pretreatment followed by 90 min of heat stress).

      • * DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily. * Authors: We thank the reviewer for proposing this experimental direction. However, in a previous study (Ganassi et al., 2016; 10.1016/j.molcel.2016.07.021), we demonstrated that DRiP accumulation during the stress granule assembly phase drives conversion to a solid-like state and delays stress granule disassembly. It is therefore critical to assess DRiP enrichment within stress granules immediately after their formation, rather than during the stress recovery phase, as done here.

      • * The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results. * Authors: We thank the reviewer for this question. The population of SGs with a DRiP enrichment > 1.5 represents SGs with a significant DRiP enrichment compared to the surrounding (background) signal. As explained in the methods, the intensity of DRiPs inside each SG is corrected by the intensity of DRiPs two pixels outside of each SG. Thus, differences in thresholds between independent experimental conditions (5B versus 5D) do not confound interpretation of the results but depend on overall staining intensity that can different between different experimental conditions. Choosing the cut-off > 3 allows to specifically highlight the population of SGs that are strongly enriched with DRiPs. MKRN2 silencing caused a strong DRiP enrichment in the majority of the SGs analyzed and therefore we chose this way of data representation. Note that the results represent the average of the analysis of 3 independent experiments with high numbers of SGs automatically segmented and analyzed/experiment. Figure 5A, B: n = 3 independent experiments; number of SGs analyzed per experiment: HS + OP-puro (695; 1216; 952); TAK-243 + HS + OP-puro (1852; 2214; 1774). Figure 5C, D: n = 3 independent experiments; number of SGs analyzed per experiment: siRNA control, HS + OP-puro (1984; 1400; 1708); siRNA MKRN2, HS + OP-puro (912; 1074; 1532).

      • * For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.

      * Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, FK2 immunofluorescence microscopy and perform SG partition coefficient analysis.

      * - Are prior studies referenced appropriately? • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature *

      Authors: We have added citations for the relevant studies.

      • *

      At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover. * Authors: The reviewer is correct. We have added to the text that G3BP1 is ubiquitinated through a Lys63-linked ubiquitin chain.

      • *

      When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly. * Authors: We thank the reviewer for this suggestion. We added to the discussion plausible explanations for why our study identified new E3 ligases.

      • *

      Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers. * Authors: We have added this detail to the discussion.

      • *

      The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference. * Authors: We have added citations to back up our claim that ZNF598, CNOT4, MKRN2, TRIM25 and TRIM26 exhibit RNA-binding activity.

      *- Are the text and figures clear and accurate?

      • In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript. *

      Authors: We have made the colors consistent across the main and supplementary figures.

      • *

      At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics. * Authors: Good catch. We now fixed the graphical mistake (Figure 1F and S6).

      • * Line 86: adjust a missing bracket * Authors: Thank you, we fixed it.

      • *

      There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.

      * Authors: This unfortunate graphical mistake has been corrected.

      • * At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation. * Authors: We thank the reviewer and clarify in the legend of Figure 3F and the Results the correct labels: indeed faster fluorescence recovery seen in MKRN2 KD is correctly interpreted as increased liquidity in the text.

      • *

      At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus. * Authors: The text has been modified to reflect both the study of MKRN2 localization to SGs and of nuclear localization.

      • *

      At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data. * Authors: Thank you for pointing out this error. We have corrected it.

      • * Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal. * Authors: We thank the reviewer for pointing the sub-optimal presentation of this figure. We modified Figure 5A to improve image quality and interpretation. Concerning the comment that “the most obvious DRIP spots are localized in the nucleus”, this is in line with our previous findings demonstrating that a fraction of DRiPs accumulates in nucleoli (Mediani et al. 2019 10.15252/embj.2018101341). To avoid misinterpretation, we modified Figure 5A as follows: (i) we provide a different image for control cells, exposed to heat shock and OP-puro; (ii) we select a ROI that only shows a few stress granules; (iii) we added arrowheads to indicate the nucleoli that are strongly enriched for DRiPs; (iv) we include a dotted line to show the nuclear membrane, helping to distinguish cytoplasm and nucleus in the red and green channel. We also include the scale bars (5 µm) in the image.

      * Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      • In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.*

      Authors: We thank the reviewer for this suggested improvement to our “storyline”. As suggested by the reviewer, we have moved the IF validation of MKRN2 to the following paragraph in order to improve the flow of the manuscript. We added additional justification to prioritizing MKRN2 citing (Youn et al. 2018 and Markmiller et al. 2018).

      • *

      At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays." * Authors: The statement has been rephrased in a softer way as suggested by the reviewer.

      • At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation. * * *Authors: We thank the reviewer for this suggestion and have altered the phrasing of this statement accordingly.

      *Reviewer #1 (Significance (Required)):

      General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

      • This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

      Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

      • The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).

      • Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.

      • Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.

      • There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.

      • A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

      Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

      The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

      • Please 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.

      I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties. Major issues: 1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time: • The peak or SG nucleation was actually at 10-30 min (Figure 1B). • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition. *

      Authors: We thank the reviewer for identifying this caveat in our methodology. We now discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.

      * Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain: • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs? • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm. • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics. *

      Authors: We thank the reviewer for this experimental suggestion. We will perform an analysis of the SG partitioning coefficient between WT-MKRN2 and a RING mutant of MKRN2.

      * Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear: • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly? • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification. • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes. *

      Authors: We thank the reviewer and will add to the Discussion a mention of a precedent for this precise phenotype from our previous work (Seguin et al., 2014).

      * Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding: • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination. *

      Authors: The DRiPs generated in the study represent truncated versions of all the proteins that were in the process of being synthesized by the cell at the moment of the stress, and therefore include both MKRN2 specific substrates and MKRN2 independent substrates. Identifying specific MKRN2 substrates, while interesting as a new research avenue, is not within the scope of the present study.

      • * Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway. * Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, VCP immunofluorescence microscopy and perform SG partition coefficient analysis.

      • * The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection. * Authors: This study is conditional results of the above study. If VCP partitioning to SGs is reduced upon MKRN2 KD, which we do not know at this point, then MKRN2/VCP double KD experiment will be performed to strengthen this connection.

      * Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype. *

      Authors: We are happy to provide alternative explanations in the Discussion in line with Reviewer #2 suggestion. The role of the proteosome is out of the scope of our study.

      • Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However: • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2. *

      • * Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects? * Authors: Our paper presents a study of the E3 ligase MKRN2. Generalizing these observations to ZNF598, CNOT4 and perhaps an even longer list of E3s, may be an interesting question, outside the scope of our mission.

      • * The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning? * Authors: We thank the reviewer for this smart question. MKRN2 has the strongest dependence on active ubiquitination as we now clarify better in the Results.

      *Reviewer #2 (Significance (Required)):

      This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

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

      In this paper, Amzallag et al. investigate the relationship between ubiquitination and the dynamics of stress granules (SGs). They utilize proximity ligation coupled mass spectrometry to identify SG components under conditions where the proteasome is inhibited by a small drug that targets UBiquitin-like modifier Activating enzyme 1 (UBA1), which is crucial for the initial step in the ubiquitination of misfolded proteins. Their findings reveal that the E3 ligase Makorin2 (MKRN2) is a novel component of SGs. Additionally, their data suggest that MKRN2 is necessary for processing damaged ribosome-associated proteins (DRIPs) during heat shock (HS). In the absence of MKRN2, DRIPs accumulate in SGs, which affects their dynamics. Major comments: Assess the knockdown efficiency (KD) for CNOT1, ZNF598, and MKRN2 to determine if the significant effect observed on SG dynamics upon MKRN2 depletion is due to the protein's function rather than any possible differences in KD efficiency. *

      Authors: To address potential variability in knockdown efficiency, we will quantify CNOT4, ZNF598, and MKRN2 mRNA levels by RT-qPCR following siRNA knockdown.

      * Since HS-induced stress granules (SGs) are influenced by the presence of TAK-243 or MKRN2 depletion, could it be that these granules become more mature and thus acquire more defective ribosomal products (DRIPs)? Do HS cells reach the same level of DRIPs, as assessed by OP-Puro staining, at a later time point? *

      Authors: an interesting question. Mateju et al. carefully characterized the time course of DRiP accumulation in stress granules during heat shock, decreasing after the 90 minutes point (Appendix Figure S7; 10.15252/embj.201695957). We therefore interpret DRiP accumulation in stress granules following TAK243 treatment as a pathological state, reflecting impaired removal and degradation of DRiPs, rather than a normal, more “mature” stress granule state.

      * Incorporating OP-Puro can lead to premature translation termination, potentially confounding results. Consider treating cells with a short pulse (i.e., 5 minutes) of OP-Puro just before fixation. *

      Authors: Thank you for this suggestion. Treating the cell with a short pulse of OP-Puro just before fixation will lead to the labelling of a small amount of proteins, likely undetectable using conventional microscopy or Western blotting. Furthermore, it will lead to the unwanted labeling of stress responsive proteins that are translated with non canonical cap-independent mechanisms upon stress.

      * Is MKRN2's dependence limited to HS-induced SGs? *

      Authors: We will test sodium arsenite–induced stress and use immunofluorescence at discrete time points to assess whether the heat shock–related observations generalize to other stress types.

      *

      Minor comments: Abstract: Introduce UBA1. Introduction: The reference [2] should be replaced with 25719440. Results: Line 70, 'G3BP1 and 2 genes,' is somewhat misleading. Consider rephrasing into 'G3BP1 and G3BP2 genes'. Line 103: considers rephrasing 'we orthogonally validated the ubiquitin-dependent interaction' to 'we orthogonally validated the ubiquitin-dependent stress granule localization'. Line 125: '(fig.3C, EI Supplementary fig. 3)' Remove 'I'. Methods: line 260: the reference is not linked (it should be ref. [26]). Line 225: Are all the KDs being performed using the same method? Please specify. *

      Authors: The text has been altered to reflect the reviewer’s suggestions.

      *Fig.2C: Consider adding 'DEPLETED' on top of the scheme.

      Reviewer #3 (Significance (Required)):

      The study offers valuable insights into the degradative processes associated with SGs. The figures are clear, and the experimental quality is high. The authors do not overstate or overinterpret their findings, and the results effectively support their claims. However, the study lacks orthogonal methods to validate the findings and enhance the results. For instance, incorporating biochemical and reporter-based methods to measure degradation-related intermediate products (DRIPs) would be beneficial. Additionally, utilizing multiple methods to block ubiquitination, studying the dynamics of MKRN2 on SGs, and examining the consequences of excessive DRIPs on the cell fitness of SGs would further strengthen the research. *

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      Referee #3

      Evidence, reproducibility and clarity

      In this paper, Amzallag et al. investigate the relationship between ubiquitination and the dynamics of stress granules (SGs). They utilize proximity ligation coupled mass spectrometry to identify SG components under conditions where the proteasome is inhibited by a small drug that targets UBiquitin-like modifier Activating enzyme 1 (UBA1), which is crucial for the initial step in the ubiquitination of misfolded proteins. Their findings reveal that the E3 ligase Makorin2 (MKRN2) is a novel component of SGs. Additionally, their data suggest that MKRN2 is necessary for processing damaged ribosome-associated proteins (DRIPs) during heat shock (HS). In the absence of MKRN2, DRIPs accumulate in SGs, which affects their dynamics.

      Major comments:

      Assess the knockdown efficiency (KD) for CNOT1, ZNF598, and MKRN2 to determine if the significant effect observed on SG dynamics upon MKRN2 depletion is due to the protein's function rather than any possible differences in KD efficiency. Since HS-induced stress granules (SGs) are influenced by the presence of TAK-243 or MKRN2 depletion, could it be that these granules become more mature and thus acquire more defective ribosomal products (DRIPs)? Do HS cells reach the same level of DRIPs, as assessed by OP-Puro staining, at a later time point? Incorporating OP-Puro can lead to premature translation termination, potentially confounding results. Consider treating cells with a short pulse (i.e., 5 minutes) of OP-Puro just before fixation. Is MKRN2's dependence limited to HS-induced SGs?

      Minor comments:

      Abstract:

      Introduce UBA1. Introduction:

      The reference [2] should be replaced with 25719440.

      Results:

      Line 70, 'G3BP1 and 2 genes,' is somewhat misleading. Consider rephrasing into 'G3BP1 and G3BP2 genes'. Line 103: considers rephrasing 'we orthogonally validated the ubiquitin-dependent interaction' to 'we orthogonally validated the ubiquitin-dependent stress granule localization'. Line 125: '(fig.3C, EI Supplementary fig. 3)' Remove 'I'. Methods:

      line 260: the reference is not linked (it should be ref. [26]). Line 225: Are all the KDs being performed using the same method? Please specify.

      Fig.2C: Consider adding 'DEPLETED' on top of the scheme.

      Significance

      The study offers valuable insights into the degradative processes associated with SGs. The figures are clear, and the experimental quality is high. The authors do not overstate or overinterpret their findings, and the results effectively support their claims. However, the study lacks orthogonal methods to validate the findings and enhance the results. For instance, incorporating biochemical and reporter-based methods to measure degradation-related intermediate products (DRIPs) would be beneficial. Additionally, utilizing multiple methods to block ubiquitination, studying the dynamics of MKRN2 on SGs, and examining the consequences of excessive DRIPs on the cell fitness of SGs would further strengthen the research.

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      Referee #2

      Evidence, reproducibility and clarity

      This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties.

      Major issues:

      1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time:
        • The peak or SG nucleation was actually at 10-30 min (Figure 1B).
        • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.
      2. Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain:
        • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs?
        • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm.
        • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics.
      3. Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear:
        • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly?
        • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification.
        • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes.
      4. Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding:
        • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination.
        • Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway.
        • The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection.
      5. Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype.
      6. Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However:
        • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2.
        • Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects?
        • The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning?

      Significance

      This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

    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

      Summary:

      In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

      Major comments:

      • Are the key conclusions convincing?

      The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary.

      1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment.

      2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity.

      3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. - 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. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation.

      1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms.

      2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance.

      3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. - 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. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. - Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. - Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. - Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

      Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning. - Are the data and the methods presented in such a way that they can be reproduced?

      Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions. - Are the experiments adequately replicated and statistical analysis adequate?

      Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential.

      Minor comments:

      • Specific experimental issues that are easily addressable.
        • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used
        • In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers.
        • In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates.
        • DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily.
        • The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results.
        • For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.
      • Are prior studies referenced appropriately?

        • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature
        • At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover.
        • When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly.
        • Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers.
        • The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference.
        • Are the text and figures clear and accurate?
        • In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript.
        • At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics.
        • Line 86: adjust a missing bracket
        • There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.
        • At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation.
        • At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus.
        • At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data.
        • Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal.

      Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      • In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.
      • At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays."
      • At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation.

      Significance

      General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

      • This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

      Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

      • The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).
      • Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.
      • Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.
      • There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.
      • A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

      Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

      The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

      Please 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.

      I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

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      Reply to the reviewers

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

      *The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

      Thank you for your positive feedback.

      *There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms? *

      In a separate technical manuscript describing the application of T-ChIC in mouse cells (Zeller, Blotenburg et al 2024, bioRxiv, 2024.05. 09.593364), we have provided a direct comparison of data quality between T-ChIC and other single-cell methods for chromatin-RNA co-profiling (Please refer to Fig. 1C,D and Fig. S1D, E, of the preprint). We show that compared to other methods, T-ChIC is able to better preserve the expected biological relationship between the histone modifications and gene expression in single cells.

      *In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors? *

      While we haven't profiled these other modifications using T-ChIC in Zebrafish, we have previously published high quality data on these histone modifications using the sortChIC method, on which T-ChIC is based (Zeller, Yeung et al 2023). In our comparison, we find that histone modification profiles between T-ChIC and sortChIC are very similar (Fig. S1C in Zeller, Blotenburg et al 2024). Therefore the method is expected to work as well for the other histone marks.

      *T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary? *

      We used the published scRNA-seq dataset with a larger number of cells to homogenize our cell type labels with these datasets, but we also cross-referenced our cluster-specific marker genes with ZFIN and homogenized the cell type labels with ZFIN ontology. This way our annotation is in line with previous datasets but not biased by it. Due the relatively smaller size of our data, we didn't expect to identify unique, rare cell types, but our full-length total RNA assay helps us identify non-coding RNAs such as miRNA previously undetected in scRNA assays, which we have now highlighted in new figure S1c .

      *Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH? *

      We appreciate that the ISH staining could be useful to validate the expression pattern of genes identified in this study. But to validate the relationships between the histone marks and gene expression, we need to combine these stainings with functional genomics experiments, such as PRC2-related knockouts. Due to their complexity, such experiments are beyond the scope of this manuscript (see also reply to reviewer #3, comment #4 for details).

      *In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern? *

      Thanks for the suggestion. In this revision, we have reanalysed a dataset of mouse ChIP-seq of H3K27me3 during mouse embryonic development by Xiang et al (Nature Genetics 2019) and find similar evidence of spreading of H3K27me3 signal from their pre-marked promoter regions at E5.5 epiblast upon differentiation (new Figure S4i). This observation, combined with the fact that the mechanism of pre-marking of promoters by PRC1-PRC2 interaction seems to be conserved between the two species (see (Hickey et al., 2022), (Mei et al., 2021) & (Chen et al., 2021)), suggests that the dynamics of H3K27me3 pattern establishment is conserved across vertebrates. But we think a high-resolution profiling via a method like T-ChIC would be more useful to demonstrate the dynamics of signal spreading during mouse embryonic development in the future. We have discussed this further in our revised manuscript.

      Reviewer #1 (Significance (Required)):

      *The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

      Thank you very much for your supportive remarks.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      *Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication. *

      We thank the reviewer for appreciating the quality of our study.

      *Major concerns: *

        • A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay. *

      We focused on the lineage-specific transcription factor activity during gastrulation in Figure 4 and S8 of the manuscript and discovered several interesting regulators active at this stage. During our analysis of the temporal dimension for the rest of the manuscript, we also classified the cells by their germ layer and "latent" developmental time by taking the full advantage of the single-cell nature of our data. Additionally, we have now added the cell-type-specific H3K27-demethylation results for 24hpf in response to your comment below. We hope that these results, together with our openly available dataset would demonstrate the advantage of the single-cell aspect of our dataset.

      1. *The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue. *

      Indeed we see that ~30% of the bins enriched in the pluripotent stage spread, while 70% do not seem to spread. In line with earlier observations(Hickey et al., 2022; Vastenhouw et al., 2010), we find that H3K27me3 is almost absent in the zygote and is still being accumulated until 24hpf and beyond. Therefore the majority of the sites in the genome still seem to be in the process of gaining H3K27me3 until 24hpf, explaining why we see mostly "spreading" and "stable" states. Considering most of these sites are at promoters and show signs of bivalency, we think that these sites are marked for activation or silencing at later stages. We have discussed this in the manuscript ("discussion"). However, in response to this and earlier comment, we went back and searched for genes that show H3K27-demethylation in the most mature cell types (at 24 hpf) in our data, and found a subset of genes that show K27 demethylation after acquiring them earlier. Interestingly, most of the top genes in this list are well-known as developmentally important for their corresponding cell types. We have added this new result and discussed it further in the manuscript (Fig. 2d,e, , Supplementary table 3).

      *Minors: *

        • The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them. *

      We have cited more single-cell chromatin and multiome studies focussed on early embryogenesis in the introduction now.

      *2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show. *

      Figure 1a. shows a summary of our Zebrafish TChIC workflow, which contains the unique sample multiplexing and sorting strategy to reduce batch effects, which was not applied in the original TChIC workflow. We have now clarified this in "Results".

      1. *It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C. *

      We have added the numbers to the corresponding legends.

      1. *Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages. *

      The citation seems to be correct (Fig. S4C supplements Fig. 2C, but shows mesodermal lineage cells) but the description of the legend was a bit misleading. We have clarified this now.

      *5. Figure 4C has not been cited or mentioned in the main text. Please check. *

      Thanks for pointing it out. We have cited it in Results now.

      Reviewer #2 (Significance (Required)):

      *Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. *

      *Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited. *

      *Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting. *

      *The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc. *

      *I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc. *

      Thank you for your remarks.

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

      *This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors. *

      *Major concerns *

      1. *Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset. *

      Thanks for pointing this out. We had, in fact, performed T-ChIC experiments in four rounds of biological replicates (independent clutch of embryos) and merged the data to create our resource. Although not all timepoints were profiled in each replicate, two timepoints (10 and 24hpf) are present in all four, and the celltype composition of these replicates from these 2 timepoints are very similar. We have added new plots in figure S2f and added (new) supplementary table (#1) to highlight the presence of biological replicates.

      2. *The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off. *

      Thank you for this suggestion. We did use 10-fold cross validation during training and obtained the R2 values of TF motifs from the independent test set as an unbiased estimate. However, the cutoff of R2 > 0.6 to select the TFs for classification was indeed arbitrary. In the revised version, we now report the FDR-adjusted p-values for these R2 estimates based on permutation tests, and select TFs with a cutoff of padj supplementary table #4 to include the p-values for all tested TFs. However, we see that our arbitrary cutoff of 0.6 was in fact, too stringent, and we can classify many more TFs based on the FDR cutoffs. We also updated our reported numbers in Fig. 4c to reflect this. Moreover, supplementary table #4 contains the complete list of TFs used in the analysis to allow others to choose their own cutoff.

      3. *Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes. *

      We agree that independent validation of the functions of our predicted TFs on target gene activity would be important. During this revision, we analysed recently published scRNA-seq data of Saunders et al. (2023) (Saunders et al., 2023), which includes CRISPR-mediated F0 knockouts of a couple of our predicted TFs, but the scRNAseq was performed at later stages (24hpf onward) compared to our H3K4me1 analysis (which was 4-12 hpf). Therefore, we saw off-target genes being affected in lineages where these TFs are clearly not expressed (attached Fig 1). We therefore didn't include these results in the manuscript. In future, we aim to systematically test the TFs predicted in our study with CRISPRi or similar experiments.

      4. *The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression. *

      We appreciate the suggestion that indeed PRC2-disruption followed by T-ChIC or other forms of validation would be needed to confirm whether the H3K27me3 spreading is indeed causally linked to the silencing of the identified target genes. But performing this validation is complicated because of multiple reasons: 1) due to the EZH2 contribution from maternal RNA and the contradicting effects of various EZH2 zygotic mutations (depending on where the mutation occurs), the only properly validated PRC2-related mutant seems to be the maternal-zygotic mutant MZezh2, which requires germ cell transplantation (see Rougeot et al. 2019 (Rougeot et al., 2019)) , and San et al. 2019 (San et al., 2019) for details). The use of inhibitors have been described in other studies (den Broeder et al., 2020; Huang et al., 2021), but they do not show a validation of the H3K27me3 loss or a similar phenotype as the MZezh2 mutants, and can present unwanted side effects and toxicity at a high dose, affecting gene expression results. Moreover, in an attempt to validate, we performed our own trials with the EZH2 inhibitor (GSK123) and saw that this time window might be too short to see the effect within 24hpf (attached Fig. 2). Therefore, this validation is a more complex endeavor beyond the scope of this study. Nevertheless, our further analysis of H3K27me3 de-methylation on developmentally important genes (new Fig. 2e-f, Sup. table 3) adds more confidence that the polycomb repression plays an important role, and provides enough ground for future follow up studies.

      *Minor concerns *

      1. *Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development. *

      We agree that H3K27me3 alone would not be sufficient to fully understand the repressive chromatin state. Extension to other chromatin marks and DNA methylation would be the focus of our follow up works.

      *2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided. *

      As mentioned in the manuscript, we provide an open-source pre-processing pipeline "scChICflow" to perform all these steps (github.com/bhardwaj-lab/scChICflow). We have now also provided the configuration files on our zenodo repository (see below), which can simply be plugged into this pipeline together with the fastq files from GEO to obtain the processed dataset that we describe in the manuscript. Additionally, we have also clarified the peak calling and post-processing steps in the manuscript now.

      *3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added. *

      We have now publicly released the .h5ad files with raw counts, normalized counts, and complete gene and cell-level metadata, along with signal tracks (bigwigs) and peaks on GEO. Additionally, we now also released the source datasets and notebooks (.Rmarkdown format) on Zenodo that can be used to replicate the figures in the manuscript, and updated our statements on "Data and code availability".

      *4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference. *

      Thank you for spotting them. We have fixed these issues.

      Reviewer #3 (Significance (Required)):

      The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

      Thank you for your comments. We hope to have addressed your concerns in this revised version of our manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors.

      Major concerns

      1. Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset.
      2. The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off.
      3. Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes.
      4. The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression.

      Minor concerns

      1. Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development.
      2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided.
      3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added.
      4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference.

      Significance

      The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

    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

      Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication.

      Major concerns:

      1. A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay.
      2. The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue.

      Minors:

      1. The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them.
      2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show.
      3. It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C.
      4. Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages.
      5. Figure 4C has not been cited or mentioned in the main text. Please check.

      Significance

      Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

      Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting.

      The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc.

      I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc.

    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 have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

      There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms?

      In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors?

      T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary?

      Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH?

      In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern?

      Significance

      The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

    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

      1. General Statements

      We thank the reviewers for their overall support, thorough review, and thoughtful comments. The points raised were all warranted and we feel that addressing them has improved the quality of our manuscript. Below we respond to each of the points raised.

      2. Point-by-point description of the revisions

      Reviewer #1

      Minor comments:

      Are the lgl-1; pac-1 M-Z- double mutants dead? Only the phenotype of pac-1(M-Z-); lgl-1 (M+Z-) is shown. In figures and text throughout, it should be clear whether mutants are referring to zygotic loss or both maternal and zygotic loss, as this distinction could have major implications on the interpretation of experiments.

      Almost all experiments we performed used a combination of RNAi of lgl-1 in a homozygous pac-1 null mutant background, or the other way around. RNAi should eliminate maternal product, but we hesitate to use the terminology M/Z since it has previously been used for protein degradation strategies.

      We have updated the text and figure 1 to address the potential of maternal product masking earlier phenotypes, and performed additional RNAi experiments to demonstrate that the phenotypes obtained by RNAi for either pac-1 or lgl-1 in a homozygous mutant background for the other are the same as for the genetic double mutant. The results are shown as additional images and quantifications in figure 1B,C. We also updated the legend to figure 1 to make it clear that double genetic mutants are obtained from heterozygous lgl-1/+ parents.

      Regarding the phenotype of lgl-1; pac-1 M-Z- double mutants: assuming the reviewer refers to M-Z- double genetic mutants, we cannot make such embryos as the pac-1(M-Z-); lgl-1(M+Z-) animals are already lethal.

      In Figure 1C, it would be more appropriate to show a fully elongated WT embryo to contrast with arrested elongation in mutant embryos.

      We agree with the reviewer and have replaced the 2-fold WT embryo with a 3-fold embryo.

      Is the lateral spread of DLG-1 in double mutant embryos a result of failure to polarize DLG-1, or failure to maintain polarity? This should be straightforward to address in higher time resolution movies.

      We have analyzed additional embryos at early stages of development. In lgl-1; pac-1 embryos we never see the appearance of complete junctions: defects are apparent already at dorsal intercalation. We interpret these results as a failure to properly polarize DLG-1. We have added additional images to Figure S2 and added this sentence to the text: Imaging of embryos from early stages of development on showed that normal continuous junctional DLG-1 bands are never established in pac-1(RNAi); lgl-1(mib201) embryos (Fig. S2B).

      The lack of enhancement of hmp-1(fe4) by lgl-1(RNAi) is quite interesting, given that pac-1 does enhance hmp-1(fe4). To rule out the possibility that this result stems from incomplete lgl-1 RNAi, this experiment should be repeated using the lgl-1 null mutant.

      We have done this experiment by recreating the fe4 S823F mutation in the lgl-1(null) mutant background as well as in the wild-type CGC1 background using CRISPR/Cas9. The phenotype of both was similar, but differs from that of the original PE97 strain. In the original strain, there is ~50% embryonic lethality but worms that complete embryogenesis grow up to be fertile adults. In our new "fe4" strains, nearly all animals are severely malformed with little to no elongation taking place. We are able to maintain both strains (with and without lgl-1) homozygous but with difficulty as only ~5% of animals grow up and give progeny. Apparently, there are genetic differences between PE97 and our CGC1 background that cause phenotypic differences despite having the same amino acid change in HMP-1.

      Nevertheless, using our original embryonic viability criterium of 'hatching', loss of lgl-1 does not enhance the S823F mutation. We have included the following text in the manuscript:

      To rule out that the lack of enhancement by lgl-1(RNAi) is due to incomplete inactivation of lgl-1, we also re-created the hmp-1(fe4) mutation (S823F) by CRISPR in lgl-1(mib201) mutant animals and wild-type controls. The phenotype of the S823F mutant we created is more severe than that of the original PE97 hmp-1(fe4) strain, with only ~5% of animals becoming fertile adults (Fig. S2F). This likely represents the presence of compensatory changes that have accumulated over time in PE97. Nevertheless, consistent with our RNAi results, the presence of lgl-1(mib201) did not further exacerbate the phenotype of HMP-1(S823F) (Fig. S2E, F). Taken together, the lack of enhancement of hmp-1(S823F) mutants by inactivation of loss of lgl-1 This observation argues against a primary role for lgl-1 in regulating cell junctions.

      • Related to point 4, do pac-1 or lgl-1 null mutants enhance partial knockdown of junction protein DLG-1, or is this effect (of pac-1) specific to HMP-1/AJs?*

      We have attempted to address this point using feeding RNAi against dlg-1. However, we were not able to obtain partial depletion of DLG-1. On RNAi feeding plates, control, pac-1, and lgl-1 animals did not show significant embryonic lethality. We checked RNAi effectiveness with a DLG-1::mCherry strain and found RNAi by feeding to be very ineffective. Since we could not deplete DLG-1 to a level that results in partial embryonic lethality, we were not able to address this question properly.

      Does lgl-1 loss affect PAC-1 protein localization and vice versa?

      It does not. We have added the following text and a figure panel: Loss-of-function mutants that strongly enhance a phenotype are often interpreted as acting in parallel pathways. We therefore examined whether loss of lgl-1 or pac-1 alters the localization of endogenously GFP-tagged LGL-1 or PAC-1. In neither null background did we detect changes in the subcellular localization of the other protein, consistent with LGL-1 and PAC-1 functioning in parallel pathways (Fig. S1D).

      Reviewer #2

      Very little of the imaging data are analyzed quantitatively, and in many cases it is not clear how many embryos were analyzed. While the images that are presented show clear defects, readers cannot determine how reproducible, strong or significant the phenotypes are.

      We completely agree with the reviewer that interpretation of our data requires this information and apologize for the omission in the first manuscript version. The phenotypes are highly penetrant and consistent (timing of arrest, % lethality, junctional defects), and we have now added quantifications throughout the manuscript.

      In particular, the data below should be quantified and, where possible, analyzed statistically:

      • The frequency of the various junctional phenotypes shown in 2C

      We have now quantified the junctional phenotypes. The junctional defects are highly penetrant: >90% of lgl-1; pac-1 embryos have junctional defects (new Fig. 2B). We used airy-scan confocal imaging to analyze the distribution of the different phenotypes (unaffected, spread laterally, and ring-like pattern). The results are shown in Fig. 2G.

      • The expansion of DLG-1::mCherry in pac-1 lgl-1 embryos should be quantified (related to Figure 2B). For example, the percentage of membrane (marked by PH::GFP) occupied by DLG-1 could be quantified.

      We have performed this quantification, shown in Fig. 2D.

      - Similarly, the expansion of the aPKC domain should be quantified (Figure 3A).

      An objective quantification of aPKC signal is difficult due to the relatively weak expression of aPKC::GFP and the lack of a clear demarcating boundary. This is part of the reason we measured tortuosity as a more quantifyable indicator of apical domain expansion. We have now added a qualitative observation table as Figure 3B. In addition, we have expanded the quantification of cell geometry by measuring lateral and basal surfaces. Lateral surfaces were decreased. We added the following text:

      To better understand the reason for the change in geometry, we also measured the lengths of the lateral and basal surfaces (Fig. 3F). We found that the absolute lengths of the apical surfaces were not significantly different between pac-1(RNAi); lgl-1(mib201) and control animals. Instead, the lengths of the lateral domain were reduced (Fig. 3F). Hence, the more dome-shaped appearance of epidermal cells in pac-1; lgl-1 double mutant animals is due to a decrease in lateral domain size, which is consistent with the observed lateral spreading of aPKC.

      • How many embryos were analyzed for each marker shown in Figure 2A, and what proportion showed the described phenotypes? This could be given in the text or in a panel.

      We have added these numbers to panel 2B, and indicated the percentage in the text.

      • The frequency of the various junctional phenotypes shown in 4F.

      To address this, we have changed figure 4F to show three types of phenotype (strong, mild, no phenotype) and added how frequently we observed each to the panels. In rescue experiments, 18/24 embryos showed no junctional defects, while 6/24 showed a mild defect (compared to 100% severe in non-rescued embryos). To make room for this and other quantifications in Figure 4, we moved the demonstration that PAC-1 is depleted by RNAi to supplemental figure S4.

      Because the genetic perturbations used are global (either deletions or RNAi), it is not established whether PAC-1/LGL-1 act in epidermal epithelial cells per se (versus an earlier requirement that manifests in epidermal epithelial cells). While I agree that this is the most likely scenario, other mechanisms are possible.

      Our experiments indeed use global depletion/deletion of lgl-1 and pac-1. We cannot exclude therefore that other tissues do not contribute to the epithelial phenotypes. We assume that other tissues would be affected as well, and in fact have observed abnormal looking pharynx tissue (see our response to reviewer 3 below for examples). As the epidermis is one of the first tissue to develop it is likely the first in which phenotypes become apparent.

      In particular, the overall GFP::aPKC levels appear notably higher in pac-1 lgl-1 embryos in Figure 3A. aPKC levels should be quantified to determine if this is true of pac-1 lgl-1 embryos. If so, couldn't that explain (or at least contribute to) the observed phenotypes?

      Overall higher levels could indeed contribute to the phenotype. However, we have now quantified total aPKC levels in control and pac-1; lgl-1 embryos found no difference between them. We have added the following text to the manuscript: To determine if increased expression of aPKC might explain the broadened apical localization, we measured total intensity levels of aPKC::GFP. However, we detected no differences in fluorescence levels between control and pac-1(RNAi); lgl-1(mib201) animals (Fig. S3B, C).

      Minor

      Figure 4: For completeness, please include the embryonic viability of pac-1 lgl-1 +/- embryos treated with EV and cdc-42(RNAi), as was done for pac-1 lgl-1 pkc-3(ts) in Figure 4E. Presumably the increased proportion of viable embryos with the lgl-1 deletion allele is reflected in an overall increase in embryonic viability.

      The embryonic viability indeed increases, but not as much as one might think because 15% of embryos die from the cdc-42 RNAi itself. The most important rescue argument is that we can obtain adult pac-1; lgl-1 animals with cdc-42 RNAi.

      We have now included the overall rescue and the following text: Overall, cdc-42 RNAi caused a mild increase in embryonic viability (Fig. 4A). However, total embryonic viability may underestimate rescue of pac-1; lgl-1 embryonic lethality, because it also includes the ~15% lethality caused by cdc-42 inactivation itself, even among animals wild type for lgl-1.

      The orientation of the inset images in Figures 2C, 3A and 3D is confusing. An illustration showing how these images are oriented relative to each other would be helpful.

      We have added a figure showing how the junctions are oriented in the figures (Fig. 2E). We have also added supplemental videos S3 and S4 that should illustrate the phenotype more clearly as well.

      For completeness, it would be good to test whether lgl-1(delta) is also synthetically lethal with picc-1(RNAi) (Zilberman 2017).

      We like this idea and had already looked into this. Lgl-1 and picc-1 are not synthetic lethal (see graph in word file submitted). However, PICC-1 is not the only junctional localization signal for PAC-1, as demonstrated by the Nance lab. We find the data interesting but feel that it deserves a more thorough structure/function investigation of PAC-1 than we can provide here. Therefore we would prefer not to include this data.

      Reviewer #3

      We thank the reviewer for their support of our manuscript.

      A few small areas to improve this manuscript:

      p. 6 like 139: "remain" should be "remaining"

      We have fixed this typo.

      Could the authors mention what is the phenotype of the 10% of pac-1 animals that die?

      Yes. They die with pleotropic phenotypes not resembling those of our pac-1; lgl-1 double mutant embryos. We have added examples of these to Figure S1.

      Based on the Supplemental figures, it made me curious to ask: Did the authors notice changes in dorsal epidermal fusions? Cadherin normally disappears in the dorsal hyp7 cells at this time. Did the timing of the fusions change at all?

      We haven't analyzed this in detail but our time-lapse videos show that dorsal fusions still take place and do not seem to be particularly delayed (overall development is slightly delayed but the delay in fusion is consistent with overall delay).

      Again, curiosity driven by the Supplemental figures: did the authors notice defects in apical regions of internal organs, like the pharynx or intestine? The CDC-42 biosensor is asymmetrical in the developing intestine. See: DOI: 10.1242/bio.056911

      We did not pay much attention to the intestine as PAC-1 is barely detectable in this tissue. The pharynx is formed, which we can easily detect in arrested embryos as we use GFP or BFP expressed under the myo-2 promoter to mark the deletion of pac-1. While we did not look closely, we do observe defects in pharynx development.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Jarosinska and colleagues addresses a long-standing mystery in the apical/basal polarity field: why LGL-1 and PAC-1/RhoGAP19D, which are essential in Drosophila, and in some tissue culture contexts, are not essential in C. elegans embryos.

      The authors take an open-ended approach by using genetics, in the form of a genome wide RNAi screen, to find other proteins that enhance the mild phenotypes of lgl-1 mutant embryos. They uncover strong synthetic lethality when they reduce pac-1, a well-documented CDC-42 GAP that supports apical/basal polarity during early embryogenesis, and yet is also only partially required during embryogenesis.

      The phenotypic analysis to understand why the embryos die when missing both lgl-1 and pac-1 leads to a careful analysis of known junctional molecules in C. elegans. Using newly made endogenously tagged junctional proteins, including DLG-1 and AFD, so that they can examine all three C. elegans apical junction complexes, the authors find a penetrant defect in the epidermal junctions as the embryos undergo elongation, an actomyosin dependent contractile event that dramatically reshapes the embryos into long, skinny tubes. With disorganized junctions, the embryos die due to ruptures, or hernias, as shown in the Supplemental Movie 2. In addition, and quite excitingly, the apical domains of the embryos are expanded. These defects are then partially rescued by removing CDC-42 or aPKC using RNAi depletion.

      Major comments:

      The claims and conclusions are supported by the data.

      The data is presented in such a way that it is easy to understand what was done, and how measurements were obtained and evaluated.

      Rigorous documentation of how the strains were built and how the genome wide RNAi screen was conducted is included in the Supplemental files.

      Beautiful use of CRISPR to do the genetics:

      since when they made the deletion of lgl-1 they replaced the coding sequence with GFP, they could use GFP to count the animals carrying the deletion in their double mutant analysis with pac-1 deletion mutants.

      Figures are very nicely done.

      The writing is clear.

      Minor comments:

      A few small areas to improve this manuscript:

      p. 6 like 139: "remain" should be "remaining"

      Could the authors mention what is the phenotype of the 10% of pac-1 animals that die?

      Based on the Supplemental figures, it made me curious to ask: Did the authors notice changes in dorsal epidermal fusions? Cadherin normally disappears in the dorsal hyp7 cells at this time. Did the timing of the fusions change at all?

      Again, curiosity driven by the Supplemental figures: did the authors notice defects in apical regions of internal organs, like the pharynx or intestine? The CDC-42 biosensor is asymmetrical in the developing intestine. See: DOI: 10.1242/bio.056911

      Significance

      This study raises interesting and important questions for the general polarity field. Early embryos have hugely redundant methods to maintain apical/basal polarity, which in C. elegans masked the roles for lgl-1 and pac-1 at earlier events, like compaction, when apical/basal polarity is first established. However, during elongation, when healthy strong junctions are a requirement, the double mutant loss of LGL-1 and PAC-1 results in expanded apical domain, that is lethal.

      The study will be of interest to the broader polarity community, and to developmental biologist interested in how the apical junctions are assembled and strengthened during morphogenesis. The Discussion does a good job of showing what aspects of this study are novel, and which support prior findings that suggested, for example, that PAC-1 may have roles independent of CDC-42. I appreciate the comment that our field needs more and more sensitive biosensors to fully address the changes of key polarity regulators.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This study focuses on the polarization of epidermal epithelial cells in C. elegans. Whereas the basolateral polarity protein is LGL-1 is required for epithelial polarity in flies, LGL-1 is dispensable for polarization and viability in C. elegans. Through a whole-genome RNAi screen, Jarosinska et al discover that the depletion of the RhoGAP PAC-1 is synthetically lethal with an lgl-1 deletion mutant. pac-1 lgl-1 double mutants have significant polarity defects in the epidermal epithelial, including mislocalization of junctional markers and expansion of the apical aPKC domain. As a result pac-1 lgl-1 double mutants fail to maintain surface epithelial and arrest development. Genetic interaction data suggest that increased CDC42 and aPKC activity in pac-1 lgl-1 contributes, as least in part, to the polarity defects and resulting embryonic lethality.

      Major comments:

      Very little of the imaging data are analyzed quantitatively, and in many cases it is not clear how many embryos were analyzed. While the images that are presented show clear defects, readers cannot determine how reproducible, strong or significant the phenotypes are. In particular, the data below should be quantified and, where possible, analyzed statistically:

      • The frequency of the various junctional phenotypes shown in 2C
      • The expansion of DLG-1::mCherry in pac-1 lgl-1 embryos should be quantified(related to Figure 2B). For example, the percentage of membrane (marked by PH::GFP) occupied by DLG-1 could be quantified.
      • Similarly, the expansion of the aPKC domain should be quantified (Figure 3A).
      • How many embryos were analyzed for each marker shown in Figure 2A, and what proportion showed the described phenotypes? This could be given in the text or in a panel.
      • The frequency of the various junctional phenotypes shown in 4F.

      Because the genetic perturbations used are global (either deletions or RNAi), it is not established whether PAC-1/LGL-1 act in epidermal epithelial cells per se (versus an earlier requirement that manifests in epidermal epithelial cells). While I agree that this is the most likely scenario, other mechanisms are possible. In particular, the overall GFP::aPKC levels appear notably higher in pac-1 lgl-1 embryos in Figure 3A. aPKC levels should be quantified to determine if this is true of pac-1 lgl-1 embryos. If so, couldn't that explain (or at least contribute to) the observed phenotypes?

      Minor

      Figure 4: For completeness, please include the embryonic viability of pac-1 lgl-1 +/- embryos treated with EV and cdc-42(RNAi), as was done for pac-1 lgl-1 pkc-3(ts) in Figure 4E. Presumably the increased proportion of viable embryos with the lgl-1 deletion allele is reflected in an overall increase in embryonic viability.

      The orientation of the inset images in Figures 2C, 3A and 3D is confusing. An illustration showing how these images are oriented relative to each other would be helpful.

      For completeness, it would be good to test whether lgl-1(delta) is also synthetically lethal with picc-1(RNAi) (Zilberman 2017).

      Significance

      LGL-1 is a conserved polarity protein that is essential for viability in Drosophila. In contrast, lgl-1 mutants are viable and have weak polarity phenotypes in C. elegans. A previous study showed that LGL-1 acts redundantly with the posterior polarity proteins PAR-2 during establishment of anterior/posterior polarity in the one-cell worm embryo. Here, Jarosinska et al show that LGL-1 acts redundantly with another protein, the RhoGAP protein PAC-1, in the polarization of the embryonic epidermal epithelial. The strength of this study is the identification of redundant roles for PAC-1 and LGL-1, the apparent strength of the polarity defects in the double mutant and the broader implication that LGL-1 may act in a range of redundant, cell/tissue specific pathways to regulate polarity. The primary weakness of this study is the lack of quantification. Additionally, the aPKC and CDC42 genetic interaction data hint at potential pathways, but fall short of establishing LGL-1's or PAC-1's mechanism of action.

      Advance: This works identifies a redundant genetic interaction between LGL-1 and PAC-1. While the data require additional quantification, the phenotypes presented appear clear and strong. Although the molecular mechanism by which LGL-1 and PAC-1 act is not well established in the current work, the core observation is significant and should provide a foundation for future studies dissecting the molecular mechanisms.

      Audience: This work will be of interest to a broad audience. LGL-1 is conserved and its role in cell polarization and epithelial polarity is very actively studied, including in mammalian systems.

      Field of expertise. C elegans embryonic development; cell polarity.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Jarosinska and colleagues address the roles of two polarity regulators, pac-1 and lgl-1, in C. elegans epidermal polarity. Loss of function mutations in either of these gene individually does not block polarization, but through a genome-wide RNAi screen, the authors find that pac-1 and lgl-1 enhance each other to cause apical-basal polarity defects and arrest during epidermal morphogenesis. The remainder of the paper focuses on testing genetic interactions between both proteins and AJ proteins (HMP-1) as well as apical proteins (CDC-42, PKC-3). These experiments reveal some interesting differences in how lgl-1 and pac-1 interface with junctional proteins (pac-1 enhances hmp-1 but lgl-1 does not) and apical proteins (lgl-1 suppresses pkc-3 or cdc-42 partial loss but pac-1 does not).

      Minor comments:

      1. Are the lgl-1; pac-1 M-Z- double mutants dead? Only the phenotype of pac-1(M-Z-); lgl-1 (M+Z-) is shown. In figures and text throughout, it should be clear whether mutants are referring to zygotic loss or both maternal and zygotic loss, as this distinction could have major implications on the interpretation of experiments.
      2. In Figure 1C, it would be more appropriate to show a fully elongated WT embryo to contrast with arrested elongation in mutant embryos.
      3. Is the lateral spread of DLG-1 in double mutant embryos a result of failure to polarize DLG-1, or failure to maintain polarity? This should be straightforward to address in higher time resolution movies.
      4. The lack of enhancement of hmp-1(fe4) by lgl-1(RNAi) is quite interesting, given that pac-1 does enhance hmp-1(fe4). To rule out the possibility that this result stems from incomplete lgl-1 RNAi, this experiment should be repeated using the lgl-1 null mutant.
      5. Related to point 4, do pac-1 or lgl-1 null mutants enhance partial knockdown of junction protein DLG-1, or is this effect (of pac-1) specific to HMP-1/AJs?
      6. Does lgl-1 loss affect PAC-1 protein localization and vice versa?

      Significance

      Overall, the manuscript provides additional insights into apical-basal polarization in C. elegans and demonstrates that lgl-1 is likely working in a similar way as in Drosophila, despite the lack of a phenotype in single lgl-1 mutants. I found the experiments to be done rigorously and interpretations of the data appropriate. All of my suggestions on improving the manuscript are minor; suggested experiments should be viewed as optional ways to strengthen the conclusions/impact of the study.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      In this paper, Wang and Shu et al. investigate the extent to which the negative binomial (NB) distribution captures the statistical properties of single-cell like count data and the effects of using this model to interpret biophysical parameters. Assuming an underlying telegraph model of transcription, they demonstrate how the NB can produce similar if not equivalent fits to simulated data from various parameter regimes, regimes which can, notably, fall outside of the bursty transcription limit in which the telegraph model is known to have a NB form. The authors then assess how model selection favors the NB or Poisson models over the underlying telegraph model, and how technical noise can lead to greater selection/representation of the NB over the parameter regime. Finally, they demonstrate how the broader applicability of the NB impacts inference of burst size and frequency (commonly inferred from NB fits on single-cell data), preserving relative rather than absolute information.

      The authors use both method of moments and MLE-based approaches to obtain and compare model fits over the same parameter regimes. They also develop the aeBIC metric which balances parametric complexity and distributional similarity to the desired, ground truth distribution, to more quickly approximate the BIC (used for model selection).

      Major comments:

      The likelihood of model fits is used as a main criteria for model selection and comparison (e.g., in the BIC/aeBIC metrics), however it is possible that analysis of the curvature of the likelihood may suggest greater uncertainty/less information about parameter estimates for the different statistical models across the transcriptional regimes tested. Since a major component of this study is to demonstrate to readers that nuanced model selection is important for interpreting single-cell data, it would support these efforts to see if the telegraph versus NB model fits, for example, demonstrate differences in their respective Hessian matrices for the MLE estimates. This would help determine, for those interested in comparing these fits on their data, if there is potential here to distinguish the more optimal/true model or not (i.e., what the extent of the limitations are). The authors describe how in the infinite limit of N_sigma the NB and telegraph models converge to the same distribution, which provides another biological scenario outside transcriptional bursting where the NB can be interpreted as a good statistical model. However, though many parameter regimes are possible not all are observed in real data. Thus for readers to understand how likely these regimes are to be present in the data it would be helpful to discuss in what biological scenarios such a limit may appear and if it is likely to be a common instance, etc (perhaps given the ranges of on/off times observed in the literature https://pmc.ncbi.nlm.nih.gov/articles/PMC10860890/). This would parallel the discussion in the study on the bursty transcription model, often described in the literature as a widespread phenomenon. The p_cap parameter is described as representing technical capture and affords the conclusion in the Discussion that the NB can improve capture of technical noise beyond the biological noise in the system. However, as mentioned later in the Discussion, this effect could also arise from cell to cell differences in transcription rate (extrinsic, biological noise), which cannot be distinguished in this model. This point should be made clearer earlier on, as without use of control genes/spike-ins/etc we cannot distinguish the biological and technical components encompassed by the p_cap term (i.e., whether or not a spread in total UMIs observed over droplets is due to biological or technical capture differences). Since the aeBIC is being presented as a new, faster method in this study, the timing and memory usage in performing these calculations, for each model, should be presented somewhere. The Methods should also have a more explicit description of the steps/tools used to calculate the aeBIC.

      Minor comments:

      Figure S4 mentioned comparison of scRNA-seq with smFISH data to approximate p_cap, however given that smFISH data would have its own technical biases it does not seem exactly clear how a map from smFISH to scRNA-seq would work such as to illuminate the gap incurred by technical bias/capture. Perhaps previous literature/methods doing this can be cited here, or this idea can be fleshed out in the Discussion text for readers interested in better estimating p_cap. In Figure 4 the pink color of the Poisson in c is hard to see, and it may be easier to write the names of the different models in the respective regions that they cover (similarly in Figure 5 c) For Figure 8, it may be easier for the reader to interpret the several plots in a row by repeating the x-axis labels under each set of plots and collating all the legend labels into one box somewhere near the first plots.

      Significance

      General assessment: Overall, the paper is a clear and concise view on the use of the NB in analysis of sparse, transcriptomic count data, the potential effects of technical and biological noise on the pertinence of the NB as the statistical representation, and the impacts on user interpretation of biophysical parameters from these model fits. This study is useful for both biologists and computational scientists looking to gain mechanistic insight from single-cell data.

      The strength of the paper is that the methodology is straightforward and uses simple numerical experiments to demonstrate how and when several common distributions can describe the type of data we encounter in single-cell genomics. They additionally connect these results to common biological interpretations from single-cell measurements and outline regimes in which inferences are likely to be incorrect.

      The paper could benefit from more discussion on the biological interpretations of the findings and regimes analyzed, particularly to help readers interested in how this impacts their data analysis. Supplemental analysis on whether other criteria could potentially distinguish the models in question would also help support the conclusions of model selection/identifiability and if other properties of these model fits can be used for selection or not.

      Advance: The study builds on others in the field by not just fitting several common models to this type of sparse, transcriptomic count data but also describing why these overlapping fits arise and how that affects biological interpretation. Often the focus is more on choosing a sufficient statistical representation without the underlying, mechanistic connections between the models. The results here are thus more technical and mechanistic in nature, describing both the theoretical connections between common single-cell count models and their biophysical interpretations.

      Audience: This result is likely to be of interest to scientists performing data analysis and method development in single-cell genomics, particularly with mechanistic insight in mind. This would be more of interest within the domain of transcriptomics, but it also presents a methodology for studying limitations of identifiability in noisy systems which could be of interest to other biological domains.

      My expertise is in developing representation learning methods and stochastic models of transcription for single-cell biology, which covers the classical models described in this study.

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      Referee #2

      Evidence, reproducibility and clarity

      The study is generally well reasoned and thorough, and should be of interest to the community. My only critique relates to the treatment of extrinsic noise and the related discussion: Many studies have concluded that extrinsic noise (e.g., cell-to-cell variability in the transcription rate) is a larger contribution to noise in gene expression than intrinsic noise. (For example, see the seminal review by Raj and van Oudenaarden (PMID: 18957198) and early examples such as Raser and O'Shea: Raser JM, O'Shea EK. Science. 2004;304:1811. doi: 10.1126/science.1098641). For this reason, one must be careful in assuming that the telegraph model by itself fully captures biological variability. I believe this point could be more clearly made in the paper. Did the authors treat a case in which the gene undergoes state switching, but where there is also a significant contribution of extrinsic noise, for example, through variability in the transcription rate and/or other papers? I could not tell for sure if this was explicitly studied. This would be an important scenario to study, because it may be the most likely. I would have thought that this is the most biologically realistic scenario (i.e., strong contributions of both intrinsic and extrinsic noise, along with state switching). My prior assumption has been that the NB model is often empirically indicated because it somehow well captures this combination of intrinsic (including state switching) + extrinsic noise. Could the authors comment on whether this assumption is consistent with their findings? (Neither Case I or Case II in the manuscript captures this scenario). Related to the treatment of extrinsic noise, I was confused by this sentence: "Any variation in the effective transcription rate due to variability in the transcription rate (extrinsic noise on the transcription rate) between cells is indistinguishable from variability in the transcript capture probability and hence is automatically accounted for in our present method. " But doesn't the distribution of transcription rates vary significantly, depending on whether the variation comes from technical noise versus extrinsic biological variability? For example, one source of extrinsic biological variability is differences in RNA polymerase concentrations in different cells. Wouldn't one need to know what kind of distribution to use to capture these effects? In this case, I believe one would need to study various types of compound distributions, depending on the assumptions underlying the biological extrinsic variability.

      Significance

      This paper presents a thorough study of the conditions under which the negative binomial model of transcript distributions can map onto other widely used models, namely the telegraph model of stochastic gene expression. The study is generally well reasoned and thorough, and should be of interest to the community (namely: single cell transcriptomics community, bio mathematicians, biological noise community).

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      Referee #1

      Evidence, reproducibility and clarity

      The authors present an investigation into the surprising effectiveness of the negative-binomial distribution in modelling transcript counts in single cell RNA sequencing experiments. With experimentally motivated ground-truth models that incorporate transcriptional bursting, they show that when transcription activity is large compared to degradation these distributions coincide. With a novel model selection metric, they indicate the regions of parameter space in which the negative-binomial model is a good approximation to the underlying true model. With this procedure, they also indicate that transcriptional burst parameters are unlikely to be reconstructed by an effective negative-binomial function, but that nevertheless, relative rankings between genes can be identified robustly.

      I would like to commend the authors on an interesting and fairly comprehensive investigation on a topic of considerable importance in the interpretation of single cell RNA sequencing experiments, and on a well written paper. I have no major comments on issues that affect the conclusions of the paper, although I have a few minor suggestions that might aid reader's understanding of the results and their applicability.

      General

      It would be nice to have a comparison with some real data for the burst frequency and size, just to indicate to the reader how important these regions are compared to what might be measured. For example, if most genes are outside of the region that does not accommodate the NB distribution, then the conclusion is quite different than if most real counts are unlikely to accommodated by the NB.

      Inter-cellular variability of transcription dynamics is quite a significant point of interest, so it would be good to have stated earlier that this is not considered, with the mitigation that is noted later. This is particularly important given that in the introduction, the cases mentioned seem to imply that an NB distribution would be more likely with higher inter-cellular variability.

      Introduction

      It would be nice to have a bit more detail here, for example on what UMIs are, and what the parameters of the NB distribution represent in general.

      For smFISH, I would have thought that the more simple explanation is that the NB is often the simplest distribution with some overdispersion that fits the data, and the parameters don't necessarily need to be biologically interpretable?

      It's noted later that the capture probability of modern RNASeq protocols can be ~0.3, which doesn't seem very different compared to 0.7-0.9 of smFISH, so some context here would be good.

      Results

      Eq 1: I don't think you lose anything by giving the Pochammer symbol and Kummer confluent geometric function explicitly here, and it would make it it a lot easier to read. That said, this equation also seems to come out of nowhere, so a reference would be nice.

      I think the moment matching is reasonably convincing, but it might require a little more explicit motivation for a more general audience.

      Thm. 1: Do these converge at similar rates, and if not, does that have any implications for the interpretation of the comparisons (as these are evaluated with specific values)? This might be worth a short comment.

      Fig 3. In the description for this in the text, it would be nice to have an expression of the KL divergence (and what order the arguments are in), for anyone unfamiliar.

      The discussion of the aeBIC seems a bit circuitous. A reasonable prior intention might be to average (or apply a voting function) to individual BIC values, rather than the aeBIC constructed here. And in fact the text goes on to note after the description that this is a good estimate of the expectation of the BIC after all, with some computational advantages. So it might be better to have a more straightforward presentation where this is proposed as an approximation to the expectation of the BIC in the first place.

      Section 2.4: The intro to this section could do with a bit more background of the capture, PCR, sequencing, etc, stages, and what exactly the data generated here represents. Otherwise the discussion of zero inflation and UMIs is a little confusing.

      It would also be nice to have a comment here on the effect of sequencing depth, or similar (compared to capture probability), even if this wouldn't change the interpretation.

      Significance

      The paper provides novel arguments towards the support of the negative-binomial distribution in describing single cell RNA sequencing data, with particular relevance to transcriptional bursting observed in numerous datasets. The paper follows from some notable prior work in the field, and integrates these into a more consistent description, particularly in relation to newer techniques such as UMIs.

      The ubiquity of the negative-binomial distribution means that these arguments will be of relevance to those that perform theoretical or statistical modelling of single cell RNA sequencing data, and theoretically justifies many widely held assumptions. However, the paper does not make any reference to specific reference datasets or commonly observed values, so where in the parameter space data likely lies would still need to be evaluated on a case-by-case basis.

      My expertise is in mathematical modelling and statistics, with some experience of the analysis of single cell RNA sequencing data.

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      Reply to the reviewers

      The PDF version of point-by-point response includes figures (I, II, III,... IX) that are not included in the manuscript nor in this post but serve to illustrate and clarify our replies to the reviewers' comments.

      Dear Editor,

      Many thanks for forwarding the comments from reviewers #1-#4 regarding our manuscript (Preprint #RC-2025-03087144), entitled "HIV-1 Envelope glycoprotein modulates CXCR4 clustering and dynamics on the T cell membrane", by Quijada-Freire A. et al.

      We have carefully reviewed all reviewer comments and prepared our specific, detailed responses. Alongside this, we have created a revised version of the manuscript to post them on BioRxiv, and we are pleased to announce that we will transfer this new version to an affiliate journal for consideration.

      Reviewer #1

      Thank you very much for considering that our manuscript evaluates an important question and that the reagents used are well prepared and characterized. We also much appreciate that you consider the information generated as potentially useful for those studying HIV infection processes and strategies to prevent infection.

      • While a single particle tracking routine was applied to the data, it's not clear how the signal from a single GFP was defined and if movement during the 100 ms acquisition time impacts this. My concern would be that the routine is tracking fluctuations, and these are related to single particle dynamics, it appears from the movies that the density or the GFP tagged receptors in the cells is too high to allow clear tracking of single molecules. SPT with GFP is very difficult due to bleaching and relatively low quantum yield. Current efforts in this direction that are more successful include using SNAP tags with very photostable organic fluorophores. The data likely does mean something is happening with the receptor, but they need to be more conservative about the interpretation. *

      Some of the paradoxical effects might be better understood through deeper analysis of the SPT data, particularly investigation of active transport and more detailed analysis of "immobile" objects. Comments on early figures illustrate how this could be approached. This would require selecting acquisitions where the GFP density is low enough for SPT and performing a more detailed analysis, but this may be difficult to do with GFP.

      When the authors discuss clusters of 3, how do they calibrate the value of GFP and the impact of diffusion on the measurement. One way to approach this might be single molecules measurements of dilute samples on glass vs in a supported lipid bilayer to map the streams of true immobility to diffusion at >1 µm2/sec.

      We fully understand the reviewer's apprehensions regarding the application of these high-end biophysical techniques, in particular the associated complexity of the data analysis. We provide below extensive explanations on our methodology, which we hope will satisfactorily address all of the reviewer's concerns.

      We would first like to emphasize that the experimental conditions and the quantitative analysis used in our current experiments are similar to the established protocols and methodologies applied by our group previously (Martinez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022; Gardeta et al. Frontiers in Immunol., 2022; García-Cuesta et al.eLife, 2024; Gardeta et al. Cell. Commun. Signal., 2025) and by others (Calebiro et al. PNAS, 2013; Jaqaman et al. Cell,2011; Mattila et al. Immunity, 2013; Torreno-Pina et al. PNAS, 2014; Torreno-Pina et al. PNAS, 2016).

      As SPT (single-particle tracking) experiments require low-expressing conditions in order to follow individual trajectories (Manzo & García-Parajo Rep. Prog. Phys., 2015), we transiently transfected Jurkat CD4+ cells with CXCR4-AcGFP or CXCR4R334X-AcGFP. At 24 h post-transfection, cells expressing low CXCR4-AcGFP levels were selected by a MoFlo Astrios Cell Sorter (Beckman-Coulter) to ensure optimal conditions for SPT. Using Dako Qifikit (DakoCytomation), we quantified the number of CXCR4 receptors and found ∼8,500 - 22,000 CXCR4-AcGFP receptors/cell, which correspond to a particle density ∼2 - 4.5 particles/mm2 (Figure I, only for review purposes) and are similar to the expression levels found in primary human lymphocytes.

      These cells were resuspended in RPMI supplemented with 2% FBS, NaPyr and L-glutamine and plated on 96-well plates for at least 2 h. Cells were centrifuged and resuspended in a buffer with HBSS, 25 mM HEPES, 2% FBS (pH 7.3) and plated on glass-bottomed microwell dishes (MatTek Corp.) coated with fibronectin (FN) (Sigma-Aldrich, 20 mg/ml, 1 h, 37{degree sign}C). To observe the effect of the ligand, we coated dishes with FN + CXCL12; FN + X4-gp120 or FN + VLPs, as described in material and methods; cells were incubated (20 min, 37{degree sign}C, 5% CO2) before image acquisition.

      For SPT measurements, we use a total internal reflection fluorescence (TIRF) microscope (Leica AM TIRF inverted) equipped with an EM-CCD camera (Andor DU 885-CS0-#10-VP), a 100x oil-immersion objective (HCX PL APO 100x/1.46 NA) and a 488-nm diode laser. The microscope was equipped with incubator and temperature control units; experiments were performed at 37{degree sign}C with 5% CO2. To minimize photobleaching effects before image acquisition, cells were located and focused using the bright field, and a fine focus adjustment in TIRF mode was made at 5% laser power, an intensity insufficient for single-particle detection that ensures negligible photobleaching. Image sequences of individual particles (500 frames) were acquired at 49% laser power with a frame rate of 10 Hz (100 ms/frame). The penetration depth of the evanescent field used was 90 nm.

      We performed automatic tracking of individual particles using a very well established and common algorithm first described by Jaqaman (Jaqaman et al. Nat. Methods, 2008). Nevertheless, we would stress that we implemented this algorithm in a supervised fashion, i.e., we visually inspect each individual trajectory reconstruction in a separate window. Indeed, this algorithm is not able to quantify merging or splitting events.

      We follow each individual fluorescence spot frame-by-frame using a three-by-three matrix around the centroid position of the spot, as it diffuses on the cell membrane. To minimize the effect of photon fluctuations, we averaged the intensity over 20 frames. Nevertheless, to assure the reviewer that most of the single molecule traces last for at least 50 frames (i.e., 5 seconds), we provide the following data and arguments. We currently measure the photobleaching times from individual CD86-AcGFP spots exclusively having one single photobleaching step to guarantee that we are looking at individual CD86-AcGFP molecules. The distribution of the photobleaching times is shown below (Figure II, only for review purposes). Fitting of the distribution to a single exponential decay renders a t0 value of ~5 s. Thus, with 20 frames averaging, we are essentially measuring the whole population of monomers in our experiments. As the survival time of a molecule before photobleaching will strongly depend on the excitation conditions, we used low excitation conditions (2 mW laser power, which corresponds to an excitation power density of ~0.015 kW/cm2 considering the illumination region) and longer integration times (100 ms/frame) to increase the signal-to-background for single GFP detection while minimizing photobleaching.

      To infer the stoichiometry of receptor complexes, we also perform single-step photobleaching analysis of the TIRF trajectories to establish the existence of different populations of monomers, dimers, trimers and nanoclusters and extract their percentage. Some representative trajectories of CXCR4-AcGFP with the number of steps detected are shown in new Supplementary Figure 1.

      The emitted fluorescence (arbitrary units, a.u.) of each spot in the cells is quantified and normalized to the intensity emitted by monomeric CD86-AcGFP spots that strictly showed a single photobleaching step (Dorsch et al. Nat. Methods,2009). We have preferred to use CD86-AcGFP in cells rather than AcGFP on glass to exclude any potential effect on the different photodynamics exhibited by AcGFP when bound directly to glass. We have also previously shown pharmacological controls to exclude CXCL12-mediated receptor clustering due to internalization processes (Martinez-Muñoz et al. Mol. Cell, 2018) that, together with the evaluation of single photobleaching steps and intensity histograms, allow us to exclude the presence of vesicles in our data. Thus, the dimers, trimers and nanoclusters found in our data do correspond to CXCR4 molecules on the cell surface. Finally, distribution of monomeric particle intensities, obtained from the photobleaching analysis, was analyzed by Gaussian fitting, rendering a mean value of 980 {plus minus} 86 a.u. This value was then used as the monomer reference to estimate the number of receptors per particle in both cases, CXCR4-AcGFP and CXCR4R334X-AcGFP (new Supplementary Figure 1).

      • I understand that the CXCL12 or gp120 are attached to the substrate with fibronectin for adhesion. I'm less clear how how that VLPs are integrated. Were these added to cells already attached to FN?*

      For TIRF-M experiments, cells were adhered to glass-bottomed microwell dishes coated with fibronectin, fibronectin + CXCL12, fibronectin + X4-gp120, or fibronectin + VLPs. As for CXCL12 and X4-gp120, the VLPs were attached to fibronectin taking advantage of electrostatic interactions. To clarify the integration of the VLPs in these assays, we have stained the microwell dishes coated with fibronectin and those coated with fibronectin + VLPs with wheat germ agglutinin (WGA) coupled to Alexa647 (Figure III, only for review purposes) and evaluated the staining by confocal microscopy. These results indicate the presence of carbohydrates on the VLPs and are, therefore, indicative of the presence of VLPs on the fibronectin layer.

      Moreover, it is important to remark that the effect of the VLPs on CXCR4 behavior at the cell surface observed by TIRF-M confirmed that the VLPs remained attached to the substrate during the experiment.

      • Fig 1A- The classification of particle tracks into mobile and immobile is overly simplistic description that goes back to bulk FRAP measurements and it not really applicable to single molecule tracking data, where it's rare to see anything that is immobile and alive. An alternative classification strategy uses sub-diffusion, normal diffusion and active diffusion (or active transport) to descriptions and particles can transition between these classes over the tracking period. Fig 1B- this data might be better displayed as histograms showing distributions within the different movement classes.*

      In agreement with the reviewer's commentary, the majority of the particles detected in our TIRF-M experiments were indeed mobile. However, we also detected a variable, and biologically appreciable, percentage of immobile particles depending on the experimental condition analyzed (Figure 1A in the main manuscript). To establish a stringent threshold for identifying these immobile particles under our specific experimental conditions, we used purified monomeric AcGFP proteins immobilized on glass coverslips. Our analysis demonstrated that 95% of these immobilized proteins showed a diffusion coefficient £0.0015 mm2/s; consequently, this value was established as the cutoff to distinguish immobile from mobile trajectories. While the observation of truly immobile entities in a dynamic, living system is rare, the presence of these particles under our conditions is biologically significant. For instance, the detection of large, immobile receptor nanoclusters at the plasma membrane is entirely consistent with facilitating key cellular processes, such as enabling the robust signaling cascade triggered by ligand binding or promoting the crucial events required for efficient viral entry into the cells.

      Regarding the mobile receptors (defined as those with D1-4 values exceeding 0.0015 mm2/s), we observed distinct diffusion profiles derived from mean square displacement (MSD) plots (Figure V) (Manzo & García-Parajo Rep. Prog. Phys., 2015), which were further classified based on motion, using the moment scaling spectrum (MSS) (Ewers et al. PNAS, 2005). Under all experimental conditions, the majority of mobile particles, ∼85%, showed confined diffusion: for example under basal conditions, without ligand addition, ∼90% of mobile particles showed confined diffusion, ∼8.5% showed Brownian-free diffusion and ∼1.5% exhibited directed motion (new Supplementary Figure 5A in the main manuscript). These data have been also included in the revised manuscript to show, in detail, the dynamic parameters of CXCR4.

      Due to the space constraints, it is very difficult to include all the figures generated. However, to ensure comprehensive assessment and transparency (for the purpose of this review), we have included below representative plots of the MSD values as a function of time from individual trajectories, showing different types of motion obtained in our experiments (Figure IV, only for review purposes).

      • Fig 1C,D- It would be helpful to see a plot of D vs MSI at a single particle level. In comparing C and D I'm surprised there is not a larger difference between CXCL12 and X4-gp120. It would also be very important to see the behaviour of X4-gp120 on the CXCR4 deficient Jurkat that would provide a picture of CD4 diffusion. The CXCR4 nanoclustering related to the X4-gp120 could be dominated by CD4 behaviour.*

      As previously described, all analyses were performed under SPT conditions (see previous response to point 1 in this reply). Figure 1C details the percentage of oligomers (>3 receptors/particle) calibrated using Jurkat CD4+ cells electroporated with monomeric CD86-AcGFP (Dorsch et al. Nat. Methods, 2009). The monomer value was determined by analyzing photobleaching steps as described in our previous response to point 1.

      In our experiments, we observed a trend towards a higher number of oligomers upon activation with CXCL12 compared with X4-gp120. This trend was further supported by measurements of Mean Spot Intensity. However, the values are also influenced by the number of larger spots, which represents a minor fraction of the total spots detected.

      The differences between the effect triggered by CXCL12 or X4-gp120 might also be attributed to a combination of factors related to differences in ligand concentration, their structure, and even to the technical requirements of TIRF-M. Both ligands are in contact with the substrate (fibronectin) and the specific nature of this interaction may differ between both ligands and influence their accessibility to CXCR4. Moreover, the requirement of the prior binding of gp120 to CD4 before CXCR4 engagement, in contrast to the direct binding of CXCL12 to CXCR4, might also contribute to the differences observed.

      We previously reported that CXCL12-mediated CXCR4 dynamics are modulated by CD4 co-expression (Martinez-Muñoz et al. Mol. Cell, 2018). We have now detected the formation of CD4 heterodimers with both CXCR4 and CXCR4R334X, and found that these conformations are influenced by gp120-VLPs. In the present manuscript, we did not focus on CD4 clustering as it has been extensively characterized previously (Barrero-Villar et al. J. Cell Sci., 2009; Jiménez-Baranda et al. Nat. Cell. Biol., 2007; Yuan et al. Viruses, 2021). Regarding the investigation of the effects of X4-gp120 on CXCR4-deficient Jurkat cells, which would provide a picture of CD4 diffusion, we would note that a previous report has already addressed this issue using single-molecule super-resolution imaging, and revealed that CD4 molecules on the cell membrane are predominantly found as individual molecules or small clusters of up to 4 molecules, and that the size and number of these clusters increases upon virus binding or gp120 activation (Yuan et al. Viruses, 2021).

      • Fig S1D- This data is really interesting. However, if both the CD4 and the gp120 have his tags they need to be careful as poly-His tags can bind weakly to cells and increasing valency could generate some background. So, they should make the control is fair here. Ideally, using non-his tagged person of sCD4 and gp120 would be needed ideal or they need a His-tagged Fab binding to gp120 that doesn't induce CXCR4 binding.*

      New Supplementary Figure 2D shows that X4-gp120 does not bind Daudi cells (these cells do not express CD4) in the absence of soluble CD4. While the reviewer is correct to state that both proteins contain a Histidine Tag, cell binding is only detected if X4-gp120 binds sCD4. Nonetheless, we have included in the revised Supplementary Figure 2D a control showing the negative binding of sCD4 to Daudi cells in the absence of X4-gp120. Altogether, these results confirm that only sCD4/X4-gp120 complexes bind these cells.

      • Fig S4- Panel D needs a scale bar. I can't figure out what I'm being shown without this.*

      Apologies. A scale bar has been included in this panel (new Supplementary Figure 6D).

      Reviewer #2

      • This study is well described in both the main text and figures. Introduction provides adequate background and cites the literature appropriately. Materials and Methods are detailed. Authors are careful in their interpretations, statistical comparisons, and include necessary controls in each experiment. The Discussion presents a reasonable interpretation of the results. Overall, there are no major weaknesses with this manuscript.*

      We very much appreciate the positive comments of the reviewer regarding the broad interest and strength of our work.

      • NL4-3deltaIN and immature HIV virions are found to have less associated gp120 relative to wild-type particles. It is not obvious why this is the case for the deltaIN particles or genetically immature particles. Can the authors provide possible explanations? (A prior paper was cited, Chojnacki et al Science, 2012 but can the current authors provide their own interpretation.)*

      Our conclusion from the data is actually exactly the opposite. As shown in Figure 2D, the gp120 staining intensity was higher for NL4-3DIN particles (1,786 a.u.) than for gp120-VLPs (1,223 a.u.), indicating lower expression of Env proteins in the latter. Furthermore, analysis of gp120 intensity per particle (Figure 2E) confirmed that gp120-VLPs contained fewer gp120 molecules per particle than NL4-3DIN virions. These levels were comparable with, or even lower than, those observed in primary HIV-1 viruses (Zhu et al. Nature, 2006). This reduction was a direct consequence of the method used to generate the VLPs, as our goal was to produce viral particles with minimal gp120 content to prevent artifacts in receptor clustering that might occur using high levels of Env proteins in the VLPs to activate the receptors.

      This misunderstanding may arise from the fact that we also compared Gag condensation and Env distribution on the surface of gp120-VLPs with those observed in genetically immature particles and integrase-defective NL4-3ΔIN virions, which served as controls. STED microscopy data revealed differences in Env distribution between gp120-VLPs and NL4-3ΔIN virions, supporting the classification of gp120-VLPs as mature particles (Figure 2 A,B).

      Reviewer #3

      We thank the reviewer for considering that our work offers new insights into the spatial organization of receptors during HIV-1 entry and infection and that the manuscript is well written, and the findings significant.

      • For mechanistic basis of gp120-CXCR4 versus CXCL12-CXCR4 differences. Provide additional structural or biochemical evidence to support the claim that gp120 stabilises a distinct CXCR4 conformation compared to CXCL12. If feasible, include molecular modelling, mutagenesis, or cross-linking experiments to corroborate the proposed conformational differences.*

      We appreciate the opportunity to clarify this point. The specific claim that gp120 stabilizes a conformation of CXCR4 that is distinct from the CXCL12-bound state was not explicitly stated in our manuscript, although we agree that our data strongly support this possibility. It is important to consider that CXCL12 binds directly to CXCR4, whereas gp120 requires prior sequential binding to CD4, and its subsequent interaction is with a CXCR4 molecule that is already forming part of the CD4/CXCR4 complex, as demonstrated by our FRET experiments and supported by previous studies (Zaitseva et al. J. Leuk. Biol., 2005; Busillo & Benovic Biochim. Biophys. Acta, 2007; Martínez-Muñoz et al. PNAS, 2014). This difference makes it inherently complex to compare the conformational changes induced by gp120 and CXCL12 on CXCR4.

      However, our findings show that both stimuli induce oligomerization of CXCR4, a phenomenon not observed when mutant CXCR4R334X was exposed to the chemokine CXCL12 (García-Cuesta et al. PNAS, 2022).

      1. CXCL12 induced oligomerization of CXCR4 but did not affect the dynamics of CXCR4R334X (Martinez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022). By contrast, X4-gp120 and the corresponding VLPs-which require initial binding to CD4 to engage the chemokine receptor-stabilized oligomers of both CXCR4 and CXCR4R334X.

      FRET analysis revealed distinct FRET50 values for CD4/CXCR4 (2.713) and CD4/CXCR4R334X (0.399) complexes, suggesting different conformations for each complex. Consistent with previous reports (Balabanian et al. Blood, 2005; Zmajkovicova et al. Front. Immunol., 2024; García-Cuesta et al. PNAS, 2022), the molecular mechanisms activated by CXCL12 are distinct when comparing CXCR4 with CXCR4R334X. For instance, CXCL12 induces internalization of CXCR4, but not of mutant CXCR4R334X. Conversely, X4-gp120 triggers approximately 25% internalization of both receptors. Similarly, CXCL12 does not promote CD4 internalization in cells co-expressing CXCR4 or CXCR4R334X, whereas X4-gp120 does, although CD4 internalization was significantly higher in cells co-expressing CXCR4.

      These findings suggest that CD4 influences the conformation and the oligomerization state of both co-receptors. To further support this hypothesis, we have conducted new in silico molecular modeling of CD4 in complex with either CXCR4 or its mutant CXCR4R334X using AlphaFold 3.0 (Abramson et al. Nature, 2024). The server was provided with both sequences, and the interaction between the two molecules for each protein was requested. It produced a number of solutions, which were then analyzed using the software ChimeraX 1.10 (Meng et al. Protein Sci., 2023). CXCR4 and its mutant, CXCR4R334X bound to CD4, were superposed using one of the CD4 molecules from each complex, with the aim of comparing the spatial positioning of CD4 molecules when interacting with CXCR4.

      As illustrated in Figure V (only for review purposes), the superposition of the CD4/CXCR4 complexes was complete. However, when CD4/CXCR4 complexes were superimposed with CD4/CXCR4R334X complexes using the same CD4 molecule as a reference, indicated by an arrow in the figure, a clear structural deviation became evident. The main structural difference detected was the positioning of the CD4 transmembrane domains when interacting with either the wild-type or mutant CXCR4. While in complexes with CXCR4, the angle formed by the lines connecting residues E416 at the C-terminus end of CD4 with N196 in CXCR4 was 12{degree sign}, for the CXCR4R334X complex, this angle increased to 24{degree sign}, resulting in a distinct orientation of the CD4 extracellular domain (Figure VI, only for review purposes).

      To further analyze the models obtained, we employed PDBsum software (Laskowski & Thornton Protein Sci., 2021) to predict the CD4/CXCR4 interface residues. Data indicated that at least 50% of the interaction residues differed when the CD4/CXCR4 interaction surface was compared with that of the CD4/CXCR4R334X complex (Figure VII, only for review purposes). It is important to note that while some hydrogen bonds were present in both complex models, others were exclusive to one of them. For instance, whereas Cys394(CD4)-Tyr139 and Lys299(CD4)-Glu272 were present in both CD4/CXCR4 and CD4/CXCR4R334X complexes, the pairs Asn337(CD4)-Ser27(CXCR4R334X) and Lys325(CD4)-Asp26(CXCR4R334X) were only found in CD4/CXCR4R334X complexes.

      These findings, which are consistent with our FRET results, suggest distinct interaction surfaces between CD4 and the two chemokine receptors. Overall, these results are compatible with differences in the spatial conformation adopted by these complexes.

      • For Empty VLP effects on CXCR4 dynamics: Explore potential causes for the observed effects of Env-deficient VLPs. It's valuable to include additional controls such as particles from non-producer cells, lipid composition analysis, or blocking experiments to assess nonspecific interactions. *

      As VLPs are complex entities, we thought that the relevant results should be obtained comparing the effects of Env(-) VLPs with gp120-VLPs. Therefore, we would first remark that regardless of the effect of Env(-) VLPs on CXCR4 dynamics, the most evident finding in this study is the strong effect of gp120-VLPs compared with control Env(-) VLPs. Nevertheless, regarding the effect of the Env(-) VLPs compared with medium, we propose several hypotheses. As several virions can be tethered to the cell surface via glycosaminoglycans (GAGs), we hypothesized that VLPs-GAGs interactions might indirectly influence the dynamics of CXCR4 and CXCR4R334X at the plasma membrane. Additionally, membrane fluidity is essential for receptor dynamics, therefore VLPs interactions with proteins, lipids or any other component of the cell membrane could also alter receptor behavior. It is well known that lipid rafts participate in the interaction of different viruses with target cells (Nayak & Hu Subcell. Biochem., 2004; Manes et al. Nat. Rev. Immunol., 2003; Rioethmullwer et al. Biochim. Biophys. Acta, 2006) and both the lipid composition and the presence of co-expressed proteins modulate ligand-mediated receptor oligomerization (Gardeta et al. Frontiers in Immunol., 2022; Gardeta et al. Cell. Commun. Signal., 2025). We have thus performed Raster Image Correlation Spectroscopy (RICS) analysis to assess membrane fluidity through membrane diffusion measurements on cells treated with Env(-) VLPs.

      Jurkat cells were labeled with Di-4-ANEPPDHG and seeded on FN and on FN + VLPs prior to analysis by RICS on confocal microscopy. The results indicated no significant differences in membrane diffusion under the treatment tested, thereby discarding an effect of VLPs on overall membrane fluidity (Figure VIII, only for review purposes).

      Nonetheless, these results do not rule out other non-specific interactions of Env(-) VLPs with membrane proteins that could affect receptor dynamics. For instance, it has been reported that C-type lectin DC-SIGN acts as an efficient docking site for HIV-1 (Cambi et al. J. Cell. Biol., 2004; Wu & KewalRamani Nat. Rev. Immunol., 2006). However, a detailed investigation of these possible mechanisms is beyond the scope of this manuscript.

      • For Direct link between clustering and infection efficiency - Test whether disruption of CXCR4 clustering (e.g., using actin cytoskeleton inhibitors, membrane lipid perturbants, or clustering-deficient mutants) alters HIV-1 fusion or infection efficiency*.

      Designing experiments using tools that disrupt receptor clustering by interacting with the receptors themselves is difficult and challenging, as these tools bind the receptor and can therefore alter parameters such as its conformation and/or its distribution at the cell membrane, as well as affect some cellular processes such as HIV-1 attachment and cell entry. Moreover, effects on actin polymerization or lipids dynamics can affect not only receptor clustering but also impact on other molecular mechanisms essential for efficient infection.

      Many previous reports have, nonetheless, indirectly correlated receptor clustering with cell infection efficiency. Cholesterol plays a key role in the entry of several viruses. Its depletion in primary cells and cell lines has been shown to confer strong resistance to HIV-1-mediated syncytium formation and infection by both CXCR4- and CCR5-tropic viruses (Liao et al. AIDS Res. Hum. Retrovisruses, 2021). Moderate cholesterol depletion also reduces CXCL12-induced CXCR4 oligomerization and alters receptor dynamics (Gardeta et al. Cell. Commun. Signal., 2025). By restricting the lateral diffusion of CD4, sphingomyelinase treatment inhibits HIV-1 fusion (Finnegan et al. J. Virol., 2007). Depletion of sphingomyelins also disrupts CXCL12-mediated CXCR4 oligomerization and its lateral diffusion (Gardeta et al. Front Immunol., 2022). Additional reports highlight the role of actin polymerization at the viral entry site, which facilitates clustering of HIV-1 receptors, a crucial step for membrane fusion (Serrano et al. Biol. Cell., 2023). Blockade of actin dynamics by Latrunculin A treatment, a drug that sequesters actin monomers and prevents its polymerization, blocks CXCL12-induced CXCR4 dynamics and oligomerization (Martínez-Muñoz et al. Mol. Cell, 2018).

      Altogether, these findings strongly support our hypothesis of a direct link between CXCR4 clustering and the efficiency of HIV-1 infection.

      • CD4/CXCR4 co-endocytosis hypothesis - Support the proposed model with direct evidence from live-cell imaging or co-localization experiments during viral entry. Clarification is needed on whether internalization is simultaneous or sequential for CD4 and CXCR4.*

      When referring to endocytosis of CD4 and CXCR4, we only hypothesized that HIV-1 might promote the internalization of both receptors either sequentially or simultaneously. The hypothesis was based in several findings:

      1) Previous studies have suggested that HIV-1 glycoproteins can reduce CD4 and CXCR4 levels during HIV-1 entry (Choi et al. Virol. J., 2008; Geleziunas et al. FASEB J, 1994; Hubert et al. Eur. J. Immunol., 1995).

      2) Receptor endocytosis has been proposed as a mechanism for HIV-1 entry (Daecke et al. J. Virol., 2005; Aggarwal et al.Traffick, 2017; Miyauchi et al. Cell, 2009; Carter et al. Virology, 2011).

      3) Our data from cells activated with X4-gp120 demonstrated internalization of CD4 and chemokine receptors, which correlated with HIV-1 infection in PBMCs from WHIM patients and healthy donors.

      4) CD4 and CXCR4 have been shown to co-localize in lipid rafts during HIV-1 infection (Manes et al. EMBO Rep., 2000; Popik et al. J. Virol., 2002)

      5) Our FRET data demonstrated that CD4 and CXCR4 form heterocomplexes and that FRET efficiency increased after gp120-VLPs treatment.

      We agree with the reviewer that further experiments are required to test this hypothesis, however, we believe that this is beyond the scope of the current manuscript.

      Minor Comments:

      • The conclusions rely solely on the HXB2 X4-tropic Env. It would strengthen the study to assess whether other X4 or dual-tropic strains induce similar receptor clustering and dynamics.*

      The primary goal of our current study was to investigate the dynamics of the co-receptor CXCR4 during HIV-1 infection, motivated by previous reports showing CD4 oligomerization upon HIV-1 binding and gp120 stimulation (Yuan et al.Viruses, 2021). We initially used a recombinant X4-gp120, a soluble protein that does not fully replicate the functional properties of the native HIV-1 Env. Previous studies have shown that Env consists of gp120 trimers, which redistribute and cluster on the surface of virions following proteolytic Gag cleavage during maturation (Chojnacki et al. Nat. Commun., 2017). An important consideration in receptor oligomerization studies is the concentration of recombinant gp120 used, as it does not accurately reflect the low number of Env trimers present on native HIV-1 particles (Hart et al. J. Histochem. Cytochem., 1993; Zhu et al. Nature, 2006). To address these limitations, we generated virus-like particles (VLPs) containing low levels of X4-gp120 and repeated the dynamic analysis of CXCR4. The use of primary HIV-1 isolates was limited, in this project, to confirm that PBMCs from both healthy donors and WHIM patients were equally susceptible to infection. This result using a primary HIV-1 virus supports the conclusion drawn from our in vitroapproaches. We thus believe that although the use of other X4- and dual-tropic strains may complement and reinforce the analysis, it is far beyond the scope of the current manuscript.

      • Given the observed clustering effects, it would be valuable to explore whether gp120-induced rearrangements alter epitope exposure to broadly neutralizing antibodies like 17b or 3BNC117. This would help connect the mechanistic insights to therapeutic relevance.*

      As 3BNC117, VRC01 and b12 are broadly neutralizing mAbs that recognize conformational epitopes on gp120 (Li et al. J. Virol., 2011; Mata-Fink et al. J. Mol. Biol., 2013), they will struggle to bind the gp120/CD4/CXCR4 complex and therefore may not be ideal for detecting changes within the CD4/CXCR4 complex. The experiment suggested by the reviewer is thus challenging but also very complex. It would require evaluating antibody binding in two experimental conditions, in the absence and in the presence of oligomers. However, our data indicate that receptor oligomerization is promoted by X4-gp120 binding, and the selected antibodies are neutralizing mAbs, so they should block or hinder the binding of gp120 and, consequently, receptor oligomerization. An alternative approach would be to study the neutralizing capacity of these mAbs on cells expressing CD4/CXCR4 or CD4/CXCR4R334X complexes. Variations in their neutralizing activity could be then extrapolated to distinct gp120 conformations, which in turn may reflect differences between CD4/CXCR4 and CD4/CXCR4R334X complexes.

      We thus assessed the ability of the VRC01 and b12, anti-gp120 mAbs, which were available in our laboratory, to neutralize gp120 binding on cells expressing CD4/CXCR4 or CD4/CXCR4R334X. Specifically, increasing concentrations of each antibody were preincubated (60 min, 37ºC) with a fixed amount of X4-gp120 (0.05 mg/ml). The resulting complexes were then incubated with Jurkat cells expressing CD4/CXCR4 or CD4/CXCR4R334X (30 min, 37ºC) and, finally, their binding was analyzed by flow cytometry. Although we did not observe statistically significant differences in the neutralization capacity of b12 or VRC01 for the binding of X4-gp120 depending on the presence of CXCR4 or CXCR4334X, we observed a trend for greater concentrations of both mAbs to neutralize X4-gp120 binding in Jurkat CD4/CXCR4 cells than in Jurkat CD4/CXCR4R334X cells (Figure IX, only for review purposes).

      These slight alterations in the neutralizing capacity of b12 and VRC01 mAbs may thus suggest minimal differences in the conformations of gp120 depending of the coreceptor used. We also detected that X4-gp120 and VLPs expressing gp120, which require initial binding to CD4 to engage the chemokine receptor, stabilized oligomers of both CXCR4 and CXCR4R334X, but FRET data indicated distinct FRET50 values between the partners, (2.713) for CD4/CXCR4 and (0.399) for CD4/CXCR4R334X (Figure 5A,B in the main manuscript). Moreover, we also detected significantly more CD4 internalization mediated by X4-gp120 in cells co-expressing CD4 and CXCR4 than in those co-expressing CD4 and CXCR4R334X (Figure 6 in the main manuscript). Overall these latter data and those included in Figures V, VI and VII of this reply, indicate distinct conformations within each receptor complexes.

      • TIRF imaging limits analysis to the cell substrate interface. It would be useful to clarify whether CXCR4 receptor clustering occurs elsewhere, such as at immunological synapses or during cell-to-cell contact.*

      In recent years, chemokine receptor oligomerization has gained significant research interest due to its role in modulating the ability of cells to sense chemoattractant gradients. This molecular organization is now recognized as a critical factor in governing directed cell migration (Martínez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022, Hauser et al.Immunity, 2016). In addition, advanced imaging techniques such as single-molecule and super-resolution microscopy have been used to investigate the spatial distribution and dynamic behaviour of CXCR4 within the immunological synapse in T cells (Felce et al. Front. Cell Dev. Biol., 2020). Building on these findings, we are currently conducting a project focused on characterizing CXCR4 clustering specifically within this specialized cellular region.

      • In LVP experiments, it would be useful to report transduction efficiency (% GFP+ cells) alongside MSI data to relate VLP infectivity with receptor clustering functionally.*

      These experiments were designed to validate the functional integrity of the gp120 conformation on the LVPs, confirming their suitability for subsequent TIRF microscopy. Our objective was to establish a robust experimental tool rather than to perform a high-throughput quantification of transduction efficiency. It is for that reason that these experiments were included in new Supplementary Figure S6, which also contains the complete characterization of gp120-VLPs and LVPs. In such experimental conditions, quantifying the percentage of GFP-positive cells relative to the total number of cells plated in each well is very difficult. However, in line with the reviewer's commentary and as we used the same number of cells in each experimental condition, we have included, in the revised manuscript, a complementary graph illustrating the GFP intensity (arbitrary units) detected in all the wells analyzed (new Supplementary Fig. 6E).

      • To ensure that differences in fusion events (Figure 7B) are attributable to target cell receptor properties, consider confirming that effector cells express similar levels of HIV-1 Env. Quantifying gp120 expression by flow cytometry or western blot would rule out the confounding effects of variable Env surface density.*

      In these assays (Figure 7B), we used the same effector cells (cells expressing X4-gp120) in both experimental conditions, ensuring that any observed differences should be attributable solely to the target cells, either JKCD4X4 or JKCD4X4R334X. For this reason, in Figure 7A we included only the binding of X4-gp120 to the target cells which demonstrated similar levels of the receptors expressed by the cells.

      • HIV-mediated receptor downregulation may occur more slowly than ligand-induced internalization. Including a 24-hour time point would help assess whether gp120 induces delayed CD4 or CXCR4 loss beyond the early effects shown and to better capture potential delayed downregulation induced by gp120.*

      The reviewer suggests using a 24-hour time point to facilitate detection of receptor internalization. However, such an extended incubation time may introduce some confounding factors, including receptor degradation, recycling and even de novo synthesis, which could affect the interpretation of the results. Under our experimental conditions, we observed that CXCL12 did not trigger CD4 internalization whereas X4-gp120 did. Interestingly, CD4 internalization depended on the co-receptor expressed by the cells.

      • Increase label font size in microscopy panels for improved readability.*

      Of course; the font size of these panels has been increased in the revised version.

      • Consider adding more references on ligand-induced co-endocytosis of CD4 and chemokine receptors during HIV-1 entry.*

      We have added more references to support this hypothesis (Toyoda et al. J. Virol., 2015; Venzke et al. J. Virol., 2006; Gobeil et al J. Virol., 2013).

      • For Statistical analysis. Biological replicates are adequate, and statistical tests are generally appropriate. For transparency, report n values, exact p-values, and the statistical test used in every figure legend and discussed in the results.*

      Thank you for highlighting the importance of transparency in statistical reporting. We confirm that the n values for all experiments have been included in the figure legends. The statistical tests used for each analysis are also clearly indicated in the figure legends, and the interpretation of these results is discussed in detail in the Results section. Furthermore, the Methods section specifies the tests applied and the thresholds for significance, ensuring full transparency regarding our analytical approach.

      In accordance with established conventions in the field, we have utilized categorical significance indicators (e.g., n.s., *, **, ***) within our figures to enhance readability and focus on biological trends. This approach is widely adopted in high-impact literature to prevent visual clutter. However, to ensure full transparency and reproducibility, we have ensured that the underlying statistical tests and thresholds are clearly defined in the respective figure legends and Methods section.

      Reviewer #4

      We thank the reviewer for considering that this work is presented in a clear fashion, and the main findings are properly highlighted, and for remarking that the paper is of interest to the retrovirology community and possibly to the broader virology community.

      We also agree on the interest that X4-gp120 clusters CXCR4R334X suggests a different binding mechanism for X4-gp120 from that of the natural ligand CXCL12, an aspect that we are now evaluating. These data also indicate that WHIM patients can be infected by HIV-1 similarly to healthy people.

      • The observation that "empty VLPs" reduce CXCR4 diffusivity is potentially interesting. However, it is not supported by the data owing to insufficient controls. The authors correctly discuss the limitations of that observation in the Discussion section (lines 702-704). However, they overinterpret the observation in the Results section (lines 509-512), suggesting non-specific interactions between empty VLPs, CD4 and CXCR4. I suggest either removing the sentence from the Results section or replacing it with a sentence similar to the one in the Discussion section.*

      In accordance with the reviewer`s suggestion, the sentence in the result section has been replaced with one similar to that found in the discussion section. In addition, we have performed Raster Image Correlation Spectroscopy (RICS) analysis using the Di-4-ANEPPDHQ lipid probe to assess membrane fluidity by means of membrane diffusion, and compared the results with those of cells treated with Env(-) VLPs. The results indicated that VLPs did not modulate membrane fluidity (Figure VIII in this reply). Nonetheless, these results do not rule out other potential non-specific interactions of the Env(-) VLPs with other components of the cell membrane that might affect receptor dynamics (see our response to point 2 of reviewer #3 p. 14-15 of this reply).

      • In the case of the WHIM mutant CXCR4-R334X, the addition of "empty VLPs" did not cause a significant change in the diffusivity of CXCR4-R334X (Figure 4B). This result is in contrast with the addition of empty VLPs to WT CXCR4. However, the authors neither mention nor comment on that result in the results section. Please mention the result in the paper and comment on it in relation to the addition of empty VLPs to WT CXCR4.*

      We would remark that the main observation in these experiments should focus on the effect of gp120-VLPs, and the results indicates that gp120-VLPs promoted clustering of CXCR4 and of CXCR4R334X and reduced their diffusion at the cell membrane. The Env(- ) VLPs were included as a negative control in the experiments, to compare the data with those obtained using gp120-VLPs. However, once we observed some residual effect of the Env(-) VLPs, we decided to give a potential explanation, formulated as a hypothesis, that the Env(-) VLPs modulated membrane fluidity. We have now performed a RICS analysis using Di-4-ANEPPDHQ as a lipid probe (Figure IX only for review purposes). The results suggest that Env(-) VLPs do not modulate cell membrane fluidity, although we do not rule out other potential interactions with membrane proteins that might alter receptor dynamics. We appreciate the reviewer's observation and agree that this result can be noted. However, since the main purpose of Figure 4B is to show that gp120-VLPs modulate the dynamics of CXCR4R334X rather than to remark that the Env(-) VLPs also have some effects, we consider that a detailed discussion of this specific aspect would detract from the central finding and may dilute the primary narrative of the study.

      Minor comments

      • It would be helpful for the reader to combine thematically or experimentally linked figures, e.g., Figures 3 and 4.*

      • Figures 3 and 4 are very similar. Please unify the colours in them and the order of the panels (e.g. Figure 3 panel A shows diffusivity of CXCR4, while Figure 4 panel A shows MSI of CXCR4-R334X).*

      While we considered consolidating Figures 3 and 4, we believe that maintaining them as separate entities enhances conceptual clarity. Since Figure 3 establishes the baseline dynamics for wild-type CXCR4 and Figure 4 details the distinct behavior of the CXCR4R334X mutant, keeping them separate allows the reader to fully appreciate the specificities of each system before making a cross-comparison.

      • Some parts of the Discussion section could be shortened, moved to the Introduction (e.g.,lines 648-651), or entirely removed (e.g.,lines 633-635 about GPCRs).*

      In accordance, the Discussion section has been reorganized and shortened to improve clarity.

      • I suggest renaming "empty VLPs" to "Env(−) VLPs" (or similar). The name empty VLPs can mislead the reader into thinking that these are empty vesicles.*

      The term empty VLPs has been renamed to Env(−) VLPs throughout the manuscript to more accurately reflect their composition. Many thanks for this suggestion.

      • Line 492 - please rephrase "...lower expression of Env..." to "...lower expression of Env or its incorporation into the VLPs...".*

      The sentence has been rephrased

      • Line 527 - The data on CXCL12 modulating CXCR4-R334X dynamics and clustering are not present in Figure 4 (or any other Figure). Please add them or rephrase the sentence with an appropriate reference. Make clear which results are yours.*

      • Line 532 - Do the data in the paper really support a model in which CXCL12 binds to CXCR4-R334X? If not, please rephrase with an appropriate reference.*

      Previous studies support the association of CXCL12 with CXCR4R334X (Balabanian et al. Blood, 2005; Hernandez et al. Nat Genet., 2003; Busillo & Benovic Biochim. Biophys. Acta, 2007). In fact, this receptor has been characterized as a gain-of-function variant for this ligand (McDermott et al. J. Cell. Mol. Med., 2011). The revised manuscript now includes these bibliographic references to support this commentary. In any case, our previous data indicate that CXCL12 binding does not affect CXCR4R334X dynamics (García-Cuesta et al. PNAS, 2022).

      • Line 695 - "...lipid rafts during HIV-1 (missing word?) and their ability to..." During what?*

      Many thanks for catching this mistake. The sentence now reads: "Although direct evidence for the internalization of CD4 and CXCR4 as complexes is lacking, their co-localization in lipid rafts during HIV-1 infection (97-99) and their ability to form heterocomplexes (22) strongly suggest they could be endocytosed together."

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      Referee #4

      Evidence, reproducibility and clarity

      This paper provides new insights into the organisational changes of the X4-tropic HIV-1 co-receptor CXCR4 upon binding of the viral receptor-binding protein X4-gp120, either in its soluble form or when displayed on virus-like particles (VLPs) as Env. The study employs single-particle tracking total internal reflection fluorescence (SPT-TIRF) microscopy to quantify the dynamics and clustering of CXCR4 on CD4+ T cells. The data show that CXCR4 clusters in the presence of X4-gp120 and VLPs, a phenomenon also observed for the primary HIV-1 receptor CD4. The authors also show that a WHIM mutant of CXCR4 (CXCR4-R334X) that does not cluster in the presence of its natural ligand, CXCL12, clusters in the presence of X4-gp120 and VLPs.

      The following points should be clarified or improved prior to publication:

      Major comments:

      1. The observation that "empty VLPs" reduce CXCR4 diffusivity is potentially interesting. However, it is not supported by the data owing to insufficient controls. The authors correctly discuss the limitations of that observation in the Discussion section (lines 702-704). However, they overinterpret the observation in the Results section (lines 509-512), suggesting non-specific interactions between empty VLPs, CD4 and CXCR4. I suggest either removing the sentence from the Results section or replacing it with a sentence similar to the one in the Discussion section.
      2. In the case of the WHIM mutant CXCR4-R334X, the addition of "empty VLPs" did not cause a significant change in the diffusivity of CXCR4-R334X (Figure 4B). This result is in contrast with the addition of empty VLPs to WT CXCR4. However, the authors neither mention nor comment on that result in the results section. Please mention the result in the paper and comment on it in relation to the addition of empty VLPs to WT CXCR4.

      Minor comments:

      1. It would be helpful for the reader to combine thematically or experimentally linked figures, e.g., Figures 3 and 4.
      2. Figures 3 and 4 are very similar. Please unify the colours in them and the order of the panels (e.g. Figure 3 panel A shows diffusivity of CXCR4, while Figure 4 panel A shows MSI of CXCR4-R334X).
      3. Some parts of the Discussion section could be shortened, moved to the Introduction (e.g., lines 648-651), or entirely removed (e.g., lines 633-635 about GPCRs).
      4. I suggest renaming "empty VLPs" to "Env(−) VLPs" (or similar). The name empty VLPs can mislead the reader into thinking that these are empty vesicles.
      5. Line 492 - please rephrase "...lower expression of Env..." to "...lower expression of Env or its incorporation into the VLPs...".
      6. Line 527 - The data on CXCL12 modulating CXCR4-R334X dynamics and clustering are not present in Figure 4 (or any other Figure). Please add them or rephrase the sentence with an appropriate reference. Make clear which results are yours.
      7. Line 532 - Do the data in the paper really support a model in which CXCL12 binds to CXCR4-R334X? If not, please rephrase with an appropriate reference.
      8. Line 695 - "...lipid rafts during HIV-1 (missing word?) and their ability to..." During what?

      Significance

      In summary, the work is presented in a clear fashion, and the main findings are properly highlighted. The paper is of interest to the retrovirology community and possibly to the broader virology community. The findings are not entirely surprising because it has been shown previously that the binding of Env to CD4 mediates CD4 clustering, which would also suggest clustering of the co-receptor. Nonetheless, the paper provides strong evidence that CXCR4 clusters and changes its dynamics in the presence of CD4 and X4-gp120. Moreover, the evidence that X4-gp120 clusters CXCR4-R334X is of high interest because it suggests a different binding mechanism for X4-gp120 from that of the natural ligand CXCL12, raising questions for further research. The diffusivity data with empty VLPs require additional controls to strengthen the evidence. My expertise is in virology and structural biology. I did not comment on the technical aspects of the light-microscopy experiments in the study because these are beyond my expertise.

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      Referee #3

      Evidence, reproducibility and clarity

      The author investigates how the HIV-1 Env glycoprotein modulates the nanoscale organisation and dynamics of the CXCR4 co-receptor on CD4⁺ T cells. The author demonstrates that HIV-1 Env induces CXCR4 clustering distinct from that triggered by its natural ligand (CXCL12), implicating spatial receptor organization as a determinant of infection. This study investigates how HIV-1 Env (specifically X4-tropic gp120) alters the membrane organization and dynamics of the chemokine receptor CXCR4 and its WHIM-associated mutant, CXCR4R334X, in a CD4-dependent manner. Using single-particle tracking total internal reflection fluorescence microscopy (SPT-TIRF-M), the authors demonstrate that both soluble gp120 and virus-like particles (VLPs) displaying gp120 induce CXCR4 nanoclustering, reduce receptor diffusivity, and promote immobile nanoclusters of CXCR4 at the membrane of Jurkat T cells and primary CD4⁺ T cell blasts.The work offers new insights into the spatial organisation of receptors during HIV-1 entry and infection. The manuscript is well-written, and the findings are significant.

      Major Comments: 1. For mechanistic basis of gp120-CXCR4 versus CXCL12-CXCR4 differences

      Provide additional structural or biochemical evidence to support the claim that gp120 stabilises a distinct CXCR4 conformation compared to CXCL12.

      If feasible, include molecular modelling, mutagenesis, or cross-linking experiments to corroborate the proposed conformational differences. 2. For Empty VLP effects on CXCR4 dynamics

      Explore potential causes for the observed effects of Env-deficient VLPs. It's valuable to include additional controls such as particles from non-producer cells, lipid composition analysis, or blocking experiments to assess nonspecific interactions. 3. For Direct link between clustering and infection efficiency - Test whether disruption of CXCR4 clustering (e.g., using actin cytoskeleton inhibitors, membrane lipid perturbants, or clustering-deficient mutants) alters HIV-1 fusion or infection efficiency. 4. CD4/CXCR4 co-endocytosis hypothesis - Support the proposed model with direct evidence from live-cell imaging or co-localization experiments during viral entry. Clarification is needed on whether internalization is simultaneous or sequential for CD4 and CXCR4.

      Minor Comments: 1. The conclusions rely solely on the HXB2 X4-tropic Env. It would strengthen the study to assess whether other X4 or dual-tropic strains induce similar receptor clustering and dynamics. 2. Given the observed clustering effects, it would be valuable to explore whether gp120-induced rearrangements alter epitope exposure to broadly neutralizing antibodies like 17b or 3BNC117. This would help connect the mechanistic insights to therapeutic relevance. 3 . TIRF imaging limits analysis to the cell substrate interface. It would be useful to clarify whether CXCR4 receptor clustering occurs elsewhere, such as at immunological synapses or during cell-to-cell contact. 4. In LVP experiments, it would be useful to report transduction efficiency (% GFP+ cells) alongside MSI data to relate VLP infectivity with receptor clustering functionally. 5. To ensure that differences in fusion events (Figure 7B) are attributable to target cell receptor properties, consider confirming that effector cells express similar levels of HIV-1 Env. Quantifying gp120 expression by flow cytometry or western blot would rule out the confounding effects of variable Env surface density 6. HIV-mediated receptor downregulation may occur more slowly than ligand-induced internalization. Including a 24-hour time point would help assess whether gp120 induces delayed CD4 or CXCR4 loss beyond the early effects shown and to better capture potential delayed downregulation induced by gp120. 7. Increase label font size in microscopy panels for improved readability. 8. Consider adding more references on ligand-induced co-endocytosis of CD4 and chemokine receptors during HIV-1 entry. For Statistical analysis. Biological replicates are adequate, and statistical tests are generally appropriate. For transparency, report n values, exact p-values, and the statistical test used in every figure legend and discussed in the results.

      Referee cross-commenting

      Overall, the manuscript provides compelling mechanistic insight into HIV-1 entry by demonstrating Env-induced CXCR4 clustering, including in WHIM mutant receptors. While the core findings are well supported and of high interest, clarifications regarding Env trimer densities, receptor internalization, and the contribution of empty VLPs would further strengthen the work.

      Significance

      Nature and significance of the advance

      This work marks a conceptual and mechanistic breakthrough in understanding HIV-1 entry. It goes beyond the static view of Env-co-receptor interaction to show that nanoscale reorganization of CXCR4, distinct from chemokine-induced clustering, occurs during HIV-1 Env engagement and may be essential for infection Context within existing literature. Previous studies established Env-induced CD4 clustering (Yin et al., 2020) and chemokine-induced CXCR4 nanocluster formation (Martínez-Muñoz et al., 2018), but the exact nanoscale rearrangement of CXCR4 in the context of HIV-1 Env and physiological Env densities remains unquantified. This study addresses this gap using SPT-TIRF, STED microscopy, and functional assays.

      Audience and influence

      The findings will be of interest to researchers in HIV virology, membrane receptor biology, viral entry mechanisms, and therapeutic target development. The receptor-clustering aspect could also influence broader fields of study, such as GPCR organization and immune receptor signalling.

      Reviewer expertise

      I can evaluate HIV-1 entry mechanisms, viral glycoprotein-host-host-host receptor interactions, single-molecule fluorescence microscopy, and membrane protein dynamics. I am less equipped to evaluate the deep structural modelling aspects, though the in silico AlphaFold results are straightforward to interpret in context.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors examine the distribution of CXCR4 on the cell surface following exposure to gp120 and HIV virus-like particles (VLPs) using single particle tracking total internal reflection fluorescence (SPT-TIRF) microscopy. They show that gp120 and VLPs promote clustering of wild-type CXCR4 and CXCR4.R334X from a person with WHIM syndrome. The HIV Env-induced clustering involves heterodimeric interactions between CXCR4 and CD4 and spatial distribution and dynamics are distinct from that induced by CXCR4's natural ligand, CXCL12. The authors suggest the CD4-CXCR4 interaction may be targeted to specifically block HIV infection.

      Major comments

      This study is well described in both the main text and figures. Introduction provides adequate background and cites the literature appropriately. Materials and Methods are detailed. Authors are careful in their interpretations, statistical comparisons, and include necessary controls in each experiment. The Discussion presents a reasonable interpretation of the results. Overall, there are no major weaknesses with this manuscript.

      Minor comments

      Ln 477-497. NL4-3deltaIN and immature HIV virions are found to have less associated gp120 relative to wild-type particles. It is not obvious why this is the case for the deltaIN particles or genetically immature particles. Can the authors provide possible explanations? (A prior paper was cited, Chojnacki et al Science, 2012 but can the current authors provide their own interpretation.)

      Significance

      The current study builds on prior works that examined CXCR4 distribution, HIV pseudotyped infection in CXCR4.R334X cells, but goes beyond these studies in resolution and depth of analysis of CXCR4/CD4 nanoclustering, AF3 modeling of CXCR4/CD4 heterodimer, as well as demonstration of replication of HIV in CXCR4.R334X cells.

      Audience:

      Scientists interested in HIV-1, cell biologists and virologists interested in receptor nanoclustering

      Reviewer expertise:

      HIV-1 Envelope glycoproteins and entry assays, HIV broadly neutralizing antibodies, HIV vaccine design

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      Referee #1

      Evidence, reproducibility and clarity

      The authors investigate the impact of surface bound HIV gp120 and VLPs on CXCR4 dynamics in Jurkat T cells expressing WT or WHIM syndrome mutated CXCR4, which has a defective response to CXCL12. Jurkat cells were transfected with CXCR4-AcGFP. Images were acquired and a single particle tracking routine was applied to generate information about nanoclustering and diffusion, and FRET was used to investigate CD4-CXCR4 proximity. They compare effects of soluble gp120 to immature and mature VLPs, which include varying degrees of gp120 clustering. They find that solid phase gp120 or VLP can increase CXCR4 clustering size and decrease diffusion in Jurkat cells. Surprisingly, VLP lacking gp120 could increase CXCR4 clustering and speed, which is paradoxical as there were no known ligands on the VLPs, but they likely carry many cellular proteins with potential interactions. The impact of CXCL12 and gp120 binding to CXCR4 was different in terms of clustering and receptor down-regulation.

      While a single particle tracking routine was applied to the data, it's not clear how the signal from a single GFP was defined and if movement during the 100 ms acquisition time impacts this. My concern would be that the routine is tracking fluctuations, and these are related to single particle dynamics, it appears from the movies that the density or the GFP tagged receptors in the cells is too high to allow clear tracking of single molecules. SPT with GFP is very difficult due to bleaching and relatively low quantum yield. Current efforts in this direction that are more successful include using SNAP tags with very photostable organic fluorophores. The data likely does mean something is happening with the receptor, but they need to be more conservative about the interpretation.

      Some of the paradoxical effects might be better understood through deeper analysis of the SPT data, particularly investigation of active transport and more detailed analysis of "immobile" objects. Comments on early figures illustrate how this could be approached. This would require selecting acquisitions where the GFP density is low enough for SPT and performing a more detailed analysis, but this may be difficult to do with GFP.

      When the authors discuss clusters of <2 or >3, how do they calibrate the value of GFP and the impact of diffusion on the measurement. One way to approach this might be single molecules measurements of dilute samples on glass vs in a supported lipid bilayer to map the streams of true immobility to diffusion at >1 µm2/sec.

      I understand that the CXCL12 or gp120 are attached to the substrate with fibronectin for adhesion. I'm less clear how how that VLPs are integrated. Were these added to cells already attached to FN? Fig 1A- The classification of particle tracks into mobile and immobile is overly simplistic description that goes back to bulk FRAP measurements and it not really applicable to single molecule tracking data, where it's rare to see anything that is immobile and alive. An alternative classification strategy uses sub-diffusion, normal diffusion and active diffusion (or active transport) to descriptions and particles can transition between these classes over the tracking period. Fig 1B- this data might be better displayed as histograms showing distributions within the different movement classes. Fig 1C,D- It would be helpful to see a plot of D vs MSI at a single particle level. In comparing C and D I'm surprised there is not a larger difference between CXCL12 and X4-gp120. It would also be very important to see the behaviour of X4-gp120 on the CXCR4 deficient Jurkat that would provide a picture of CD4 diffusion. The CXCR4 nanoclustering related to the X4-gp120 could be dominated by CD4 behaviour.

      Fig S1D- This data is really interesting. However, if both the CD4 and the gp120 have his tags they need to be careful as poly-His tags can bind weakly to cells and increasing valency could generate some background. So, they should make the control is fair here. Ideally, using non-his tagged person of sCD4 and gp120 would be needed ideal or they need a His-tagged Fab binding to gp120 that doesn't induce CXCR4 binding.

      Fig S4- Panel D needs a scale bar. I can't figure out what I'm being shown without this.

      Significance

      The strengths are that its an important question and the reagents are well prepared and characterised. They are detecting quantitative effects that will likely be reproducible. The information generated is potentially useful for those studying HIV infection processes and strategies to prevent infection.

      The major weakness is that the conditions for the SPT experiments are not ideal in that the density of particles is too high for SPT and the single molecule basis for assessing nanoclusters is not clear. This means that the data is getting at complex molecules phenomena and less likely be generating pure single molecules measurements.

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      Reply to the reviewers

      Manuscript number: RC-2025-03206

      Corresponding author(s____): Teresa M. Przytycka

      General Statements

      We thank all the reviewers for their time and their constructive criticism, based on which we have revised our manuscript. All review comments in are italics. Our responses are indicated in normal font except the excerpts from manuscript which are shown within double quote and in italics. The line numbers indicated here refer to those in the revised manuscript.

      Point-by-point description of the revisions

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

      This paper addresses the interesting question of how cell size may scale with organ size in different tissues. The approach is to mine data from the fly single cell atlas (FCA) which despite its name is a database of gene expression levels in single isolated nuclei. Using this data, they infer cell size based on ribosomal protein gene expression, and based on this approach infer that there are tissue and sex specific differences in scaling, some of which may be driven by differences in ribosomal protein gene expression.

      Response: Indeed, using the FCA dataset, we infer sex-specific differences in both cell size and cell number, which we validated with targeted experiments. We show that Drosophila cell types scale through distinct strategies-via cell size, cell number, or a mix of both-in an allometric rather than uniform fashion. We further propose that these scaling differences are driven, at least in part, by variation in translational activity, reflected in the expression of ribosomal proteins, translation elongation factors, and Myc.

      -----------------------------------------------------------

      I think the idea of mining this database is a clever one, however there a number of concerns about whether the existing data can really be used to draw the conclusions that are stated.

      __Response: __We are pleased to see that the reviewer found the question and our approach interesting.

      -----------------------------------------------------------

      *One concern has to do with the assumption that RP (ribosome protein) expression is a proxy for cell size. It is well established that ribosome abundance scales with cell size, but is there reason to believe that ribosome nuclear gene EXPRESSION correlates with ribosome abundance? *

      I'm not saying that this can't be true, but it seems like a big assumption that needs to be justified with some data. Maybe this is well known in the Drosophila literature, but in that case the relevant literature really needs to be cited.

      __Response: __To avoid any misunderstanding: we use sex-biased RP expression as an indicator of sex differences in cell size only within the same cell type or subtype, as defined by expression-based clustering in the FCA-not as a general estimator of cell size. This measure is applied strictly within the same clusters, never between different ones. To prevent overinterpretation, we replaced the term 'proxy' with 'indicator,' since the earlier wording might have implied that ribosomal gene expression was being used to estimate cell size more broadly.

      We should have begun by providing more background on the well-established link between ribosomal protein gene dosage and cell growth. This context was missing from the introduction, so we have now added a full paragraph outlining what is known about this connection:

      *Added at line 85: *

      "Cell growth, which supports both cell enlargement and cell division, demands elevated protein synthesis, accomplished by boosting translation rates. Indeed, ribosome abundance is known to scale with cell size in many organisms (Schmoller and Skotheim 2015; Cadart and Heald 2022; Serbanescu et al. 2022). Long before it was known that DNA was the carrier of genetic information, Drosophila researchers had identified a large class of mutations known as "Minutes" (Schultz 1929). These were universally haplo-insufficient. A single wild type copy resulted in a tiny slowly growing fly, and the homozygous loss-of-function alleles were lethal. In clones, the Minute cells are clearly smaller and compete poorly with surrounding wild type cells. We now know that most of the Minute loci encode ribosomal proteins (Marygold et al. 2007). Similarly, the Drosophila diminutive locus, also characterized by small flies almost a century ago, is now known to encode the Myc oncogene (Gallant 2013). This is significant as Myc is a regulator of ribosomal protein encoding genes in metazoans, including Drosophila (Grewal et al. 2005). The ribosome is assembled in a specialized nuclear structure called the nucleolus (Ponti 2025). Across species, including Drosophila (Diegmiller et al. 2021) and C. elegans (Ma et al. 2018), nucleolar size scales with cell size and is broadly correlated with growth in cell size and/or cell number, processes that are directly relevant to sex-specific allometry. Collectively, these and many other studies offer compelling evidence that ribosomal biogenesis is positively associated with cell size and growth, underscoring the value of measuring ribosome biogenesis as a metric."

      We understand that the reviewer is asking whether reduced RP mRNA expression directly leads to reduced functional ribosome assembly. We do not have a definitive answer to that specific question. However, we directly measured translation in fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex-biased protein synthesis), and the results show a clear correlation between RP gene expression and biosynthetic activity; even though we did not track every step from transcription to ribosome assembly to polysome loading across all cell types. This would indeed be an excellent direction for future work, including polysome profiling and related assays. Importantly, we did examine the nucleolus (Figure 4), where ribosome assembly occurs, and showed that nucleolar volume scales with RP gene expression. This strongly supports the presence of sex-specific differences in ribosome biogenesis.

      Added at line 115:

      "Building on the earlier studies noted above, as well as our direct measurements of translation bias in the fat body, nucleolar size, and cell size, we used sex-biased expression of ribosomal proteins as an indicator of sex differences in per-nucleus cell size."

      -----------------------------------------------------------

      Second, the interpretation of RP expression as a proxy for cell size seems potentially at odds with the fact that some cells are multi-nucleate. Those cells are big because of multiple nuclei, and so they might not show any increase in ribosome expression per nucleus. presumably for multi-nucleate cells, RP expression if it reflects anything at all would be something to do with cell size PER nucleus.

      Response: Yes, this is a very important point, and this is why we chose multinucleated indirect flight muscles for our direct experimental analysis. We show that in indirect flight muscle cells, adult cell size is greatly influenced by the sex-specific number of nuclei per cell. The female muscle cells are larger and have larger nuclei count per cell. Additionally, they also have higher expression of ribosomal protein coding genes. As the latter data are from the single nucleus sequencing atlas, this already demonstrates what this reviewer is asking for: per nucleus, female muscle cells express more ribosome protein coding mRNAs.

      -----------------------------------------------------------

      *Third, it is well known that many tissues in Drosophila are polyploid or polytene. I don't know enough about the methodology used to produce the FCA to know whether this is somehow normalized. Otherwise, my hypothesis would be that nuclei showing higher RP expression might just be polyploid or polytene. You might say that this could be controlled by asking if all genes are similary upregulated, but that isn't the case since at least in polytene chromosomes it is well known that only a small number of genes are expressed at a given time, while many are silent. *

      Response: Yes, this is an excellent point. As noted above, our study does not distinguish among the different potential causes of sex differences in ribosomal mRNA copy number, as these may vary across cell types. We now explicitly acknowledge it in the discussion (line 327). Importantly, even in the cases when ribosomal gene expression bias primarily reflects differences in DNA content, this still represents a plausible mechanistic route linking ribosomal gene expression to increased nucleolar ribosome biogenesis and, ultimately, larger cell size. This possibility does not alter our main conclusions.

      -----------------------------------------------------------

      Overall, I think a lot more foundational work would need to be done in order to allow the inference of cell size from RP expression. In a way, it is a bit unfortunate that they chose to do this work in Drosophila where so many cells are polyploid, although I gather that even in humans some tissues have this issue, for example large neurons in the brain.

      Response: We acknowledge that we did not clearly reference some of the foundational work in the literature. To address this, we have expanded the introduction to provide additional background and context. We also clarify that our fat body experiment offers independent support for the relationship between ribosomal gene expression bias, nuclear size bias, and corresponding biases in protein synthesis, thereby reinforcing the use of sex-specific ribosomal gene expression as an indicator of sex-specific cell size. Importantly, we assess this bias only within clusters, not between them. These clusters are derived from gene-expression-based clustering and are therefore relatively homogeneous. For example, as discussed in our response to Reviewer #3, the fat body contains several clusters that correspond to expression-defined subtypes of fat body cells. Our previous terminology may have inadvertently implied that we were using ribosomal gene expression to estimate cell size more broadly, which was not our intention.

      As for the choice of the organism, most of the authors are Drosophila researchers and we benefit from the unique, highly replicated data from whole head and whole body of both sexes. Such data is necessary for a non-biased estimation of the differences in nuclear number.

      -----------------------------------------------------------

      *Reviewer #1 (Significance (Required)):

      The idea that gene regulatory networks could "program" differences in scaling by changing levels of ribosomal protein gene expression is a tremendously important one if it can be established, because it would show a simple way for size scaling to be placed under control of developmental regulatory pathways. My original concern when I first looked at the abstract was going to be that yeah the results are interesting but a mechanism is not provided, but as I read it, that concern went away. showing that RP gene expression, which could be programmed by various driving pathways, can affect allometric scaling, would be extremely impactful and really change how we think about scaling, but putting it into the framework of gene expression networks that control other aspects of developmewnht. it would not be necessary to show which pathways actually drive these expression differences, the fact that they are different would be interesting enough to make everyone want to read this paper. But as discussed above I am not, however, convinced by the evidence presented here. So while I think it would be very significant if true, I am not convinced that the conclusion is well supported. This doesn't mean I have a reason to think it is false, just that its not well supported for the reasons I have given.*

      Response: We are grateful to the reviewer for this positive assessment of our findings despite lack of a specific mechanism. We also regret that our initial writing did not clearly situate our work within the foundational literature on the relationship between ribosomal biogenesis and scaling. The key contribution of our study is to demonstrate that sex-biased ribosomal biogenesis plays a role in allometric scaling, providing a basis for future mechanistic exploration. We hope that the revised manuscript now offers clear and compelling support for the conclusion that RP gene expression bias can influence allometric scaling.

      -----------------------------------------------------------

      I hasten to point out that I could be entirely wrong, if the missing bits of logic (i.e. that RP expression matches ribosome abundance and that gene expression in the FCA dataset isn't influenced by ploidy of the nucleus). If suitable references can be provided to support these underlying assumptions, then in fact I think these concerns could be answered with very little effort. Otherwise, I think experiments would be needed to support these assumptions, and that might be non-trivial to do in a reasonable time frame. for that reason, in the next question I have put "cannot tell" for the time estimate.

      Response: While gene expression in some FCA cell types may indeed be influenced by ploidy, our analysis does not depend on distinguishing among the possible sources of gene expression bias, which may vary across cell types. Rather, our key point is that-regardless of its origin-an increase in ribosomal gene expression is associated with enhanced ribosome biogenesis in the nucleolus and, ultimately, larger cell size. Thus, our main conclusions do not rely on any specific mechanism underlying RP gene expression upregulation. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1.

      We thank the reviewer for their thoughtful and constructive comments, which have prompted us to clarify both our reasoning and the relevant literature more fully.

      -----------------------------------------------------------

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The authors analyzed the FlyAtlas single-nucleus dataset to identify sex differences in gene expression and cell numbers. This led them to focus on muscles, cardiomyocytes, and fat body cells. They then measured cell and nucleolus size across different tissues and showed that reducing Myc function decreases sex differences in fat body cells. Overall, the manuscript provides a characterization of dimorphic differences in cell and organ size across three tissues.*

      Response: This is a nice synopsis of the work.

      -----------------------------------------------------------

      Major Comments: The major claims of the manuscript are well supported by the reported experiments and analyses. While Reviewer #2 considered the major claims of the manuscript to be well supported, by the reported experiments and analysesStatistical analyses appear adequate.

      Response: We agree, and we are glad that the reviewer found our work well supported.

      -----------------------------------------------------------

      *Minor Comments: The following minor issues should be addressed through textual edits:In the Introduction:

      "Disruptions in proportionality, whether due to undergrowth or overgrowth, can lead to reduced fitness or diseases such as cancer." Could the authors provide a reference for this statement, particularly for the claim that disruptions in proportion*

      Response: We apologize for this omission. The following explanation is now included starting at line 39:

      "For example, scaled cell growth is a driver of symmetry in Myc-dependent scaling of bone growth in the skeleton by chondrocyte proliferation (Ota et al. 2007; Zhou et al. 2011). Increased nucleolus size is a well known marker of cancer progression in a histopathological setting (Pianese 1896; Derenzini et al. 1998; Elhamamsy et al. 2022)."

      -----------------------------------------------------------

      *The authors state:

      "This study offers a comprehensive, cellular-resolution analysis of sexual size dimorphism in a model organism, uncovering how differences in cell number and size contribute to sex-specific body plans."*

      The study cannot be considered comprehensive, as not all organs were examined.

      Response: Indeed, "comprehensive" is a loaded word and in the revised manuscript we just omitted it.

      -----------------------------------------------------------

      *The following sentence from the abstract is unclear:

      "By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies..."*

      * What do the authors mean by a conserved developmental system? Do they refer to a commonly used developmental model, or to a developmental system that is evolutionarily conserved?*

      Response: We acknowledge that the use of the word 'conserved' was inappropriate, and we have therefore removed it from the statement.

      -----------------------------------------------------------

      *Reviewer #2 (Significance (Required)):

      The manuscript presents a relevant exploration of sex-specific differences in cell size and cell number in Drosophila males and females. The limitations of the study are clearly acknowledged in the "Limitations" section. The work does not provide mechanistic insight into the causes or functional consequences of the observed differences. Nonetheless, the study extends our understanding of sexual dimorphism and establishes a foundation for future investigations into the autonomous and systemic mechanistic factors that regulate these differences.*

      Response: Thank you.

      -----------------------------------------------------------

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

      The manuscript by Pal and colleagues addresses an important question: the cellular mechanisms underlying sex differences in organ size. By leveraging single-nucleus transcriptomic data from the adult Drosophila Cell Atlas, the authors show that different cell types adopt distinct strategies to achieve sex differences in organ size-either by increasing cell size or by altering cell number. They then focus on three organs-the indirect flight muscles, the heart, and the fat body-and provide supporting evidence for their transcriptomic analyses.*

      Response: This is a nice summary of the study. Thank you.

      -----------------------------------------------------------

      This study tackles a highly relevant and often overlooked question, as our understanding of the molecular and cellular events driving sex differences remains incomplete. The work presents interesting observations; however, it is largely descriptive, establishing correlations without providing functional evidence or mechanistic insight.

      Response: We agree that this is an often overlooked problem that has been difficult to address experimentally without single-cell genomics. Our work aims to help fill this gap. While the paper does contain descriptive elements, we believe such characterization is important at the early stages of developing a new area of inquiry. The study explores a unique dataset and includes experimental validation to support key observations. We also propose how allometry may be shaped by cell division and cell size, drawing on well-established molecular mechanisms. Thus, the reviewer's comment regarding a lack of mechanistic insight likely pertains to the absence of a direct connection to the sex-determination pathway, which is beyond the scope of the current study.

      -----------------------------------------------------------

      Below are four main points that should be addressed before publication: 1. Introduction and contextualisation of prior work The introduction does not adequately present the current state of knowledge. Several key studies are missing or insufficiently discussed. In particular, the following works should be included and integrated into the introduction: - PMID: 26710087 - shows that the sex determination gene transformer regulates male-female differences in Drosophila body size. - PMID: 28064166 - describes how differences in Myc gene dosage contribute to sex differences in body size. - PMID: 26887495 - demonstrates that the intrinsic sexual identity of adult stem cells can control sex-biased organ size through sex-biased proliferation. - PMID: 28976974 - reveals that Sxl modulates body growth through both tissue-autonomous and non-autonomous mechanisms. - PMID: 39138201 - shows that transformer drives sex differences in organ size and body weight. Incorporating and discussing these references would provide a more comprehensive and up-to-date framework for the study.

      Response: We agree that the literature suggested by the reviewer strengthens the introduction and improves the contextualization of prior work relevant to our study. Although much of it was previously included in the discussion section on cell-autonomous and hormonal regulation, it has now been moved to the introduction, along with the discussion of the papers suggested by the reviewer (beginning at line 58).

      "In Drosophila melanogaster, adult females are substantially larger than males (Fig. 1A1), yet both sexes develop from genetically similar zygotes and share most organs and cell types. In wild type flies, sex is determined by the number of X chromosomes in embryos, with XX flies developing as females and X(Y) flies developing as males due to the activation and stable expression of Sex-lethal only in XX flies (Erickson and Quintero 2007). While it is not entirely clear how sexually dimorphic size is regulated, the sex determination pathway is implicated in size regulation. Sex-reversed flies often show a size based on the X chromosome number rather than sexual morphology. Female Sex-lethal contributes to larger female size independently of sexual identity (Cline 1984), and Sex-lethal expression in insulin producing neurons in the brain also impacts body size (Sawala and Gould 2017). Female-specific Transformer protein is produced as a consequence of female-specific Sex-lethal and also contributes to increased female size (Rideout et al. 2015). This size scaling also applies to individual organs. For example, the Drosophila female gut is longer than the male gut due Transformer activity (Hudry et al. 2016). It has also been suggested that Myc dose (it is X-linked) is a regulator of body size (Mathews et al. 2017), although the failed dosage compensation model proposed has not been demonstrated."

      And again at line 74:

      "These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for sexual size differences (SSD). The origins of SSD can, in principle, arise from differences in (i) gene expression, (ii) the presence of sex-specific cell types, (iii) the number of cell-specific nuclei, or (iv) the size (per nucleus) of those cells. Previous research in Drosophila has largely focused on gene expression in sex-specific organs like the gonads (Arbeitman et al. 2002; Parisi et al. 2004; Graveley et al. 2011; Pal et al. 2023), which are governed by a well-characterized sex-determination pathway (Salz and Erickson 2010; Clough and Oliver 2012; Raz et al. 2023) However, whether and how scaling differences in shared, non-sex-specific tissues are achieved via changes in cell size and number remains largely unexamined (Fig. 1A2). These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for size differences."

      -----------------------------------------------------------

      2. Use of ribosomal gene expression as a proxy for cell size The authors use ribosomal gene expression levels as a proxy for cell size, but this assumption is not adequately justified. The cited references (refs. 20-22) focus on unicellular organisms (bacteria and yeast) or cleavage divisions in frog embryos, which are fundamentally different from adult Drosophila tissues. The authors should provide evidence that ribosome abundance scales with cell size across the distinct adult Drosophila cell types. Given that most adult fly tissues are post-mitotic, it is more likely that ribosomal gene expression reflects protein synthesis activity rather than cell size, particularly in secretory cell types.

      Response: Reviewer 1 raised a similar point, and we agree. We recognize that the term "proxy" may have been misleading. We use this measure only in the context of sex bias within homogeneous cell clusters, and not between clusters, even when such clusters share the same cell-type annotation. To avoid overinterpretation, we changed "poxy" to "indicator".

      In response to the reviewer's concern, we have expanded our discussion of the relevant supporting literature (additional text starting line 75). We have also directly measured translation in the fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex biased protein synthesis), which clearly demonstrates a correlation between ribosomal protein gene expression and biosynthetic activity. Although, we have not traced the chain of events from expression to ribosome assembly to polysome loading in all cell types, we did examine the nucleolus (Figure 4), where ribosomes are assembled, and we make a strong point that the volume of the nucleolus scales like ribosome protein gene expression. This provides strong evidence for sex-specific ribosome biogenesis contributing to cell size.

      Furthermore, the observation that ribosomal gene expression likely reflects protein synthesis activity is not at odds with increased cell size: biosynthesis increases in larger cells (Schmoller and Skotheim 2015). We have added a panel to Figure 4 showing the relationship between ribosomal gene expression bias and the average expression bias of Eukaryotic Elongation Factor 1 (eEF1).

      -----------------------------------------------------------

      3. Relationship between Myc and sex-biased Rp expression The proposed link between Myc and sex-biased Rp expression is unclear. Panels D and E of Figure 1 show no consistent relationship: some cell types with strong Rp sex bias exhibit either high or low female Myc bias, or even a male bias. The linear regression in Figure 4I (R = 0.07, p = 0.59) confirms the lack of correlation. The authors should clarify this point and adopt a more cautious interpretation regarding Myc as a potential regulator of sex-biased Rp expression and cell size differences. Experimentally, using Myc hypomorph or heterozygous conditions would be more appropriate than complete knockdown to test its role.

      Response: Thank you for noting that the relationship between Myc expression bias and sex-biased RP expression required clarification. This response was prepared in consultation with Myc expert Dr. David Levens.

      We demonstrate that both Myc and RP gene expression exhibit an overall female bias in the body. The absence of a strong correlation across cell clusters does not invalidate this conclusion. Myc is a well-established master regulator of ribosome biogenesis, but its quantitative effects are complex. According to recent models of Myc-mediated gene regulation (Nie et al. 2012; Lin et al. 2012), Myc upregulates all actively transcribed genes. Because this regulation is global, the relationship between changes in Myc expression and corresponding changes in ribosomal protein gene expression depends on cell type. Moreover, (Lorenzin et al. 2016) demonstrated that ribosomal protein genes saturate at relatively low levels of Myc, which helps explain why we observe a correlation in head cell clusters-where Myc expression is lower-but not in body clusters.

      Importantly, on average, the female-specific Myc expression bias is stronger in body cell clusters than in head cell clusters, consistent with the stronger female bias in ribosomal protein gene expression observed in the head relative to the body.

      To make this relationship more transparent, we combined the head and body clusters, which yielded a strong overall correlation (Fig. 4J, replacing the previous Fig. 4H).

      To further strengthen the evidence linking ribosomal gene expression to cell size, we also examined the relationship between ribosomal gene expression bias and Elongation Factor 1 (eEF1) expression bias, a key component of protein biosynthesis during the elongation step of translation. The resulting correlation exceeds 0.9 (new Fig. 4H, added as an additional panel in Fig. 4).

      -----------------------------------------------------------

      4. Conclusions about fat body cell number I have concerns about drawing conclusions on sex differences in fat body cell number from single-nucleus transcriptomic data for two reasons:

      1- Drosophila fat body tissue is heterogeneous, comprising distinct subpopulations (e.g., visceral fat cells, subcuticular fat cells), some of which are sex-specific-such as fat cells associated with the spermathecae in females.

      Response: Thank you for giving us the opportunity to clarify our analysis of the FCA data. Our approach does account for subpopulations within the fat body as well as within other cell types. Based on gene expression profiles, we identify three fat body clusters, all of which are reported in Table S3. One small female-specific cluster (

      When all fat body clusters are combined into a single supercluster, this supercluster still shows a male bias. We have now clarified this point in the manuscript (line 113). Note that both subclusters of fat body are already shown in Fig. 1C and 1D.

      -----------------------------------------------------------

      2- Adult fat body cells can be multinucleated (PMID: 13723227). Apparent sex differences in nucleus number may reflect differences in specific subpopulations or degrees of multinucleation rather than true differences in cell number. To strengthen the conclusions, the analysis should be performed at the level of fat body subpopulations, distinguishing clusters where possible. Additionally, quantifying nuclei relative to actual cell number-as done for muscle tissue-would clarify whether observed sex differences reflect true variation in cell number or differences in nuclear content per cell.

      Response: Yes, some cells can be multinucleate. We specifically address this in the context of muscle cells, where multinucleation is prominent, and we also conducted experimental validation in this tissue. As noted above, our analysis is performed at the subpopulation level, since clusters are defined by expression similarity (Leiden resolution 4.0) rather than by annotation.

      Because our work relies on single-nucleus data, each nucleus is treated as an individual unit of analysis. Nevertheless, we observe genuine nuclear differences within each cluster. Importantly, the presence of multinucleated cells does not alter our conclusions; it simply represents one form of variation in cell number that can be thought of as a subcomponent of cell/nuclei number.

      -----------------------------------------------------------

      Minor corrections/points: 1-The term body size in the title does not accurately reflect the content of the paper. I recommend replacing it with organ size to better align with the study's focus.

      Response: Thank you for the suggestion.

      ----------------------------------------------------------- 2-The term sexual size dimorphism is somewhat inaccurate in this context. Sex differences in size would be more appropriate. The term sexual dimorphism typically refers to traits that exhibit two distinct forms in males and females-such as primary or secondary sexual characteristics like sex organs or sex combs. In contrast, size is a quantitative trait that follows a normal distribution. Although the average female may be larger than the average male, the distributions overlap, making the term dimorphism imprecise.

      Response: Thank you for the suggestion.

      -----------------------------------------------------------

      3-In Figure 2E, there appears to be an inconsistency between the text, figure legend, and the data presented. The text and legend state that the total volume of dorsal longitudinal flight muscle cells was quantified, whereas the graph indicates measurements of nuclear size. This discrepancy should be clarified.

      Response: Thank you for pointing this out. We figured out that Y-axis label in the graph was incorrect and it is now fixed.

      -----------------------------------------------------------

      4-The authors proposed: "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication". Please note that this hypothesis has already been tested functionally in PMID: 39138201 and was shown to be incorrect. Sex differences in body size are completely independent of fat body sexual identity or any intrinsic sex differences within fat cells.

      __Response: __We thank the reviewer for the opportunity to discuss why the data shown in PMID 39138201 (Hérault et al. 2024) do not rule out a model in which the fat body contributes to the sex-specific regulation of body size via interorgan communication. The main reason data in Herault et al cannot rule out such a model is that they use wing size as a proxy for body size. This is in contrast to prior studies, such as (Rideout et al. 2015), in which pupal volume was used to directly measure body size and show a non-autonomous effect of sex determination gene transformer on body size. Measuring body size directly is a more precise readout of growth during the larval stages of development, as opposed to using adult wing area which reflects the growth of a single organ. It is also important to note that the diets used to rear flies in Herault and Rideout differ, which is an important consideration as females do not achieve their maximal size without high dietary protein levels (Millington et al. 2021). To ensure all these points are communicated to readers, we added text to this effect in the revised version of our manuscript.

      Added at line 254:

      "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication (Colombani et al. 2003; Géminard et al. 2009; Rajan and Perrimon 2012; Sano et al. 2015; Koyama and Mirth 2016). Indeed, one study showed the sexual identity of the fat body influenced pupal volume, which is an accurate readout of larval growth (Rideout et al. 2015; Delanoue et al. 2010). While a recent study suggests that male-female differences in body size were regulated independently of fat body sexual identity (Hérault et al. 2024), this study measured the growth of a single organ, the wing, as a proxy for body size. Additional studies are therefore needed to resolve whether fat body protein synthesis plays an important role in regulating sex differences in body size."

      -----------------------------------------------------------

      *5-The authors state: "This demonstrate that Myc plays a key role in regulating the sex difference in nucleolar size." This is an overstatement given the functional data presented. The claim should be toned down to reflect the limited evidence.

      **Referee cross-commenting**

      I completely agree with the main comments of Reviewer 1, as they address the paper's core.*

      Response: We have addressed the comments of Reviewer 1 in the response to reviewer's comments above.

      -----------------------------------------------------------

      *Reviewer #3 (Significance (Required)):

      The main novelty and strongest aspect of this study is its use of single-nucleus transcriptomic data from the adult Drosophila Cell Atlas to investigate how different cell types adopt distinct strategies to generate sex differences in organ size-either by increasing cell size or by altering cell number. Previous studies have largely focused on specific tissues, whereas this work provides a comprehensive, organism-wide view that encompasses all tissues, enabling direct cross-comparison between organs. This represents a clear advance in the field, primarily from a technical perspective, by leveraging organism-wide single-cell transcriptomics. The main limitations lie in the lack of functional experiments and mechanistic insights. Moreover, the proposed mechanism-differences in Myc gene dosage or expression levels-is not entirely novel, as Myc dosage has previously been implicated in contributing to sex differences in body size (PMID: 28064166).*

      Response: We do have some functional testing in the 3 tissues, flight muscle, heart and fat body, however, providing mechanistic insights is beyond the scope of this paper. The paper suggested by the reviewer is an example of one attempt to provide such a mechanism, probably not the only one. We hope that our rich data that we have assembled in this paper provide resources for generating hypotheses and stimulate further research.

      -----------------------------------------------------------

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      Diegmiller, Rocky, Caroline A. Doherty, Tomer Stern, Jasmin Imran Alsous, and Stanislav Y. Shvartsman. 2021. "Size Scaling in Collective Cell Growth." Development (Cambridge, England) 148 (18): dev199663. https://doi.org/10.1242/dev.199663.

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      Marygold, Steven J., John Roote, Gunter Reuter, et al. 2007. "The Ribosomal Protein Genes and Minute Loci of Drosophila Melanogaster." Genome Biology 8 (10): R216. https://doi.org/10.1186/gb-2007-8-10-r216.

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      Nie, Zuqin, Gangqing Hu, Gang Wei, et al. 2012. "C-Myc Is a Universal Amplifier of Expressed Genes in Lymphocytes and Embryonic Stem Cells." Cell 151 (1): 68-79. https://doi.org/10.1016/j.cell.2012.08.033.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by Pal and colleagues addresses an important question: the cellular mechanisms underlying sex differences in organ size. By leveraging single-nucleus transcriptomic data from the adult Drosophila Cell Atlas, the authors show that different cell types adopt distinct strategies to achieve sex differences in organ size-either by increasing cell size or by altering cell number. They then focus on three organs-the indirect flight muscles, the heart, and the fat body-and provide supporting evidence for their transcriptomic analyses.

      This study tackles a highly relevant and often overlooked question, as our understanding of the molecular and cellular events driving sex differences remains incomplete. The work presents interesting observations; however, it is largely descriptive, establishing correlations without providing functional evidence or mechanistic insight.

      Below are four main points that should be addressed before publication:

      1. Introduction and contextualisation of prior work The introduction does not adequately present the current state of knowledge. Several key studies are missing or insufficiently discussed. In particular, the following works should be included and integrated into the introduction:
        • PMID: 26710087 - shows that the sex determination gene transformer regulates male-female differences in Drosophila body size.
        • PMID: 28064166 - describes how differences in Myc gene dosage contribute to sex differences in body size.
        • PMID: 2688749 - demonstrates that the intrinsic sexual identity of adult stem cells can control sex-biased organ size through sex-biased proliferation.
        • PMID: 28976974 - reveals that Sxl modulates body growth through both tissue-autonomous and non-autonomous mechanisms.
        • PMID: 39138201 - shows that transformer drives sex differences in organ size and body weight. Incorporating and discussing these references would provide a more comprehensive and up-to-date framework for the study.
      2. Use of ribosomal gene expression as a proxy for cell size The authors use ribosomal gene expression levels as a proxy for cell size, but this assumption is not adequately justified. The cited references (refs. 20-22) focus on unicellular organisms (bacteria and yeast) or cleavage divisions in frog embryos, which are fundamentally different from adult Drosophila tissues. The authors should provide evidence that ribosome abundance scales with cell size across the distinct adult Drosophila cell types. Given that most adult fly tissues are post-mitotic, it is more likely that ribosomal gene expression reflects protein synthesis activity rather than cell size, particularly in secretory cell types.
      3. Relationship between Myc and sex-biased Rp expression The proposed link between Myc and sex-biased Rp expression is unclear. Panels D and E of Figure 1 show no consistent relationship: some cell types with strong Rp sex bias exhibit either high or low female Myc bias, or even a male bias. The linear regression in Figure 4I (R = 0.07, p = 0.59) confirms the lack of correlation. The authors should clarify this point and adopt a more cautious interpretation regarding Myc as a potential regulator of sex-biased Rp expression and cell size differences. Experimentally, using Myc hypomorph or heterozygous conditions would be more appropriate than complete knockdown to test its role.
      4. Conclusions about fat body cell number I have concerns about drawing conclusions on sex differences in fat body cell number from single-nucleus transcriptomic data for two reasons:

      1) Drosophila fat body tissue is heterogeneous, comprising distinct subpopulations (e.g., visceral fat cells, subcuticular fat cells), some of which are sex-specific-such as fat cells associated with the spermathecae in females.

      2) Adult fat body cells can be multinucleated (PMID: 13723227). Apparent sex differences in nucleus number may reflect differences in specific subpopulations or degrees of multinucleation rather than true differences in cell number. To strengthen the conclusions, the analysis should be performed at the level of fat body subpopulations, distinguishing clusters where possible. Additionally, quantifying nuclei relative to actual cell number-as done for muscle tissue-would clarify whether observed sex differences reflect true variation in cell number or differences in nuclear content per cell.

      Minor corrections/points:

      1. The term body size in the title does not accurately reflect the content of the paper. I recommend replacing it with organ size to better align with the study's focus.
      2. The term sexual size dimorphism is somewhat inaccurate in this context. Sex differences in size would be more appropriate. The term sexual dimorphism typically refers to traits that exhibit two distinct forms in males and females-such as primary or secondary sexual characteristics like sex organs or sex combs. In contrast, size is a quantitative trait that follows a normal distribution. Although the average female may be larger than the average male, the distributions overlap, making the term dimorphism imprecise.
      3. In Figure 2E, there appears to be an inconsistency between the text, figure legend, and the data presented. The text and legend state that the total volume of dorsal longitudinal flight muscle cells was quantified, whereas the graph indicates measurements of nuclear size. This discrepancy should be clarified.
      4. The authors proposed: "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication". Please note that this hypothesis has already been tested functionally in PMID: 39138201 and was shown to be incorrect. Sex differences in body size are completely independent of fat body sexual identity or any intrinsic sex differences within fat cells.
      5. The authors state: "This demonstrate that Myc plays a key role in regulating the sex difference in nucleolar size." This is an overstatement given the functional data presented. The claim should be toned down to reflect the limited evidence.

      Referee cross-commenting

      I completely agree with the main comments of Reviewer 1, as they address the paper's core.

      Significance

      The main novelty and strongest aspect of this study is its use of single-nucleus transcriptomic data from the adult Drosophila Cell Atlas to investigate how different cell types adopt distinct strategies to generate sex differences in organ size-either by increasing cell size or by altering cell number. Previous studies have largely focused on specific tissues, whereas this work provides a comprehensive, organism-wide view that encompasses all tissues, enabling direct cross-comparison between organs. This represents a clear advance in the field, primarily from a technical perspective, by leveraging organism-wide single-cell transcriptomics. The main limitations lie in the lack of functional experiments and mechanistic insights. Moreover, the proposed mechanism-differences in Myc gene dosage or expression levels-is not entirely novel, as Myc dosage has previously been implicated in contributing to sex differences in body size (PMID: 28064166).

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      Referee #2

      Evidence, reproducibility and clarity

      The authors analyzed the FlyAtlas single-nucleus dataset to identify sex differences in gene expression and cell numbers. This led them to focus on muscles, cardiomyocytes, and fat body cells. They then measured cell and nucleolus size across different tissues and showed that reducing Myc function decreases sex differences in fat body cells. Overall, the manuscript provides a characterization of dimorphic differences in cell and organ size across three tissues.

      Major Comments:

      The major claims of the manuscript are well supported by the reported experiments and analyses. Statistical analyses appear adequate.

      Minor Comments:

      The following minor issues should be addressed through textual edits:In the Introduction:

      "Disruptions in proportionality, whether due to undergrowth or overgrowth, can lead to reduced fitness or diseases such as cancer."

      Could the authors provide a reference for this statement, particularly for the claim that disruptions in proportionality can lead to cancer?

      The authors state:

      "This study offers a comprehensive, cellular-resolution analysis of sexual size dimorphism in a model organism, uncovering how differences in cell number and size contribute to sex-specific body plans."

      The study cannot be considered comprehensive, as not all organs were examined.

      The following sentence from the abstract is unclear:

      "By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies..."

      What do the authors mean by a conserved developmental system? Do they refer to a commonly used developmental model, or to a developmental system that is evolutionarily conserved?

      Significance

      The manuscript presents a relevant exploration of sex-specific differences in cell size and cell number in Drosophila males and females. The limitations of the study are clearly acknowledged in the "Limitations" section. The work does not provide mechanistic insight into the causes or functional consequences of the observed differences. Nonetheless, the study extends our understanding of sexual dimorphism and establishes a foundation for future investigations into the autonomous and systemic mechanistic factors that regulate these differences.

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      Referee #1

      Evidence, reproducibility and clarity

      This paper addresses the interesting question of how cell size may scale with organ size in different tissues. The approach is to mine data from the fly single cell atlas (FCA) which despite its name is a databse of gene expression levels in single isolated nuclei. Using this data, they infer cell size based on ribosomal protein gene expression, and based on this approach infer that there are tissue and sex specific differences in scaling, some of which may be driven by differences in ribosomal protein gene expression.

      I think the idea of mining this database is a clever one, however there a number of concerns about whether the existing data can really be used to draw the conclusions that are stated.

      One concern has to do with the assumption that RP (ribosome protein) expression is a proxy for cell size. It is well established that ribosome abundance sclse with cell size, but is there reason to believe that ribosome nuclear gene EXPRESSION correlates with ribosome abundance? I'm not saying that this can't be true, but it seems like a big assumption that needs to be justified with some data. Maybe this is well known in the Drosophila literature, but in that case the relevant literature really needs to be cited.

      Second, the interpretation of RP expression as a proxy for cell size seems potentially at odds with the fact that some cells are multi-nucleate. those cells are big because of multiple nuclei, and so they might not show any increase in ribosome expression per nucleus. presumably for a multi-nucleate cells, RP expression if it reflects anythnig at all would be something to do with cell size PER nucleus.

      Third, it is well known that many tissues in Drosophila are polyploid or polytene. I don't know enough about the methodology used to produce the FCA to know whether this is somehow normalized. Otherwise, my hypothesis would be that nuclei showing higher RP expression might just be polyploid or polytene. You might say that this could be controlled by asking if all genes are similary upregulated, but that isn't the case since at least in polytene chromosomes it is well known that only a small number of genes are expressed at a given time, while many are silent.

      Overall, I think a lot more foundational work would need to be done in order to allow the inference of cell size from RP expression. In a way, it is a bit unfortunate that they chose to do this work in Drosophila where so many cells are polyploid, although I gather that even in humans some tissues have this issue, for example large neurons in the brain.

      Significance

      The idea that gene regulatory networks could "program" differences in scaling by changing levels of ribosomal protein gene expression is a tremendously important one if it can be established, because it would show a simple way for size scaling to be placed under control of developmental regulatory pathways. My original concern when I first looked at the abstract was going to be that yeah the results are interesting but a mechanism is not provided, but as I read it, that concern went away. showing that RP gene expression, which could be programmed by various driving pathways, can affect allometric scaling, would be extremely impactful and really change how we think about scaling, but putting it into the framework of gene expression networks that control other aspects of developmewnht. it would not be necessary to show which pathways actually drive these expression differences, the fact that they are different would be interesting enough to make everyone want to read this paper. But as discussed above I am not, however, convinced by the evidence presented here. So while I think it would be very significant if true, I am not convinced that the conclusion is well supported. This doesn't mean I have a reason to think it is false, just that its not well supported for the reasons I have given.

      I hasten to point out that I could be entirely wrong, if the missing bits of logic (i.e. that RP expression matches ribosome abundance and that gene expression in the FCA dataset isn't influenced by ploidy of the nucleus). If suitable references can be provided to support these underlying assumptions, then in fact I think these concerns could be answered with very little effort. Otherwise, I think experiments would be needed to support these assumptions, and that might be non-trivial to do in a reasonable time frame. for that reason, in the next question I have put "cannot tell" for the time estimate.

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      Reply to the reviewers

      Manuscript number: RC-2025-03091

      Corresponding author(s): Chia-Tsen, Tsai, Liuh-Yow Chen

      1. General Statements [optional]

      We thank the reviewers for their valuable time and constructive feedback on our study, which ultimately improved our manuscript. Herein, we provide a detailed response to each of the reviewers' comments, supported by new data that have been integrated into both the main text and the supplementary figures.

      2. Point-by-point description of the revisions

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

      Summary This manuscript builds upon the authors' prior findings that targeting COUP-TF2 to TRF1 induces ALT-associated phenotypes and G2-mediated synthesis in telomerase-immortalised BJT human fibroblasts. In this study, the authors show that telomere-coupled COUP-TF2 promotes H3K9me3 enrichment in these cells, and that this effect is blocked by TRIM28 depletion. Furthermore, TRIM28 depletion also suppresses the formation of ALT phenotypes in VA13 ALT cells. Given that TRIM28 has been implicated in regulating H3K9me3 deposition via SETDB1, and has been reported to co-purify with TR2 and TR4 (though not previously in the context of ALT telomeres), these findings add mechanistic depth to how heterochromatin regulators contribute to ALT activity. Overall, the manuscript's conclusions are generally supported by the presented data, but several aspects require clarification or additional experimental validation.

      The authors report a modest reduction in telomeric H3K9me3 following COUP-TF2 and TR4 depletion in U-2 OS and VA13 cells (Figure 1B). To strengthen the claim that these orphan receptors specifically regulate H3K9me3, the authors should 1) Assess additional heterochromatic histone marks (e.g., H4K20me3) at telomeres, 2) Normalize telomeric signals to both parental histone levels and input, and 3) Evaluate whether global H3K9me3 levels also decrease upon receptor depletion

      Response: We appreciate the reviewer's suggestion. To address the concern regarding specificity, we assessed H3K27me3 and H4K20me3 levels upon COUP-TF2/TR4 depletion and found no significant changes (Supplementary Fig. 1C). Furthermore, we reprocessed the telomeric ChIP data, normalizing to both input DNA and parental histone levels (Figure 1B). This refined analysis reinforces our original conclusion. Finally, Western blot analysis showed no significant changes in global H3 or H3K9me3 levels upon COUP-TF2/TR4 depletion (Figure 1A). Altogether, these results further support the specificity of COUP-TF2/TR4 for H3K9me3 at telomeres. We have revised the main text (page 3) and updated Figure 1A, 1B, and Supplementary Figure 1C for these changes.

      Most experiments explore chromatin changes in telomerase-positive BJT fibroblasts (Figure 2, Figure 4D). It remains unclear whether similar manipulations in ALT cells yield consistent effects, which would give a broader context for ALT phenotype induction. Are ALT phenotypes similarly induced in ALT cells? Does altered chromatin status affect telomere length or telomerase recruitment/activity? Can these pathways drive ALT phenotypes in non-immortalised cells?

      Response: We appreciate the reviewer's suggestion and have explored chromatin changes in telomerase-negative BJ and IMR90 primary fibroblasts (Supplementary Fig. 2C, D). Consistent to the result in BJ-telomerase cells, we found that VP64-TRF1 decreased telomeric H3, H4, and H3K9me3 levels, whereas KRAB-TRF1 increased these marks. Moreover, expression of either VP64-TRF1 or KRAB-TRF1 was sufficient to induce APB formation and ATDs in BJ and IMR90 cells. These results indicate that the chromatin changes at telomeres can drive ALT phenotypes in both primary and telomerase-immortalized fibroblast cells.

          Additionally, regarding whether chromatin alteration affects telomere length or telomere regulation, we have explored telomere length changes in BJT cells expressing vector, TRF1, KRAB-TRF1 or VP64-TRF1. The result of telomere restriction fragment (TRF) assay showed that the cells of all conditions maintained static telomere lengths through 30 days in culture (data shown below), suggesting that the chromatin alterations may not impact telomerase recruitment or activity. As this result is beyond the scope of current study, this data is only shown here in the rebuttal letter for a reference and is not included in the revised manuscript.
      
          Moreover, according to the reviewer's suggestion, we also carried out VP64-TRF1 or KRAB-TRF1 expression experiments in WI38-VA13/2RA cells that express high TERRA and have altered chromatin structures. Our data revealed that VP64-TRF1 suppresses telomere H3K9me3 and ALT activity, while KRAB-TRF1 increases both (Supplementary Figure 2E), suggesting an association of heterochromatin state with ALT activation in WI38-VA13/2RA cells.
      
          The observation that VP64-TRF1 reduces ALT activity in WI38-2RA/VA13 cells contrasts with findings in BJT cells. It is worth noting that studies from the Azzalian and Linger groups demonstrated that experimentally induced TERRA expression promotes ALT activity in ALT and non-ALT cells (PMID: 36122232, PMID: 40624280). Therefore, we propose that TERRA upregulation by VP64-TRF1 may contribute to the ALT induction observed in BJT cells (Supplementary Figure 2A, B), whereas the ability of VP64-TRF1 to suppress ALT activity in WI38-2RA/VA13 cells could be attributed to the reduction of telomere H3K9me3 and heterochromatin loss. Importantly, KRAB-TRF1 concurrently enhanced histone H3, H4, and H3K9me3 occupancy and ATL activity in both human fibroblasts and ALT cells. Altogether, these results support the notion that heterochromatin formation triggers ALT.
      
          We also examined TRIM28 recruitment to telomeres by telomere-ChIP and found that COUP-TF2LBD-TRF1 promotes TRIM28 telomere enrichment in BJ, IMR90 and U2OS, similar to BJT cells (Supplementary Fig. 5A-D).  Moreover, in ALT cell lines WI38-2RA/VA13, U2OS, and Saos-2, depletion of COUP-TF2 or TR4 reduced TRIM28 telomeric association (Figure 4A, B). Together, the data from human fibroblasts and ALT cells supports a role of orphan NRs in recruiting TRIM28 to ALT telomeres.
      

      We acknowledge the reviewer's suggestions, which allow us to clarify and strengthen the conclusions. The corresponding data are presented in Figure 4A-B and Supplementary Figure 2B-D and 5E-F, and the main text has been modified on page 4-6 in the revised manuscript.

      When referring to Figure 3G, the authors state that that telomeric H3K9me3 was abolished upon depleting TRIM28 from the U2OS and WI38-VA13/2RA cells. Abolished is a strong word for a 50% decrease, and this sentence should be revised. The reduction appears greater than that seen with COUP-TF2/TR4 depletion. Are the effects additive? If so, might TRIM28 act, at least in part, independently of COUP-TF2/TR4?

      Response: We appreciate the reviewer's comments. We have revised the manuscript on page 5, replacing "abolished" with "significantly reduced" to better describe the effect of TRIM28 depletion on telomeric H3K9me3. To further investigate the interplay between TRIM28 and orphan NRs in regulating telomeric H3K9me3, we conducted single and combined knockdown experiments in U2OS and WI38-VA13/2RA cells, followed by telomere-ChIP analysis (Supplementary Figures 4D, E). Our results showed that single depletion of either orphan NRs or TRIM28 lead to a similar decrease in telomeric H3K9me3, and that combined knockdown do not result in any further reduction. These findings support an epistatic interaction between orphan NRs and TRIM28 in the regulation of telomeric H3K9me3. We have expanded on this interpretation in the main text (page 6) and included the relevant data in Supplementary Figures 4D, E.

      VA13 cells consistently exhibit stronger effects than U-2 OS (e.g., Figures 1 and 3). This discrepancy could be linked to the high content of variant repeats in VA13 cells. The authors should assess whether variant repeat content underlies the differential response. Repeating key experiments in additional ALT lines with varied repeat compositions would be informative.

      Response: We appreciate the reviewer's suggestion and have extended our analyses to two additional ALT osteosarcoma cell lines, SAOS-2 and G292. In both lines, depletion of orphan NRs resulted in a consistent decrease in telomeric H3K9me3 levels (Supplementary Figures 1A, B). We also examined the contribution of TRIM28 to telomeric H3K9me3 in these cells. siRNA-mediated depletion of TRIM28 in SAOS-2 and G292 cells similarly caused a significant reduction in telomeric H3K9me3 and ALT phenotypes (Supplementary Figure 4A-C). Together, these results from 4 ALT cell lines confirm that orphan NRs and TRIM28 promote telomeric H3K9me3 formation in ALT cells. We have modified the main text on page 3 and 5-6 for these results.

      In line with the previous point, it would be useful to show whether TRIM28 telomeric enrichment is affected by COUP-TF2/TR4 depletion in U2OS cells (Figure 4C). To improve confidence in these findings, the authors should perform telomeric ChIP assays, especially with the COUP-TF2^LBDΔAF2-TRF1 mutant construct.

      Response: Following the reviewer's suggestion, we performed telomere-ChIP assays to assess TRIM28 enrichment at telomeres upon expression of COUP-TF2LBD-TRF1 and its ΔAF2 mutant in U2OS cells. Consistent with our immunofluorescence results, telomere-ChIP revealed that COUP-TF2LBD-TRF1 expression promotes TRIM28 telomere enrichment, while the AF2 deletion mutant failed to recruit TRIM28 (Supplementary Figure 5D). We have modified the main text on page 6 for this result.

      The immunoprecipitation experiments showing TRIM28 association with orphan receptors should include benzonase treatment to rule out DNA-mediated co-association (Figure 4F-G).

      Response: We appreciate the reviewer's suggestion. To address the possibility of DNA-mediated interactions, we pre-incubated cell lysates with benzonase prior to Co-IP (Page 7). This treatment did not disrupt the association between TRIM28 and COUP-TF2 or TR4 in WI38-VA13/2RA and BJT cells (Supplementary Figures 5E-G), indicating a DNA-independent interaction. We have modified the main text on page 7 for this result.

      The study would benefit from a direct assessment of whether COUP-TF2LBDΔAF2-TRF1 fails to induce ALT phenotypes in BJTfibroblasts.

      Response: We thank the reviewer for this suggestion. As the role of the COUP-TF2 AF2 domain in ALT induction in BJT fibroblasts has recently been thoroughly investigated and published by our group (PMID: 38752489), we have directed the current study towards a more detailed mechanistic question. Specifically, we have carried out experiments to further demonstrate that COUP-TF2 recruits TRIM28 to telomeres via its AF2 domain in both human fibroblasts and ALT cells (Supplementary Figures 5A-D). On Page 6, we have modified the main text for these results and included a citation to our previous publication to provide the necessary background.

      The experiments performed in Figure 5E-H lack a vector-only + siCtrl control.• In Figure 5E, the observation that APB formation is restored in siTRIM28 + Vector-treated cells is unexpected. The authors should address this finding and clarify whether this reflects biological noise or a compensatory effect.

      Response: We thank the reviewer for this suggestion. We have repeated the experiments with a revised design, ensuring a consistent vector background across all groups (Vector + siCtrl, Vector + siTRIM28, TRIM28 WT + siTRIM28, and TRIM28 ΔRBCC + siTRIM28) (Figure 5E-H). This improved design confirms that expression of wild-type TRIM28, but not TRIM28 ΔRBCC, restores APB formation, ATDS, ssTeloC, and telomeric H3K9me3 levels in TRIM28-depleted cells. The updated dataset also resolves the previous unexpected increase in APB formation in the siTRIM28 + Vector condition, which is now excluded. We have modified the main text accordingly on page 8.

      Reviewer #1 (Significance (Required)):

      This work offers valuable mechanistic insight into how COUP-TF2 and TRIM28 coordinate to regulate heterochromatin deposition and ALT phenotype formation. It adds to the growing understanding of chromatin-mediated telomere regulation. What remains unclear is how important this interaction is for ALT maintenance, as H3K9me3 is only moderately altered upon TRIM28 depletion in ALT cells. Depletion of TRIM28 has been shown previously to induce APB formation and telomere elongation in U-2 OS ALT cells (Wang et al., 2021), the opposite to what the authors observed here in VA13 cells (Figure 5E-H). Clarifying whether these differences are variant repeat-dependent, or reflect intrinsic features of specific ALT cell lines, would substantially elevate the study's impact.

      Response: We appreciate the reviewer's recognition of the significance of our work in elucidating the molecular basis of ALT regulation through COUP-TF2-TRIM28-mediated heterochromatin formation. In response to the reviewer's insightful comment regarding the importance of this interaction for ALT maintenance, we have expanded our study. We now include data from three additional primary human fibroblasts and a total of four ALT cancer cell lines (Figure 4, Supplementary Figure 4). These new data further strengthen the conclusion that TRIM28 promotes telomeric H3K9me3 and ALT-associated features. Furthermore, our rescue experiments support the model that the ALT-promoting function of TRIM28 in both fibroblasts and ALT cell lines is mediated through its physical interaction with COUP-TF2 (Supplementary Figure 5). We believe these results provide a solid foundation for demonstrating a cooperative role of COUP-TF2 and TRIM28 in ALT maintenance, and address the reviewer's concern regarding the generalizability of our findings.

      Reviewer #2 (Evidence, reproducibility and clarity (Required):

      Summary This manuscript investigates the role of orphan nuclear receptors (ORs), specifically COUP-TF2 and TR4, in promoting H3K9me3 enrichment at ALT telomeres via recruitment of TRIM28 (KAP1). The authors propose that the AF2 domain of COUP-TF2, located in its ligand-binding domain (LBD), is sufficient to recruit TRIM28 to telomeres. This, in turn, promotes heterochromatinization and induces hallmarks of the Alternative Lengthening of Telomeres (ALT) pathway, including APB formation and telomeric DNA synthesis outside of S-phase. This study addresses one important and unresolved question in the field: by what mechanism is the heterochromatic state established at ALT telomeres? Another timely question, not addressed here is: how is heterochromatin (specifically H3K9me3) functionally linked to ALT? The findings are potentially novel and mechanistically insightful. However, key elements of the study, particularly the central tethering experiments, require stronger quantification and clarity. Additional mechanistic tests and literature adjustments would also improve the manuscript.

      Major Concerns

      Central TRF1-COUP-TF2-LBD result lacks quantification and clarity: the tethering of COUP-TF2's LBD to telomeres via TRF1 is a core result of the paper. This experiment demonstrates that this domain is sufficient to induce weak H3K9me3 enrichment and ALT features (APBs and ATDS). However, the supporting ALT data are presented only in Supplementary Figures S1A and S1B, and are not quantified. These data should be quantified with appropriate statistics and moved to a main figure.

      Response: The current study builds upon our recent publication (PMID: 38752489), which comprehensively analyzed ALT induction (APBs, ATDS, C-circles, T-SCEs) by orphan NR-TRF1 expression (COUP-TF1, COUP-TF2, TR2, and TR4; full-length and LBD) in various human fibroblast cell lines. To avoid potential duplicate publication concerns, particularly regarding APB and ATDS results for COUP-TF2LBD-TRF1 in BJT cells, we have put the data with revised quantification results in Supplementary Figure 1D-E. We will follow the reviewer's suggestion and move this data to the main figures if the editors agree.

      Furthermore, the broader functional implication is not explored. Does this tethering induce a fully functional ALT pathway? For example, can telomerase knockout cells expressing TRF1-COUP-TF2-LBD maintain long-term proliferation? Such evidence would significantly strengthen the impact of the study.

      Response: While COUP-TF2LBD-TRF1 expression rapidly induces key ALT phenotypes, we acknowledge that this alone is insufficient to directly promote telomere lengthening and long-term proliferation of primary fibroblasts, as discussed in Gaela et al., 2024 (PMID: 38752489). However, our ongoing, unpublished studies indicate that COUP-TF2LBD-TRF1 can drive immortalization of primary BJ fibroblasts expressing SV40LT by promoting ALT-mediated telomere elongation (Attached Figure A-C; additional data not shown). These findings suggest that COUP-TF2 may cooperate with additional genetic or epigenetic alterations to facilitate ALT development. We appreciate the reviewer's recognition of this critical aspect. As our immortalization study is still in progress and will be the subject of a separate manuscript, we hope the reviewer understands that the data shown in this letter will not be included in the revised manuscript.

      Chromatin manipulation experiments lead to ambiguous conclusions: the authors propose that telomeric heterochromatin promotes ALT activity, but their own experiments (e.g., Figure 2) show that both heterochromatin-inducing (KRAB-TRF1) and euchromatin-inducing (VP64-TRF1) tethering can trigger ALT-like features. This makes it difficult to conclude that heterochromatin is specifically required.

      To clarify:

      -Did the authors express TRF1-VP64 in an ALT cell line? According to their model, this should suppress ALT activity.

      -More broadly, do chromatin alterations per se (regardless of direction) trigger ALT features? Clarifying these points is important for interpretation.

      Response: In response to the reviewer's suggestion, we expressed VP64-TRF1 and KRAB-TRF1 in WI38-2RA/VA13 cells to investigate telomere chromatin changes and ALT activity. Our data indeed revealed that VP64-TRF1 suppresses telomere H3K9me3 and ALT activity, while KRAB-TRF1 increases both (Supplementary Figure 2E), suggesting that heterochromatin triggers ALT activation.

      The observation that VP64-TRF1 reduces ALT activity in WI38-2RA/VA13 cells contrasts with findings in BJT cells. Of note, studies from the Azzalian and Lingner groups demonstrated that experimentally induced TERRA expression promotes ALT activity in ALT and non-ALT cells (PMID: 36122232, PMID: 40624280). Therefore, we propose that TERRA upregulation may contribute to the ALT induction observed in BJT cells (Figure 2A, Supplementary Figure 2A, B). Given the high basal TERRA expression, expression of VP64-TRF1 and KRAB-TRF1 did not result in a consistent change in TERRA levels (Supplementary Figure 2F). Thus, the ability of VP64-TRF1 to suppress ALT activity in WI38-2RA/VA13 cells could be attributed to the reduction of telomere H3K9me3 and heterochromatin loss. Altogether, our results support the hypothesis that heterochromatin formation, rather than euchromatin triggers ALT.

      We thank the reviewer's insightful comments, which have allowed us to resolve the ambiguity of our results and strengthen the notion that heterochromatin formation promotes ALT. We think that the heterochromatin features and high TERRA expression represent two independent, coexisting mechanisms within ALT cancer cells to guarantee ALT activation. We have modified the main text on page 4-5 accordingly.

      TERRA downregulation contradicts current models: while TERRA upregulation is often observed in ALT cells and is thought to contribute to replication stress and recombination at telomeres, the authors show that TRF1-KAP1 expression induces ALT features while TERRA is downregulated. This observation is not addressed in the manuscript. The authors should at least discuss this discrepancy and propose whether this reflects a cell line-specific phenomenon or a decoupling between TERRA levels and ALT induction in this context.

      Response: We thank the reviewer for the comments. As mentioned above (Major Concerns 2), heterochromatin formation and TERRA expression are two mechanisms that can independently promote ALT. Unlike ALT cell lines that have high TERRA levels, human fibroblasts BJ cells have low TERRA that does not induce ALT phenotypes. Thus, the effect of KRAB-TRF1 on ALT induction in BJ cells could be attributed to the heterochromatin formation, but not reduction of TERRA. We have modified the main text on page 5 to clarify the result.

      Minor Comments

      Introduction (p. 3): The authors cite Episkopou et al. as showing increased H3K9me3 at ALT telomeres. This is incorrect; that paper suggests the opposite. The first study to clearly demonstrate H3K9me3 enrichment at ALT telomeres is Cubiles et al., 2018 and should be cited instead. Results (p. 5, first paragraph): The manuscript should cite Déjardin and Kingston, 2009 as the first to report COUP-TF2 and TR4 localization at ALT telomeres. The studies by Conomos et al., 2012 and Gaela et al., 2024 build on this prior evidence. Please also include this citation in the bibliography.

      Response: We appreciate the reviewer's careful reading and for pointing out these errors. The citation errors on pages 2 and 3 have now been corrected.Broader relevance of TRIM28-OR interaction: TRIM28 is a complex protein with roles in SUMOylation, heterochromatin formation, and transcriptional initiation/elongation regulation.

      The authors should explore whether similar COUP-TF2/TRIM28 interactions occur at other genomic loci. Public ChIP-seq data for COUP-TF2, TR4, and TRIM28 could be mined to investigate whether these factors co-occupy regulatory regions elsewhere in the genome, and how this relates to gene expression states.

      Response: We appreciate the reviewer's insightful suggestion regarding a potential genome-wild functional interaction between TRIM28 and COUP-TF2. To address this, we analyzed public ENCODE ChIP-seq data from K562 cells (TRIM28: ENCSR000BRW; COUP-TF2: ENCSR000BRS). This analysis revealed 3,326 co-binding sites for TRIM28 and COUP-TF2 (Attached Figure A). Interestingly, these co-binding sites were preferentially located within gene bodies (70.7%) and promoter regions (4.3%) (Attached Figures B-D), suggesting a potential cooperative role in gene regulation that aligns with our observation of physical interaction. While the finding is intriguing, a full exploration is beyond the scope of this manuscript, which focuses on ALT telomere regulation. We consider this is an important insight and have briefly noted it in the discussion (p. 9), although the corresponding analyses are not included in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      This work contributes mechanistic insight into how heterochromatin is established at ALT telomeres-an important and timely question in telomere biology and cancer research. It offers a noncanonical recruitment mechanism for TRIM28, independent of KRAB-ZNFs, and highlights the functional role of orphan nuclear receptors in telomeric chromatin regulation. The study has potential implications for understanding ALT regulation and for identifying new intervention points in ALT-positive cancers. The work is conceptually interesting, but the conclusions are currently limited by insufficient quantification, some interpretative ambiguities, and a few overlooked references. Addressing the concerns listed above would significantly enhance the rigor and impact of the manuscript.

      Response: We appreciate the reviewer's recognition of the significance of our work in elucidating the molecular basis of ALT regulation through COUP-TF2-TRIM28-mediated heterochromatin formation. We also thank the reviewer for the valuable feedback, which has significantly strengthened our manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This manuscript investigates the role of orphan nuclear receptors (ORs), specifically COUP-TF2 and TR4, in promoting H3K9me3 enrichment at ALT telomeres via recruitment of TRIM28 (KAP1). The authors propose that the AF2 domain of COUP-TF2, located in its ligand-binding domain (LBD), is sufficient to recruit TRIM28 to telomeres. This, in turn, promotes heterochromatinization and induces hallmarks of the Alternative Lengthening of Telomeres (ALT) pathway, including APB formation and telomeric DNA synthesis outside of S-phase. This study addresses one important and unresolved question in the field: by what mechanism is the heterochromatic state established at ALT telomeres? Another timely question, not addressed here is: how is heterochromatin (specifically H3K9me3) functionally linked to ALT? The findings are potentially novel and mechanistically insightful. However, key elements of the study, particularly the central tethering experiments, require stronger quantification and clarity. Additional mechanistic tests and literature adjustments would also improve the manuscript.

      Major Concerns

      1. Central TRF1-COUP-TF2-LBD result lacks quantification and clarity: the tethering of COUP-TF2's LBD to telomeres via TRF1 is a core result of the paper. This experiment demonstrates that this domain is sufficient to induce weak H3K9me3 enrichment and ALT features (APBs and ATDS). However, the supporting ALT data are presented only in Supplementary Figures S1A and S1B, and are not quantified. These data should be quantified with appropriate statistics and moved to a main figure. Furthermore, the broader functional implication is not explored. Does this tethering induce a fully functional ALT pathway? For example, can telomerase knockout cells expressing TRF1-COUP-TF2-LBD maintain long-term proliferation? Such evidence would significantly strengthen the impact of the study.
      2. Chromatin manipulation experiments lead to ambiguous conclusions: the authors propose that telomeric heterochromatin promotes ALT activity, but their own experiments (e.g., Figure 2) show that both heterochromatin-inducing (KRAB-TRF1) and euchromatin-inducing (VP64-TRF1) tethering can trigger ALT-like features. This makes it difficult to conclude that heterochromatin is specifically required. To clarify:
      3. Did the authors express TRF1-VP64 in an ALT cell line? According to their model, this should suppress ALT activity.
      4. More broadly, do chromatin alterations per se (regardless of direction) trigger ALT features? Clarifying these points is important for interpretation.
      5. TERRA downregulation contradicts current models: while TERRA upregulation is often observed in ALT cells and is thought to contribute to replication stress and recombination at telomeres, the authors show that TRF1-KAP1 expression induces ALT features while TERRA is downregulated. This observation is not addressed in the manuscript. The authors should at least discuss this discrepancy and propose whether this reflects a cell line-specific phenomenon or a decoupling between TERRA levels and ALT induction in this context.

      Minor Comments

      Introduction (p. 3): The authors cite Episkopou et al. as showing increased H3K9me3 at ALT telomeres. This is incorrect; that paper suggests the opposite. The first study to clearly demonstrate H3K9me3 enrichment at ALT telomeres is Cubiles et al., 2018 and should be cited instead. Results (p. 5, first paragraph): The manuscript should cite Déjardin and Kingston, 2009 as the first to report COUP-TF2 and TR4 localization at ALT telomeres. The studies by Conomos et al., 2012 and Gaela et al., 2024 build on this prior evidence. Please also include this citation in the bibliography. Broader relevance of TRIM28-OR interaction: TRIM28 is a complex protein with roles in SUMOylation, heterochromatin formation, and transcriptional initiation/elongation regulation. The authors should explore whether similar COUP-TF2/TRIM28 interactions occur at other genomic loci. Public ChIP-seq data for COUP-TF2, TR4, and TRIM28 could be mined to investigate whether these factors co-occupy regulatory regions elsewhere in the genome, and how this relates to gene expression states.

      Significance

      This work contributes mechanistic insight into how heterochromatin is established at ALT telomeres-an important and timely question in telomere biology and cancer research. It offers a noncanonical recruitment mechanism for TRIM28, independent of KRAB-ZNFs, and highlights the functional role of orphan nuclear receptors in telomeric chromatin regulation. The study has potential implications for understanding ALT regulation and for identifying new intervention points in ALT-positive cancers.

      The work is conceptually interesting, but the conclusions are currently limited by insufficient quantification, some interpretative ambiguities, and a few overlooked references. Addressing the concerns listed above would significantly enhance the rigor and impact of the manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This manuscript builds upon the authors' prior findings that targeting COUP-TF2 to TRF1 induces ALT-associated phenotypes and G2-mediated synthesis in telomerase-immortalised BJT human fibroblasts. In this study, the authors show that telomere-coupled COUP-TF2 promotes H3K9me3 enrichment in these cells, and that this effect is blocked by TRIM28 depletion. Furthermore, TRIM28 depletion also suppresses the formation of ALT phenotypes in VA13 ALT cells. Given that TRIM28 has been implicated in regulating H3K9me3 deposition via SETDB1, and has been reported to co-purify with TR2 and TR4 (though not previously in the context of ALT telomeres), these findings add mechanistic depth to how heterochromatin regulators contribute to ALT activity. Overall, the manuscript's conclusions are generally supported by the presented data, but several aspects require clarification or additional experimental validation.

      • The authors report a modest reduction in telomeric H3K9me3 following COUP-TF2 and TR4 depletion in U-2 OS and VA13 cells (Figure 1B). To strengthen the claim that these orphan receptors specifically regulate H3K9me3, the authors should 1) Assess additional heterochromatic histone marks (e.g., H4K20me3) at telomeres, 2) Normalize telomeric signals to both parental histone levels and input, and 3) Evaluate whether global H3K9me3 levels also decrease upon receptor depletion
      • Most experiments explore chromatin changes in telomerase-positive BJT fibroblasts (Figure 2, Figure 4D). It remains unclear whether similar manipulations in ALT cells yield consistent effects, which would give a broader context for ALT phenotype induction. Are ALT phenotypes similarly induced in ALT cells? Does altered chromatin status affect telomere length or telomerase recruitment/activity? Can these pathways drive ALT phenotypes in non-immortalised cells?
      • When referring to Figure 3G, the authors state that that telomeric H3K9me3 was abolished upon depleting TRIM28 from the U2OS and WI38-VA13/2RA cells. Abolished is a strong word for a 50% decrease, and this sentence should be revised. The reduction appears greater than that seen with COUP-TF2/TR4 depletion. Are the effects additive? If so, might TRIM28 act, at least in part, independently of COUP-TF2/TR4?
      • VA13 cells consistently exhibit stronger effects than U-2 OS (e.g., Figures 1 and 3). This discrepancy could be linked to the high content of variant repeats in VA13 cells. The authors should assess whether variant repeat content underlies the differential response. Repeating key experiments in additional ALT lines with varied repeat compositions would be informative.
      • In line with the previous point, it would be useful to show whether TRIM28 telomeric enrichment is affected by COUP-TF2/TR4 depletion in U-2 OS cells (Figure 4C). To improve confidence in these findings, the authors should perform telomeric ChIP assays, especially with the COUP-TF2^LBDΔAF2-TRF1 mutant construct.
      • The immunoprecipitation experiments showing TRIM28 association with orphan receptors should include benzonase treatment to rule out DNA-mediated co-association (Figure 4F-G).
      • The study would benefit from a direct assessment of whether COUP-TF2LBDΔAF2-TRF1 fails to induce ALT phenotypes in BJT fibroblasts.
      • The experiments performed in Figure 5E-H lack a vector-only + siCtrl control.
      • In Figure 5E, the observation that APB formation is restored in siTRIM28 + Vector-treated cells is unexpected. The authors should address this finding and clarify whether this reflects biological noise or a compensatory effect.

      Significance

      This work offers valuable mechanistic insight into how COUP-TF2 and TRIM28 coordinate to regulate heterochromatin deposition and ALT phenotype formation. It adds to the growing understanding of chromatin-mediated telomere regulation. What remains unclear is how important this interaction is for ALT maintenance, as H3K9me3 is only moderately altered upon TRIM28 depletion in ALT cells. Depletion of TRIM28 has been shown previously to induce APB formation and telomere elongation in U-2 OS ALT cells (Wang et al., 2021), the opposite to what the authors observed here in VA13 cells (Figure 5E-H). Clarifying whether these differences are variant repeat-dependent, or reflect intrinsic features of specific ALT cell lines, would substantially elevate the study's impact.

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      Reply to the reviewers

      1. General Statements

      In this study, we mechanistically define a new molecular interaction linking two of the cell's major morphological regulatory pathways-the Rho GTPase and Hippo signaling networks. These two major signaling pathways are both required for life across huge swaths of the tree of life. They are required for the dynamic organization and reorganization of proteins, lipids, and genetic material that occurs in essential cellular processes such as division, motility and differentiation. For decades these pathways have been almost exclusively studied independently, however, they are known to act in concert in cancer to drive cytoskeletal remodeling and morphological changes that promote proliferation and metastasis. However, mechanistic insight into how they are coordinated is lacking.

      Our data reveal a mechanistic model where coordination is mediated by the RhoA GTPase-activating protein ARHGAP18, which forms molecular interactions with both the tumor suppressor Merlin (NF2) and the transcriptional co-regulator YAP (YAP1). Using a combination of state-of-the-art super-resolution microscopy (STORM, SORA-confocal) in cultured human cells, biochemical pulldown assays with purified proteins, and analyses of tissue-derived samples, we characterize ARHGAP18's function from the molecular to the tissue level in both native and cancer model systems.

      Together, these findings establish a previously unrecognized molecular connection between the RhoA and Hippo pathways and culminate in a working model that integrates our current results with prior work from our group and decades of prior studies. This model provides a new conceptual framework for understanding how RhoA and Hippo signaling are coordinated to regulate cell morphology and tumor progression in human cells.

      In this substantially revised manuscript, we have addressed all comments from the expert reviewers described point-by-point below. A shared major comment from the reviewers was the request for direct evidence of the proposed mechanistic model. To address these constructive comments, we've added new experiments, new quantification, new text, new control data, and have added two expert authors, adding super-resolution mouse tissue imaging data for the endogenous study of ARHGAP18 in its native condition. We believe that these additions greatly enhance the manuscript and collectively address the overall message from the reviewer's collective comments.

      2. Point-by-point description of the revisions

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

      This manuscript describes a dual mechanism by which ARHGAP18 regulates the actin cytoskeleton. The authors propose that in addition to the known role for ARHGAP18 in regulating Rho GTPases, it also affects the cytoskeleton through regulation of the Hippo pathway transcriptional regulator YAP. ARHGAP18 knockout Jeg3 cells are were generated and show a clear loss of basal stress fiber like F-actin bundles. The authors further characterize the effects of ARHGAP18 knockout and overexpression. It is also discovered that ARHGAP18 binds to the Hippo pathway regulator Merlin and to YAP. Ultimately it is concluded that ARHGAP18 regulates the F-actin cytoskeleton through dual regulation of RHO GTPases and of YAP. While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      • *

      *We appreciate the reviewers' constructive comments and have added substantial new data and quantifications to address their concerns. We have focused these new data on directly testing the proposed mechanisms, adding controls, and performing quantitative analysis with statistical testing. Additionally, we have edited our language to make our rationale clearer and to present our conclusions as a more moderate assessment of our experimental results. Below we respond to the specific comments made by the reviewer, followed by a list of additional editorial changes we've made based on the reviewer's overarching comments on clarity and rationale. *

      Specific Comments

      1) The authors make a big point about the effects of ARHGAP18 on myosin light chain phosphorylation. However, this result is not quantified and tested for statistical significance and reproducibility.

      *We thank the reviewer for their comments on our western blotting quantification, which in the original submission version had quantification of RhoA downstream signaling of pCofilin/ Cofilin and pLIMK/ LIMK. We had withheld the pMLC and MLC quantification as the result was previously published with quantification, reproducibility, and statistical significance by our group in our prior manuscript on ARHGAP18 published in Elife in 2024 (Fig. 4E of *

      https://doi.org/10.7554/eLife.83526 ). However, these prior results lacked the new overexpression data. We recognize the need to add these data to this manuscript as requested by the reviewer.

      • *

      *To address the reviewer's comment, we have added quantification of pMLC/MLC (Fig. 1F) *

      2) Along similar lines in Figure 2C they state that overexpression of ARHGAP18 causes cells to invade over the top of their neighbors. This might be true and interesting, but only a single cell is shown and there is no quantification or controls for simply overexpressing something in that cell. The authors also conclude from this image that the overexpression phenotype is independent of its GAP activity on Rho. It is not clear how this conclusion is made based on the data. It would seem like a more definitive experiment would be to see if a similar phenotype was induced by an ARHGAP18 mutant deficient in GAP activity.

      Based on the reviewer's comment, we recognize the qualitative statements made in Figure 2C (now Figure 3) should've been made more quantitative. We have added the control of Jeg 3 WT cells expressed with empty vector flag to show that WT cells do not invade over the top of each other (Fig. 3F). Additionally, we have added the quantification found in Fig. 3E, which shows the % invasive/ non-invasive cells between WT and ARHGAP18 overexpression cells. We have clarified our conclusions to make clear that these data do not directly test if the invasive phenotype derives from a Rho-independent mechanism. The text now states the following conclusion alongside others, which can be seen in our tracked changes:

      • *

      "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin. However, it was not clear whether this activity occurred through a Rho-independent or a Rho-dependent mechanism."

      • *

      We have added new data of cells expressing an ARHGAP18 mutant deficient in GAP activity, which is explained in detail in the following response below.

      3) In Figure 3 the authors compare gene expression profiles of ARHGAP18 knockout cells to wild-type cells. They see lots of differences in focal adhesion and cytoskeletal proteins and conclude that this supports their conclusion that ARHGAP18 is not just acting through RHO. The rationale for this in not clear. In addition, they observe changes in expression profiles consistent with changes in YAP activity. They conclude that the effects are direct. This very well might be true. However RHO is a potent regulator of YAP activity and the results seem quite consistent with ARHGAP18 acting through RHO to affect YAP.

      • *

      We thank the reviewer for their comment and believe the revised manuscript now presents direct evidence to support the conclusions made through the editing text and the incorporation of new data.

      • *

      First, the reviewer highlighted that we were not clear in our rationale and explanation of the conclusions made from our RNAseq data in the new Figure 4 (Previously Figure 3). We agree with the reviewer that the RNAseq data alone is not sufficient rationale for the conclusion that ARHGAP18 is acting through YAP directly. In the revised manuscript, the conclusion is now made based on the combination of our multi-faceted investigation of the relationship between ARHGAP18 and YAP (most importantly, new Figure 5). It's important for us to argue that our RNAseq analysis is much more robust and specific than simply reporting a descriptive assay seeing lots of differences in cytoskeletal proteins. We recruited an outside RNAseq expert collaborator; Dr. Yongho Bae, to perform state-of-the-art IPA analysis and a grueling manual curation of the top hit genes to identify the predominant signaling pathways linking the loss of ARHGAP18 to known YAP translational products. We've provided a supplemental table listing each citation supporting the identified YAP pathway associations from this manual curation. We also have added a new discussion paragraph on RNAseq data to clarify our specific RNAseq data results and analysis. In the revised manuscript, we have moderated our language in the results text regarding the RNAseq data to reflect the reviewer's suggestion:

      • *

      "Our RNAseq data alone could not independently confirm if the alterations to transcriptional signaling and expression of actin cytoskeleton proteins were through a Rho-dependent or Rho-independent mechanism."

      • *

      • *

      Second, in this comment and the above, the reviewer highlights the need for a new experiment to directly test the Rho Independent effects of ARHGAP18, which we now provide in the new Figure 5. In this new data, we've applied an experimental design suggested by reviewer 2 regarding the same concern. In short, we've produced and expressed a point mutant variant ARHGAP18(R365A), which abolishes the Rho GAP activity while maintaining the remainder of the protein intact. This construct allows us to directly test the effects of ARHGAP18 independent from its RhoA GAP activity. We find that the GAP-deficient ARHGAP18 is able to fully rescue basal focal adhesions, indicating that the basal actin phenotype is at least in part regulated through a Rho-independent mechanism.

      • *

      • *

      *We believe the revised manuscript, when taken in totality, provides the definitive proof requested by the reviewer. Specifically, the combination of Figure 5, where we show new data using the ARHGAP18(R365A) variant, and the result that ARHGAP18 forms a stable complex with YAP (Fig. 6G) or Merlin (Fig.6A), is supportive of direct Rho-independent molecular interactions between YAP, Merlin, and ARHGAP18. *

      4) In Figure 4A showing Merlin binding to ARHGAP18 there is no control for the amount of Merlin sticking to the column as was done in Figure 4F for binding experiments with YAP. This makes it difficult to determine the significance of the observed binding.

      We have performed the requested control experiment and added the results to Figure 6A.

      5) The images in Figure 4C showing YAP being maintained in the nucleus more in ARHGAP18 knockout cells compared to wild-type. However the images only show a few cells and YAP localization can be highly variable depending on where you look in a field. Images with more cells and some sort of quantification would bolster this result.

      We have provided quantification (Figure 6D) of what was originally Figure 4C (now Figure 6C).

      Reviewer #1 (Significance (Required)):

      While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      In the above comments, we detail the specific definitive experiments, proper controls, and statistical tests for significance, requested by the reviewer, which we believe greatly strengthen our manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript investigates the Rho effector, ARHGAP18 in Jegs cells, a trophoblastic cell line. It presents a number of new pieces of data, which increase our understanding of the importance of this GAP on cell function and explains at a molecular level previous results of other workers in the field. ARHGAP18 was originally given the name "conundrum' and continues to stand apart from the majority of other GAP proteins and their functions. Hence the data here is significant and of high standard.

      The data is clear, and the images are of high quality and extremely impressive in their resolution. It is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      • *

      We appreciate the reviewer's comments and supportive insights.

      The data is based on the use of the cell line Jeg3. Even the authors previous publication in eLife is based only on this cell line. They need to show the conclusions are general and not specific to this line of cells. As an extension of this, is the ARHGAP18 function shown here only in transformed cells? Does the same mechanisms operate in normal cells, which respond to activation to proliferate or migrate?

      • *
      • We respectfully point out that the critical experiments of the prior eLife publication were validated in DLD-1 colorectal cells and not Jeg-3 cells alone (Figure 1-figure supplement 2). Our newly independent lab, established just over a year ago, is unable to perform a full expansion of the manuscript using untransformed cells, however, we agree with the reviewer's perspective and wish to address the comment to the best of our current capability. To answer the reviewers' suggestions, we have recruited Dr. Christine Schaner Tooley, an expert in mouse model system studies. In the revised manuscript, we've added new Super-Resolution SORA confocal images of endogenous ARHGAP18's localization in the intact intestinal villi tissue, and apical junctions of WT mice (Fig.1A-C). These data indicate that endogenous ARHGAP18 is enriched (but not exclusively localized) at the apical plasma membranes of normal WT epithelial cells. This localization, where both Merlin and Ezrin are present at apical membrane/ junctions under normal conditions, is a major component of the working model proposed in Fig. 7. These data also indicate that ARHGAP18 is capable of entering the nucleus in WT cells, another critical aspect of our proposed model. Collectively, our DLD-1 studies published previously and or new studies using WT mice tissue samples support the conclusion that at least some of ARHGAP18's functions described in this manuscript are not limited to Jeg3 cells.*

      In endothelial cells, Lovelace et al 2017 showed localization to microtubules and that depletion of ARHGAP18 resulted in microtubule instability. The authors may like to comment on the differences. Is this a cell type difference or RhoA versus RhoC difference?

      • *

      In our previous publication (Lombardo Elife), we validated the finding that ARHGAP18 forms a complex with microtubules, as we detected tubulin in the ARHGAP18 pulldown experiment (Figure 1- Source Data). However, our data indicate that in Jeg3 cells ARHGAP18 does not localize to the same microtubule associated spheres observed in the Lovelace publication. We now comment on the shared conclusions and differences between this manuscript and the Lovelace et al 2017 in the discussion section.

      • *

      "In endothelial cells, ARHGAP18 has been reported to localize microtubules and plays a role in maintaining proper microtubule stability (Lovelace et al., 2017). In our epithelial cell culture models and WT mouse intestine, we have been unable to detect ARHGAP18 at microtubules suggesting ARHGAP18 may have additional functions is various cell types."

      On pages 7,9 they conclude that MLC and basal and junctional actin are regulated through a GAP independent mechanism. The best way to show this is with overexpression of a GAP mutant.

      We appreciate the reviewer's insight and have produced and expressed a GAP mutant, ARHGAP18(R365A), in our cells, directly testing our conclusion that ARHGAP18 has a GAP-independent function. These data are now presented in revised Figure 5 and explained further in response to reviewer #1.

      There is a huge amount of data presented in Figure 3, but their 2 genes which they focus on, LOP1 and CORO1A, are discussed but no actual data presented in support.

      We now validate the CORO1A by qPCR in Figure 4J.

      • *

      Reviewer #2 (Significance (Required)):

      The data is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli. This manuscript will be of significance to an basic science audience in the field of RhoGTPases and cell migration.

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

      The study by Murray et al explores the effects of ARHGAP18 on the actin cytoskeleton, Rho effector kinases, non-muscle myosin, and transcription. Using super resolution microscopy, they show that in ARHGAP18 KO cells there is a mixed and unexpected cytoskeleton phenotype where myosin phosphorylation appears to be increased, but actin is disorganised with reduced stress fibres, diminished focal adhesions and augmented invasiveness. They conclude that the underlying mechanisms are likely independent from RhoA. Next, they perform RNAseq using the KO cells and identify an array of dysregulated genes, including those that play crucial roles in microvilli (related to previously published findings). Analysis of the data identify gene expression changes that are relevant for altered focal adhesion (integrins). Further analysis reveals that a large cohort of the dysregulated genes are YAP targets. They then show that in ARHGAP18 KO cells YAP nuclear localization, as detected by immunostaining, is augmented; and demonstrate that immobilized ARHGAP18 protein can bind the Hippo regulator merlin as well as YAP itself.

      Major comments:

      1, The premise of the study (that ARHGAP18 is a RhoA effector or may acts independently of RhoA) remains not proven.

      We have added new evidence of direct RhoA independent activity for ARHGAP18 described in the above comments. Specifically, we've added data using a RhoA-GAP dead variant of ARHGAP18 in Figure 5, which we believe addresses this comment.

      • *

      At several places (including in the title) the authors refer to ARHGAP18 as a Rho effector, which would suggest that it is downstream form Rho, but the basis for this is not clear. In fact, their own previous study suggested that ARHGAP is a RhoA regulator, rather than an effector. In general, the connection of the described effects to RhoA remains unclear, and not addressed in this study. The authors seem to go back and forth in their conclusions regarding the connection between ARHGAP18 and RhoA. For example, the first section of results is finished by stating (line 194): "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin through Rho-independent mechanism". But the next section starts by stating (line 198): "We hypothesized that the invasive and cytoskeletal phenotypes observed at the basal surface of cells devoid of ARHGAP18 may be a result of changes in regulation at the transcriptional level either directly through RhoA signaling or through an additional mechanism specific to ARHGAP18". The paper would be strengthened by adding data that show whether the effects are indeed downstream, from RhoA or RhoA independent. If there is no sufficient demonstration that ARHGAP18 is downstream of RhoA and is an effector, this needs to be stated explicitly, and the wording should be changed.

      *We now provide new data in Figure 5, which directly tests the RhoA independent functions of ARHGAP18 as recommended by the reviewer. Our understanding of the term effector is 'a molecule that activates, controls, or inactivates a process or action.' Based on this understanding, we used the term to convey ARHGAP18's functional role within the feedback loop, rather than to imply that it acts exclusively downstream. *

      • *

      We seek to clarify our perspective with the reviewer's assertion that we go "back and forth" as to if ARHGAP18 functions in a Rho Dependent or Rho Independent manner. It was our intent to propose a model where ARHGAP 18 acts in two separate circuits that regulate cell signaling. The first circuit involves ARHGAP18's canonical RhoA GAP activity, which involves ERMs and LOK/SLK, and is limited to the apical plasma membrane. This first signaling circuit was characterized in our prior Elife manuscript (Lombardo et al., 2024) and in an earlier JCB manuscript (Zaman and Lombardo et al., 2021). In this newly revised manuscript, we provide a partial mechanistic characterization of the second circuit, which we freely admit is much more complex and will likely require additional study to fully characterize.

      • *

      As both circuits operate as signaling feedback loops, we find the terms 'upstream' and 'downstream' to be of limited value, and we attempt to avoid their use when possible. We retain their use only when referring to the Hippo and ROCK signaling cascades, where these designations are well established. We suggest that the conceptual inconsistencies of Conundrum/ARHGAP18 may have arisen from the tendency to view it in strictly binary terms as upstream or downstream. Here, we propose a third possibility that ARHGAP18 functions as both, participating in a negative feedback loop.

      • *

      *We have edited and added data testing if the effects are Rho independent and discussion text in response to the reviewer's comments and clarify the molecular function of ARHGAP18.

      "Additionally, focal adhesions and basal actin bundles are restored to WT levels when the ARHGAP18(R365A) GAP-ablated mutant is expressed in ARHGAP18 KO cells (Fig. 5A, B). These results represent the strongest argument that ARHGAP18 functions in additional pathways to RhoA/C alone. Our data suggests that at least one of the alternative pathways is through ARHGAP18's interaction with YAP and Merlin. From these data we conclude that ARHGAP18 has important functions in both RhoA signaling through both its GAP activity and in Hippo signaling through its GAP independent binding partners. "*

      • *

      • *

      The study is descriptive and contains a series of observations that are not connected. Because of this, the study's conclusions are not well supported, and key mechanistic insight is limited. The study feels like a set of separate observations, that remain incompletely worked out and have some preliminary feel to them. The model in the last figure also seems to contain hypotheses based on the observations, several of which remains to be proven.

      • *

      *We present our revised manuscript, in which we've more clearly outlined our rationale and conclusions, as detailed in the above responses, to emphasize the overall connectivity of the study. We have also updated the title of Figure 7 to read "__Theoretical __Model of ARHGAP18's coordination of RhoA and Hippo signaling pathways in Human epithelial cells." To make it clear that we are presenting a working model, which has elements that will require additional investigation. Throughout the manuscript, we highlight the unknown elements that remain to be tested or other outstanding questions. Thus, we do not aim to characterize this complex signaling coordination completely. Instead, this manuscript represents the 3rd iteration in our systematic advances to describe this entirely new signaling pathway. We agree that, despite three separate manuscripts (this one included) to date, this work represents an early stage in understanding the system, many additional studies will be needed to characterize this signaling system fully. Figure 7 is presented as a working model that results from a thoughtful combination of our collective data and that of other researchers, derived from numerous species across decades of study. We firmly believe that proposing such integrative models is valuable for advancing the field. We also recognize the importance of clearly indicating which aspects remain hypothetical. We now explicitly note in several places within the discussion which components of the model will require further validation and experimental confirmation. For example, regarding our theoretical mechanism in Figure 7 we state: *

      "Validation of the direct mechanism by which YAP/TAZ transcriptional changes drive basal actin changes in ARHGAP18 KO cells will require further investigation based on predictions from RNAseq results."

      • *

      Addressing any possible connection between key effects of ARHGAP18 KO (changes in actin, focal adhesion, integrins, Yap and merlin binding) could strengthen the manuscript. One such specific question is the whether the changes in integrin expression (RNAseq) are indeed connected to the actin alterations and reduction ion focal adhesions (Fig 1). Staining for these integrins to show they are indeed altered, and/or manipulating any of them to reproduce changes could provide and exciting addition.

      • *

      *We attempted to stain cells for Integrins by purchasing three separate antibodies. However, despite extensive optimization and careful selection of the specific integrins using our RNAseq results we were unable to get any of these antibodies to work in any cell type or condition. We believe that there is a technical challenge to staining for integrins due to their transmembrane and extracellular components, which we were unable to overcome. As an attempt to address the reviewers comment, we alternatively stained cells for paxillin which directly binds the cytoplasmic tails of integrins (Fig. 3&5). *

      Some of the experimental findings are not convincing or lack controls. Fig 1: some of the western blots are not convincing or poor quality. [...] On the same figure, the quality of LIM kinase blots is poor. [...] The signal is weak, and the blot does not appear to support the quantification. The last condition (expression of flag-ARHGAP18) results in a large drop in pLIMK and pcofilin on the blot, which is not reflected by the graph. Addition of *a better blot and the use of strong positive or negative control would boost confidence in these data. *

      • *

      In response to this and other reviewers' comments, we have added new western data and quantification to Figure 1. We now focus on MLC/pMLC data as we believe these data highlight the potential Rho-independent mechanism of ARHGAP18, and we were able to greatly improve the quality of the blots through careful optimization. We hope the reviewer finds these blots and quantifications (Fig. 1E and F) more convincing.

      *We note that phospho-specific Western blotting presents considerably greater technical challenges than conventional blotting. We believe that the appearance of an attractive looking blot does not always correlate to quality or reproducibility and have focused on taking extraordinarily careful steps in the blotting of our phospho-specific antibodies, which at times comes at the cost of the blot's attractiveness in appearance. For example, all phospho-specific antibodies are run using two color fluorescent markers to blot against both the total protein and the phospho-protein on the same blot. This approach often leads to blots that have reduced signal to noise compared to chemiluminescent Westerns. Additionally, we use phospho-specific blocking buffer reagents which do not contain phosphate-based buffers or agents that attract non-specific phospho-staining signals. These blocking buffers are not as effective as non-fat milk in pbs at blocking the background signal, however, they are ultimately cleaner for phospho-specific primary antibodies. We use carefully optimized protocols, from cell treatment to lysis, transfer, and antibody incubation, including methods developed by laboratories where the corresponding author of the manuscript was trained. Nonetheless, despite these efforts, we have now removed the LIMK and cofilin data because we deemed them unnecessary for the main conclusions of this manuscript and were unable to improve their quality to satisfy the reviewer. *

      The changes in pMLC on the western blots are very small, and for any conclusion, these studies require quantification. Further, the expression levels of Flag-ARHGAP18 needs to be shown to support the statement that the protein is expressed, and indeed overexpressed under these conditions (vs just re-expressed).

      In continuation of the above comment, we have made significant effort to improve the quality of our pMLC western blots and now provide quantification in Figure 1. We also now provide the Flag-ARHGAP18 signal as requested by the reviewer.

      Fig 4: the differences in YAP nuclear localization under the various conditions are not well visible. Quantitation of nuclear/cytosolic signal ratio should be provided. Please provide a rationale and more context for using serum starvation and re-addition. What is the expected effect? Serum removal and addition is referred to as nutrient removal and re-addition, but this is inaccurate, as it does not equal nutrient removal, since serum contains a variety of other important components, e.g. growth factors too.

      We have provided new quantification of the nuclear/cytosolic signal ratio in Figure 6D. We have explained our rational for the study through the following new text:

      "Merlin is activated and localized to junctions upon signaling, promoting growth and proliferation; among these signals is the availability of growth factors and other components of serum (Bretscher et al., 2002). We hypothesized that since ARHGAP18 formed a complex with Merlin that ARHGAP18's localization may localize to junctions under conditions which promote Merlin activation."

      • *

      We have altered our use of "nutrient removal" to "serum removal"

      The binding between ARHGAP18 and merlin is interesting, but a key limitation is the use of expressed proteins. Can the binding be shown for the endogenous proteins (IP, colocalization). Another important unaddressed question is the relevance of this binding, and the relation of this to altered YAP nuclear localization.

      • *

      *Our data in Fig. 6G shows binding of a resin bound human ARHGAP18 to endogenous YAP from human cells as suggested by the reviewer. In Fig. 6A, we have selected to use GFP-Merlin as Merlin shares approximately 60% sequence identity with Ezrin, Radixin, and Moesin (ERMs). Their similarity is such that Merlin was named for Moesin-Ezrin-Radixin-Like Protein. In our experience, nearly all Merlin or ERM antibodies have some cross-contaminating signal. Thus, a major concern is that if we were to blot for endogenous Merlin in the pull-down experiment, we may see a band that could in fact be ERMs. To avoid this, we tagged Merlin with GFP to ensure that the product pulled down by ARHGAP18 was Merlin, not an ERM. Regarding the ARHGAP18-resin bound column, our homemade ARHGAP18 antibody is polyclonal. We have extensive experience in pulldown assays and have found that the binding of a polyclonal antibody to the bait protein can produce less accurate results, as the binding site for the antibody is unknown and can sterically hinder attachment of target proteins like Merlin. In our experience, attachment to a flag-tag, which is expressed after a flexible linker at the N- or C-terminus, allows us to overcome this limitation, which we've used in this manuscript. *

      Minor comments:

      Introduction line 99: "When localized to the nucleus, YAP/TAZ promotes the activation of cytoskeletal transcription factors associated with cell proliferation and actin polymerization" Please clarify what you mean by this statement, that is inaccurate in its present for. Did you mean effects on transcription factors that control cytoskeletal proteins, or do you mean that Yap/Taz affect these proteins? Please also provide reference for this.

      We've altered the sentence as suggested by the reviewer, which now reads the following:

      "When localized to the nucleus, YAP/TAZ promotes transcriptional changes associated with cell proliferation and actin polymerization."

      • *

      *The full mechanism for how YAP/TAZ promotes proliferation and actin polymerization is a currently debated issue. We do not think introducing the various current proposed models is required for this manuscript, and we simply intend to convey that when in the nucleus, YAP/TAZ promotes transcriptional changes that drive actin polymerization and cell proliferation. *

      -What is the cell confluence in these experiments? For epithelial cells confluence affects actin structure. Please comment on similarity of confluency across experimental conditions?

      • *

      All cellular experiments are paired where WT and ARHGAP18 KO cells are plated at the same time under identical conditions. For imaging, we plate all cells onto glass coverslips in a 6 well dish so that each condition is literally in the same cell culture plate and gets identical treatment. In our prior Elife paper studying ARHGAP18, we characterized that ARHGAP18 KO cells and WT cells divide at a similar rate and have similar proliferation characteristics. The epithelial cell cultures are maintained for experiments around 70-80% confluency. For the focal adhesion staining experiments, the confluency is slightly lower, between 50-60% to capture the focal adhesions towards the leading edge. We have added the following new text to further describe these methods: "Cell cultures for experiments were maintained at 70%-80% confluency. For focal adhesion experiments, the cell cultures were maintained at 50%-60% confluency."

      -Fig 2 legend: please indicate that the protein detected was non-muscle myosin heavy chain (distinct from the light chain detected in Fig 1).

      • *

      We have altered original Figure 2 (new Figure 3) legend.

      -Line 339-340: please check the syntax of this sentence -Western blot quantification: the comparison of experiments with samples run on different gels/blots requires careful normalization and experimental consistency. Please describe how this was achieved.

      • *

      We have added the following new text to further describe these methods:

      "For blots which required quantification of antibodies that were only rabbit primaries (e.g., pMLC/MLC antibodies listed above), samples were loaded onto a single gel and transferred onto a single membrane at the same time. After transfer, the membrane was cut in half and subsequent steps were done in parallel. All quantified blots were checked for equal loading using either anti-tubulin as a housekeeping protein or total protein as detected by Coomassie staining"

      Reviewer #3 (Significance (Required)):

      Rho signalling is a central regulator of an array of normal and pathological cell functions, and our understanding of the context dependent regulation of this key pathway remains very incomplete. Therefore, new knowledge on the role of specific regulators, such as ARHGAP18, is of interest to a very broad range of researchers. A further exciting aspect of this protein, that despite indications by many studies that it acts as a GAP (inhibitor) for Rho proteins, there are findings in the literature that suggest that its manipulation can affect actin in unexpected (opposite) manner. These point to possible Rho-independent roles, and warranted further in-depth exploration.

      One of the strength of the study is that it explores possible roles of ARHGAP18 beyond RhoA and describes some new and interesting observations, which advance our knowledge. The authors use some excellent tools (e.g. ARHGAP KO cells and re-expression) and approaches (e.g. super resolution microscopy to analyze actin changes, RNAseq and bioinformatics to find genes that may be downstream from ARHGAP18). A key limitation of the study however, is that it is not clear whether the observed findings are indeed independent from RhoA. Further limitation is that potential causal relationships between the described findings are not studied, and therefore the findings are in some cases overinterpreted, and limited mechanistic insights are provided. In some cases the exclusive use of expressed proteins is also a limitation. Finally, some of the experiments also need improvement.

      Reviewer expertise: RhoA signalling, guanine nucleotide exchange factors, epithelial biology, cell migration, intercellular junctions.

      In the above comments, we detail the new experimental data addressing reviewer 3's listed key limitations. We've added new data using the Rho GAP deficient ARHGAP18(R365A) variant which allows for the direct characterization of ARHGAP18's Rho independent activity. We have introduced new data in WT cells studying endogenous proteins to address the limitations from expressed proteins. Finally, we have moderated our language to address overinterpretation. Collectively, we believe that our revised manuscript addresses the constructive reviewer's comments.

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      Referee #3

      Evidence, reproducibility and clarity

      The study by Murray et al explores the effects of ARHGAP18 on the actin cytoskeleton, Rho effector kinases, non-muscle myosin, and transcription. Using super resolution microscopy, they show that in ARHGAP18 KO cells there is a mixed and unexpected cytoskeleton phenotype where myosin phosphorylation appears to be increased, but actin is disorganised with reduced stress fibres, diminished focal adhesions and augmented invasiveness. They conclude that the underlying mechanisms are likely independent from RhoA. Next, they perform RNAseq using the KO cells and identify an array of dysregulated genes, including those that play crucial roles in microvilli (related to previously published findings). Analysis of the data identify gene expression changes that are relevant for altered focal adhesion (integrins). Further analysis reveals that a large cohort of the dysregulated genes are YAP targets. They then show that in ARHGAP18 KO cells YAP nuclear localization, as detected by immunostaining, is augmented; and demonstrate that immobilized ARHGAP18 protein can bind the Hippo regulator merlin as well as YAP itself.

      Major comments:

      1. The premise of the study (that ARHGAP18 is a RhoA effector or may acts independently of RhoA) remains not proven. At several places (including in the title) the authors refer to ARHGAP18 as a Rho effector, which would suggest that it is downstream form Rho, but the basis for this is not clear. In fact, their own previous study suggested that ARHGAP is a RhoA regulator, rather than an effector. In general, the connection of the described effects to RhoA remains unclear, and not addressed in this study. The authors seem to go back and forth in their conclusions regarding the connection between ARHGAP18 and RhoA. For example, the first section of results is finished by stating (line 194): "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin through Rho-independent mechanism". But the next section starts by stating (line 198): "We hypothesized that the invasive and cytoskeletal phenotypes observed at the basal surface of cells devoid of ARHGAP18 may be a result of changes in regulation at the transcriptional level either directly through RhoA signaling or through an additional mechanism specific to ARHGAP18". The paper would be strengthened by adding data that show whether the effects are indeed downstream, from RhoA or RhoA independent. If there is no sufficient demonstration that ARHGAP18 is downstream of RhoA and is an effector, this needs to be stated explicitly and the wording should be changed.
      2. The study is descriptive and contains a series of observations that are not connected. Because of this, the study's conclusions are not well supported, and key mechanistic insight is limited. The study feels like a set of separate observations, that remain incompletely worked out and have some preliminary feel to them. The model in the last figure also seems to contain hypotheses based on the observations, several of which remains to be proven. Addressing any possible connection between key effects of ARHGAP18 KO (changes in actin, focal adhesion, integrins, Yap and merlin binding) could strengthen the manuscript. One such specific question is the whether the changes in integrin expression (RNAseq) are indeed connected to the actin alterations and reduction ion focal adhesions (Fig 1). Staining for these integrins to show they are indeed altered, and/or manipulating any of them to reproduce changes could provide and exciting addition.
      3. Some of the experimental findings are not convincing or lack controls.

      Fig 1: some of the western blots are not convincing or poor quality. The changes in pMLC on the western blots are very small, and for any conclusion, these studies require quantification. Further, the expression levels of Flag-ARHGAP18 needs to be shown to support the statement that the protein is expressed, and indeed overexpressed under these conditions (vs just re-expressed). On the same figure, the quality of LIM kinase blots is poor. The signal is weak, and the blot does not appear to support the quantification. The last condition (expression of flag-ARHGAP18) results in a large drop in pLIMK and pcofilin on the blot, which is not reflected by the graph. Addition of a better blot and the use of a strong positive or negative control would boost confidence in these data.

      Fig 4: the differences in YAP nuclear localization under the various conditions are not well visible. Quantitation of nuclear/cytosolic signal ratio should be provided. 4. Please provide a rationale and more context for using serum starvation and re-addition. What is the expected effect? Serum removal and addition is referred to as nutrient removal and re-addition, but this is inaccurate, as it does not equal nutrient removal, since serum contains a variety of other important components, e.g. growth factors too. 5. The binding between ARHGAP18 and merlin is interesting, but a key limitation is the use of expressed proteins. Can the binding be shown for the endogenous proteins (IP, colocalization). Another important unaddressed question is the relevance of this binding, and the relation of this to altered YAP nuclear localization.

      Minor comments:

      • Introduction line 99: "When localized to the nucleus, YAP/TAZ promotes the activation of cytoskeletal transcription factors associated with cell proliferation and actin polymerization" Please clarify what you mean by this statement, that is inaccurate in its present for. Did you mean effects on transcription factors that control cytoskeletal proteins, or do you mean that Yap/Taz affect these proteins? Please also provide reference for this.
      • What is the cell confluence in these experiments? For epithelial cells confluence affects actin structure. Please comment on similarity of confluency across experimental conditions?
      • Fig 2 legend: please indicate that the protein detected was non-muscle myosin heavy chain (distinct from the light chain detected in Fig 1).
      • Line 339-340: please check the syntax of this sentence
      • Western blot quantification: the comparison of experiments with samples run on different gels/blots requires careful normalization and experimental consistency. Please describe how this was achieved.

      Significance

      Rho signalling is a central regulator of an array of normal and pathological cell functions, and our understanding of the context dependent regulation of this key pathway remains very incomplete. Therefore, new knowledge on the role of specific regulators, such as ARHGAP18, is of interest to a very broad range of researchers. A further exciting aspect of this protein, that despite indications by many studies that it acts as a GAP (inhibitor) for Rho proteins, there are findings in the literature that suggest that its manipulation can affect actin in unexpected (opposite) manner. These point to possible Rho-independent roles, and warranted further in-depth exploration. One of the strength of the study is that it explores possible roles of ARHGAP18 beyond RhoA and describes some new and interesting observations, which advance our knowledge. The authors use some excellent tools (e.g. ARHGAP KO cells and re-expression) and approaches (e.g. super resolution microscopy to analyze actin changes, RNAseq and bioinformatics to find genes that may be downstream from ARHGAP18). A key limitation of the study however, is that it is not clear whether the observed findings are indeed independent from RhoA. Further limitation is that potential causal relationships between the described findings are not studied, and therefore the findings are in some cases overinterpreted, and limited mechanistic insights are provided. In some cases the exclusive use of expressed proteins is also a limitation. Finally, some of the experiments also need improvement.<br /> Reviewer expertise: RhoA signalling, guanine nucleotide exchange factors, epithelial biology, cell migration, intercellular junctions.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the Rho effector, ARHGAP18 in Jegs cells, a trophoblastic cell line. It presents a number of new pieces of data, which increase our understanding of the importance of this GAP on cell function and explains at a molecular level previous results of other workers in the field. ARHGAP18 was originally given the name "conundrum' and continues to stand apart from the majority of other GAP proteins and their functions. Hence the data here is significant and of high standard.

      The data is clear, and the images are of high quality and extremely impressive in their resolution. It is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      The data is based on the use of the cell line Jeg3. Even the authors previous publication in eLife is based only on this cell line. They need to show the conclusions are general and not specific to this line of cells. As an extension of this, is the ARHGAP18 function shown here only in transformed cells? Does the same mechanisms operate in normal cells, which respond to activation to proliferate or migrate? In endothelial cells, Lovelace et al 2017 showed localisation to microtubules and that depletion of ARHGAP18 resulted in microtubule instability. The authors may like to comment on the differences. Is this a cell type difference or RhoA versus RhoC difference?

      On pages 7,9 they conclude that MLC and basal and junctional actin are regulated through a GAP independent mechanism. The best way to show this is with overexpression of a GAP mutant.

      There is a huge amount of data presented in Figure 3, but their 2 genes which they focus on, LOP1 and CORO1A, are discussed but no actual data presented in support.

      Significance

      The data is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      This manuscript will be of significance to an basic science audience in the field of RhoGTPases and cell migration.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript describes a dual mechanism by which ARHGAP18 regulates the actin cytoskeleton. The authors propose that in addition to the known role for ARHGAP18 in regulating Rho GTPases, it also affects the cytoskeleton through regulation of the Hippo pathway transcriptional regulator YAP. ARHGAP18 knockout Jeg3 cells are were generated and show a clear loss of basal stress fiber like F-actin bundles. The authors further characterize the effects of ARHGAP18 knockout and overexpression. It is also discovered that ARHGAP18 binds to the Hippo pathway regulator Merlin and to YAP. Ultimately it is concluded that ARHGAP18 regulates the F-actin cytoskeleton through dual regulation of RHO GTPases and of YAP. While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      Specific Comments

      1) The authors make a big point about the effects of ARHGAP18 on myosin light chain phosphorylation. However this result is not quantified and tested for statistical significance and reproducibility.

      2) Along similar lines in Figure 2C they state that overexpression of ARHGAP18 causes cells to invade over the top of their neighbors. This might be true and interesting, but only a single cell is shown and there is no quantification or controls for simply overexpressing something in that cell. The authors also conclude from this image that the overexpression phenotype is independent of its GAP activity on Rho. It is not clear how this conclusion is made based on the data. It would seem like a more definitive experiment would be to see if a similar phenotype was induced by an ARHGAP18 mutant deficient in GAP activity.

      3) In Figure 3 the authors compare gene expression profiles of ARHGAP18 knockout cells to wild-type cells. They see lots of differences in focal adhesion and cytoskeletal proteins and conclude that this supports their conclusion that ARHGAP18 is not just acting through RHO. The rationale for this in not clear. In addition, they observe changes in expression profiles consistent with changes in YAP activity. They conclude that the effects are direct. This very well might be true. However RHO is a potent regulator of YAP activity and the results seem quite consistent with ARHGAP18 acting through RHO to affect YAP.

      4) In Figure 4A showing Merlin binding to ARHGAP18 there is no control for the amount of Merlin sticking to the column as was done in Figure 4F for binding experiments with YAP. This makes it difficult to determine the significance of the observed binding.

      5) The images in Figure 4C showing YAP being maintained in the nucleus more in ARHGAP18 knockout cells compared to wild-type. However the images only show a few cells and YAP localization can be highly variable depending on where you look in a field. Images with more cells and some sort of quantification would bolster this result.

      Significance

      While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

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      Reply to the reviewers

      Dear editor and reviewers,

      We sincerely thank you for your thoughtful comments and constructive suggestions, which have greatly improved the quality and clarity of our manuscript. In response, we have implemented all requested changes, which are highlighted in yellow throughout the revised text, and updated several figures accordingly. Furthermore, we have performed all additional experiments recommended by the reviewers and incorporated the new data into the manuscript. To enhance clarity, we have also included a schematic representation of our proposed model in an additional figure, providing a concise visual summary of our findings.

      We hope that these revisions fully address all concerns raised by the reviewers and meet all the expectations for publication.

      Below, we answer the reviewers point by point (in blue).


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

      In this paper, the authors address the important question of the role of centrosomes during neuronal development. They use Drosophila as an in vivo model. The field is somewhat unclear on the role and importance of centrosomes during neuronal development, although the current data would suggest they are dispensable for axon specification and growth. Early studies in cultured mammalian neurons showed that centrosomes are active and that their microtubules can be cut and transported into the neurites. But a study then showed that centrosomes in these cultured neurons are deactivated relatively early during neuronal development in vitro and that ablating centrosomes even when they are active had no obvious effect on axon specification and growth. Consistent with this, a study in Drosophila provided evidence that centrosomes were not active or necessary in different types of neurons. More recently, a study showed that centrosomal microtubules are dispensable for axon specification and growth in mice in vivo but are required for neuronal migration in the cerebral cortex. However, another study has linked the generation of acetylated microtubules at centrosomes with axon development. In this current study, the authors examine the effect of centrosome loss on various motor and sensory neurons and muscles mainly by examining mutants in essential centriole duplication genes. They associate axonal routing and morphology defects with centrosome loss and provide some evidence that centrosomes could still be active in the developing neurons. Overall, they conclude that centrosomes are active during at least early neuronal development and that this activity is important for proper axonal morphology and routing.

      While I think this study addressing a very interesting and important question, I think as it stands the data is not sufficient to be conclusive on a role for centrosomes during neuronal development. My biggest concern is that most phenotypes have not yet been shown to be cell autonomous, as whole animal mutants have been analysed rather than analysing the effect of cell-specific depletion, and the evidence for active centrosomes needs to be strengthened. If the authors can provide stronger evidence for a role of centrosomes in axonal development then the paper will certainly be of interest to a broad readership.

      We thank the reviewer for the clear and concise summary and fully agree that our study addresses a critical gap in understanding. Centrosomes have long been implicated in morphogenesis, yet their precise contribution to nervous system development has remained unclear. Our findings provide compelling evidence that centrosomes are indispensable for proper nervous system formation and that their absence also triggers muscular defects, highlighting their broader role in tissue organization.

      We acknowledge that the original manuscript lacked some key details; therefore, we have now strengthened our conclusions with additional experiments. Specifically, we demonstrate that these effects are cell-autonomous by using two independent RNAi lines targeted to a subset of motor neurons. Furthermore, we present new data showing that neuronal centrosomes remain active during the early stages of axonal development, emphasising their functional relevance in morphogenesis. All new experiments, figures, and corresponding text revisions are detailed below.

      Major comments 1) The sas-6 transallelic combination shows only 17% embryonic lethality compared to 50% embryonic lethality with sas-4 mutants. Given that both mutants should result in the same degree of centrosome loss (this should be quantified in sas-6 mutants) it would suggest that either sas-4 has other roles away from centrosomes or that the sas-4 mutant chromosome used in the experiment has other mutations that affect viability. The effect of picking up "second-site lethal" mutations on mutant chromosomes is common and so I would not be surprised if this is the reason for the difference in phenotypes. This can be addressed either by "cleaning up" the sas-4 mutant chromosome by backcrossing to wild-type lines, allowing recombination to occur and replace the potential second site mutations, or by using transallelic combinations of sas-4, as they did for sas-6. The "easier" option may just be to analyse all the phenotypes with the sas-6 transallelic combination.

      We appreciate this comment, as it brought to light an issue with the CRISPR line Sas-6-Δa. Upon reanalysing all the data, we determined that this line is embryonic lethal both in homozygosis and when combined with the deficiency uncovering the genomic region, Df(3R)BSC794. In contrast, Sas-6-Δb homozygotes are viable. The inconsistency between these results raised concerns about whether the Δa and Δb Sas-6 mutants carry deletions confined to the Sas-6 coding region. Although this would not hinder our cell biology analysis, it could represent a problem in viability tests. To address this, we repeated all analyses using Sas-6-Δb homozygotes and Sas-6-Δb combined with Df(3R)BSC794. These new results are more consistent and indicate that approximately 50% of Sas-6/Def individuals hatch as adults. Fig. 3 was redone and the manuscript text changed in view of these results.

      2) Using "whole animal" mutants for assessing neuronal morphology is risky due to non-cell-autonomous effects. The authors have carried out some phenotypic analysis of neurons depleted of Sas-4 by cell-specific RNAi, but I feel they need to do this for all of their analysis. This includes embryonic lethality measures, quantification of centrosome numbers, and all axonal phenotypes in Sas-4 RNAi neurons. It would also be prudent to use 2 distinct RNAi lines to help ensure any phenotypes are not off-target effects (and this may help clarify why the authors see some additional phenotypes with RNAi). Indeed, there are relatively weak phenotypes in muscles when using RNAi compared to the mutants and these potential non-cell-autonomous effects could then have a knock-on effect on neuronal morphology. If the authors were concerned that RNAi is not very efficient (explaining any potential weaker phenotypes than in mutants) the authors could examine the effectiveness of RNAi lines by analysing protein depletion by western blotting or mRNA depletion by rt-qPCR (although this has to be done in a different cell type due to the difficulty in obtaining a neuronal extract).

      We have now added a new panel to supplementary Figure 1, showing how the expression of a different Sas-4 RNAi line (2) induces similar nervous system phenotypes when expressed only in aCC, pCC and RP2 pioneer neurons (Sup. Fig. 1 M-O).

      3) When analysing centriole presence or absence it is a good idea to stain with two different centriole markers e.g. Asl and Plp. This helps rule out unspecific staining. It is clear from the images that similar sized foci can be observed outside of the cells (see Figure 5A for example), so clearly some of the foci that appear to be within the cells may also be unspecific staining.

      In a new supplementary figure, we now show that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). In addition, and we apologise for the confusion, but the reason why there are foci outside the marked cells is because these are wholemount embryonic stainings and the anti-Plp antibody marks all centrosomes in all cells in the embryo.

      4) The evidence for active centrosomes is not that convincing. Acetylated tubulin is associated with stable MTs, which are not normally organised by "active" centrosomes that nucleate dynamic microtubules. Moreover, it is plausible that centriole foci happen to overlap with the acetylated tubulin staining by chance. This would explain why not all centrosomes colocalise with acetylated tubulin signal. The authors could better test centrosome activity by performing live imaging with EB1-GFP. If centrosomes are active, it is very easy to observe the many comets produced by the centrosomes.

      We appreciate the reviewer’s comment and agree that acetylated tubulin alone is not an ideal marker for centrosome activity. To address this, we performed live imaging of aCC neurons expressing EB1-GFP together with Asl-Tomato. This was technically challenging because we were imaging only two neurons per segment in live embryos, under significant limitations in fluorescence detection and timing. Despite these constraints, we were able to clearly observe EB1 comets emerging from the centrosome and moving toward the cell periphery, providing direct evidence of microtubule nucleation from centrosomes in neurons.

      Importantly, we complemented this with a microtubule depolymerization/polymerization assay, which provides unequivocal evidence that polymerization initiates at the centrosome. After depolymerization, we observed microtubule regrowth from the centrosome, confirming its role as an active microtubule-organizing centre in these neurons. Together, we hope that these results are enough to demonstrate that neuronal centrosomes are functionally active during early axonal development. These experiments are presented in Figure 6 and corresponding text in the manuscript.

      5) If the authors believe that centrosomes have a role in axon pathfinding in sensory neurons, they should show that these centrosomes are active, at least during early stages (again using EB1-GFP imaging).

      We appreciate the reviewer’s suggestion and agree that EB1-GFP imaging would be the most direct way to assess centrosome activity in sensory neurons. However, performing time-lapse imaging in these neurons is technically very demanding due to their location and accessibility in live embryos, and we did not attempt this approach. Instead, we now provide new evidence showing that sensory neuron centrosomes colocalize with both α-tubulin and γ-tubulin. This strongly supports that these centrosomes are associated with microtubule nucleation machinery and are as likely as motor neuron centrosomes to be active during early stages of axon development. These new data have been included in the revised manuscript (see Figure 5 and corresponding text).

      6) The authors mention in the discussion that "increased JNK activity, can result in axonal wiggliness (Karkali et al, 2023)". I therefore wonder whether centrosome loss may induce JNK activation (the stress response), as this would then indicate an indirect effect of centrosome loss on axonal structure rather than a direct influence of centrosome-generated microtubules. The authors could assess whether the DNK-JNK pathway is activated in neurons lacking centrosomes by expression UAS-Puc-GFP and quantifying the nuclear signal.

      In a new supplementary figure, we now show by using a reporter for JNK signalling, as requested, that Sas-4 neurons do not activate the JNK pathway (Supl. Fig 4).

      7) In Figure 5, the authors claim that they find "a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". I don't think this is a strong correlation. The difference in centriole number between embryos with no defects and those with defects is very small. In contrast, the difference between centriole numbers in control (no defects) and mutant (no defects) is very large. So, there does not appear to be a strong correlation between centrosome number and phenotype.

      We agree and we have corrected this sentence to better explain the results.

      Minor comments

      1) I don't understand Figure 3C - why do the % of surviving homozygotes and heterozygotes add up to 100%? Should the grey boxes not relate to dead and the white to surviving?

      Thank you for pointing this out. Figures 1B and 3C represent only the surviving individuals. The grey boxes correspond to surviving homozygotes, and the white boxes correspond to surviving heterozygotes. The percentages add up to 100% only at embryonic stages because all embryos reach late embryonic stages. The grey and white boxes reflect the proportion of these two genotypes among the survivors, not the total number of embryos including those that died. We have changed the text to convey this.

      2) "In mouse fibroblasts, myoblasts and endothelial cells, centrosome orientation is important for nuclear positioning and cell migration(Chang et al, 2015; Gomes et al, 2005; Kushner et al, 2014)." Do you mean "centrosome position"?

      Yes, text changed, thank you for spotting it.

      3) In the introduction, the authors mention Meka et al. when saying the centrosomal microtubules are important for axonal development, but they should also discuss the counter argument from Vinopal et al., 2023 (Neuron) that showed how centrosomes were required for neuronal migration but not axon growth, which was instead mediated by Golgi-derived microtubules.

      Done, thank you very much.

      4) Lines 228-230 - repeated sentence

      Corrected, thank you very much.

      5) Additionally, we did not detect centrioles in the quadrant opposite the axon exit point (Fig. 2B n=75) - this data is not in Fig 2B

      Correct, it is in figure 4B, thank you very much.

      6) "This significant decrease in the humber of centrioles further supports the critical role of Sas-4 in pioneer neurons of the ventral nerve cord (VNC) during Drosophila embryogenesis". It rather highlights that Sas-4 is required for centriole formation in these neurons. Also, humber = number.

      We agree, and have changed the text, thank you very much.

      7) Result title: Non-ciliated sensory neurons have centrioles. This is kind of obvious. A better title may be "axon phenotypes correlate with centriole numbers in sensory neurons" but unfortunately i don't think there is good evidence for this (See major point above).

      We agree and we have changed. We now believe we have strong evidence to support it. We hope the additional data presented in the revision convincingly demonstrate this point.

      Reviewer #1 (Significance (Required)):

      As mentioned above, the advance will be important if more evidence is provided. In this case, the paper will be interesting to a broad readership. But currently the paper is limited by the lack of evidence for centrosome function and activity in the neurons.

      We hope that reviewer 1, now considers that the manuscript is not limited anymore and that it shows convincing evidence for centrosome function and activity in embryonic neurons.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: In this manuscript, Gonzalez et al. examine the potential function of centrosomes in the neurons and muscle cells of Drosophila embryos. By studying various mutant and RNAi lines in which centriole duplication has been disrupted, they conclude that the loss of centrioles disrupts axonal pathfinding and muscle integrity.

      Major points: 1. Throughout the manuscript, the phenotypes presented are often quite subtle. For this reason, I would really recommend that these experiments are scored blind. Perhaps the authors did this, but I didn't see any mention of this.

      All our phenotypic analyses are performed blind. We apologize for not having originally included this information in the Methods section; it has now been added. Embryos are stained using colorimetric methods (DAB) to label the nervous system, while balancer chromosomes are marked with a fluorescent antibody. This approach allows us to assess and quantify phenotypes using white light without knowing whether the embryos are homozygous mutants or heterozygous, which can only be detected by changing the channels to fluorescence.

      1. The authors conclude that neurons have active centrioles that function as centrosomes (Figure 6), but the data here is confusing. The authors state that in these cells they observe acetylated MTs extending from the centrosomes and these colocalised with g-tubulin. But the authors don't show the overlap between centrosomes, g-tubulin and MTs, as they stain for these separately. This is problematic, as it was not clear from these images that the majority of the MTs really are extending from the centrosome: the centrosome may just associate or be close by to these MT cables (Figure 6A,B). Moreover, the authors show that only a fraction of the centrosomes in these cells associate with g-tubulin, so presumably in cells where the centrosomes lack g-tubulin they would not expect the centrosomes to be associated with the MTs-but they do not show that this is the case. Perhaps the authors can't test this, but an alternative would be to show that these MT arrays are absent in Sas-4 mutants. This would give more confidence that these MTs arise from the centrosomes.

      We agree that the initial data based on acetylated microtubules and γ-tubulin colocalization were not sufficient to conclude that microtubules originate from the centrosome, as these markers can only suggest association. To address this, we have now included additional experiments that provide direct evidence of centrosome activity.

      First, we performed live imaging of aCC neurons expressing EB1-GFP together with Asl-Tomato. Despite the technical challenges of imaging only two neurons per segment in live embryos under strict fluorescence and timing constraints, we were able to clearly observe EB1 comets emerging from the centrosome and moving toward the cell periphery. This demonstrates active microtubule nucleation from centrosomes rather than mere proximity to microtubule bundles.

      Second, we carried out a microtubule depolymerization/polymerization assay, which provides unequivocal evidence that polymerization initiates at the centrosome. After depolymerization, microtubules regrew from the centrosome, confirming its role as an active microtubule-organizing center. These experiments go beyond colocalization and directly address the concern that centrosomes might simply be adjacent to microtubule cables.

      Regarding the suggestion to use Sas-4 mutants, while we did not perform this experiment, the regrowth assay combined with EB1 imaging strongly supports that these microtubules originate from the centrosome. All new data are presented in Figure 6 and the corresponding text in the revised manuscript.

      1. The authors show that muscle cell integrity is compromised by centriole-loss (Figure 2). This is very surprising as it is widely believed that centrosomes are non-functional in muscle cells, and the MTs are instead organised around the nuclear envelope. I'm not aware of the situation in Drosophila muscle cells, but the authors should ideally try to examine if the centrioles are functioning as centrosomes in these cells. At the very least they should discuss how they think centriole-loss is influencing the muscle integrity when it is widely believed they are inactive in these cells.

      We do not claim that centrosomes are active in muscle cells at these developmental stages. The observed muscle defects could result from earlier processes such as cell division, migration, or muscle fusion. We agree that this is an intriguing observation; however, pursuing this question further would go beyond the scope of the current manuscript. As requested by the reviewer, we have now expanded the discussion to consider how centriole loss might impact muscle integrity.

      Regardless of the strength of the supporting data, I think the authors should tone down their conclusions. The title and abstract led me to believe that centriole loss would cause significant problems in axonal pathfinding and muscle integrity. In all the mutant specimens examined (and certainly the low magnification views shown in Figure 1D'-F', Figure 1I'-K' and Figure 2D'-F') the mutants look very similar to the WT. Many readers may not get past the title and abstract, so the authors should make it clearer that these defects are very subtle.

      We have changed the text to convey this idea.

      Minor points: 1. In Figures 4 and 5, CP309 staining is relied on to identify centrioles, but there is quite a background of non-specific dots, making it hard to be certain what is a centriole and what isn't. For example, in Figure 5D' there are lots of dots within some of the cells - are any of these centrioles? How can the authors be certain which dot is a centriole in some of the cells shown in Figure 5C'? Is it possible to use a second marker and only count as centrioles dots that are recognised by both antibodies?

      We thank the reviewer for this suggestion and agree that using a second marker improves confidence in centriole identification. In a new supplementary figure (Supplementary Fig. 3), we now show that Asl and Plp colocalize in neurons and provide a quantification of the frequency of this colocalization. This dual labelling confirms the identity of centrioles and addresses the concern about non-specific background.

      We also apologize for any confusion regarding the presence of foci outside the marked cells. These images are whole-mount embryonic stainings, and the anti-Plp antibody labels all centrosomes in all cells of the embryo, which explains the additional foci observed.

      In the abstract that authors state that traditionally centrosomes have been considered to be non-essential in terminally differentiated cells. I don't think this is correct. In the standard "textbook" view of a cell, the centrosome is normally positioned in the centre of the cell organising an extensive array of MTs that are thought play an important role in organising intracellular transport, the positioning and movement of organelles and the maintenance and establishment of cell polarity. I don't think it is only recent evidence that suggests they play vital roles in terminally differentiated cells.

      We thank the reviewer for this correction and we have changed the text accordingly.

      1. Line 162 the authors state that in the RNAi knockdown lines they observe several additional phenotypes, but then in the same sentence (Line 164) they say that these defects were also observed in the original mutant and mutant/Df lines.

      We apologise for this confusion, we have rearranged the sentence for clearance.

      The sentences in Line281-287 don't reference any of the Figures, so it seems the authors are just stating these results without presenting any data (e.g. "Significantly, we also found a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". If they've tested this correlation, they should show it.

      We have rearranged the sentences for better understanding.

      In Figure 7 I did not understand how the authors measured tortuosity (wiggliness) and could see no description in the methods. This is important as, again the defect seems quite subtle, but perhaps I am not understanding which bits of the axon are being measures. Is it just the small bit of the axons close to the asterixis that is being measured, or the whole FasII track?

      We have now added another quantification and additional descriptions in the methods section.

      Reviewer #2 (Significance (Required)):

      The potential function of centrosomes in axonal outgrowth is quite controversial, so this study is potentially of considerable interest.

      However, several aspects of the data presented here were confusing or not terribly convincing. In its present state, I don't think the main conclusions are strongly enough supported by the data.

      We hope that reviewer 2, now considers that the manuscript is not confusing anymore and that it shows convincing evidence for centrosome function and activity in embryonic neurons.

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

      The manuscript of González et al. entitled "Centriole Loss in Embryonic Development Disrupts Axonal Pathfinding and Muscle Integrity" deals with the role of centrosomes in shaping axonal morphology. To this aim the AA analysed Drosophila Sas-4 mutants that are reported to develop until adult stage without centrioles. Remarkably, the AA observe that 50% of the homozygous mutant embryos fail to hatch as larvae. The present observations suggest that centrosome loss results in axonemal shaping defects and muscle developmental abnormalities. Finally, the AA show the presence of functional centrosomes in neurons. In my opinion, the manuscript is interesting because shows unexpected findings. However, to justify these new findings the AA are required to improve some experimental observations.

      We thank the reviewer for his summary of our work and for considering it interesting. We have taken into account all the comments and believe that these have helped improve our manuscript.

      Major: Abstract- It is unclear in which phenotypic condition the observations of centrosome loss or centrosome presence have been found. Please better explain. l.36. embryos, larvae, adult, from Sas4 or controls? If mutants, the observations are very interesting since Sas4 would be without centrioles. Indeed, Basto et al., show that chemosensory neurons do not develop an axoneme in the absence of centrioles, but extend dendrites toward the sensory bristle.

      We have made clear which refer to wild-type and which are Centriole Loss (CL) conditions. CL conditions refer to mutant and downregulation conditions, whereas targeted downregulation refers to RNAi downregulation only in neurons.

      I do not think appropriate the use of "centriole" in the main title since the centrioles would be localized by true centriolar antigens rather than by centrosomal antigens. This problem occurs throughout the text and some figures where the AA image centrioles by centrosomal material. In Gig. 5A only the AA properly look at Asl localization. The other pictures of presumptive centrioles or centriole quantification report CP309 dots. This localization does not unequivocally reveal centrioles, since CP309 is essentially required for centrosome-mediated Mt nucleation. There are differentiated Drosophila tissues in which centrioles are present, but inactivated, and unable to recruit pericentriolar material. Mt are nucleated by ncMTOCs that contain centrosomal material and gamma-tubulin. Thus, the centrosomal antigens do not colocalize with centrioles.

      We have changed centrioles to centrosomes in the title and most sections in the manuscript. We have also included an extra control, showing that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). Asl is a reliable and widely used marker for centrioles, as it localizes specifically to the centriole structure (Varmark H, Llamazares S, Rebollo E, Lange B, Reina J, Schwarz H, Gonzalez C. Asterless is a centriolar protein required for centrosome function and embryo development in Drosophila. Curr Biol. 2007 Oct 23;17(20):1735-45. doi: 10.1016/j.cub.2007.09.031. PMID: 17935995.)

      Minor: l. 58. The early arrest is mainly due to a checkpoint control. In double mutant for Sas4 and P53 the embryos survive longer, even if their further development is asrrested.

      We thank the reviewer for this comment, and we have changed the text accordingly.

      1. Previous works, also quoted by the AA, reported that in mature neurons the centrosome are inactivated, whereas the present manuscript describes functional centrosomes in Drosophila motor and peripheral nervous system. This is an intriguing observations that needs a better explanation in Discussion section.

      We thank the reviewer for this comment, and we have changed the discussion accordingly.

      l.143-145. I understand that 50% of the Sas4 embryos that reach the adult stage have centrioles. Is it correct? But if it is so, how the AA explain the absence of centrioles in sensory neurons of adult flies as reported by Basto et al. ?

      According to our results they have less centrioles than controls already at embryonic stages. In addition, as reported in Basto et al. they continue losing centrioles during larval stages and metamorphosis, which explains why centrioles are not detected at adult stages.

      l.215. It is unclear for me why the AA analyse Sas6 flies, unless explain the mutant phenotype.

      To strengthen our conclusions with Sas-4 and exclude the possibility that the observed phenotypes arise from a centrosome-independent function of Sas-4. For this reason, we have taken additional steps to confirm that the effects are specifically due to centrosome loss and we used Sas-6 mutants as one of these.

      l.221. How the centrioles have been quantified? What antibody, the AA used.

      We have quantified centrosomes using antibodies agains Plp (CP309) and Asl-YFP expression.

      l.244. and Fig 4C,D. I see high background with CP309. As reported previously I think better to use antibodies against centriolar proteins, such as Sas6, Ana1, Asl, or Sas4 ( if centrioles are present in 50% of mutants as the AA claim, the antibody could be also useful). In addition, I can see some CP309 spots in Fig 4E,F. Are they centrioles?

      Indeed, as we report, Sas-4 mutant embryos are not totally devoid of centrosomes. In addition, and we apologise for the confusion, but the reason why there are foci outside the marked cells in control embryos is because these are wholemount embryonic stainings and the anti-Plp antibody marks all centrosomes in all cells in the embryo, not just in the neurons.

      l.270 and Fig. 5A and Fig.5 C-E. Why the AA localize Cp309 and not Asl (Fig. 5A) to detect centrioles?

      In a new supplementary figure, we now show that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). So, we can use CP309 in neurons to the same effect as Asl-

      L295-296. I cannot see Mts, but only a diffuse staining. I am expecting to see distinct Mt bundles.

      In figure 5 it is now easier to see the MT bundles in the new experiment in Fig. 5F-I , where we performed MT depolymerisation/repolymerisation: Nevertheless, we need to stress out that we are doing these analyses in wholemount embryonic stainings.

      326-327. How the AA explain this different lethality, even if both the proteins are involved in centriole assembly?

      We have now redone all the viability and mutant phenotype analysis using Sas-6 CRISPR mutant over the Deficiency, which is a better way to access the phenotype.

      335-337. In my opinion the quoted publications are not relevant.

      We believe that these references back up our hypothesis because:

      • Metzger et al 2012 stress the importance of nuclear position in muscle development in Drosophila
      • Loh et al 2023, relate centrosomes with nuclear migration in Drosophila
      • Tillery et al 2018, is a review describing MTs in muscle development in Drosophila.

      358-359. Does maternal contribution persist after gastrulation?

      While bulk degradation occurs by midblastula transition, some stable maternal products persist beyond gastrulation. In our case, if centrioles are formed due to the maternal contribution, they will only be diluted by cell division, which explains why we can detect centrioles at late embryonic stages.

      l.366. This is an intriguing point, but as previously observed I have some problem with centriole localization. References. Please uniform Journal abbreviations and control page numbers.

      I hope we have clarified this problem with the new experiments showing MT repolarization from the centrosomes in neurons.

      Reviewer #3 (Significance (Required)):

      The manuscript is potentially interesting for peoples working of cell and molecular biology, and development. However, the paper needs an additional working to be suitable for publication.

      We hope that reviewer 3, considers that the additional work and revision make this manuscript suitable for publication.

    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 manuscript of González et al. entitled "Centriole Loss in Embryonic Development Disrupts Axonal Pathfinding and Muscle Integrity" deals with the role of centrosomes in shaping axonal morphology. To this aim the AA analysed Drosophila Sas-4 mutants that are reported to develop until adult stage without centrioles. Remarkably, the AA observe that 50% of the homozygous mutant embryos fail to hatch as larvae. The present observations suggest that centrosome loss results in axonemal shaping defects and muscle developmental abnormalities. Finally, the AA show the presence of functional centrosomes in neurons. In my opinion, the manuscript is interesting because shows unexpected findings. However, to justify these new findings the AA are required to improve some experimental observations.

      Major:

      Abstract- It is unclear in which phenotypic condition the observations of centrosome loss or centrosome presence have been found. Please better explain. l.36. embryos, larvae, adult, from Sas4 or controls? If mutants, the observations are very interesting since Sas4 would be without centrioles. Indeed, Basto et al., show that chemosensory neurons do not develop an axoneme in the absence of centrioles, but extend dendrites toward the sensory bristle.

      I do not think appropriate the use of "centriole" in the main title since the centrioles would be localized by true centriolar antigens rather than by centrosomal antigens. This problem occurs throughout the text and some figures where the AA image centrioles by centrosomal material. In Gig. 5A only the AA properly look at Asl localization. The other pictures of presumptive centrioles or centriole quantification report CP309 dots. This localization does not unequivocally reveal centrioles, since CP309 is essentially required for centrosome-mediated Mt nucleation. There are differentiated Drosophila tissues in which centrioles are present, but inactivated, and unable to recruit pericentriolar material. Mt are nucleated by ncMTOCs that contain centrosomal material and gamma-tubulin. Thus, the centrosomal antigens do not colocalize with centrioles.

      Minor:

      l. 58. The early arrest is mainly due to a checkpoint control. In double mutant for Sas4 and P53 the embryos survive longer, even if their further development is asrrested.

      l. 102. Previous works, also quoted by the AA, reported that in mature neurons the centrosome are inactivated, whereas the present manuscript describes functional centrosomes in Drosophila motor and peripheral nervous system. This is an intriguing observations that needs a better explanation in Discussion section.

      l.143-145. I understand that 50% of the Sas4 embryos that reach the adult stage have centrioles. Is it correct? But if it is so, how the AA explain the absence of centrioles in sensory neurons of adult flies as reported by Basto et al. ?

      l.215. It is unclear for me why the AA analyse Sas6 flies, unless explain the mutant phenotype.

      l.221. How the centrioles have been quantified? What antibody, the AA used.

      l.244. and Fig 4C,D. I see high background with CP309. As reported previously I think better to use antibodies against centriolar proteins, such as Sas6, Ana1, Asl, or Sas4 ( if centrioles are present in 50% of mutants as the AA claim, the antibody could be also useful). In addition, I can see some CP309 spots in Fig 4E,F. Are they centrioles?

      l.270 and Fig. 5A and Fig.5 C-E. Why the AA localize Cp309 and not Asl (Fig. 5A) to detect centrioles?

      L295-296. I cannot see Mts, but only a diffuse staining. I am expecting to see distinct Mt bundles.

      L. 326-327. How the AA explain this different lethality, even if both the proteins are involved in centriole assembly?

      l. 335-337. In my opinion the quoted publications are not relevant.

      l. 358-359. Does maternal contribution persist after gastrulation?

      l.366. This is an intriguing point, but as previously observed I have some problem with centriole localization.

      References. Please uniform Journal abbreviations and control page numbers.

      Significance

      The manuscript is potentially interesting for peoples working of cell and molecular biology, and development. However, the paper needs an additional working to be suitable for publication.

    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: In this manuscript, Gonzalez et al. examine the potential function of centrosomes in the neurons and muscle cells of Drosophila embryos. By studying various mutant and RNAi lines in which centriole duplication has been disrupted, they conclude that the loss of centrioles disrupts axonal pathfinding and muscle integrity.

      Major points:

      1. Throughout the manuscript, the phenotypes presented are often quite subtle. For this reason, I would really recommend that these experiments are scored blind. Perhaps the authors did this, but I didn't see any mention of this.
      2. The authors conclude that neurons have active centrioles that function as centrosomes (Figure 6), but the data here is confusing. The authors state that in these cells they observe acetylated MTs extending from the centrosomes and these colocalised with g-tubulin. But the authors don't show the overlap between centrosomes, g-tubulin and MTs, as they stain for these separately. This is problematic, as it was not clear from these images that the majority of the MTs really are extending from the centrosome: the centrosome may just associate or be close by to these MT cables (Figure 6A,B). Moreover, the authors show that only a fraction of the centrosomes in these cells associate with g-tubulin, so presumably in cells where the centrosomes lack g-tubulin they would not expect the centrosomes to be associated with the MTs-but they do not show that this is the case. Perhaps the authors can't test this, but an alternative would be to show that these MT arrays are absent in Sas-4 mutants. This would give more confidence that these MTs arise from the centrosomes.
      3. The authors show that muscle cell integrity is compromised by centriole-loss (Figure 2). This is very surprising as it is widely believed that centrosomes are non-functional in muscle cells, and the MTs are instead organised around the nuclear envelope. I'm not aware of the situation in Drosophila muscle cells, but the authors should ideally try to examine if the centrioles are functioning as centrosomes in these cells. At the very least they should discuss how they think centriole-loss is influencing the muscle integrity when it is widely believed they are inactive in these cells.
      4. Regardless of the strength of the supporting data, I think the authors should tone down their conclusions. The title and abstract led me to believe that centriole loss would cause significant problems in axonal pathfinding and muscle integrity. In all the mutant specimens examined (and certainly the low magnification views shown in Figure 1D'-F', Figure 1I'-K' and Figure 2D'-F') the mutants look very similar to the WT. Many readers may not get past the title and abstract, so the authors should make it clearer that these defects are very subtle.

      Minor points:

      1. In Figures 4 and 5, CP309 staining is relied on to identify centrioles, but there is quite a background of non-specific dots, making it hard to be certain what is a centriole and what isn't. For example, in Figure 5D' there are lots of dots within some of the cells - are any of these centrioles? How can the authors be certain which dot is a centriole in some of the cells shown in Figure 5C'? Is it possible to use a second marker and only count as centrioles dots that are recognised by both antibodies?
      2. In the abstract that authors state that traditionally centrosomes have been considered to be non-essential in terminally differentiated cells. I don't think this is correct. In the standard "textbook" view of a cell, the centrosome is normally positioned in the centre of the cell organising an extensive array of MTs that are thought play an important role in organising intracellular transport, the positioning and movement of organelles and the maintenance and establishment of cell polarity. I don't think it is only recent evidence that suggests they play vital roles in terminally differentiated cells.
      3. Line 162 the authors state that in the RNAi knockdown lines they observe several additional phenotypes, but then in the same sentence (Line 164) they say that these defects were also observed in the original mutant and mutant/Df lines.
      4. The sentences in Line281-287 don't reference any of the Figures, so it seems the authors are just stating these results without presenting any data (e.g. "Significantly, we also found a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". If they've tested this correlation, they should show it.
      5. In Figure 7 I did not understand how the authors measured tortuosity (wiggliness) and could see no description in the methods. This is important as, again the defect seems quite subtle, but perhaps I am not understanding which bits of the axon are being measures. Is it just the small bit of the axons close to the asterixis that is being measured, or the whole FasII track?

      Significance

      The potential function of centrosomes in axonal outgrowth is quite controversial, so this study is potentially of considerable interest.

      However, several aspects of the data presented here were confusing or not terribly convincing. In its present state, I don't think the main conclusions are strongly enough supported by the data.

    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

      In this paper, the authors address the important question of the role of centrosomes during neuronal development. They use Drosophila as an in vivo model. The field is somewhat unclear on the role and importance of centrosomes during neuronal development, although the current data would suggest they are dispensable for axon specification and growth. Early studies in cultured mammalian neurons showed that centrosomes are active and that their microtubules can be cut and transported into the neurites. But a study then showed that centrosomes in these cultured neurons are deactivated relatively early during neuronal development in vitro and that ablating centrosomes even when they are active had no obvious effect on axon specification and growth. Consistent with this, a study in Drosophila provided evidence that centrosomes were not active or necessary in different types of neurons. More recently, a study showed that centrosomal microtubules are dispensable for axon specification and growth in mice in vivo but are required for neuronal migration in the cerebral cortex. However, another study has linked the generation of acetylated microtubules at centrosomes with axon development. In this current study, the authors examine the effect of centrosome loss on various motor and sensory neurons and muscles mainly by examining mutants in essential centriole duplication genes. They associate axonal routing and morphology defects with centrosome loss and provide some evidence that centrosomes could still be active in the developing neurons. Overall, they conclude that centrosomes are active during at least early neuronal development and that this activity is important for proper axonal morphology and routing.

      While I think this study addressing a very interesting and important question, I think as it stands the data is not sufficient to be conclusive on a role for centrosomes during neuronal development. My biggest concern is that most phenotypes have not yet been shown to be cell autonomous, as whole animal mutants have been analysed rather than analysing the effect of cell-specific depletion, and the evidence for active centrosomes needs to be strengthened. If the authors can provide stronger evidence for a role of centrosomes in axonal development then the paper will certainly be of interest to a broad readership.

      Major comments

      1. The sas-6 transallelic combination shows only 17% embryonic lethality compared to 50% embryonic lethality with sas-4 mutants. Given that both mutants should result in the same degree of centrosome loss (this should be quantified in sas-6 mutants) it would suggest that either sas-4 has other roles away from centrosomes or that the sas-4 mutant chromosome used in the experiment has other mutations that affect viability. The effect of picking up "second-site lethal" mutations on mutant chromosomes is common and so I would not be surprised if this is the reason for the difference in phenotypes. This can be addressed either by "cleaning up" the sas-4 mutant chromosome by backcrossing to wild-type lines, allowing recombination to occur and replace the potential second site mutations, or by using transallelic combinations of sas-4, as they did for sas-6. The "easier" option may just be to analyse all the phenotypes with the sas-6 transallelic combination.
      2. Using "whole animal" mutants for assessing neuronal morphology is risky due to non-cell-autonomous effects. The authors have carried out some phenotypic analysis of neurons depleted of Sas-4 by cell-specific RNAi, but I feel they need to do this for all of their analysis. This includes embryonic lethality measures, quantification of centrosome numbers, and all axonal phenotypes in Sas-4 RNAi neurons. It would also be prudent to use 2 distinct RNAi lines to help ensure any phenotypes are not off-target effects (and this may help clarify why the authors see some additional phenotypes with RNAi). Indeed, there are relatively weak phenotypes in muscles when using RNAi compared to the mutants and these potential non-cell-autonomous effects could then have a knock-on effect on neuronal morphology. If the authors were concerned that RNAi is not very efficient (explaining any potential weaker phenotypes than in mutants) the authors could examine the effectiveness of RNAi lines by analysing protein depletion by western blotting or mRNA depletion by rt-qPCR (although this has to be done in a different cell type due to the difficulty in obtaining a neuronal extract).
      3. When analysing centriole presence or absence it is a good idea to stain with two different centriole markers e.g. Asl and Plp. This helps rule out unspecific staining. It is clear from the images that similar sized foci can be observed outside of the cells (see Figure 5A for example), so clearly some of the foci that appear to be within the cells may also be unspecific staining.
      4. The evidence for active centrosomes is not that convincing. Acetylated tubulin is associated with stable MTs, which are not normally organised by "active" centrosomes that nucleate dynamic microtubules. Moreover, it is plausible that centriole foci happen to overlap with the acetylated tubulin staining by chance. This would explain why not all centrosomes colocalise with acetylated tubulin signal. The authors could better test centrosome activity by performing live imaging with EB1-GFP. If centrosomes are active, it is very easy to observe the many comets produced by the centrosomes.
      5. If the authors believe that centrosomes have a role in axon pathfinding in sensory neurons, they should show that these centrosomes are active, at least during early stages (again using EB1-GFP imaging).
      6. The authors mention in the discussion that "increased JNK activity, can result in axonal wiggliness (Karkali et al, 2023)". I therefore wonder whether centrosome loss may induce JNK activation (the stress response), as this would then indicate an indirect effect of centrosome loss on axonal structure rather than a direct influence of centrosome-generated microtubules. The authors could assess whether the DNK-JNK pathway is activated in neurons lacking centrosomes by expression UAS-Puc-GFP and quantifying the nuclear signal.
      7. In Figure 5, the authors claim that they find "a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". I don't think this is a strong correlation. The difference in centriole number between embryos with no defects and those with defects is very small. In contrast, the difference between centriole numbers in control (no defects) and mutant (no defects) is very large. So, there does not appear to be a strong correlation between centrosome number and phenotype.

      Minor comments

      1. I don't understand Figure 3C - why do the % of surviving homozygotes and heterozygotes add up to 100%? Should the grey boxes not relate to dead and the white to surviving?
      2. "In mouse fibroblasts, myoblasts and endothelial cells, centrosome orientation is important for nuclear positioning and cell migration(Chang et al, 2015; Gomes et al, 2005; Kushner et al, 2014)." Do you mean "centrosome position"?
      3. In the introduction, the authors mention Meka et al. when saying the centrosomal microtubules are important for axonal development, but they should also discuss the counter argument from Vinopal et al., 2023 (Neuron) that showed how centrosomes were required for neuronal migration but not axon growth, which was instead mediated by Golgi-derived microtubules.
      4. Lines 228-230 - repeated sentence
      5. Additionally, we did not detect centrioles in the quadrant opposite the axon exit point (Fig. 2B n=75) - this data is not in Fig 2B
      6. "This significant decrease in the humber of centrioles further supports the critical role of Sas-4 in pioneer neurons of the ventral nerve cord (VNC) during Drosophila embryogenesis". It rather highlights that Sas-4 is required for centriole formation in these neurons. Also, humber = number.
      7. Result title: Non-ciliated sensory neurons have centrioles. This is kind of obvious. A better title may be "axon phenotypes correlate with centriole numbers in sensory neurons" but unfortunately i don't think there is good evidence for this (See major point above).

      Significance

      As mentioned above, the advance will be important if more evidence is provided. In this case, the paper will be interesting to a broad readership. But currently the paper is limited by the lack of evidence for centrosome function and activity in the neurons.

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

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      A previous study by Komada et al. demonstrated that MAP7 is expressed in both Sertoli and germ cells, and that Map7 gene-trap mutant mice display disrupted microtubule bundle formation in Sertoli cells, accompanied by defects in spermatid manchettes and germ cell loss. In the current study, Kikuchi et al. investigated the role of MAP7 in the formation of the Sertoli cell apical domain during the first wave of spermatogenesis. They generated a GFP-tagged MAP7 mouse line and demonstrated that the endogenous MAP7 protein localizes to the apical microtubules in Sertoli cells and to the manchette microtubules in step 9-11 spermatids. They also generated a new Map7 knockout (KO) mouse line in a genetic background distinct from the one used in the previous study. Focusing on stages before the emergence of step 9-11 spermatids, the authors aimed to isolate defects caused by the function of MAP7 in Sertoli cells. They report that loss of MAP7 impairs Sertoli cell polarity and apical domain formation, accompanied by the microtubule remodeling defect. Using the GFP-tagged MAP7 line, they performed immunoprecipitation-mass spectrometry and identified several MAP7-interacting proteins in the testis, including MYH9. They further observed that MAP7 deletion alters the distribution of MYH9. Single-cell RNA sequencing revealed that the loss of MAP7 in Sertoli cells resulted in slight transcriptomic shifts but had no significant impact on their functional differentiation. Single-cell RNA sequencing analysis also showed delayed meiotic progression in the MAP7-deficient testis. Overall, while the study provides some interesting discoveries of early Sertoli cell defects in MAP7-deficient testes, some conclusions are premature and not fully supported by the presented data. The mechanistic investigations remain limited in depth.

      Response: We thank the reviewer for this insightful summary. We agree that some of our initial interpretations were speculative and have revised the relevant sections to more accurately reflect the limitations of the current data. We also acknowledge that further mechanistic studies will be important to strengthen our conclusions, and we have outlined these plans in the individual responses below.

      Major comments:

      Although the infertility phenotype of the Map7 gene-trap mutant mice has been reported previously, it remains essential to assess fertility in this newly generated MAP7 knockout line. While the authors present testis size and histological differences between WT and KO mice (Extended Fig. 2e and 2f), there is no corresponding description or interpretation in the main text regarding fertility outcomes.

      Response: We thank the reviewer for raising this point. Although we had presented the differences in testis size and histology between wild-type and Map7-/- mice, we agree that a description of the corresponding fertility outcomes was missing from the main text. We have now revised the relevant part of the Results section as follows: “Consistent with observations in Map7 gene-trap mice, Map7-/- males exhibited reduced testis size and spermatogenic defects (Supplemental Fig. 2E, F). Notably, the cauda epididymis of Map7-/- males contained no mature spermatozoa (Supplemental Fig. 2F), indicating male infertility.” (page 5, line 33–page 6, line 2)

      • In Figure 2C, the authors identified Sertoli cells, spermatogonia cells, and spermatocytes using SEM, based on their cell morphology and adhesion to the basement membrane. Given that the loss of MAP7 disrupts the polarity and architecture of Sertoli cells, the position of germ cells will be affected, making this identification criterion less reliable.

      Response: We appreciate the reviewer’s comment. While the reviewer notes that cell identification was based on cell morphology and adhesion to the basement membrane, we clarify that nuclear morphology was also considered, as described in the original manuscript. Specifically, germ cells have spherical nuclei, whereas Sertoli cell nuclei are irregularly shaped (representative segmentation results can be provided as an additional Supplemental Figure upon request). Round spermatids at P21 can be distinguished from spermatocytes by their smaller nuclear size. In addition, spermatogonia remain attached to the basement membrane even in Map7-/- testes, as confirmed by GFRα1-positive spermatogonial stem cells (Figure 6A). Together, these features ensure reliable identification of each cell type, independent of the altered polarity observed in Map7-deficient Sertoli cells.

      • In Figure 2e, the number of Sox9-positive Sertoli cells in MAP7 knockout mice appears higher than that in the control at P17. Quantification of total Sox9-positive cells should be done to determine whether MAP7 deletion increases Sertoli cell numbers.

      Response: As suggested by the reviewer, we will quantify the density of SOX9-positive Sertoli cells per unit area of seminiferous tubule at P10 and P17 in Map7+/- and Map7-/- testes, and include the results in the revised manuscript.

      • To determine whether MAP7's role in regulating Sertoli cell polarity relies on germ cells, the authors treated mice with busulfan at P28 to delete germ cells, a stage after Sertoli cell polarity defect has developed in MAP7 knockout mice. This data is insufficient to support the conclusion that MAP7 regulates Sertoli cell polarity independently of the presence of germ cells. Germ cell deletion should be done before the Sertoli cell defect develops to address this question.

      Response: We appreciate the reviewer’s thoughtful comment regarding the interpretation of the busulfan experiments. While depletion of germ cells at P28 enabled us to assess Sertoli cell polarity in the absence of postnatal spermatogonia, these experiments do not definitively determine whether MAP7 regulates Sertoli cell polarity independently of germ cells. Neonatal germ-cell depletion would more directly test germ cell–independent effects; however, systemic busulfan administration at early developmental stages is highly toxic, often causing bone marrow failure and multi-organ damage, which precludes survival and confounds analysis of testis-specific effects. Although germ cell ablation could, in principle, be achieved using transgenic approaches that exploit the natural resistance of mice to diphtheria toxin (DTX) (reviewed in Smith et al., Andrology, 2015), these strategies require multiple transgenes and show minor variability in efficiency, making them impractical for our current experiments. Generating the necessary genetic combinations would require considerable time. We therefore plan to pursue alternative genetic approaches in future work.

      In the revised manuscript, we have modified the relevant section to more accurately reflect the limitations of the current experiments, as follows: “Busulfan was administered at P28, and testes were analyzed 6 weeks later, after complete elimination of germ cell lineages. Following treatment, Map7+/- mice showed testis-to-body weight ratios comparable to untreated Map7-/- mice (Supplemental Fig. 3D), and hematoxylin-eosin (HE) staining confirmed germ cell depletion (Fig. 2F; Supplemental Fig. 3E). In Map7+/- testes, most Sertoli nuclei remained basally positioned, indicating that once apical–basal polarity is established, it is stably maintained even in the absence of germ cells. In contrast, Map7-/- Sertoli nuclei were frequently misoriented toward the lumen under the same conditions (Fig. 2F; Supplemental Fig. 3E), suggesting that polarity defects in Map7-deficient Sertoli cells occur independently of germ cell presence.” (page 7, lines 20–28)

      In addition, we have added the following sentences to the Discussion section to highlight the implication of these findings: “In addition, even after germ cell depletion by busulfan treatment, Map7-deficient Sertoli cells failed to reestablish basal nuclear positioning, indicating that loss of MAP7 causes an intrinsic polarity defect. These findings suggest that MAP7 acts as a cell-autonomous regulator of Sertoli cell polarity, rather than mediating effects indirectly through germ cell–Sertoli cell interactions.” (page 15, lines17–21)

      • The resolution of the SEM images in Figure 3c is insufficient to evaluate tight and adherens junctions clearly. As such, these images do not convincingly support the claim that adherens junctions are absent in the KO testes.

      Response: We thank the reviewer for this insightful comment. Tight junctions can be reliably identified in SEM images as dense intercellular structures accompanied by endoplasmic reticulum aligned along the cell boundaries. The region immediately apical to the tight junctions likely corresponds to adherens junctions, which are also associated with the endoplasmic reticulum. Unlike tight junctions, these regions exhibit wider intercellular spaces, consistent with the looser membrane apposition characteristic of adherens junctions, although they cannot be unambiguously distinguished from gap junctions or desmosomes based on morphology alone. In the original figure, 2× binning reduced image resolution, which may have contributed to the reviewer’s concern.

      In the revised manuscript, we have re-acquired the SEM images in high-resolution mode, focusing on the relevant regions. The new high-resolution images have replaced the original panels in revised Figure 3C, providing clearer visualization of junctional structures at P10 and P21 in Map7+/- and Map7-/- testes. The original Figure 3C images have been moved to Supplemental Figure 4B for reference.

      The corresponding section in the Results has been revised as follows in the updated manuscript: “We then performed SEM to examine the effects of Map7 KO. In P21 Map7+/- testes, electron-dense regions along the basal side of Sertoli–Sertoli junctions corresponded to tight junctions closely associated with the endoplasmic reticulum, consistent with previous reports (Luaces et al. 2023) (Fig. 3C; Supplemental Fig. 4B). The region immediately apical to the tight junctions likely represents adherens junctions, which were also associated with the endoplasmic reticulum. Unlike tight junctions, these regions displayed wider intercellular spaces, reflecting the looser membrane apposition typical of adherens junctions, though they could not be definitively distinguished from gap junctions or desmosomes based on morphology alone (Fig. 3C; Supplemental Fig. 4B). At P10, both Map7+/- and Map7-/- testes lacked clearly defined tight junctions and adherens junction–like structures (Fig. 3C; Supplemental Fig. 4B). In P21 Map7-/- mice, Sertoli cells formed expanded basal tight junctions but failed to establish adherens junction–like structures (Fig. 3C; Supplemental Fig. 4B).” (page 8, line 34–page 9, line 12)

      • GFP-tagged reporter mice and HeLa cells were used for immunoprecipitation-mass spectrometry to identify proteins that interact with MAP7. Given that the authors aimed to elucidate the mechanism by which MAP7 regulates Sertoli cell cytoskeleton organization, the rationale for including HeLa cells is unclear and should be better justified or reconsidered.

      Response: We thank the reviewer for this comment. MAP7-egfpKI HeLa cells were used as a complementary system to identify MAP7-associated proteins, providing sufficient material and a controlled environment for robust detection. By comparing IP-MS results from MAP7-egfpKI HeLa cells and P17–P20 Map7-egfpKI testes, we can distinguish proteins that are specific to polarized Sertoli cells: proteins detected exclusively in P17–P20 testes may be involved in Sertoli cell polarization, whereas proteins detected in both systems likely represent general MAP7-associated factors that are not specific to Sertoli cell polarity.

      This rationale has been clarified in the revised manuscript by adding the following sentence to the Results section: “MAP7-egfpKI HeLa cells were used as a complementary system, providing sufficient material and a controlled environment for robust detection of MAP7-associated proteins. Comparison of IP-MS results between MAP7-egfpKI HeLa cells and P17–P20 Map7-egfpKI testes allows identification of MAP7-associated proteins that are specific to polarized Sertoli cells, whereas proteins detected in both systems likely represent general MAP7-associated proteins.” (page 9 lines 27-32)

      • The authors observed that MYH9, one of the MAP7-interacting proteins, does not colocalize with ectopic microtubule and F-actin structures in MAP7 KO testes and concluded that MAP7 facilitates the integration of microtubules and F-actin via interaction with NMII heavy chains. This conclusion is speculative and not adequately supported by the presented data.

      Response: We thank the reviewer for this insightful comment. We agree that our initial conclusion was speculative and have revised the relevant section to more accurately reflect the limitations of the current data. The revised text now reads as follows: “These findings indicate that MYH9 localization at the luminal interface depends on MAP7, and suggest that MAP7 helps coordinate microtubules and F-actin, potentially via its association with NMII heavy chains.” (page 10, lines 13–15)

      To further elucidate this mechanism, we will perform biochemical domain-mapping to define the MAP7 region responsible for MYH9 complex formation. We have already established a series of human MAP7 deletion mutants (as reported previously, EMBO Rep., 2018) and will conduct co-immunoprecipitation assays in HEK293 cells to identify the specific MAP7 domain required for complex formation with MYH9. Based on these results, we plan to use AlphaFold3 to predict the three-dimensional structure of the MAP7–MYH9 complex. These analyses will help clarify how MAP7 associates with the actomyosin network and provide additional mechanistic insights that complement our in vivo observations of MYH9 mislocalization in Map7-/- testes.

      • The authors used Spearman correlation coefficients to analyze six Sertoli cell clusters and generated a minimum spanning tree to infer differentiation trajectories. However, details on the method used for constructing the tree are lacking. Moreover, relying solely on Spearman correlation to define differentiation topology is oversimplified.

      Response: We appreciate the reviewer’s valuable feedback. We agree that Spearman correlation alone is insufficient to infer differentiation topology. In response, we reanalyzed the data using Monocle3, which implements branch-aware pseudotime inference to capture both cluster continuity and differentiation directionality. This reanalysis provides a more accurate reconstruction of differentiation trajectories among the six Sertoli cell clusters. Although the overall trajectories appeared different and a higher proportion of Map7-/- Sertoli cells exhibited very low pseudotime values, comparison of the control and Map7-/- trajectories revealed that the average node degree was nearly identical, indicating that the local graph structure—reflecting the connectivity among neighboring cells—was largely preserved. The numbers of branch points and the graph diameter differed slightly, likely due to differences in sample size (311 control vs. 434 Map7-/- Sertoli cells) and distribution bias rather than major topological changes. Accordingly, Figures 5C and 5D have been replaced with the updated Monocle3-based trajectory analysis, and the corresponding text in the Results section and figure legend have been revised as follows:

      “To reconstruct differentiation trajectories among the six Sertoli cell clusters, we reanalyzed the datasets using Monocle3, which incorporates branch-aware pseudotime inference. Cluster C1 was selected as the root based on shared specificity and entropy scores, consistent with its metabolically active and transcriptionally diverse profile (Fig. 5B, C; Supplemental Fig. 7). While the overall trajectories appeared altered, the proportion of Map7-/- Sertoli cells with very low pseudotime values was only modestly increased (Fig. 5D). Comparison with controls showed that the average node degree was nearly identical (Fig. 5C), indicating that the local graph structure, reflecting connectivity among neighboring cells, remained largely intact. Minor differences in branch points and graph diameter likely reflect inherent variability in the data rather than major topological changes (Supplemental Fig. 6B). Consistent with this, the relative proportions of the six clusters showed only modest shifts, suggesting that the overall architecture of Sertoli cell differentiation is largely preserved in the absence of MAP7.” (page 11, lines 7-18)

      “(C) Control and Map7-/- Sertoli cells were visualized separately using UMAPs constructed in Seurat. Using the same datasets, pseudotime trajectories were inferred with Monocle3. For root selection, shared_score (cluster overlap), specificity_score (cluster uniqueness), and entropy_score (transcriptional diversity) were computed, resulting in cluster 1 being selected as the root. The numbers of nodes, edges, branch points, average degree, and diameter of each trajectory are shown below the corresponding UMAPs. (D) Parallel comparison of pseudotime distributions between control and Map7-/- populations.” (page 30, lines 5-12)

      Minor comments:

      • Several extended data figures are redundant with main figures and do not provide additional value (e.g., Fig. 2d vs. Extended Data Fig. 3a; Fig. 2f vs. Extended Data Fig. 3d; Fig. 2C vs. Extended Data Fig. 4b; Fig. 3d vs. Extended Data Fig. 4c). The authors should consolidate or remove duplicates.

      Response: Regarding the concerns about redundancy between main and Supplemental figures, we would like to clarify the rationale for retaining certain Supplemental figures.

      Fig. 2D vs. Supplemental Fig. 3A: Due to space limitations in the main figure, only the merged three-color image was shown. We believe that the single-color grayscale images in Supplemental Fig. 3A provide additional clarity, allowing easier visualization of SOX9-positive Sertoli cell distribution and differences in F-actin structure.

      Fig. 2F vs. Supplemental Fig. 3E: In the main figure, only the high-magnification image was shown due to space constraints. The lower-magnification image in Supplemental Fig. 3E demonstrates that the selected field was not chosen arbitrarily, providing context for the observed structures. In addition, Supplemental Fig. 3E includes both low- and high-magnification images of age-matched busulfan (-) testes as a control for the busulfan (+) condition, further supporting the validity of the comparison.

      For the above-mentioned cases (Fig. 2D vs. Supplemental. 3A; Fig. 2F vs. Supplemental Fig. 3E), as well as other potentially overlapping figures (e.g., Fig. 3D vs. Supplemental Fig. 4C), we believe that the additional single-channel and lower-magnification images provide important context that cannot be fully conveyed in the main figures due to space limitations. Nevertheless, to address the reviewer’s concern, we will (i) clearly state the purpose of each Supplemental figure in the corresponding legends, and (ii) re-evaluate all figures to consolidate or remove any truly redundant panels. Our goal is to ensure that all figures collectively convey the data in the most concise and informative manner.

      • Figure citations in the main text do not consistently match figure content. For example, on page 7 (lines 5-6), the text refers to Extended Data Fig. 4a for SOX9 staining. Yet, it is the extended Data Fig. 3a that contains the relevant data. Similarly, the reference to Extended Data Fig. 4b and 4c on page 7 (lines 7-8) for adult defects is inaccurate.

      Response: We thank the reviewer for drawing attention to these inconsistencies. We have carefully checked all figure citations throughout the main text and corrected them so that they consistently match the figure content. The revised manuscript reflects these corrections.

      • In Figure 2e, percentages of Sertoli cells across three layers are shown. The figure legend should specify which layer(s) show statistically significant differences between WT and KO.

      Response: We are grateful to the reviewer for highlighting this point. Statistical comparisons were performed between Map7+/- and Map7-/- mice within each corresponding layer at P17. Statistical significance was assessed using Student’s t-test, and all three layers showed significant differences between Map7+/- and Map7-/- (P < 2.20 × 10⁻⁴). The figure legend has been revised accordingly as follows: “Statistical comparisons between Map7+/- and Map7-/- mice were performed for each corresponding layer at P17 using Student’s t-test. All three layers showed significant differences between Map7+/- and Map7-/- mice (*, P<2.20 × 10⁻⁴).” (page 28, lines 5-8)

      • The current color scheme for F-actin and TUBB3 in Figure 3 lacks sufficient contrast. Adjusting to more distinguishable colors would improve readability.

      Response: Response: We thank the reviewer for this helpful suggestion. In the original merged images, four channels (DNA, TUBB3, F-actin, and β-catenin) were displayed together, which reduced contrast between cytoskeletal signals. To improve clarity, we generated new merged images showing only TUBB3 and F-actin, allowing better visual distinction between these components. In addition, β-catenin and DNA are now displayed together as a separate merged image (β-catenin in yellow and DNA in blue) in the final column, highlighting the altered localization of β-catenin in Map7-/- testes.

      • Since multiple scale bars with different units are present within the same figures, adding units directly above or beside each scale bar would improve readability.

      Response: We thank the reviewer for the suggestion. Following this recommendation, we have added units directly above each scale bar in all figures to improve readability.

      • It is recommended to directly mark Sertoli cells, spermatogonia, and spermatocytes on the SEM images in Figure 2C for clearer visualization.

      Response: We thank the reviewer for the suggestion. We will follow this recommendation by performing segmentation and directly marking Sertoli cells, spermatogonia, and spermatocytes on the SEM images in Figure 2C to improve visualization.

      • The quantification of Sertoli cell positioning shown in Fig. 2C is already described in the main text and is unnecessary in the figure.

      Response: We appreciate the reviewer’s comment regarding the quantification of Sertoli cell positioning. Although the results are described in the main text, we believe that the visual presentation in Figure 2C is essential for conveying the spatial distribution pattern in an intuitive and comparative manner. To address the concern about redundancy, we have slightly revised the figure legend (page 27, lines 28–29) to clarify that this panel provides a visual summary of the quantitative data described in the text, thereby improving clarity without unnecessary duplication.

      _Referee cross-commenting_

      I concur with Reviewer 2 that the Map7-eGFP mouse model is a valuable tool for the research community. I also agree that performing MAP7-MYH9 double immunofluorescence staining to demonstrate their colocalization would further strengthen the authors' conclusions regarding their interaction. My overall assessment of the manuscript remains unchanged: the study represents an incremental advance that extends previous findings on MAP7 function but provides limited new mechanistic insight.

      Reviewer #1 (Significance):

      This study investigates the role of the microtubule-associated protein MAP7 in Sertoli cell polarity and apical domain formation during early stages of spermatogenesis. Using GFP-tagged and MAP7 knockout mouse models, the authors show that MAP7 localizes to apical microtubules and is required for Sertoli cell cytoskeletal organization and germ cell development. While the study identifies early Sertoli cell defects and candidate MAP7-interacting proteins, the mechanistic insights remain limited, and several conclusions require stronger experimental support. Overall, the discovery represents an incremental advance that extends prior findings on MAP7 function, providing additional but modest insights into the role of MAP7 in cytoskeletal regulation in male reproduction.

      Response: We thank the reviewer for their constructive comments and thoughtful evaluation of our manuscript. We appreciate the positive feedback regarding the value of the Map7-egfpKI mouse model for the research community. We also thank the reviewer for the suggestion to perform MAP7–MYH9 double immunofluorescence staining to demonstrate colocalization, which we agree will further strengthen the mechanistic support.

      We would like to clarify that several aspects of our findings represent novel contributions within a field where the mechanisms of microtubule remodeling during apical domain formation have remained largely unresolved. In particular, our study provides evidence that MAP7 is asymmetrically enriched at the apical microtubule network in Sertoli cells and contributes to the directional organization of these microtubules—an aspect of Sertoli cell polarity that has not been previously characterized. Our results further indicate that dynamic microtubule turnover, rather than stabilization alone, is required for proper apical domain formation, addressing a gap in current understanding of how microtubules are reorganized during early polarity establishment. In addition, the data support a role for MAP7 in coordinating microtubule and actomyosin organization, suggesting a scaffolding function that links these cytoskeletal systems. We also observe that Sertoli cell polarity can be functionally separated from cell identity and that disruptions in apical domain architecture precede delays in germ cell developmental progression. Taken together, these observations provide mechanistic insight that expands upon previous studies of MAP7 function at the cellular level.

      The conclusions are supported by multiple, complementary lines of evidence, including knockout and Map7-egfpKI mouse models, high-resolution electron microscopy, immunoprecipitation–mass spectrometry, and single-cell RNA sequencing. While we agree that further experiments, such as MAP7–MYH9 double staining, will strengthen the mechanistic framework, we will also perform complementary biochemical analyses to provide additional insight. Specifically, we plan to conduct domain-mapping experiments to identify the MAP7 region required for MYH9 complex formation, coupled with co-immunoprecipitation assays in cultured cells to validate this association.

      Although generating new mutant mouse lines is not feasible within the scope of this revision, and no in vitro system fully recapitulates Sertoli cell polarization, these complementary approaches will provide further mechanistic support. We believe that these planned experiments, together with the current dataset, will clarify the underlying mechanisms and reinforce the significance of our findings, while appropriately acknowledging the current limits of experimental evidence.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In this manuscript the authors evaluate the role of Microtubule Associated Protein 7 (MAP7) in postnatal Sertoli cell development. The authors build two novel transgenic mouse lines (Map7-eGFP, Map7 knockout) which will be useful tools to the community. The transgenic mouse lines are used in paired advanced sequencing experiments and advanced imaging experiments to determine how Sertoli cell MAP7 is involved in the first wave of spermatogenesis. The authors identify MAP7 as an important regulator of Sertoli cell polarity and junction formation with loss of MAP7 disrupting intracellular microtubule and F-actin arrangement and Sertoli cell morphology. These structural issues impact the first wave of spermatogenesis causing a meiotic delay that limits round spermatid numbers. The authors also identify possible binding partners for MAP7, key among those MYH9.

      The authors did a great job building a complex multi-modal project that addressed the question of MAP7 function from many angles. The is an excellent balance of using many advanced methods while still keeping the project narrowed, to use only tools to address the real questions. The lack of quality testing on the germ cells outside of TUNEL is disappointing, but the Conclusion section implies that this sort of work is being done currently so the omission in this manuscript is acceptable. However, there is an issue with the imaging portion of the work on MYH9. The conclusions from the MYH9 data is currently overstated, super-resolution imaging of Map7 knockouts with microtubule and F-actin stains, and imaging that uses MYH9 with either Map7-eGFP or anti-MAP7 are also needed to both support the MAP7-MYH9 interaction normally and lack of interaction with failure of MYH9 to localize to microtubules and F-actin in knockouts. Since a Leica SP8 was used for the imaging, using either Leica LIGHTNING or just higher magnification will likely be the easiest solution.

      Response: We sincerely appreciate the reviewer’s thorough and positive evaluation of our study. We are encouraged that the reviewer recognized the overall strength of our multi-modal approach and the scientific value of the Map7-egfp knock-in and Map7 knockout genome-edited mouse models that we generated. We also thank the reviewer for highlighting the balance between methodological breadth and focused, hypothesis-driven investigation in our work.

      Regarding the reviewer’s valuable comments on the imaging data, we have addressed them as follows. We improved the cytoskeletal imaging data as described in response to the reviewer’s minor comments. Specifically, in the revised Figure 3B, we replaced the original images with higher-resolution confocal images to provide a clearer view of cytoskeletal organization. In addition, following Reviewer #1’s suggestion, we modified the panel layout to enlarge each field and enhance the contrast between TUBB3 and F-actin channels, allowing better visualization of their altered localization in Map7-/- testes.

      We agree that super-resolution imaging comparing control and Map7-/- testes stained for TUBB3 and F-actin would further strengthen the analysis. If the current resolution is still considered insufficient, we plan to perform additional imaging using a Carl Zeiss Airyscan or Leica Stellaris 5 system to further improve spatial resolution and confirm the observed cytoskeletal phenotypes. Finally, we will perform co-imaging of MYH9 with MAP7 to validate their spatial relationship under normal conditions, complementing the existing data obtained from Map7-/- testes.

      This manuscript is nicely organized with almost all of the results spelled out very clearly and almost always paired with figures that make compelling and convincing support for the conclusions. There are minor revision suggestions for improving the manuscript listed below. These include synching up Figure and Supplemental Figure reference mismatches. There are also many minor, but important, details that need to be added to the Methods section including many catalog numbers and some references.

      - Some of the imaging, especially Fig4F could benefit and be more convincing with super-resolution imaging in the 150nm range (SIM, Airyscan, LIGHTNING, SoRa) possibly even just imaging with a higher magnification objective (60x or 100x)

      Response: We appreciate the reviewer’s suggestion to improve the resolution of the imaging data. In addition to revising Figure 3B as described above, we have also replaced the images in Figure 4F with higher-resolution confocal images to provide a clearer view of MYH9 localization relative to microtubules and F-actin. These revised images highlight that MYH9 specifically accumulates at apical regions where microtubules and F-actin intersect, forming the apical ES, but is not localized to the basal ES-associated F-actin structures. To retain spatial context and allow readers to appreciate the overall distribution pattern, the original lower-magnification images from Figure 4F have been moved to Supplemental Figure 5.

      - SuppFig1D: Please add context in the legend to the meaning of the Yellow Stars and "O->U" labels. The latter would seem to be to indicate the Ovarian and Uterine sides of the image

      Response: In response to this comment, we revised the figure legend to clarify the annotations. The legend now states: “O, ovary side; U, uterus side. Asterisks indicate secretory cells that lack planar cell polarity.”

      - Pg6Line7: up to P23 or up to P35?

      Response: We appreciate the reviewer’s attention to this detail. The text has been revised for clarity as follows: “To examine the temporal dynamics of Sertoli cell polarity establishment, we analyzed seminiferous tubule morphology across the first wave of spermatogenesis, from postnatal day (P)10 to P35. To specifically assess the role of MAP7 in Sertoli cells while minimizing contributions from germ cells, our analysis focused on stages up to P23, before MAP7 expression becomes detectable in step 9–11 spermatids (Fig. 1), to exclude potential secondary effects resulting from MAP7 loss in germ cells.” (page 6, lines 5-10)

      - SuppFig4B: Does SuppFig4B reference back to Fig3B or Fig3C? If the latter please update this in the legend.

      - Pg7Line21-23: Is SuppFig3D,E meant to be referenced and not SuppFig5A,B?

      - Pg8Line22-25: Is SuppFig4A meant to be reference and not SuppFig5?

      - Pg8Line34-Pg9Line: Is SuppFig4B meant to be reference and not SuppFig5B?

      Response: We appreciate the reviewer’s careful reading. All mismatches in Supplemental figure references have been corrected, ensuring that each reference in the text now accurately corresponds to the appropriate data.

      - Pg9Line28-33: Would the authors be willing to rework this figure to include images that more closely match the reported findings? The current version does not strongly support the idea that MYH9 fails to localize to microtubule and F-actin domains in Map7 knockout P17 seminiferous tubules. This could also just be a matter of acquiring these images at a higher magnification or with a lower-end (150nm range) super-resolution system (SIM, Airyscan, LIGHTNING, SoRa etc)

      Response: Following the reviewer’s recommendation, we replaced the images in Figure 4F with higher-resolution confocal images to better visualize MYH9 localization relative to microtubules and F-actin in Map7+/- and Map7-/- testes. These revised images demonstrate that MYH9 specifically accumulates at apical regions where microtubules and F-actin intersect, but not at the basal ES-associated F-actin structures. To preserve spatial context, the original low-magnification images have been moved to Supplemental Figure 5. If additional resolution is required, we are prepared to acquire further images using an Airyscan or Stellaris 5 system.

      - SuppFig7A: The legend notes these are P23 samples but the image label says 8W. Please update this to whichever is the correct age.

      Response: We thank the reviewer for pointing out this discrepancy. The figure legend for Supplemental Figure 7A (now revised as Supplemental Figure 8A) has been corrected to indicate that the samples are from 8-week-old mice, consistent with the image label.

      - Pg16Line4-5: Please include in the text the vendor and catalog number for the C57BL/6 mice

      Response: The text now specifies: “C57BL/6NJcl mice were purchased from CLEA Japan (Tokyo, Japan)” (page 17, line 4). CLEA Japan does not assign catalog numbers to mouse strains.

      - Pg16Line18-19: Please include in the text the catalog number for the DMEM

      - Pg16Line19-20: Please include in the text the vendor and catalog number for the FBS

      - Pg16Line20: Please include in the text the vendor and catalog number for the Pen-Strep

      Response: We have added vendor and catalog information as follows: “Wild-type and MAP7-EGFPKI HeLa cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, 043-30085; Fujifilm Wako Pure Chemical, Osaka, Japan) supplemented with 10% fetal bovine serum (FBS, 35-015-CV; Corning, Corning, NY, USA) and penicillin–streptomycin (26253-84; Nacalai, Kyoto, Japan) at 37 °C in a humidified atmosphere containing 5% CO₂ 18.” (page 17, lines 18-22)

      - Pg17Line6-12: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7 knockout genotyping

      - Pg17Line20-27: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7-eGFP genotyping

      Response: The text has been updated to include the PCR conditions used for genotyping as follows: “Genotyping PCR was routinely performed as follows. Genomic DNA was prepared by incubating a small piece of the cut toe in 180 µL of 50 mM NaOH at 95 °C for 15 min, followed by neutralization with 20 µL of 1 M Tris-HCl (pH 8.0). After centrifugation for 20 min, 1 µL of the resulting DNA solution was used as the PCR template. Each reaction (8 µL total volume) contained 4 µL of Quick Taq HS DyeMix (DTM-101; Toyobo, Osaka, Japan) and a primer mix. PCR cycling conditions were as follows: 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 65 °C for 30 s, and 72 °C for 1 min; followed by a final extension at 72 °C for 2 min and a hold at 4 °C. PCR products were analyzed using agarose gel electrophoresis. This protocol was also applied to other mouse lines and alleles generated in this study.” (page 18, lines 17–25)

      - Pg17Line30: Please include in the text the vendor and catalog number for the Laemmli sample buffer

      Response: We clarified that the buffer was prepared in-house.

      - Pg17Line32&SuppTable1: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information

      - Pg17Line32: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1

      Response: Supplementary Table 1 has been updated to include both primary and HRP-conjugated secondary antibodies. In the Immunoblotting section of the Materials and Methods, we specified the antibodies used: “The following primary antibodies were used: mouse anti-Actin (C4, 0869100-CF; MP Biomedicals, Irvine, CA, USA), mouse anti-Clathrin heavy chain (610500; BD Biosciences, Franklin Lakes, NJ, USA), rat anti-GFP (GF090R; Nacalai, 04404-84), rabbit anti-MAP7 (SAB1408648; Sigma-Aldrich, St. Louis, MO, USA), rabbit anti-MAP7 (C2C3, GTX120907; GeneTex, Irvine, CA, USA), and mouse anti-α-tubulin (DM1A, T6199; Sigma-Aldrich). Corresponding HRP-conjugated secondary antibodies were used for detection: goat anti-mouse IgG (12-349; Sigma-Aldrich), goat anti-rabbit IgG (12-348; Sigma-Aldrich), and goat anti-rat IgG (AP136P; Sigma-Aldrich). Detailed information for all primary and secondary antibodies is provided in Supplementary Table 1.” (page 19, lines 14-22)

      - Pg18Line2: Please include in the text the vendor and catalog number for the Bouin's

      Response: The text has been updated to indicate that Bouin’s solution was prepared in-house

      - Pg18Line3: Please include in the text the catalog number for the CREST-coated glass slides

      - Pg18Line7: Please include in the text the catalog number for the OCT compound

      - Pg18Line11: Please include in the text the vendor and catalog number for the Donkey Serum

      - Pg18Line11: Please include in the text the vendor and catalog number for the Goat Serum

      Response: The text now includes vendor and catalog information for all these reagents, including CREST-coated slides (SCRE-01; Matsunami Glass, Osaka, Japan), OCT compound (4583; Sakura Finetechnical, Tokyo, Japan), donkey serum (017-000-121; Jackson ImmunoResearch Laboratories, PA, USA), and goat serum (005-000-121; Jackson ImmunoResearch Laboratories).

      - Pg18Line13: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information

      Response: We thank the reviewer for the suggestion. Supplementary Table 1 already includes information for the antibodies used for immunoblotting, and we have now added information for the Alexa Fluor-conjugated secondary antibodies used for immunofluorescence in this study.

      - Pg18Line18: Please include in the text the vendor and catalog number for the DAPI

      Response: The text has been updated to include the vendor and catalog number for DAPI (D9542; Sigma-Aldrich).

      - Pg18Line19: Please also include information about the objectives used including catalog numbers, detectors used (PMT vs HyD)

      Response: We thank the reviewer for the suggestion. The following information has been added to the Histological analysis section in Materials and Methods: “Objectives used were HC PL APO 40×/1.30 OIL CS2 (11506428; Leica) and HC PL APO 63×/1.40 OIL CS2 (11506350; Leica), with digital zoom applied as needed for high-magnification imaging. DAPI was detected using PMT detectors, while Alexa Fluor 488, 594, and 647 signals were captured using HyD detectors. Images were acquired in sequential mode with detector settings adjusted to prevent signal bleed-through.” (page 20, lines 13-17)

      - Pg18Line23: Please cite in the text the reference paper for Fiji (Schindelin et al. 2012 Nature Methods PMID: 22743772) and note the version of Fiji used

      - Pg18Line24: Please note the version of Aivia used

      Response: We have revised the text accordingly by citing the reference paper for Fiji (Schindelin et al., 2012, Nature Methods, PMID: 22743772) and noting the version used (v.2.16/1.54p). In addition, we have added the version of Aivia used in this study (version 14.1).

      - Pg18Line25: If possible, please use a more robust and reliable system than Microsoft Excel to do statistics (Graphpad Prism, Stata, R, etc), if this is not possible please note the version of Microsoft Excel used

      Response: We appreciate the reviewer’s suggestion. For basic statistical analyses such as the Student’s t-test, we used Microsoft Excel (Microsoft Office LTSC Professional Plus 2021), which has been sufficient for these standard calculations. For more advanced analyses, including ANOVA and single-cell RNA-seq analyses, we used R. These details have now been added to the text.

      - Pg18Line25: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing") and note the version of R used

      - Pg18Line25: Please note the specific R package with version used to do ANOVA, and cite in the text the reference for this package

      Response: We have cited the reference for R (R Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) and noted the version used (version 4.4.0) in the text. In addition, regarding ANOVA, we have added the following description: “For ANOVA analysis, linear models were fitted using the base stats package (lm function), and analysis of variance was conducted with the anova function.” (page 20, lines 23-25)

      - Pg18Line25: Please clarify, was a R package called "AVNOVA" used to do ANOVA or is this a typo?

      Response: We thank the reviewer for pointing this out. It was a typographical error — the correct term is “ANOVA”. The text has been corrected accordingly.

      - Pg18Line32: Please include in the text the catalog number for the EPON 812 Resin

      - Pg19Line3: Please include the version number for Stacker Neo

      - Pg19Line5: Please include the vendor and version number for Amira 2022

      - Pg19Line5: Please include the version number for Microscopy Image Browser

      - Pg19Line5: Please include the version number for MATLAB that was used to run Microscopy Image Browser

      Response: We added the catalog number for the EPON 812 resin and the vendor and version information for the software used. The following details have been included in the revised text:

      EPON 812 resin: TAAB Embedding Resin Kit with DMP-30 (T004; TAAB Laboratory and Microscopy, Berks, UK)

      Stacker Neo: version 3.5.3.0; JEOL

      Amira 2022: version 2022.1; Thermo Fisher Scientific

      Microscopy Image Browser: version 2.91

      Note that although Microscopy Image Browser is written in MATLAB, we used the standalone version that does not require a separate MATLAB installation.

      - Pg19Line: 9-10: Please include in the text the catalog number for the complete protease inhibitor

      - Pg19Line14: Please include in the text the catalog number for the Magnetic Agarose Beads

      - Pg19Line16: Please include in the text the catalog number for the GFP-Trap Magnetic Agarose Beads

      Response: We have added the catalog numbers for the complete protease inhibitor (4693116001), control magnetic agarose beads (bmab), and GFP-Trap magnetic agarose beads (gtma).

      - Pg19Line21: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1

      - Pg19Line21-22: Please include in the text the catalog number for the ECL Prime

      Response: We thank the reviewer for the helpful suggestions. The description regarding immunoblotting (“Eluted samples were separated by SDS–PAGE, transferred to PVDF membranes…”) was reorganized: overlapping content has been removed, and the necessary information has been integrated into the “Immunoblotting” section, where details of the primary and secondary antibodies (listed in Supplementary Table 1) are already provided. In addition, the information for ECL Prime has been updated to “Amersham ECL Prime (RPN2236; Cytiva, Tokyo, Japan)”.

      - Pg20Line2: Please include the version number for Xcalibur

      Response: The version of Xcalibur used in this study (version 4.0.27.19) has been added to the text.

      - Pg20Line5: Please cite in the text the reference paper for SWISS-PROT (Bairoch and Apweiler 1999 Nucleic Acid Research PMID: 9847139)

      Response: The reference paper for SWISS-PROT (Bairoch and Apweiler, 1999, Nucleic Acids Research, PMID: 9847139) has been cited in the text.

      - Pg19Line26: Please include in the text the catalog number for the NuPAGE gels

      - Pg19Line28: Please include in the text the catalog number for the SimpleBlue SafeStain

      Response: Both catalog numbers have been added in the Mass spectrometry section as follows: 4–12% NuPAGE gels (NP0321PK2; Thermo Fisher Scientific) and SimplyBlue SafeStain (LC6060; Thermo Fisher Scientific).

      - Pg20Line26: Please include in the text the catalog number for the Chromium Singel Cell 3' Reagent Kits v3

      Response: The catalog number for the Chromium Single Cell 3′ Reagent Kits v3 (PN-1000075; 10x Genomics) has been added to the text.

      - Pg21Line3: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing")

      Response: The reference for R (R Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) has already been cited in the “Histological analysis” section, where ANOVA analysis is described.

      - Pg21Line3 Please cite in the text the reference for RStudio (Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.)

      Response: The reference for RStudio (Posit team, 2025. RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA, USA. URL: http://www.posit.co/) has been added to the text.

      - Pg21Line23: Please include the version number for Metascape

      Response: The version of Metascape used in this study (v3.5.20250701) has been added to the text.

      - SuppFig12: please update the legend to include a description after the title and update the figure labeling to correspond to the legend. Also, this figure is currently not referenced anywhere in the text.

      Response: We have updated the legend for Supplemental Figure 12 (Supplemental Figure 13) to include a descriptive sentence after the title and have adjusted the figure labeling to match the legend. The revised legend now reads: “Full-scan images of the agarose gels shown in Supplemental Figs. 1B and 2C are displayed in the upper and lower left panels, respectively, while the corresponding full-scan images of the immunoblots shown in Supplemental Figs. 1C and 2D are presented in the upper and lower right panels, respectively.”

      As these images serve as source data, they are not referenced directly in the main text.

      _Referee cross-commenting_

      I generally agree with Reviewer 1 and specifically concur related to adding details about fertility assessment of the Map7 Knockout line, and enhancing the SEM imaging.

      Response: As noted in our response to Reviewer #1, we have re-acquired the SEM images in high-resolution mode, focusing on the relevant regions. The new high-resolution images have replaced the original panels in revised Figure 3C, providing clearer visualization of junctional structures at P10 and P21 in Map7+/- and Map7-/- testes. The original Figure 3C images have been moved to Supplemental Figure 4B for reference.

      Reviewer #2 (Significance):

      There are mouse lines, and datasets that will be useful resources to the field. This work also advances our understanding of a period in Sertoli cell development that is critical to fertility but very understudied.

      Response: We thank the reviewer for the positive comments and for recognizing the potential value of our mouse lines and datasets to the field, as well as the significance of our work in advancing the understanding of this critical but understudied period in Sertoli cell development.

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

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary:

      The manuscript titled "Unravelling the Progression of the Zebrafish Primary Body Axis with Reconstructed Spatiotemporal Transcriptomics" presents a comprehensive analysis of the development of the primary body axis in zebrafish by integrating bulk RNA-seq, 3D images, and Stereo-Seq. The authors first clearly demonstrate the application of Palette for integrating RNA-seq and Stereo-Seq using published spatial transcriptomics data of Drosophila embryos. Subsequently, they produced serial bulk RNA-seq data for certain developmental stages of Danio rerio embryos and utilized published Stereo-Seq data. Through robust validation, the authors observe the molecular network involved in AP axis formation. While the authors show that integrating bulk RNA-seq data with Stereo-Seq improves spatial resolution, additional proof is required to demonstrate the extent of this improvement.

      Response: We thank the reviewer for the positive feedback on our Palette pipeline, zSTEP construction and analysis of primary body axis development. We appreciate the constructive suggestions provided, which we can implement to improve our manuscript. As pointed out by the reviewer, some analysis procedures were not described in sufficient detail. To address this, we have added more explanatory texts and additional schematic diagrams to make the methods clearer and more understandable. We also thank the reviewer for the meticulous reading and for reminding us to include parameters, references and essential texts, which significantly improve the manuscript quality and make the manuscript more rigorous. Furthermore, as suggested by the reviewer, the extent of the improvement on the spatial resolution was not clearly demonstrated in the manuscript. Therefore, we have provided an additional figure to show the original expression on the stacked Stereo-seq slices and 3D live image compared to the expression from zSTEP, and the results indicate that zSTEP provides better, more continuous expression patterns. We still have two remaining tasks that are expected to be completed within the next month. We hope our responses have address the concerns raised by the reviewer, and we are pleased to provide any additional proof as needed.

      Major Comments:

      1. Lines 66-68: Discuss the limitations of existing tools and explicitly state the advantages of using Palette.

      Response: We thank the reviewer for the valuable suggestion. We have added the following new texts after line 68 to emphasize the features and advantages of Palette.

      "Newly developed tools are committed to integrating bulk and/or scRNA-seq data with ST data to enhance spatial resolution, focusing on expression at the spot level. However, gene expression patterns are closely correlated to the biological functions and are more critical for understanding biological processes. Therefore, a tool focusing on inferring spatial gene expression patterns would be desirable."

      1. Body Pattern Genes Analysis: For both Drosophila and Danio rerio, it would be valuable to examine body pattern genes in Stereo-Seq and apply Palette to determine if the resolution of the segments improves or merges. The resolution of the A-P axis is convincing, but further evidence for other segments would be beneficial.

      Response: We thank the reviewer for the suggestions. For the Drosophila data, we only used two adjacent slices for Palette performance assessment, and thus were only able to evaluate the expression patterns within the slice.

      For the zebrafish data, although we have construct zSTEP as a 3D transcriptomic atlas, we have to admit that the left-right (LR) and dorsal-ventral (DV) patterning is not satisfactory enough. Here we show a section from the dorsal part of 16 hpf zSTEP that displays a relatively well-defined left-right pattern (Fig. 2). Along the left-right axis, the notochord cells are centrally located, flanked by somite cells on either side, with the outermost cells being pronephros.

      One reason for the limited LR and DV patterning is that the original annotation of the ST data does not clearly distinguish all the cell types. Another reason is likely due to the disordered cell positions when stacking ST slices. Thus, our zSTEP is most suitable for investigating the AP patterns, while the performances on LR and DV patterns may not achieve the same level of accuracy.

      See response letter for the figure.

      1. Figure 2d: Include the A-P line for which the intensity profile was plotted in the main figure, rather than just in the supplementary material. Additionally, consider simplifying the plot by not combining three lines into one, as it complicates the interpretation of observations.

      Response: We thank the reviewer for the helpful suggestions. We have updated Figure 2d and Figure S1b by adding a A-P line on each subfigure (Fig. 3). Additionally, as the reviewer suggested, we have separated the intensity plots so that each subfigure now includes a dedicated intensity plot along A-P axis.

      See response letter for the figure.

      1. Drosophila Data Analysis: While the alignment and validation of Danio rerio sections are clearly explained, the analysis and validation of Drosophila data are insufficiently detailed. Provide a more thorough explanation of how the intensity profiles between BDGP in situ data and Stereo-Seq data are adjusted.

      Response: We thank the reviewer for raising this issue. To make the analysis procedure clearer, we have updated Figure 2a (Fig. 4) and added explanatory texts in the figure legends to describe the processing procedure for the Drosophila ST data.

      See response letter for the figure.

      Additionally, the following sentences have been added into the Methods section to describe the generation of the intensity profiles.

      "The intensity plot profiles along AP axis were generated through the following steps: The expression pattern plot images or in situ hybridization images were imported into ImageJ and converted to grayscale. The colour was then inverted, and a line of a certain width (here set as 10) was drawn across from the anterior part to the posterior part (Fig. S1a). The signal intensities along the width of the line were measured and imported into R for generating intensity plots."

      1. Figure 3d: Present a plot with the expected expression profiles of the three genes if the embryo is aligned as anticipated.

      Response: We thank the reviewer for this helpful suggestion, which improves the clarity of our manuscript. We have added the following subfigure in as Figure 3d (Fig. 5) to show the expected expression profiles of the three midline genes along left-right axis.

      See response letter for the figure.

      1. Analysis Without Palette: Between lines 277-438, the outcome of using Palette with bulk RNA-seq and Stereo-Seq is convincing. However, consider the following:

      o What would be the observations if the analysis were conducted solely with Stereo-Seq data, without incorporating bulk RNA-seq data and employing Palette?

      Response: We thank the reviewer for raising this important question. Here we show the comparison of ST expression on stacked Stereo-seq slices, ST expression projected on 3D live images, and the Palette-inferred expression (Fig. 6). The stacked ST slices do not fully reflect the zebrafish morphology, and the gene expression appears sparse, making it look massive (the first row). While after projecting ST expression onto the live image, the expression patterns can be observed on zebrafish morphology, but the expression is still sparsely distributed in spots (the second row). However, the expression patterns captured by Palette in zSTEP show more continuous expression patterns (the third row), which are more similar to the observations in in situ hybridization images (the fourth row). We are considering put these analyses into the supplementary figure.

      See response letter for the figure.

      o This study uses only Stereo-Seq as the spatial transcriptomics reference. It would strengthen the argument to use at least one other spatial transcriptomics method, such as Visium or MERFISH, in conjunction with bulk RNA-seq and Palette, to demonstrate whether Palette consistently improves gene expression resolution.

      Response: We thank the reviewer for raising this professional question. To demonstrate a broad application of Palette, it would be necessary to test Palette performance using different types of ST references. We plan to perform extra analyses to evaluate Palette performance using Visium and MERFISH data as ST references, respectively. Additionally, our Palette pipeline only takes the overlapped genes for inference. As only hundreds of genes can be detected by MERFISH, Palette can only infer the expression patterns of these genes. As mentioned in the work of Liu et al. (2023), MERFISH can independently resolve distinct cell types and spatial structures, and thus we believe Palette will also show great performance when using MERFISH as ST reference. We've already started the analyses and expect to accomplish it within the next month. And we will update the analyses as separated tutorials to the GitHub repository.

      Reference:

      Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance 6 (2023).

      1. PDAC Data Analysis: Provide a more detailed explanation of the PDAC data analysis and use appropriate colors in the tissue images to clearly distinguish cell types.

      Response: We thank the reviewer for the suggestions. We have updated the colours used in the tissue images to be consistent to the colours in tissue clustering analysis. Additionally, we have added an additional subfigure in supplementary figure (Fig. 7) with more explanatory texts in the figure legends to provide a more thorough explanation for the analysis.

      See response letter for the figure.

      1. Comparison with Other Methods: State the limitations of not using STitch3D and Spateo for alignment and explain why these methods were not employed.

      Response: We thank the reviewer for raising this constructive comment. We fully agree with you that the introduction of published alignment algorithms would be helpful in our analysis. Currently, the slice alignment is adjusted manually, and thus the main limitation of not using these tools is that manual operation may induce bias compared to the alignment generated by computational algorithm. Unfortunately, STitch3D and Spateo are not included in this study because of two reasons. First, these two newly developed tools have been recently posted, and our analyses were largely completed before that. Therefore, we only mentioned these tools in the Discussion section. Second, we do not want to embed too many external tools into our analysis, which may increase the difficulties for researchers' operation. Specifically, STitch3D and Spateo are configured to run in Python environment, while Palette is based on R packages. Moreover, without these tools, our current manual alignment also achieves desired performance. However, we value this enlightening suggestion by the reviewer and therefore plan to further compare the performance of manual alignment versus the mentioned two alignment tools. At present, we have a preliminary comparison scheme and collected relevant datasets. Hopefully, we will complete this analysis within the next 1 to 2 weeks.

      Minor Comments:

      1. References: Add references to the statements in lines 51-53.

      Response: We thank the reviewer for reminding us of the missing references. We have added the works of Junker et al. (2014), Liu et al. (2022), Chen et al. (2022), Wang et al. (2022), Shi et al. (2023) and Satija et al. (2015) as references in line 53 as follows.

      "Thus, great efforts are ongoing to construct gene expression maps of these models with higher resolution, depth, and comprehensiveness1-6."

      References:

      1. Junker, J.P. et al. Genome-wide RNA Tomography in the zebrafish embryo. Cell 159, 662-675 (2014).
      2. Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell 57, 1284-1298 e1285 (2022).
      3. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777-1792 e1721 (2022).
      4. Wang, M. et al. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev Cell 57, 1271-1283 e1274 (2022).
      5. Shi, H. et al. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 622, 552-561 (2023).
      6. Satija, R. et al. Spatial reconstruction of single-cell gene expression data. Nature biotechnology 33, 495-502 (2015)
      1. Scientific Name Consistency: Ensure consistency in using either "Danio rerio" or "zebrafish" throughout the manuscript.

      Response: We thank the reviewer for this suggestion. We have changed "Danio rerio" to "zebrafish" to make "zebrafish" consistent throughout the manuscript.

      1. Related References: Include the following relevant references:

      o https://academic.oup.com/bib/article/25/4/bbae316/7705532

      o https://www.life-science-alliance.org/content/6/1/e202201701

      Response: We thank the reviewer for bringing these two relevant works to us. Baul et al. (2024) presented STGAT leveraging Graph Attention Networks for integrating spatial transcriptomics and bulk RNA-seq, and Liu et al. (2023) demonstrated the concordance of MERFISH ST with bulk and single-cell RNA-seq. Both are excellent works and relevant to our work. We have added these two references in line 61 and line 68, respectively.

      References:

      Baul, S. et al. Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks. Brief Bioinform 25 (2024).

      Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance 6 (2023).

      1. Figure 1a: In the Venn diagram, include the number of genes in the bulk and Stereo-Seq datasets, as well as the number of overlapping genes.

      Response: We thank the reviewer reminding us to include these important numbers. And in our current manuscript, we have added the following sentences in the Methods section to provide the gene numbers (Fig. 8). While the Venn diagram in Figure 1a serves as a schematic representation, so we did not include the gene numbers, as these may vary depending on the actual data.

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      1. Figure 1 Improvement: Enlarge Figure 1 and reduce repetitive elements, such as parts of the deconvolution and Figure 1b.

      Response: We thank the reviewer for the helpful suggestion. We agree with the reviewer that the deconvolution sections appear repetitive. We have updated Figure 1 (Fig. 9) by replacing these repetitive elements with a clearer and simpler diagram.

      See response letter for the figure.

      1. Figure 3f: Explain the black discontinuous line in the plot.

      Response: We thank the reviewer for the reminder. We are sorry about the lack of the explanation. We have added the below explanation for the black discontinuous line in the legend of Figure 3 (Fig. 10) as follows.

      See response letter for the figure.

      1. Line 610: State the percentage of unpaired imaging spots.

      Response: We thank the review for the reminder. We are sorry about not including the paired and unpaired spot number. We have added the number of paired spots with the percentage in the total spots in the Method section as follows.

      "The numbers of mapped spots for the 10 hpf, 12 hpf and 16 hpf embryos are 15,379 (69.4% of the total spots), 14,697 (70.5% of the total spots) and 21,605 (77.2% of the total spots), respectively."

      1. Lines 616-618: Specify the unit for the spot diameter.

      Response: We thank the reviewer for the reminder. Again, we are sorry about not including the spot diameter information in our previous version of manuscript. We have added the spot diameter in Method section as follows.

      "In the Stereo-seq data, each spot contained 15 × 15 DNA nanoball (DNB) spots (The diameter of each spot is near 10 μm)."

      Reviewer #1 (Significance):

      This algorithm will be useful not only for the field of developmental biology but also for wider applications in spatial omics. Although I have expertise in spatial omics technology development, my understanding of computational biology is limited, which restricts my ability to fully evaluate the Palette algorithm presented in this paper.

      Response: We thank the reviewer for recognizing our work, and we greatly appreciate the constructive suggestions from the reviewer. Although the reviewer acknowledged limited expertise in computational biology, the comments from the reviewer are highly professional and valuable. Following the suggestions from the reviewer, we have not only included more explanatory texts and figures to make the analysis procedures clearer and more understandable, but also supplemented the important parameters that were missing in our previous manuscript. We also provided extra figure to demonstrate the improvements of zSTEP on gene expression patterns. We believe that our work is now more scientific and more understandable, and we will continue working to solve the remaining issues as planned. We express our thanks for the reviewer again.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The authors of the study introduce the Palette method, a novel approach designed to infer spatial gene expression patterns from bulk RNA-sequencing (RNA-seq) data. This method is complemented by the development of the DreSTEP 3D spatial gene expression atlas of zebrafish embryos, establishing a comprehensive resource for visualizing gene expression and investigating spatial cell-cell interactions in developmental biology.

      Response: We sincerely appreciate the reviewer's positive feedback on our Palette pipeline and the zSTEP 3D spatial expression atlas of zebrafish embryos. We also thank the reviewer for the professional comments and constructive suggestions. The reviewer raised the concerns from the aspect of algorithm design and computational biology, which we did not address well in our previous manuscript. We agree with the reviewer that we did not clarify the selection criteria of the parameters in detail, and we are now working on the additional analyses to address this issue.

      We also agree with the reviewer that we did not provide enough discussion of the strategies used in the pipeline, the features of Palette and the application scenarios of Palette and zSTEP. For wide use of our tools, it is significantly important to state these aspects. In this revised version, we have added more paragraphs in the Discussion section to address this issue. Additionally, we acknowledge that we did not adequately demonstrate the computational efficacy and computational requirements, which are important for researchers. We are also working on the additional analyses to address this issue.

      Finally, we thank the reviewer again for the professional and constructive suggestions. These suggestions are addressable, and by following them, we believe our manuscript will see a significant improvement, especially in the Palette pipeline part, making the pipeline more rigorous and easier to access. We are confident that we can complete the planned additional tasks within the next 1-2 months.

      1. The efficacy of the Palette method may be compromised by its dependency on the quality of the reference spatial transcriptomics data. As highlighted in the study, variations in data quality can lead to significant challenges in reconstructing accurate spatial expression patterns from bulk data. This underscores the necessity of evaluating quality parameters, such as the number of gene detections and spatial resolution, to ensure reliable outcomes. Additional studies should rigorously assess how these quality factors influence the accuracy and efficiency of the algorithm in various data contexts, particularly under diverse conditions of gene detection.

      Response: We thank the reviewer for this valuable suggestion. We agree with the reviewer that the quality of the reference ST data may greatly influence the performance and efficacy of the Palette, and we have added paragraphs in the Discussion section to further discuss the impact of ST data quality on Palette performance. As mentioned by the reviewer, gene detections and spatial resolution are two important parameters that can influence the Palette performance. Low gene detection may impact the clustering process, making the cell types of spots not distinguished well. To evaluate the performance of Palette when ST data shows low gene detection, we plan to applied Palette using MERFISH data as the ST reference, which only captures hundreds of genes. Moreover, we will also investigate the impact of spatial resolution on Palette performance by merging ST spots to simulate lower resolution scenarios, as well as the impact of gene detection by randomly reducing detected genes. Through the comparison among the inferred expression patterns with ST data of different spatial resolutions or different numbers of detected genes, we can better access the performance of Palette and provide guidance to researchers on the appropriate ST data requirements for optimal performance. These analyses will take another one month to accomplish after this round of revision due to the limited response time.

      1. The methodology raises pertinent questions regarding how the clustering results from different algorithms may affect the reconstructions by the Palette method. The authors would better provide a detailed discussion/comparison of clustering processes that optimize the reconstruction of spatial patterns, ensuring precision in the downstream analyses.

      Response: We thank the reviewer for the constructive comments. We agree with the reviewer that the differences in clustering results would impact the inference of the Palette. In our Palette pipeline, rather than develop a new methodology for clustering, we employ the BayesSpace for spot clustering, which considers both spot transcriptional similarity and neighbouring structure for clustering. In this case, researchers may adjust the parameters in the BayesSpace package to achieve optimal clustering results. Actually, in most cases, the spot identities were achieved through UMAP analysis, which only considers the transcriptional differences but does not consider the spatial information. This kind of clustering strategy will potentially lead to an intricate arrangement of spots belonging to different clusters, and may result in sparse gene expression in Palette outcome, which is different from the patterns in bona fide tissues. Therefore, a suitable clustering strategy will definitely help capture the local patterns.

      Moreover, our Palette pipeline also can use the clustering results from the tissue histomorphology. Using tissue histomorphology for clustering would be a good choice, as it is closer to the real case. The following Figure (Fig. 11) displays the Palette performance on PDAC datasets using both spatial clustering and histomorphology clustering strategies. The result using histomorphology clustering captures the weak pattern (indicated by the red circle) that were missed when using the spatial clustering (Fig. 11d).

      See response letter for the figure.

      1. The choice to utilize only highly expressed genes in the initial stages of the Palette algorithm also warrants further exploration. Addressing the criteria for determining which genes qualify as "highly expressed" and outlining robust cutoff will enhance the algorithm's rigor and applicability. Similarly, in the iterative estimation of gene expression across spatial spots, establishing optimal iteration conditions is crucial. Implementing a loss function may offer a systematic method for concluding iterations, thus refining computational efficiency.

      Response: We thank the reviewer for the professional suggestions. As pointed out by the reviewer, the selection of highly expressed genes and the iteration times are two important parameters in our pipeline. The definition of highly expressed genes and the number of highly expressed genes are important for achieving a satisfactory clustering performance. We tested the impact of different numbers of highly expressed genes on cluster performance in our preliminary analyses, while we did not summarize these tests and specify the parameters. Therefore, we plan to include a supplementary figure showing the clustering performances under different definitions of highly expressed genes and different numbers of highly expressed genes. Additionally, for the iteration conditions, we have tested different iteration numbers to find out a suitable iteration number to achieve a stable expression in each spot. The following figure (Fig. 1) shows the results after performing Palette with different iteration times. We randomly selected 20 cells and compared their expression across tests with varying iteration times. The results indicate that for a ST dataset with 819 spots, the expression in each spot becomes nearly stable after 5000 iteration times. We previously did not consider the computational efficiency, while here the reviewer raises a valuable and professional suggestion to implement a loss function to determine the optimal number of iterations. We greatly appreciate this suggestion, and plan to apply a loss function to summarize the optimal iteration times for ST datasets of different sizes. This will provide guidance for potential researchers in selecting iteration times and enhance computational efficiency.

      See response letter for the figure.

      1. Performance metrics relating to processing speed and computational demands remain inadequately addressed in the current framework. Understanding how the Palette method scales across varying gene counts and bulk RNA-seq datasets will be essential for potential applications in larger biological contexts. Notably, the quantitative demands of analyzing 20,000 genes when processing 10, 100, or 1,000 bulk RNA profiles must be articulated to guide researchers in planning accordingly.

      Response: We thank the reviewer for this valuable and professional suggestion. In our previous analyses, we did not consider the computation efficiency, processing speed and computational demands, which are important information for potential researchers. To address this issue, we will list our computer configuration first. And under this configuration, we plan to run Palette on datasets with different numbers of overlapped genes or ST references with varying spot numbers, and then summarize the running times into a metrics table. This will help researchers estimate the running time for their datasets and guide them in planning the analyses. We will begin the analyses soon and expect to complete the analysis within the next 1 to 2 months.

      Minor opinions:

      1. Despite the promising advances offered by the zebrafish 3D reconstruction, there is a lack of details regarding numbers of the spatial transcriptomics (ST) data utilized, and the number of bulk RNA-seq data employed in the analyses. These parameters need to be clarified.

      Response: We thank the reviewer for reminding us of these parameters. We are sorry for not including these parameters in our previous manuscript. We have now included the numbers of bulk, ST and overlap genes in the Methods section as follows (Fig. 12).

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      1. Issues regarding spatial cell-cell communication, especially concerning interactions over longer distances, necessitate careful consideration. Introducing spatial distance constraints could help formulate more realistic models of cellular interactions, a vital aspect of embryonic development.

      Response: We thank the reviewer for this essential comment. We agree with the reviewer that the spatial distance is an essential factor to investigate in vivo cell-cell communication during embryonic development. Therefore, in our analyses, we employed CellChat for spatial cell-cell communication analysis, which can be used to infer and visualize spatial cell-cell communication network for ST datasets, considering the spatial distance as constrains of the computed communication probability. However, during our analyses, we observed that there were interactions between cell types over longer distances, as mentioned by the reviewer. We then investigated how these interactions of longer distances occurred. Here, we show the FGF interaction between tail bud and neural crest cells from our spatial cell-cell analysis as an example, and the distance between these two cell types appears quite significant (Fig. 13). We labelled tail bud cells and neural crest cells on the selected midline section and observed that, although most neural crest cells are distributed anteriorly, a small number of neural crest cells are located at tail, close to the tail bud cells. Therefore, the observed interaction between tail bud and neural crest cells is likely due to their adjacent distribution in the tail region, while the anteriorly distributed of neural crest spot in spatial cell-cell communication analysis reflects the anterior positioning of most neural crest cells. As a result, the distances shown on the spatial cell-cell communication analysis are not the real distance between two cell types.

      In most cases in our spatial cell-cell communication analyses, the observed interactions over longer distances are likely influenced by this visualization strategy. Additionally, pre-processing the dataset may enhance the performance of the analyses. Here we performed systematic analyses of the entire embryo, which can make the interactions between cell types appear massive. To investigate specific biological questions, researchers can subset cell types of interest or categorize them into different subtypes based on their positions.

      See response letter for the figure.

      1. Evaluation metrics such as the Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) represent critical tools for systematically measuring the similarity of inferred spatial patterns, yet their specific application within this context should be elaborated.

      Response: We thank the reviewer for recommending these two tools. We have applied them to evaluate the similarity between the expression patterns (Fig. 14). The inclusion of these statistical values makes our comparisons of expression patterns more scientific and convincing. And we have added the following texts in the Methods section to describe the calculation of these two values.

      "The Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) were used to evaluate the similarity of the expression patterns. The expression patterns of in situ hybridization images were considered as the expected values, and the expression patterns of ST data and inferred expression patterns were compared to the expected values. Common positions along the AP axis within all three expression profiles were used, and the RMSE were calculated based on the scaled intensity of these positions. Values greater than the threshold were set to 1; otherwise, they were set to 0, and the ARI was then calculated based on the intensity category. Higher ARI and lower RMSE indicate greater similarity."

      See response letter for the figure.

      1. The study's limitations surrounding ST data quality cannot be overstated. Discussing scenarios where only limited or poor-quality ST data are available will be crucial for guiding future studies. Furthermore, a clear explanation of how enhanced specificity and accuracy translate into tangible biological insights is essential for demystifying the underlying mechanisms driving developmental processes.

      Response: We thank the reviewer for raising this essential suggestion. We have realized that in our previous manuscript, our discussion on the advantages and limitations of Palette and zSTEP was neither broad nor detailed enough.

      Therefore, in our revised manuscript, we have added the following paragraphs to further discuss the advantages and limitations of Palette and zSTEP, as well as the potential application of zSTEP in developmental biology.

      In this section, we have emphasized again the impact of ST data quality on the performance of Palette and zSTEP, and then compared Palette with the strategy that uses well-established marker genes to infer spatial information. We demonstrated that although Palette cannot achieve single cell resolution, it captures the major expression patterns, which are closely correlated to biological functions and critical for embryonic development. Furthermore, we further discussed that zSTEP is not only a valuable tool for investigating gene expression patterns, but also has the potential in evaluating the reaction-diffusion model to investigate the complicated and well-choreographed pattern formation during embryonic development.

      As here we have provided a more comprehensive discussion about Palette and zSTEP, we think that the potential researchers will better understand the application scenarios of our inference pipeline and our datasets. We hope our study can assist and inspire further research in the field of spatial transcriptomics and developmental biology.

      "Thirdly, the performance of Palette and zSTEP heavily relied on the quality of ST data. If the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots, and the performance of spot clustering could also be affected. Moreover, in this study, for example, the Stereo-seq data of 12 hpf zebrafish embryo had fewer slices on the right side (Fig. S3b), resulting in more blank spots in the right part of zSTEP for the 12 hpf embryo. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be enhanced and demonstrate even greater potential for analysing spatiotemporal gene expression.

      On the other hand, compared to the brilliant strategy that infers spatial information of scRNA-seq data from well-established genes, our Palette pipeline cannot achieve single cell resolution. However, our Palette pipeline is based on the ST reference, and thus preserves the real positional relationships between spots. Furthermore, the focus of our pipeline is to infer the gene expression patterns, which are closely correlated to biological functions and critical for embryonic development, rather than the sparse expression within individual spots. In this regard, our Palette pipeline can be advantageous, as it allows for reconstruction of the major expression profiles, which are often more relevant for understanding developmental processes. Additionally, our Palette can be applied to serial sections, enabling the construction of 3D ST atlas.

      Finally, while the current analyses demonstrated that zSTEP can serve as a valuable tool for identifying genes having specific patterns at certain developmental stages, the exploration of zSTEP is still limited. During animal development, pattern formation is always one of the most important developmental issues. As demonstrated by the reaction-diffusion (RD) model, morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification, while interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette constructed zSTEP, as a comprehensive transcriptomic expression pattern during development, could be leveraged to evaluate and prove the RD model during development, including AP patterning. Moreover, the investigation of gene expression patterns should not be limited to morphogens and TFs, and further investigation of their roles in AP patterning is desirable. Additionally, here a random forest model may be sufficient for investigating the most essential morphogens and TFs for AP axis refinement, while more sophisticated machine learning models may be required for addressing more specific biological questions."

      Reviewer #2 (Significance):

      The Palette pipeline demonstrates a marked improvement in specificity and accuracy when predicting spatial gene expression patterns. Evaluative studies on Drosophila and zebrafish datasets affirm its enhanced performance compared to existing methodologies. By effectively reconstructing spatial information from bulk transcriptomic data, the Palette method innovatively merges the philosophy of leveraging single-cell transcriptomic data for deconvolution analyses. This integration is pivotal, advancing traditional bulk RNA-seq approaches while laying the groundwork for future research.

      One of the notable achievements in this work is the construction of the DreSTEP atlas, which integrates serial bulk RNA-seq data with advanced 3D imaging techniques. This resource grants researchers unprecedented access to the visualization of gene expression patterns across the zebrafish embryo, facilitating the investigation of spatial relationships and cell-cell interactions critical for developmental processes. Such capabilities are invaluable for understanding the intricate dynamics of embryogenesis and the distinct roles of individual cell types.

      Response: We thank the reviewer for the positive evaluation of our work, either the Palette pipeline or zSTEP. The reviewer has strong expertise in algorithm development and computational biology, and the concerns and suggestions from the reviewer are significantly precious and valuable for us. Regarding the bioinformatics tool development, we did not have extensive experiences, and thus we did not thoroughly address the selection criteria or clarify the parameters used in the pipeline, which may influence the application by other researchers. Therefore, we sincerely appreciate the professional suggestions from the reviewer, which we can follow to address these issues, improve our manuscript and make our work more impactful for researchers. Additionally, we did not consider computation efficiency, processing speed and computational demands, which would be important factors for other researchers to use Palette. We would like to add extra analyses to address these aspects.

      Currently, based on the suggestions from the reviewer, we have added extra texts discussing the clustering strategy in Palette pipeline, the advantages and limitations of Palette, and the potential application of zSTEP in developmental biology. We believe that readers will now have a clearer understanding of the performance of Palette and the application scenarios of both Palette and zSTEP. We have not fully addressed the comments raised by the reviewer yet, while we are working on the planned additional analyses and expect to complete all these tasks within the next 1-2 months. We sincerely thank the reviewer for the professional and valuable suggestions, which definitely improve our work and will make it accessible for a wide range of researchers.

      Finally, through this review process, we have learned a lot about the important considerations and requirements when designing bioinformatics tools, and we benefit a lot from the thoughtful guidance. We express our thanks to the reviewer again for the guidance, and we will try our best to address the remaining issues to further improve our manuscript.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Evidence, reproducibility and clarity

      In this study, Dong and colleagues developed a computational pipeline to use spatial transcriptomics (ST) datasets as a reference to infer the spatial patterns of gene expression from bulk RNA sequencing data. This approach aims to overcome the low read depth and limited gene detection capabilities in current ST datasets, while exploiting its ability to provide highly resolved spatial information. By combining bulk RNA-seq datasets from 3 developmental stages during early zebrafish development with previously available ST and imaging datasets, the authors build DreSTEP (Danio rerio spatiotemporal expression profiles). Using this approach, they go on to identify the morphogens and transcription factors involved in anteroposterior patterning.

      The paper is well written, and the pipeline presented in this study is likely to be useful beyond the case studies included in this study. There are a few questions that, in my view, would be important to clarify to increase the impact of this work:

      Response: We sincerely appreciate the positive feedback from the reviewer on the Palette pipeline and zebrafish spatiotemporal expression profiles zSTEP. We thank the reviewer for the constructive suggestions, which have inspired us to think deeply about application and advantages of Palette and zSTEP for future studies.

      We fully agree with the reviewer that we do not sufficiently clarify the advantages and limitations of our inference pipeline in the original manuscript. The questions raised by the reviewer are very insightful. For example, while the inference expression patterns may closely resemble the in situ hybridization observation, which we consider as good performance, the reviewer pointed out that we should consider whether weak, yet real expression may have been removed. These questions have motivated us to think more deeply about the underlying principles and assumptions of our inference pipeline. Following the reviewer's questions, we have expanded our discussion on the application of zSTEP in developmental biology and the features of Palette compared to the existing strategies.

      We believe that after incorporating the revisions, our current manuscript now demonstrates the application scenario of Palette clearer and suggested the application of zSTEP for investigating biological questions in developmental biology. We are grateful for the reviewer's guidance, which helps us increase the impact of our work.

      1. The authors mention that they used a variable factor to adjust expression differences between the ST and bulk RNA-seq datasets. It would be important for the authors to comment on how much overlap in gene expression is necessary between the datasets for an accurate calculation of this variable factor? Can this be directly tested, for instance, by testing how their conclusions vary if expression is adjusted by a variable factor calculated from only a smaller set of genes?

      Response: We thank the reviewer for the professional questions. We are sorry about not including the gene numbers in our previous manuscript. And now we have provided the numbers of genes in bulk and ST data and the numbers of the overlapped genes (Fig. 15).

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      For Palette implementation, we took all the overlapped genes. To calculate the variable factor, we aggregated the expression of each gene in the ST data, and then used the expression of the bulk data to divide the aggregated expression for variable factor calculation. As a result, each overlapped gene was assigned a variable factor to adjust its expression, based on its difference between bulk and ST data. The rationale behind this approach is that by considering the ST data as a whole, we can effectively reduce the variations among individual spots. This allows the variable factors to provide reasonable adjustment to gene expression.

      Above all, the variable factors can be directly calculated. Currently Palette only can infer the expression patterns of overlapped genes. It means when the number of overlapped genes is small, such as MERFISH only detecting hundreds of genes, Palette can only infer the expression patterns of these genes. However, if the MERFISH data have good quality, which enable resolving distinct cell types, we believe Palette will also show good performance when using MERFISH as ST reference. Additionally, we plan to perform Palette using MERFISH as ST reference to further demonstrate its broad application when using different ST references.

      1. Palette gives rise to highly spatially precise patterns, which closely match those found in ISH. However, the smoothening of the expression can also remove weak, yet real, local expression patterns, as shown for idgf6 in Fig. 2a. Can the authors test this more extensively for other genes?

      Response: We thank the reviewer for this essential question. We agree with the reviewer that weak, yet real expression might be removed in our Palette inference pipeline. The weak, sparse expression may be due to the ST technique itself or the variations in samples. However, that sparse gene expression may not have biological meaning, and the focus of our pipeline in to capture the expression patterns, which are closely correlated with functions and crucial for embryonic development. Therefore, our algorithm considers spot characteristics and emphasize cluster-specific expression, resulting in spatial-specific expression patterns. In most cases, the main gene expression patterns can be captured, which can help understand gene functions and roles in embryonic development. We have updated Supplementary Figure S1a (Fig. 16) to include more gene patterns to demonstrate this point.

      See response letter for the figure.

      1. Using adjacent slices for ST and "bulk RNA-seq" may provide better results than those obtained when comparing two independent datasets. Could the authors also extend the analysis of Palette's functionalities by using separate, previously available but independent datasets, for ST and bulk RNA-seq in Drosophila as well?

      Response: We thank the reviewer for the valuable question. We agree with the reviewer that using adjacent slices may provide better results. The idea here is that the inferred spatial expression patterns from pseudo bulk RNA-seq can be used to compare with the real expression of ST to evaluate Palette performance. We have updated our Figure 2a (Fig. 17) to illustrate the analysis clearer.

      See response letter for the figure.

      To demonstrate the Palette's functionalities, we have used Palette to infer zebrafish bulk RNA-seq slice (Junker et al., 2014) using Stereo-seq slice (Liu et al., 2022) as ST reference, and these two datasets are separate and independent. We agree with the reviewer that it would be good to use separate datasets to test in Drosophila to further demonstrate the Palette's functionalities. However, unfortunately, we did not find the Drosophila serial bulk RNA-seq data along left-right axis of the corresponding stages, and thus we might be unable to perform the extra analyses using independent Drosophila datasets.

      References:

      Junker, J.P. et al. Genome-wide RNA Tomography in the zebrafish embryo. Cell 159, 662-675 (2014).

      Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell 57, 1284-1298 e1285 (2022).

      1. The DreSTEP analysis in zebrafish embryos is interesting and validates well-established observations in the field. Can the authors also discuss whether and how their dataset allows them to refine our understanding of the spatial or temporal pattern of the morphogens and TFs involved in AP patterning? This would further validate their approach.

      Response: We appreciate the reviewer for recognition of our zSTEP and raising this valuable question, which has inspired us to think more deeply about the potential application of zSTEP in developmental biology. As the reviewer noted, our zSTEP analyses have validated well-established observations in the field. Rather than focusing on the sparse expression detected in ST data, zSTEP emphasizes the gene expression patterns that are closely correlated with biological functions and critical for embryonic development. Therefore, zSTEP can serve as a valuable tool for identifying the genes having specific patterns at certain developmental stages.

      Pattern formation is one of the most important developmental issues for all animals. The reaction-diffusion (RD) model is a widely recognized theoretical framework used to explain self-regulated pattern formation in developing animal embryos (Kondo & Miura, 2010). Morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification. Most importantly, interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette-constructed zSTEP provides a comprehensive transcriptomic expression pattern, including all morphogens and TFs, across the whole embryo during development. These valuable resources, in our opinion, could be leveraged to evaluate and prove the RD model during development, including AP patterning. In our current zSTEP analyses, we have already identified genes that exhibit specific expression patterns along AP axis, some of which have not been fully characterized. These genes could be potential targets for further investigation into their roles in AP patterning, although they are not the primary focus of this study. Additionally, our analyses only focused on morphogens and TFs, but zSTEP can be used to investigate the expression patterns of other genes as well. Moreover, we employed a random forest model to investigate the most essential morphogens and TFs for AP axis refinement, which is one of the basic applications of zSTEP. To investigate specific biological questions of interest, it would be worth exploring the use of more sophisticated machine learning models.

      We have added the following paragraph in the Discussion section to discuss the potential application of zSTEP in future studies.

      "Finally, while the current analyses demonstrated that zSTEP can serve as a valuable tool for identifying genes having specific patterns at certain developmental stages, the exploration of zSTEP is still limited. During animal development, pattern formation is always one of the most important developmental issues. As demonstrated by the reaction-diffusion (RD) model, morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification, while interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette constructed zSTEP, as a comprehensive transcriptomic expression pattern during development, could be leveraged to evaluate and prove the RD model during development, including AP patterning. Moreover, the investigation of gene expression patterns should not be limited to morphogens and TFs, and further investigation of their roles in AP patterning is desirable. Additionally, here a random forest model may be sufficient for investigating the most essential morphogens and TFs for AP axis refinement, while more sophisticated machine learning models may be required for addressing more specific biological questions."

      Reference

      Kondo, S. & Miura, T. Reaction-Diffusion model as a framework for understanding biological pattern formation. Science 329, 1616-1620 (2010).

      1. Can the authors comment on the limits of this inference pipeline? And how it performs as compared to single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes?

      Response: We appreciate the reviewer for this insightful question, which has inspired us to further explore the advantages and limitations of the Palette pipeline in comparison with other inference strategies. As mentioned in the Discussion section, a key limitation of the inference pipeline is its heavy reliance on the quality of ST data. It is obvious that if the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots. We think it is a common issue for any inference tools using ST data as the reference. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be improved.

      As a comparison, the single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes do not face this limitation. The ground-breaking work by Satija et al. (2015) used such a strategy that combined scRNA-seq and in situ hybridizations of well-established marker genes to infer spatial location, enabling single cell resolution, as it maintains the high read depth and gene detection. One advantages of this scRNA-seq-based strategy is that it provides the transcriptomics of individual cells, rather than a combination of cell within a ST spot, although the positional relationships between cells are not real.

      However, compared to the inference from ST data, the positional relationships between cells are not directly captured. On the other hand, as the embryonic development progresses, more cell types will be specified, and the body patterning becomes more complex. In this scenario, using well-established marker gene to infer spatial information would be much more challenging. Additionally, there are not many scRNA-seq datasets of serial sections, and thus this strategy may not be used to construct 3D ST atlas.

      In contrast, our Palette inference pipeline is based on the ST data, which preserves the real positional relationships between spots. Although our inference pipeline cannot achieve single cell resolution, it focuses on the gene expression patterns rather than the sparse expression within individual spots. By applying Palette to paired serial sections, we were able to generated a 3D spatial expression atlas of zebrafish embryos, which has showed promising performance for investigating gene expression patterns and their involvement in AP patterning.

      Reference

      Satija, R. et al. Spatial reconstruction of single-cell gene expression data. Nature biotechnology 33, 495-502 (2015)

      We have updated the following paragraphs to further demonstrating the limitation of the inference pipeline in details in the Discussion section.

      "Thirdly, the performance of Palette and zSTEP heavily relied on the quality of ST data. If the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots, and the performance of spot clustering could also be affected. Moreover, in this study, for example, the Stereo-seq data of 12 hpf zebrafish embryo had fewer slices on the right side (Fig. S3b), resulting in more blank spots in the right part of zSTEP for the 12 hpf embryo. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be enhanced and demonstrate even greater potential for analysing spatiotemporal gene expression.

      On the other hand, compared to the brilliant strategy that infers spatial information of scRNA-seq data from well-established genes, our Palette pipeline cannot achieve single cell resolution. However, our Palette pipeline is based on the ST reference, and thus preserves the real positional relationships between spots. Furthermore, the focus of our pipeline is to infer the gene expression patterns, which are closely correlated to biological functions and critical for embryonic development, rather than the sparse expression within individual spots. In this regard, our Palette pipeline can be advantageous, as it allows for reconstruction of the major expression profiles, which are often more relevant for understanding developmental processes. Additionally, our Palette can be applied to serial sections, enabling the construction of 3D ST atlas."

      Reviewer #3 (Significance):

      This study tackles an important challenge in biology - the difficult to resolve gene expression patterns with high spatial precision and in a high-throughput manner. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that their novel inference pipeline enables them to obtain high-quality spatial information simply from bulk RNA-seq datasets, using ST as a reference. The development of this pipeline - Palette - is a major part of this manuscript and its applicability is validated using datasets from Drosophila and zebrafish embryos. This in an important advance for the field, but it would be nice for the authors to further comment on i) the validity of some of their approaches and how they may influence the quality of their inference, as well as, ii) potential pitfalls/limitations of this approach as compared to others available in the field. This would synthetize both previous and current findings into a conceptual and technological framework that would have a strong impact well beyond cell and developmental biology.

      Audience: This study would be relevant for a broad audience of biologists, interested in morphogen signaling, gene regulatory networks and cell fate specification.

      Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

      Response: We thank the reviewer for providing the positive feedback, arising these valuable questions, which have motivated us to deeply consider the design concept and further application of Palette and zSTEP. Based on the insightful questions from the reviewer, we have added two extra paragraphs in the Discussion section to further discuss the potential application of zSTEP in developmental biology and application scenarios of the Palette pipeline. Specially, we have demonstrated that the performance of the inference pipeline relies on the spatial resolution and data quality of the ST data. We have then compared the advantages and limitations of Palette with the existing brilliant spatial inference strategy, which infers spatial information of scRNA-seq from well-established marker genes. Although our inference pipeline cannot achieve single cell resolution, it can capture the major expression patterns, which are closely correlated to functions and critical for embryonic development. We believe this will help readers gain a clearer understanding of the advantage and limitations of our pipeline compared to other tools, as well as the tasks for which Palette and our constructed zSTEP can be utilized. We express our thanks to the reviewer again for the valuable comments.

    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

      In this study, Dong and colleagues developed a computational pipeline to use spatial transcriptomics (ST) datasets as a reference to infer the spatial patterns of gene expression from bulk RNA sequencing data. This approach aims to overcome the low read depth and limited gene detection capabilities in current ST datasets, while exploiting its ability to provide highly resolved spatial information. By combining bulk RNAseq datasets from 3 developmental stages during early zebrafish development with previously available ST and imaging datasets, the authors build DreSTEP (Danio rerio spatiotemporal expression profiles). Using this approach, they go on to identify the morphogens and transcription factors involved in anteroposterior patterning.

      The paper is well written, and the pipeline presented in this study is likely to be useful beyond the case studies included in this study. There are a few questions that, in my view, would be important to clarify to increase the impact of this work:

      1. The authors mention that they used a variable factor to adjust expression differences between the ST and bulk RNAseq datasets. It would be important for the authors to comment on how much overlap in gene expression is necessary between the datasets for an accurate calculation of this variable factor? Can this be directly tested, for instance, by testing how their conclusions vary if expression is adjusted by a variable factor calculated from only a smaller set of genes?
      2. Palette gives rise to highly spatially precise patterns, which closely match those found in ISH. However, the smoothening of the expression can also remove weak, yet real, local expression patterns, as shown for idgf6 in Fig. 2a. Can the authors test this more extensively for other genes?
      3. Using adjacent slices for ST and "bulk RNAseq" may provide better results than those obtained when comparing two independent datasets. Could the authors also extend the analysis of Palette's functionalities by using separate, previously available but independent datasets, for ST and bulk RNAseq in Drosophila as well?
      4. The DreSTEP analysis in zebrafish embryos is interesting and validates well-established observations in the field. Can the authors also discuss whether and how their dataset allows them to refine our understanding of the spatial or temporal pattern of the morphogens and TFs involved in AP patterning? This would further validate their approach.
      5. Can the authors comment on the limits of this inference pipeline? And how it performs as compared to single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes?

      Significance

      This study tackles an important challenge in biology - the difficult to resolve gene expression patterns with high spatial precision and in a high-throughput manner. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that their novel inference pipeline enables them to obtain high-quality spatial information simply from bulk RNAseq datasets, using ST as a reference. The development of this pipeline - Palette - is a major part of this manuscript and its applicability is validated using datasets from Drosophila and zebrafish embryos. This in an important advance for the field, but it would be nice for the authors to further comment on i) the validity of some of their approaches and how they may influence the quality of their inference, as well as, ii) potential pitfalls/limitations of this approach as compared to others available in the field. This would synthetize both previous and current findings into a conceptual and technological framework that would have a strong impact well beyond cell and developmental biology.

      Audience: This study would be relevant for a broad audience of biologists, interested in morphogen signaling, gene regulatory networks and cell fate specification.

      Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors of the study introduce the Palette method, a novel approach designed to infer spatial gene expression patterns from bulk RNA-sequencing (RNA-seq) data. This method is complemented by the development of the DreSTEP 3D spatial gene expression atlas of zebrafish embryos, establishing a comprehensive resource for visualizing gene expression and investigating spatial cell-cell interactions in developmental biology.

      Major concerns:

      1. The efficacy of the Palette method may be compromised by its dependency on the quality of the reference spatial transcriptomics data. As highlighted in the study, variations in data quality can lead to significant challenges in reconstructing accurate spatial expression patterns from bulk data. This underscores the necessity of evaluating quality parameters, such as the number of gene detections and spatial resolution, to ensure reliable outcomes. Additional studies should rigorously assess how these quality factors influence the accuracy and efficiency of the algorithm in various data contexts, particularly under diverse conditions of gene detection.
      2. The methodology raises pertinent questions regarding how the clustering results from different algorithms may affect the reconstructions by the Palette method. The authors would better provide a detailed discussion/comparison of clustering processes that optimize the reconstruction of spatial patterns, ensuring precision in the downstream analyses.
      3. The choice to utilize only highly expressed genes in the initial stages of the Palette algorithm also warrants further exploration. Addressing the criteria for determining which genes qualify as "highly expressed" and outlining robust cutoff will enhance the algorithm's rigor and applicability. Similarly, in the iterative estimation of gene expression across spatial spots, establishing optimal iteration conditions is crucial. Implementing a loss function may offer a systematic method for concluding iterations, thus refining computational efficiency.
      4. Performance metrics relating to processing speed and computational demands remain inadequately addressed in the current framework. Understanding how the Palette method scales across varying gene counts and bulk RNA-seq datasets will be essential for potential applications in larger biological contexts. Notably, the quantitative demands of analyzing 20,000 genes when processing 10, 100, or 1,000 bulk RNA profiles must be articulated to guide researchers in planning accordingly.

      Minor opinions:

      1. Despite the promising advances offered by the zebrafish 3D reconstruction, there is a lack of details regarding numbers of the spatial transcriptomics (ST) data utilized, and the number of bulk RNA-seq data employed in the analyses. These parameters need to be clarified.
      2. Issues regarding spatial cell-cell communication, especially concerning interactions over longer distances, necessitate careful consideration. Introducing spatial distance constraints could help formulate more realistic models of cellular interactions, a vital aspect of embryonic development.
      3. Evaluation metrics such as the Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) represent critical tools for systematically measuring the similarity of inferred spatial patterns, yet their specific application within this context should be elaborated.
      4. The study's limitations surrounding ST data quality cannot be overstated. Discussing scenarios where only limited or poor-quality ST data are available will be crucial for guiding future studies. Furthermore, a clear explanation of how enhanced specificity and accuracy translate into tangible biological insights is essential for demystifying the underlying mechanisms driving developmental processes.

      Significance

      The Palette pipeline demonstrates a marked improvement in specificity and accuracy when predicting spatial gene expression patterns. Evaluative studies on Drosophila and zebrafish datasets affirm its enhanced performance compared to existing methodologies. By effectively reconstructing spatial information from bulk transcriptomic data, the Palette method innovatively merges the philosophy of leveraging single-cell transcriptomic data for deconvolution analyses. This integration is pivotal, advancing traditional bulk RNA-seq approaches while laying the groundwork for future research.

      One of the notable achievements in this work is the construction of the DreSTEP atlas, which integrates serial bulk RNA-seq data with advanced 3D imaging techniques. This resource grants researchers unprecedented access to the visualization of gene expression patterns across the zebrafish embryo, facilitating the investigation of spatial relationships and cell-cell interactions critical for developmental processes. Such capabilities are invaluable for understanding the intricate dynamics of embryogenesis and the distinct roles of individual cell types.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript titled "Unravelling the Progression of the Zebrafish Primary Body Axis with Reconstructed Spatiotemporal Transcriptomics" presents a comprehensive analysis of the development of the primary body axis in zebrafish by integrating bulk RNA-seq, 3D images, and Stereo-Seq. The authors first clearly demonstrate the application of Palette for integrating RNA-seq and Stereo-Seq using published spatial transcriptomics data of Drosophila embryos. Subsequently, they produced serial bulk RNA-seq data for certain developmental stages of Danio rerio embryos and utilized published Stereo-Seq data. Through robust validation, the authors observe the molecular network involved in AP axis formation. While the authors show that integrating bulk RNA-seq data with Stereo-Seq improves spatial resolution, additional proof is required to demonstrate the extent of this improvement.

      Major Comments:

      1. Lines 66-68: Discuss the limitations of existing tools and explicitly state the advantages of using Palette.
      2. Body Pattern Genes Analysis: For both Drosophila and Danio rerio, it would be valuable to examine body pattern genes in Stereo-Seq and apply Palette to determine if the resolution of the segments improves or merges. The resolution of the A-P axis is convincing, but further evidence for other segments would be beneficial.
      3. Figure 2d: Include the A-P line for which the intensity profile was plotted in the main figure, rather than just in the supplementary material. Additionally, consider simplifying the plot by not combining three lines into one, as it complicates the interpretation of observations.
      4. Drosophila Data Analysis: While the alignment and validation of Danio rerio sections are clearly explained, the analysis and validation of Drosophila data are insufficiently detailed. Provide a more thorough explanation of how the intensity profiles between BDGP in situ data and Stereo-Seq data are adjusted.
      5. Figure 3d: Present a plot with the expected expression profiles of the three genes if the embryo is aligned as anticipated.
      6. Analysis Without Palette: Between lines 277-438, the outcome of using Palette with bulk RNA-seq and Stereo-Seq is convincing. However, consider the following:<br /> o What would be the observations if the analysis were conducted solely with Stereo-Seq data, without incorporating bulk RNA-seq data and employing Palette?<br /> o This study uses only Stereo-Seq as the spatial transcriptomics reference. It would strengthen the argument to use at least one other spatial transcriptomics method, such as Visium or MERFISH, in conjunction with bulk RNA-seq and Palette, to demonstrate whether Palette consistently improves gene expression resolution.
      7. PDAC Data Analysis: Provide a more detailed explanation of the PDAC data analysis and use appropriate colors in the tissue images to clearly distinguish cell types.
      8. Comparison with Other Methods: State the limitations of not using STitch3D and Spateo for alignment and explain why these methods were not employed.

      Minor Comments:

      1. References: Add references to the statements in lines 51-53.
      2. Scientific Name Consistency: Ensure consistency in using either "Danio rerio" or "zebrafish" throughout the manuscript.
      3. Related References: Include the following relevant references:
      4. https://academic.oup.com/bib/article/25/4/bbae316/7705532
      5. https://www.life-science-alliance.org/content/6/1/e202201701
      6. Figure 1a: In the Venn diagram, include the number of genes in the bulk and Stereo-Seq datasets, as well as the number of overlapping genes.
      7. Figure 1 Improvement: Enlarge Figure 1 and reduce repetitive elements, such as parts of the deconvolution and Figure 1b.
      8. Figure 3f: Explain the black discontinuous line in the plot.
      9. Line 610: State the percentage of unpaired imaging spots.
      10. Lines 616-618: Specify the unit for the spot diameter.

      Significance

      This algorithm will be useful not only for the field of developmental biology but also for wider applications in spatial omics. Although I have expertise in spatial omics technology development, my understanding of computational biology is limited, which restricts my ability to fully evaluate the Palette algorithm presented in this paper.

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      Reply to the reviewers

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

      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 #3

      Evidence, reproducibility and clarity

      This is a good manuscript, well performed and well presented. I have several suggestions/questions to enhance the clarity of the concept, as technically the work is rather well performed.

      1. I suggest that the authors explain better the mesenchymal-to-epithelial (MET) transition in reprogramming. Perhaps, explaining that epithelial gene acquisition (e.g., CDH1) and epidermal cell fate are not exactly the same. This approach could also be used to divide the genes they study further in their analyses.
      2. KLF4 is both a repressor and an activator in different cell contexts including reprogramming. Does HIC2 act only as repressor? Is it possible that HIC2 is repressing KLF4-activated genes bad for reprogramming (including epidermal genes) and activating KLF4-suppressed genes ncessary for reprogramming? This should not be too difficult to explore with their current dataset and they also could look at available datasets for histone modifications in reprogramming.
      3. Does HIC2 bind to genes related to somatic cell identify that need to be suppressed in reprogramming before the MET phase takes place?
      4. Does HIC2 influence proliferation during reprogramming?

      Referee cross-commenting

      Comments by the other reviewers are sound and will help improve the manuscript.

      Significance

      In this manuscript, Kaji and colleagues perform a CRISPR/Cas9 screen to identify genes involved in mouse somatic cell reprogramming, identifying HIC2 as a target that they further validate. They conclude that HIC2 acts by repressing the epidermal/epithelial program induced by KLF4 during reprogramming. Studying the complex role of transcription factor interactions in the context of cell fate conversions (of any kind and not just somatic cell reprogramming) is highly relevant. This work helps clarify such complexity in a specific context but the work has wider conceptual implications.

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      Referee #2

      Evidence, reproducibility and clarity

      The study by Beniazza et al. aims to address the inefficiencies associated with OSKM-mediated reprogramming. Through a genome-wide CRISPR/Cas9 knockout screen, the authors identified 14 genes essential for iPSC reprogramming but dispensable for ESC self-renewal. Among these, HIC2 significantly enhanced reprogramming efficiency, yielding approximately a tenfold increase compared to standard conditions. scRNA-seq analyses revealed that HIC2-overexpressing cells follow a more direct trajectory toward pluripotency, bypassing the KLF4-dependent activation of keratinocyte and epidermal gene programs. ChIP-seq profiling further demonstrated that HIC2 and KLF4 co-occupy approximately 60% of their genomic targets, indicating substantial regulatory overlap. Notably, this co-binding and its functional effects are dose-dependent on KLF4, as shown by experiments comparing high KLF4 expression systems (standard OSKM and STEMCCA+9 constructs) with low KLF4 conditions (STEMCCA cassette lacking additional KLF4). The authors conclude that HIC2's modulatory effect occurs specifically under high KLF4 levels.

      Major Comments

      Figure 1D: What is the efficiency of gRNA library transduction into MEFs? What percentage of MEF cells were successfully knocked out? Figures 2B/C: To rule out the possibility that the observed variability in reprogramming efficiency among the tested factor combinations stems from differences in MKOS expression levels, the authors should provide evidence showing that the expression levels of all MKOS factors are comparable across samples. Figures 2D/E: To rule out a fibroblast-specific effect, can the authors show whether the epidermal gene signature is also upregulated during NSC reprogramming and whether Hic2 overexpression suppresses this signature? Figure 2H: Are the 13 signature genes that distinguish MKOS-Hic2-iPSCs from MKOS-iPSCs consistently identified across independent Hic2-iPSC lines, or does each reprogramming event produce a distinct gene set? If the signature is consistent, this is an important observation and should be further addressed and discussed. Figure 3K: Can the authors show the expression levels of MKOS and Hic2 transgenes in all samples? The same concern applies to Figure 4I. The reviewer wishes to be confident that the reduction in epidermal gene expression observed in MEFs is not due to variable transgene expression caused by multiple vector introductions (e.g., KLF4 alone versus KLF4 + Hic2), which could potentially lead to lower KLF4 expression through co-transfection competition. Does KLF4 overexpression in Hic2-knockdown MEFs lead to greater upregulation of the epidermal gene signature compared to the wild-type control? Figure 4C: It appears that only about half of the Hic2 binding sites overlap with KLF4 sites. What are the characteristics of the other Hic2-specific sites, and how might they contribute to reprogramming, if at all? Can the authors perform a reprogramming experiment using a combination that lacks KLF4 (e.g., replacing KLF4 with Esrrb or BMP4, as shown in PMID: 19136965 and PMID: 21135873) and test the effect of Hic2 under these conditions? Do KLF4 and HIC2 physically interact? The authors should perform a co-immunoprecipitation assay to address this question. What is the effect of Hic2 during human reprogramming? Does it play a similar regulatory role?

      Minor Comments

      • Typographical errors should be checked and avoided; for example, on page 10, the word 'colonies' was misspelled.
      • Some blank squares appear in the Methods section; please correct these formatting errors.

      Referee cross-commenting

      All suggestions are feasible within a relatively short time frame and will improve the manuscript.

      Significance

      Overall, this study is of significant interest to the stem cell community and presents a well-designed and carefully executed experimental framework. However, several concerns remain that should be addressed prior to publication.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This work identified 14 genes essential for iPSC reprogramming but not essential for ESC maintenance and MEF proliferation by analyzing three CRISPR/Cas9-mediated genome-wide KO screens. Among them, they found that overexpression of the Hic2 gene can greatly promote OSKM-driven reprogramming. By using scRNA-seq in time points of the reprogramming process, they found that Hic2 can bypass the epidermal gene expressing state during reprogramming. Then, using ChIP-seq, they found that HIC2 and KLF4 have common binding sites on epidermal genes. Finally, by expressing KLF4 alone or KLF4 and HIC2 together, they demonstrated that HIC2 can inhibit KLF4-driven epidermal gene expression.

      Major comments: The claims and conclusions are well-supported by the data and do not require additional experiments or analysis. The data and methods are presented in a reproducible way.

      Minor comments: There seem to be some typos. For example, "we selected 30 genes with low FDRs in ESC maintenance" may be "high depletion FDR," since you want nonessential genes for ESC maintenance. The "log10(-FDR)" may be "-log10(FDR)." Some figures lack P values. Perhaps it would be useful to analyze whether Hic2 reduces reprogramming heterogeneity. Validation experiments, such as trilineage differentiation, could be considered to demonstrate that Hic2 does not affect the pluripotency and differentiation capacity of iPSCs.

      Significance

      General Assessment: This work is based on three CRISPR/Cas9-mediated genome-wide KO screens, which makes it comprehensive and reliable. They discovered that HIC2 and OSKM can drive reprogramming without an epidermal gene expression intermediate. They also found extensive common binding sites of HIC2 and KLF4 at target genes. This work not only enables more efficient reprogramming but also expands our understanding of the reprogramming process. Among HIC2 and KLF4 common target genes, some are repressed while others are activated, and it will be very interesting to study the mechanism of this selective function.

      Advance: Compared to natural `embryonic development, OSKM-driven reprogramming is very inefficient, and our understanding of the mechanisms of efficient reprogramming remains poor. The specific role of the epidermal gene expression state in the reprogramming process remains unclear. This work strongly supports the idea that repression of the epidermal gene expression state can promote iPSC generation. Moreover, previous studies on Hic2 are limited, and this work enriches our understanding of its mechanisms and functions.

      Audience: This study may be of interest to those interested in basic research on reprogramming mechanisms or Hic2, as well as those developing efficient reprogramming technologies.

      My field: Reprogramming, stem cells, aging, transcription factors.

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      Reply to the reviewers

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

      In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases.

      We thank the Reviewer for appreciating our work and for their valuable suggestions to improve our manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

      In my opinion, a few aspects would improve the manuscript. Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

      1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative. Authors’ response. This point will be addressed as detailed in the Revision Plan

      If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      I also have a few minor points to highlight:

        • It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.*

      Authors’ response. We thank the Reviewer for raising this point. We now indicated the statistical analyses performed on the data presented in the mentioned figures (according also to a point of Reviewer #3). According to the conclusion that Trim32 is necessary for proper regulation of c-Myc transcript stability, using 2-way-ANOVA, the data now reported as Figure 5G show the statistically significant effect of the genotype at 6h (right-hand graph) but not at D0 (left-hand graph). In the graphs of Fig. EV5 D and E at D0 no significant changes are observed whereas at 6h the data show significant difference at the 40 min time point. We included this info in the graphs and in the corresponding legends.

      - On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

      Authors’ response. As suggested, we included the graph showing the differentiation index upon c-Myc silencing in the Trim32 KO clones and in the WT clones, as a novel panel in Figure 6 (Fig. 6D). As already reported in the text, a partial recovery of differentiation index is observed but the increase is not statistically significant. In contrast, no changes are observed applying the same silencing in the WT cells. Legend and text were modified accordingly.

      Reviewer #1 (Significance (Required)):

      The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. * * At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking. Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8. * * The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: * * In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

      We thank the Reviewer for valuing our work and for their appreciated suggestions to improve our manuscript. We have carefully addressed some of the concerns raised as detailed here, while others, which require more laborious experimental efforts, will be addressed as reported in the Revision Plan.

      Major Comments:

      The work is a bit incremental based on this:

      https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 * * And this:

      https://www.nature.com/articles/s41418-018-0129-0 * * To their credit, the authors do cite the above papers.

      Authors’ response. We thank the Reviewer for this careful evaluation of our work against the current literature and for recognising the contribution of our findings to the understanding of myogenesis complex picture in which the involvement of Trim32 and c-Myc, and of the Trim32-c-Myc axis, can occur at several stages and likely in narrow time windows along the process, thus possibly explaining some reports inconsistencies.

      The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data.

      Authors’ response. We agree with the Reviewer and we modified our phrasing that implied Trim32-c-Myc axis as the exclusive mechanism by explicitly indicated that other pathways contribute to guarantee proper myogenesis, in the Abstract and in Discussion.

      The Abstract now reads: … suggesting that the Trim32–c-Myc axis may represent an essential hub, although likely not the exclusive molecular mechanism, in muscle regeneration within LGMDR8 pathogenesis.”

      The Discussion now reads: “Functionally, we demonstrated that c-Myc contributes to the impaired myogenesis observed in Trim32 KO clones, although this is clearly not the only factor involved in the Trim32-mediated myogenic network; realistically other molecular mechanisms can participate in this process as also suggested by our transcriptomic results.”

      The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

      Authors’ response. We thank the Reviewer for appreciating our thorough analyses on cell cycle dynamics in proliferation conditions and at the onset of the differentiation process.

      The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

      Authors’ response. We thank the Reviewer for raising this point and apologise for being too brief in describing the data, leaving indeed some points excessively implicit. As suggested, we now reorganised this session and added the lists of enriched canonical pathways relative to WT vs KO comparisons at D0 and D3 (Fig. EV3B) as well as those relative to the comparison between D0 and D3 for both WT and Trim32 KO samples (Fig. EV3C), with their relative scores. We changed the Results section “Transcriptomic analysis of Trim32 WT and Trim32 KO clones along early differentiationas reported here below and modified the legends accordingly.

      The paragraph now reads: Based on our initial observations, the absence of Trim32 already exerts a significant impact by day 3 (D3) of C2C12 myogenic differentiation. To investigate how Trim32 influences early global transcriptional changes during the proliferative phase (D0) and early differentiation (D3), we performed an unbiased transcriptomic profiling of WT and Trim32 KO clones (Fig. 2A). Multidimensional Scaling (MDS) analysis revealed clear segregation of gene expression profiles based on both time of differentiation (Dim1, 44% variance) and Trim32 genotype (Dim2, 16% variance) (Fig. 2A). Likewise, hierarchical clustering grouped WT and Trim32 KO clones into distinct clusters at both timepoints, indicating consistent genotype-specific transcriptional differences (Fig. EV3A). Differentially Expressed Genes (DEGs) were detected in the Trim32 KO transcriptome relative to WT, at both D0 and D3. In proliferating conditions, 72 genes were upregulated and 189 were downregulated whereas at D3 of differentiation, 72 genes were upregulated and 212 were downregulated. Ingenuity Pathway Analysis of the DEGs revealed the top 10 Canonical Pathways displayed in Fig. EV3B as enriched at either D0 or D3 (Fig. EV3B). Several of these pathways can underscore relevant Trim32-mediated functions though most of them represent generic functions not immediately attributable to the observed myogenesis defects.

      Notably, the transcriptional divergence between WT and Trim32 KO cells is more pronounced at D3, as evidenced by a greater separation along the MSD Dim2 axis, suggesting that Trim32-dependent transcriptional regulation intensifies during early differentiation (Fig. 2A). Given our interest in the differentiation process, we therefore focused our analyses comparing the changes occurring from D0 to D3 in WT (WT D3 vs. D0) and in Trim32 KO (KO D3 vs. D0) RNAseq data.

      Pathway enrichment analysis of D3 vs. D0 DEGs allowed the selection of the top-scored pathways for both WT and Trim32 KO data. We obtained 18 top-scored pathways enriched in each genotype (-log(p-value) ³ 9 cut-off): 14 are shared while 4 are top-ranked only in WT and 4 only in Trim32 KO (Fig. EV3C). For the following analyses, we employed thus a total of 22 distinct pathways and to better mine those relevant in the passage from the proliferation stage to the early differentiation one and that are affected by the lack of Trim32, we built a bubble plot comparing side-by-side the scores and enrichment of the 22 selected top-scored pathways above in WT and Trim32 KO (Fig. 2B). A heatmap of DEGs included within these selected pathways confirms the clustering of the samples considering both the genotypes and the timepoints highlighting gene expression differences (Fig. 2C). These pathways are mainly related to muscle development, cell cycle regulation, genome stability maintenance and few other metabolic cascades.

      As expected given the results related to Figure 1, moving from D0 to D3 WT clones showed robust upregulation of key transcripts associated with the Inactive Sarcomere Protein Complex, a category encompassing most genes in the “Striated Muscle Contraction” pathway, while in Trim32 KO clones this pathway was not among those enriched in the transition from D0 to D3 (Fig. EV3C). Detailed analyses of transcripts enclosed within this pathway revealed that on the transition from proliferation to differentiation, WT clones show upregulation of several Myosin Heavy Chain isoforms (e.g., MYH3, MYH6, MYH8), α-Actin 1 (ACTA1), α-Actinin 2 (ACTN2), Desmin (DES), Tropomodulin 1 (TMOD1), and Titin (TTN), a pattern consistent with previous reports, while these same transcripts were either non-detected or only modestly upregulated in Trim32 KO clones at D3 (Fig. 2D). This genotype-specific disparity was further confirmed by gene set enrichment barcode plots, which demonstrated significant enrichment of these muscle-related transcripts in WT cells (FDR_UP = 0.0062), but not in Trim32 KO cells (FDR_UP = 0.24) (Fig. EV3D). These findings support an early transcriptional basis for the impaired myogenesis previously observed in Trim32 KO cells.

      In addition to differences in muscle-specific gene expression, we observed that also several pathways related to cell proliferation and cell cycle regulation were more enriched in Trim32 KO cells compared to WT. This suggests that altered cell proliferation may contribute to the distinct differentiation behavior observed in Trim32 KO versus WT (Fig. 2B). Given that cell cycle exit is a critical prerequisite for the onset of myogenic differentiation and considering that previous studies on Trim32 role in cell cycle regulation have reported inconsistent findings, we further examined cell cycle dynamics under our experimental conditions to clarify Trim32 contribution to this process

      The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      "On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

      Authors’ response. We thank the Reviewer for bringing to our attention these two publications, that indeed, add important piece of data to recapitulate the in vivo complexity of c-Myc role in myogenesis. We included this point in our Discussion.

      The Discussion now reads: “On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025). Other reports, instead, demonstrated the implication of c-Myc periodic pulses, mimicking resistance-exercise, in muscle growth, a role that cannot though be observed in our experimental model (Edman et al., 2024; Jones et al., 2025).”

      Minor Comments:

      Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

      Authors’ response. As suggested, we modified the z-score-representing colors using a more distinct gradient especially in the positive to negative transition in Figure 2B.

      Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

      Authors’ response. As now better explained (see comment regarding Major point: Transcriptomics), we used a cut-off of -log(p-value) above or equal to 9 for pathways enriched in DEGs of the D0 vs D3 comparison for both WT and Trim32 KO. The threshold is now included in the Results section and the pathways (shared between WT and Trim32 KO and unique) are listed as Fig. EV3C.

      The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

      Authors’ response. We thank the Reviewer for this remark, and we apologise for having overlooked it. We amended this throughout the manuscript by always using for clarity “Trim32 KO clones/cells”.

      Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

      Authors’ response. We agree with the Reviewer that C2C12 passaging can reduce the differentiation potential of this myoblast cell lines; this is indeed the main reason why we decided to employ WT clones, which underwent the same editing process as those that resulted mutated in the Trim32 gene, as reference controls throughout our study. We apologise for not indicating the passages in the first version of the manuscript that now is amended as per here below in the Methods section:

      The C2C12 parental cells used in this study were maintained within passages 3–8. All clonal cell lines (see below) were utilized within 10 passages following gene editing. In all experiments, WT and Trim32 KO clones of comparable passage numbers were used to ensure consistency and minimize passage-related variability.

      Reviewer #2 (Significance (Required)):

      General Assessment:

      This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation. * * Advance: * * To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting. * * Audience: * * This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

      Expertise: * * My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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

      • In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:*

      We thank the Reviewer for the in-depth assessment of our work and precious suggestions to improve the manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

      - TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      - The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      -Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR

      Authors’ response. We thank the reviewer for this suggestion. This point will be addressed as detailed in the Revision Plan. We have selected several transcripts that will be evaluated in independent samples in order to validate the RNAseq results.

      - The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      - There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates.

      Authors’ response. We thank the Reviewer for raising this point.

      Regarding the replicates, we clarified in the Methods and Legends that the Trim32 KO experiments have been performed on 3 biological replicates (independent clones) and the same for the reference control (3 independent WT clones), except for the Fig. 6 experiments that were performed on 2 Trim32 KO and 2 WT clones. All the Western Blots, immunofluorescence, qPCR data are representative of the results of at least 3 independent experiments unless otherwise stated. We reported the number and type of replicates as well as the microscope fields analyzed.

      We repeated the statistical analyses of the data in Figure 5G, EV5D, EV5E, employing more appropriately the 2-way-ANOVA test, as suggested, and we now reported this info in the graphs and legends.

      We thank the Reviewer for raising this point, we agree and substituted the graphs in Fig. EV5B and 6B showing the control values normalised as suggested. The statistical analyses now reflect this change.

      -Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)."

      Authors’ response. We re-edited this revised version of the manuscript as suggested.

      -Results in Figure 5A should be quantified

      Authors’ response. We amended this point by quantifying the results shown in Fig. 5A, we added the graph of the quantification of 3 experimental replicates to the Figure. Quantification confirms that no statistically significant difference is observed. The Figure and the relative legend are modified accordingly.

      -Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D

      Authors’ response. We agree with the Reviewer that the presence of p84 also in the cytoplasmic fraction is not ideal. Regrettably, we observed this faint p84 band in all the experiments performed. We think however, that this is not impacting on the result that clearly shows that c-Myc and Trim32 are never detected in the same compartment.

      -In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition.

      Authors’ response. We agree with the Reviewer and we now show the graph of the results of the 3 technical replicates for 2 biological replicates and do not indicate any statistics (Fig. 6B). The graph was also modified according to a previous point raised.

      -The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?

      Authors’ response. We agree with the Reviewer that Trim32 might also be necessary for myoblast fusion. This point is however beyond the scope of the present study and will be addressed in future work.

      - The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      Reviewer #3 (Significance (Required)):

      The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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

      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

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

      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 #3

      Evidence, reproducibility and clarity

      In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:

      • TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.
      • The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim. -Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR
      • The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.
      • There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates. -Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)." -Results in Figure 5A should be quantified -Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D -In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition. -The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?
      • The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

      Significance

      The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

      Major Comments:

      The work is a bit incremental based on this: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 And this: https://www.nature.com/articles/s41418-018-0129-0 To their credit, the authors do cite the above papers.

      The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data. The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

      The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

      The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis. Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

      "On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

      Minor Comments:

      Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

      Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

      The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

      Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

      Significance

      General Assessment:

      This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation.

      Advance:

      To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting.

      Audience:

      This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

      Expertise:

      My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases. In my opinion, a few aspects would improve the manuscript.

      Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

      1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative.
      2. If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation. An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

      I also have a few minor points to highlight:

      • It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.
      • On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

      Significance

      The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking.

      Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8.

      The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

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      Reply to the reviewers

      We are grateful to the reviewers for their thoughtful and constructive evaluations of our manuscript. Their comments helped us clarify key aspects of the study and strengthen both the presentation and interpretation of our findings. The central goal of this work is to dissect how the opposing activities of GATA4 and CTCF coordinate chromatin topology and transcriptional timing during human cardiomyogenesis. The reviewers’ feedback has allowed us to refine this message and better contextualize our results within the broader framework of chromatin regulation and cardiac development.

      In response to the reviews, in our preliminary revision we have already implemented substantial improvements to the manuscript, including additional analyses, clearer data visualization, and revisions to the text to avoid overinterpretation. These refinements enhance the robustness of our conclusions without altering the overall scope of the study. A small number of additional analyses and experiments are ongoing and will be added to the full revision, as detailed below.

      We believe that the revised manuscript, together with the planned updates, fully addresses the reviewers’ concerns and substantially strengthens the contribution of this work to the field.

      Reviewer 1 – Point 1:

      In the datasets you are examining, what are the relative percentages in each of the four groups relating compartmentalization change to expression change (A→B, expression up; A→B, down; B→A, up; B→A, down)?

      We quantified compartment–expression relationships using Hi-C and bulk RNA-seq from H9 ESCs and CMs. The percentages for each category are shown below and incorporated into updated Figure S2H.

      Group

      Downregulated in CM

      Upregulated in CM

      A-to-A

      11.92%

      8.44%

      A-to-B

      18.20%

      2.79%

      B-to-A

      7.96%

      18.07%

      B-to-B

      14.36%

      6.44%

      A chi-squared test comparing observed vs. expected distributions (based on gene density across bins) confirmed a strong association between compartment dynamics and transcriptional behavior. B-to-A genes are significantly enriched among genes upregulated in CMs, while A-to-B genes are enriched among those downregulated (updated Figure S2H).

      We next assessed with GSEA how these gene classes respond to GATA4 and CTCF knockdown. In 2D CMs, GATA4 knockdown reduces expression of CM-upregulated B-to-A genes and increases expression of CM-downregulated A-to-B genes, whereas CTCF knockdown produces the opposite pattern (updated Figure 2F).

      Applying the same analysis to cardioid bulk RNA-seq (updated Figure 4E) revealed the strongest effects in SHF-RV organoids, consistent with monolayer data. In SHF-A organoids, only GATA4 knockdown had a measurable impact on CM-upregulated B-to-A and CM-downregulated A-to-B genes. Because the subsets of CM-downregulated B-to-A and CM-upregulated A-to-B genes were very small and showed no consistent trends, Figure 4 focuses on the two informative categories only. The full classification is provided in Reviewer Figure 1 below.

      (The figure cannot be rendered in this text-only format)

      Reviewer Figure 1. GSEA for CM-upregulated B-to-A and CM-downregulated A-to-B genes. p-values by Adaptive Monte-Carlo Permutation test.

      Reviewer 1 – Point 2

      This phrase in the abstract is imprecise: ‘whereas premature CTCF depletion accelerates yet confounds cardiomyocyte maturation.’


      The abstract has been revised to: “whereas premature CTCF depletion accelerates yet alters cardiomyocyte maturation.” (lines 29-30).

      Reviewer 1 – Point 3

      Regarding this statement: "Disruption of [3D chromatin architecture] has been linked to genetic dilated cardiomyopathy (DCM) caused by lamin A/C mutations8,9, and mutations in chromatin regulators are strongly enriched in de novo congenital heart defects (CHD)10, underscoring their pathogenic relevance11." The first studies to implicate chromatin structural changes in heart disease, including the role of CTCF in that process, were PMID: 28802249, a model of acquired, rather than genetic, disease.

      We added the following sentence to the paragraph introducing CTCF: “Moreover, depletion of CTCF in the adult cardiomyocytes leads to heart failure28,29.” (line 72)

      Reviewer 1 – Point 4

      Can you quantify this statement: ‘the compartment switch coincided with progressive reduction of promoter–gene body interactions’?

      We quantified promoter–gene body contacts by calculating the area under the curve (AUC) of the virtual 4C signal derived from H9 Hi-C data across differentiation. As a result of this analysis we added the following sentence: “Quantitatively, interactions between the TTN promoter and its gene body decreased by ~55% from the pluripotent stage to day 80 cardiomyocytes.” (lines 89-91).


      Reviewer 1 – Point 5

      Regarding this statement: "six regions became less accessible in CMs, correlating with ChIP-seq signal for the ubiquitous architectural protein CTCF." I don't see 6 ATAC peaks in either TTN trace in Figure 1A.

      We corrected the text as it follows: “TTN experienced clear changes in chromatin accessibility during CM differentiation: ATAC-seq identified two CM-specific peaks that correlated with ChIP-seq signal for the cardiac pioneer TF GATA4 at the two promoters, one driving full length titin and the other the shorter cronos isoform. In contrast, two regions became less accessible in CMs, correlating with two of the six ChIP-seq peaks for the ubiquitous architectural protein CTCF” (lines 93-97). We attribute the differences between ChIP-seq and ATAC-seq profiles to methodological sensitivity and/or biological variability between datasets generated in different laboratories and cell batches.

      Reviewer 1 – Point 6

      Western blots need molecular weight markers.

      We edited the relevant panels accordingly (updated Figures 1E and 2B).

      Reviewer 1 – Point 7

      Regarding this statement: "The decrease in CTCF protein levels may explain its selective detachment from TTN during cardiomyogenesis." At face value, these findings suggest the opposite: i.e. that a massive downregulation of CTCF at protein level should affect its binding across the genome, which is not tested and is hard to evaluate between ChIP-seq studies from different groups and from different developmental timeframes.

      We revised the text to avoid implying selective detachment and performed a genome-wide analysis of CTCF occupancy using ENCODE ChIP-seq datasets generated by the same laboratory with matched protocols in hESCs and hESC-derived CMs. This analysis shows that 43.2% of CTCF sites present in ESCs are lost in CMs, whereas only 5.7% are gained, confirming a broad reduction in CTCF binding during differentiation. These results are now included in__ updated Figure 1B__.

      Reviewer 1 – Point 8a

      A couple thoughts on the FISH experiments in Figure 2. A claim of 'impaired B-A transition' would be more convincing if you show, by FISH, that the relative distance of TTN from lamin B increases with differentiation.

      Although prior work from us and others has established that TTN transitions from the nuclear periphery in hESCs to a more internal position during cardiomyogenesis (Poleshko et al. 2017; Bertero et al. 2019a), we are reproducing this trajectory in WTC11 hiPSCs as part of the FISH experiments for the full revision.

      __Reviewer 1 – Point 8b __

      In the [FISH] images: are you showing a total projection of all z planes? One assumes the quantitation is relative to a 3D reconstruction in which the lamin B signal is restricted to the periphery. Have you shown this? __

      Quantification was performed on full 3D reconstructions from Z-stacks, as detailed in the Methods (lines 721-727). While the original submission displayed maximum-intensity projections, updated Figure 2D and Figure S2E now show representative single optical sections, which more clearly highlight the spatial relationship between the TTN locus and the nuclear lamina.

      Reviewer 1 – Point 8c

      Lastly, these data are very interesting and important, provoking reexamination of your interpretation of the results in Figure 1. Figure 1 was interpreted to show that less CTCF binding led to decreased lamina (and thus B compartment) association during development. Figure 2 shows that depleting CTCF does not change association of TTN with lamina.

      Our interpretation is that by day 25 of hiPSC-CM differentiation the TTN locus may have reached its maximal radial repositioning even in control cells, limiting the ability to detect earlier effects of CTCF depletion. To test whether CTCF knockdown accelerates lamina detachment at earlier stages, we are repeating the FISH analysis for the inducible CTCF knockdown line at multiple time points during differentiation.

      Reviewer 1 – Point 9

      A thought about this statement: "Altogether, these results suggest that GATA4 and CTCF function as positive and negative regulators of B-to-A compartment switching, likely acting through global and local chromatin remodeling, respectively." GATA4 induces TTN expression and its knockdown prevents TTN expression-the evidence that GATA4 affects compartmentalization is unclear. By activating the gene, GATA4 may shift TTN to B classification.

      Our current data do not allow us to disentangle whether GATA4-driven transcriptional activation precedes or follows the B-to-A compartment shift. We have therefore removed the mechanistic speculation from this sentence to avoid overinterpretation. Nevertheless, the analyses in updated Figure 2F, discussed in the response to Reviewer 1 - Point 1, show that GATA4 knockdown preferentially reduces expression of CM-upregulated B-to-A genes, while CTCF knockdown has the opposite effect, supporting the conclusion that both factors influence the transcriptional programs associated with B-to-A transitions.

      Reviewer 1 – Point 10

      __I'm not sure what I am looking at in Figure 3C. Are those traces integration of interactions over a defined window? "Each [mutant is] clearly different from WT" is not obvious from the presentation. The histograms are plotting AUC of what? Interactions of those peaks with the mutated region? I genuinely appreciate how laborious this experiment must have been and encourage you to explain better what you are showing. __

      We revised the main text to avoid overstating the differences (“clearly” “in a similar manner”, line 192) and expanded the l__egends of updated Figures 3C–D__ to clarify what is being shown: “(C) 4C-seq in hiPSCs using the promoter-proximal region of TTN as viewpoint. The top panel shows raw interaction profiles. The lower panels plot pairwise differences between conditions to reveal subtle changes. A schematic indicating the 4C viewpoint is included for clarity. Right inset: zoom of the CBS4–5 region. Mean of n = 3 cultures. (D) AUC of the differential 4C-seq signal for defined intervals (panel C). p-values by one-sample t-test against μ = 0.”. We also added a visual cue in updated Figure 3C indicating the 4C viewpoint to facilitate interpretation.

      Reviewer 1 – Point 11

      Again acknowledging how challenging these experiments are: when you mutant a locus, you change CTCF binding but you also change the DNA. Thus, attributing the changes in interactions to presence/absence of CTCF binding is difficult, because the DNA substrate itself has changed. Perhaps you are presenting all of this as a negative result, given the modest effect on transcription, which is as important as a positive result, given the assumptions usually made about such things. But the results are not clearly described and your interpretation seems to go between implying the structural change causative and being agnostic.

      We recognize that deleting a genomic region can affect both CTCF binding and the DNA substrate itself. For this reason, we implemented two parallel genome-editing strategies:

      (1) a straightforward Cas9-mediated deletion of ~100 bp centered on each CBS, and

      (2) a more precise HDR approach replacing only the 20 bp core CTCF motif.

      Because the HDR strategy succeeded, all downstream analyses were carried out on these minimal edits, which substantially limit disruption of other transcription factor motifs and reduce the likelihood of sequence-dependent polymer effects unrelated to CTCF.

      Nevertheless, to avoid implying unwarranted causality in the absence of more conclusive evidence, we added a paragraph to the Discussion outlining these limitations, including the sentence: “Our study also reflects general challenges in separating chromatin-architectural and transcriptional mechanisms. Although the CBS edits were restricted to the core CTCF motifs, additional sequence-dependent effects cannot be fully excluded, and we therefore interpret the resulting changes as consistent with—but not exclusively due to—loss of CTCF binding.” (lines 365-368)

      Reviewer 1 - Point 12.

      Figure 4C: since you have RNA-seq data, a much more objective way to present these data would be to show all data (again, A-B, up; A-B, down; B-A, up; B-A, down) and the effects of CTCF or GATA4. Regardless, you can still focus on the cardiac specific genes. But my guess is if you examine all genes, the pattern you show in panel C will not be present in the majority of cases. Furthermore, if this hypothesis is wrong, such an analysis will allow you to identify other genes affected by the mechanisms you describe and your analysis will test whether these mechanisms are in fact conserved at different loci.

      As outlined in our response to Point 1, we extended the analysis to all genes undergoing compartment changes and incorporated this into the cardioid RNA-seq dataset. This revealed a clear and consistent relationship between GATA4 or CTCF knockdown and the expression of B-to-A and A-to-B gene classes (updated Figure 4E).

      Reviewer 2 - Point 1.1

      1. CTCF regulation at TTN locus:

      (1) Figure 1A: The claim of the authors about convergent CTCF sites and transcriptional activation of TTN is quite simplistic. This claim is only valid when we know where cohesin is loaded. If cohesin is loaded at then intragenic GATA4 binding site, then the only important CTCF sites is at the promoter of TTN. I suggest that the authors read few more publications which may help the authors to better understand how cohesin and CTCF team up to regulate transcription, such as Hsieh et al., Nature Genetics, 2022; Liu et al., Nature Genetics, 2021; Rinzema et al., Nature Structural and Molecular Biology, 2022.

      __Suggestion: The authors should add cohesin (RAD21/SMC1A) and NIPBL ChIP-seq for better interpretation. __

      In line with the reviewer’s insightful suggestion, we integrated cohesin ChIP-seq data into updated Figure 1A. Specifically, we added a RAD21 ChIP-seq track from hESCs, which provides direct evidence of cohesin occupancy across the TTN locus. RAD21 binding closely parallels CTCF binding at five sites within the gene body, supporting a model in which promoter-proximal CTCF anchors cohesin to stabilize repressive loops at this locus. This analysis substantially strengthens the mechanistic framework and is consistent with the studies recommended by the reviewer, which we have now cited (lines 68 and 104).

      Reviewer 2 - Point 1.2. (2) Figure 3B: If delta2CBS only has heterozygenous deletion of CBS6, why we would expect the binding will be weaken to 50%. However, the CTCF binding is reduced to around 1/10 in the ChIP-qPCR. How do the authors explain this?

      Sequencing of the Δ2CBS line shows that one CBS6 allele carries the intended EcoRI replacement, while the second allele contains a 2-bp deletion within the core CTCF motif (Figure S3C). Remarkably, this small deletion is sufficient to abolish CTCF binding, resulting in complete loss of occupancy at CBS6 despite heterozygosity. We clarified this in the text as follows: “CTCF ChIP-qPCR in hiPSCs confirmed complete loss of CTCF binding at the targeted sites, including CBS6 in the Δ2CBS line, indicating that the 2-bp deletion sufficed to disrupt CTCF binding while occupancy at other CBSs remained unaffected.” (lines 187–189).

      Reviewer 2 - Point 1.3a (3) Figure 3C: There are two problems with the 4C experiments: (a) The changes are really mild. In fact, none of the p-values in Figure 3D are significant.

      The effect of deleting CBS1 is indeed modest, consistent with reports that individual CTCF binding sites often show functional redundancy (i.e., Rodríguez-Carballo et al. 2017; Barutcu et al. 2018; Kang et al. 2021). Nevertheless, our 4C-seq experiments have reproducibly shown the same directional trend across biological replicates. To increase statistical power and more rigorously assess the robustness of this effect, we are generating additional 4C replicates as part of the full revision.

      Reviewer 2 - Point 1.3b [In the 4C experiments] (b) The authors should also consider a model that CTCF directly serves as a repressor. In this way, 3D genome may not be involved. B-A switch is simply caused by the activation of the locus.

      We now explicitly acknowledge this possibility in the Discussion. The revised text states: “Moreover, our data cannot unambiguously separate CTCF’s architectural role from potential direct repressive activity. Both mechanisms could contribute to the observed effects, and our findings likely reflect the combined influence of CTCF on chromatin topology and gene regulation.” (lines 368–371).

      Reviewer 2 - Point 2.1a 2. __(CTCF) detachment: The authors mentioned few times "detachment". In the context of this manuscript, the authors indicate detachment from nuclear lamina. However, the authors haven't provide convincing evidence about this. __

      In the two instances where we used the term “detachment,” we intended it to refer exclusively to reduced CTCF binding to DNA, not to lamina repositioning. To avoid ambiguity, we have replaced “detachment” with “reduced binding” in both locations (lines 123 and 329). We do not use this term to describe TTN–lamina positioning.

      Reviewer 2 - Point 2.1b (1) Figure 1D: I doubt whether such changes of CTCF protein abundance will lead to LAD detachment. Suggest the authors read van Schaik et al., Genome Biology, 2022. With the full depletion of CTCF, the effects on LADs are still very restricted.

      We agree that the observed correlation between reduced CTCF levels and the relocation of TTN away from a LAD does not establish causality. As outlined in our response to Reviewer 1 – Point 8c, we are performing additional FISH experiments at earlier differentiation stages in the CTCF inducible knockdown line to directly assess whether partial CTCF depletion is sufficient to alter the timing of TTN–lamina separation.

      Reviewer 2 - Point 2.2 (2) Figure 2D: Lamin B1 should be mostly at nuclear periphery. I have few questions: (1) is the antibody specific? (2) do these cells carry mutation in LMNB1 gene? (3) is the staining actually LMNA?

      As also clarified in response to Reviewer 1 – Point 8b, the original images displayed maximum-intensity projections of Z-stacks, which obscured the peripheral distribution of LMNB1. We have updated Figure 2D and Figure S2E to show representative individual optical sections, which more clearly display the expected peripheral LMNB1 signal. We also confirm that the antibody used is specific for LMNB1 and previously validated (Bertero et al. 2019b), and that the WTC11-derived lines used in this study carry no mutation in LMNB1.

      Reviewer 2 - Point 3

      3. Opposite functions of GATA4 and CTCF: These data in Figure 5E-H argues the opposite role of GATA4 and CTCF in transcriptional regulation. Would it be that CTCF KD just affected cell proliferation, which is actually known for many cell types, rather than affect CM differentiation process? If this is the reason, inversed correlation between CTCF KD and GATA4 KD in Figure 4D could also be explained by opposite effects on cell cycle.

      We directly evaluated this possibility. In FHF–LV cardioids, cell cycle profiling in Figure 6C and Figure S6C (now S7C) showed that CTCF knockdown does not alter the distribution of CMs across G1/S/G2–M phases, in contrast to the marked increase in proliferation observed with GATA4 knockdown.

      Because this comment referred specifically to the SHF data, we also analyzed mitotic gene expression in the SHF–RV bulk RNA-seq dataset using GSEA. CTCF knockdown did not significantly enrich any cell cycle–related gene sets, whereas GATA4 knockdown produced a strong enrichment for mitotic cell cycle terms, in line with FHF-LV data (Reviewer Figure 2).

      These results are summarized in updated Figure S5C, reporting also the results of the broader GSEA analysis, and together indicate that the transcriptional divergence between CTCF and GATA4 knockdown is not simply explained by opposing effects on proliferation.

      (The figure cannot be rendered in this text-only format)

      Reviewer Figure 2. GSEA for mitotic cell cycle in SHF-RV after inducible knockdown of CTCF (left) or GATA4 (right). p-values by Adaptive Monte-Carlo Permutation test.

      Reviewer 2 - Point 4 4. In discussion, the authors suggested that CTCF is a local chromatin remodeller. In my view, association with local chromatin compaction doesn't qualify CTCF as a chromatin remodeler. To my knowledge, CTCF does not have an enzymatic domain, then how does it remodel chromatin?

      Our intended meaning was that CTCF shapes 3D chromatin architecture through its role in organizing intergenic looping, not that it remodels chromatin enzymatically. To avoid confusion, we have removed the original sentence from the Discussion.

      Reviewer 2 - Point 5. 5. Some conclusions are drawn based on insignificant p-values, e.g. Figure 2F, Figure 3D, etc. The authors should be careful about their conclusion, and tone down their statement for the observations have borderline significance.

      The conclusions based on bulk RNA-seq have been revised in response to Reviewer 1 – Point 1 (updated Figure 2F). By subsetting B-to-A and A-to-B genes according to their expression dynamics, this analysis now yields clearer and statistically significant differences between conditions.

      Regarding the 4C-seq data, as acknowledged in Reviewer 2 – Point 3a, the observed effects are modest. We are generating additional biological replicates to increase statistical power. In the meantime, we have adjusted the text to avoid overstating these findings. The revised manuscript now states: “While the difference did not reach significance, these trends suggest …” (lines 199–200).

      Reviewer 2 - Minor comment 1. Minor comments: 1. Figure 1A: (1) I suggest to label two promoters in the gene model. It's unclear in the figure in the current version; (2) I was a bit confused with the way how the authors labeled CTCF directionality. I thought there are a lot of promoters. Why didn't they use triangles?

      We updated Figure 1A to label both TTN promoters and indicate their orientation. For CTCF sites, we now clearly display the motif direction and core binding region as determined by FIMO analysis of the CTCF ChIP-seq peaks, improving consistency and interpretability.

      Reviewer 2 - Minor comment 2. 2. Figure 2C: I think the drastical reduction of titin-mEGFP levels is only due to the way how the authors analyze their FACS data. Can the author quantify on median fluorescence intensity?

      The gating strategy for titin-mEGFP⁺ cells was defined using a reporter-negative control, and cells lacking TNNT2 expression showed no detectable titin-mEGFP signal, confirming the specificity of the gate. To complement this analysis, we also quantified the median fluorescence intensity (MFI) of titin-mEGFP⁺ cells. The MFI analysis corroborates the original findings, showing a significant decrease in GATA4 knockdown and an increase in CTCF knockdown (updated Figure S2D).

      __Reviewer 2 - Minor comment 3. 3. Figure S2G: P value should be -log10, I assume. Please label it accurately. __

      We appreciate the reviewer pointing out this labeling error. In the revised manuscript, this panel has been removed to accommodate the updated compartment–expression analysis now presented in updated Figure 2H (see response to Reviewer 1 – Point 1), and the issue is no longer applicable.

      References

      Barutcu AR, Maass PG, Lewandowski JP, Weiner CL, Rinn JL. 2018. A TAD boundary is preserved upon deletion of the CTCF-rich Firre locus. Nat Commun 9: 1444.

      Bertero A, Fields PA, Ramani V, Bonora G, Yardımcı GG, Reinecke H, Pabon L, Noble WS, Shendure J, Murry CE. 2019a. Dynamics of genome reorganization during human cardiogenesis reveal an RBM20-dependent splicing factory. Nature communications 10: 1538.

      Bertero A, Fields PA, Smith AS, Leonard A, Beussman K, Sniadecki NJ, Kim D-H, Tse H-F, Pabon L, Shendure J, et al. 2019b. Chromatin compartment dynamics in a haploinsufficient model of cardiac laminopathy. Journal of Cell Biology 218: 2919–44.

      Kang J, Kim YW, Park S, Kang Y, Kim A. 2021. Multiple CTCF sites cooperate with each other to maintain a TAD for enhancer–promoter interaction in the β-globin locus. The FASEB Journal 35: e21768.

      Poleshko A, Shah PP, Gupta M, Babu A, Morley MP, Manderfield LJ, Ifkovits JL, Calderon D, Aghajanian H, Sierra-Pagán JE, et al. 2017. Genome-Nuclear Lamina Interactions Regulate Cardiac Stem Cell Lineage Restriction. Cell 171: 573–587.

      Rodríguez-Carballo E, Lopez-Delisle L, Zhan Y, Fabre PJ, Beccari L, El-Idrissi I, Huynh THN, Ozadam H, Dekker J, Duboule D. 2017. The HoxD cluster is a dynamic and resilient TAD boundary controlling the segregation of antagonistic regulatory landscapes. Genes Dev 31: 2264–2281.

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      Referee #2

      Evidence, reproducibility and clarity

      Becca et al. characterized the functions of GATA4 and CTCF in the context of cardiomyogenesis. The authors aim to establish a link between 3D genome changes (A/B compartment and long-range chromatin interactions) and activation of cardiac specific genes such as TTN. They showed opposite effects of GATA4 and CTCF in regulating these genes as well as phenotypical traits. I have the following suggestions and questions:

      Major comments:

      1. CTCF regulation at TTN locus:

      (1) Figure 1A: The claim of the authors about convergent CTCF sites and transcriptional activation of TTN is quite simplistic. This claim is only valid when we know where cohesin is loaded. If cohesin is loaded at then intragenic GATA4 binding site, then the only important CTCF sites is at the promoter of TTN. I suggest that the authors read few more publications which may help the authors to better understand how cohesin and CTCF team up to regulate transcription, such as Hsieh et al., Nature Genetics, 2022; Liu et al., Nature Genetics, 2021; Rinzema et al., Nature Structural and Molecular Biology, 2022.

      Suggestion: The authors should add cohesin (RAD21/SMC1A) and NIPBL ChIP-seq for better interpretation. (2) Figure 3B: If delta2CBS only has heterozygenous deletion of CBS6, why we would expect the binding will be weaken to 50%. However, the CTCF binding is reduced to around 1/10 in the ChIP-qPCR. How do the authors explain this?

      (3) Figure 3C: There are two problems with the 4C experiments: (a) The changes are really mild. In fact, none of the p-values in Figure 3D are significant; (b) The authors should also consider a model that CTCF directly serves as a repressor. In this way, 3D genome may not be involved. B-A switch is simply caused by the activation of the locus. 2. (CTCF) detachment: The authors mentioned few times "detachment". In the context of this manuscript, the authors indicate detachment from nuclear lamina. However, the authors haven't provide convincing evidence about this.

      (1) Figure 1D: I doubt whether such changes of CTCF protein abundance will lead to LAD detachment. Suggest the authors read van Schaik et al., Genome Biology, 2022. With the full depletion of CTCF, the effects on LADs are still very restricted.

      (2) Figure 2D: Lamin B1 should be mostly at nuclear periphery. I have few questions: (1) is the antibody specific? (2) do these cells carry mutation in LMNB1 gene? (3) is the staining actually LMNA? 3. Opposite functions of GATA4 and CTCF: These data in Figure 5E-H argues the opposite role of GATA4 and CTCF in transcriptional regulation. Would it be that CTCF KD just affected cell proliferation, which is actually known for many cell types, rather than affect CM differentiation process? If this is the reason, inversed correlation between CTCF KD and GATA4 KD in Figure 4D could also be explained by opposite effects on cell cycle. 4. In discussion, the authors suggested that CTCF is a local chromatin remodeller. In my view, association with local chromatin compaction doesn't qualify CTCF as a chromatin remodeler. To my knowledge, CTCF does not have an enzymatic domain, then how does it remodel chromatin? 5. Some conclusions are drawn based on insignificant p-values, e.g. Figure 2F, Figure 3D, etc. The authors should be careful about their conclusion, and tone down their statement for the observations have borderline significance.

      Minor comments:

      1. Figure 1A: (1) I suggest to label two promoters in the gene model. It's unclear in the figure in the current version; (2) I was a bit confused with the way how the authors labeled CTCF directionality. I thought there are a lot of promoters. Why didn't they use triangles?
      2. Figure 2C: I think the drastical reduction of titin-mEGFP levels is only due to the way how the authors analyze their FACS data. Can the author quantify on median fluorescence intensity?
      3. Figure S2G: P value should be -log10, I assume. Please label it accurately.

      Significance

      Strengths and limitations:

      I feel that single-cell analysis and functional analysis of GATA4 and CTCF using cardiac organoid model are elegant. However, the weak part of the manuscript is the link between 3D genome and activation of TTN. I also think the authors should include more possible explanations for the interpretation of some genome organization data (CTCF site deletion, 4C, etc).

      Advance: The study does provide useful information to understand transcriptional regulation during cardiac lineage specification. The link between 3D genome and cardiac lineage specification is conceptually nice but needs more data to support.

      Audience: developmental biologists who is interested in heart development and molecular biologists with specific interests in gene regulation.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Becca and others examines the relationship between GATA4 and CTCF in chromatin organization and cardiac maturation. There are several very interesting observations that lead to potentially new insights into the relationship between genome folding, gene expression and the relationship between transcription factors and chromatin structural proteins. To better justify their interpretations and provide a more objective analysis of the data, the authors may consider the following:

      In the datasets you are examining, what are the relative percentages in each of the four groups relating compartmentalization change to expression change (A to B, expression up; A-B, down; B-A, up; B-A, down)?

      This phrase in the abstract is imprecise: "whereas premature CTCF depletion accelerates yet confounds cardiomyocyte maturation."

      Regarding this statement: "Disruption of [3D chromatin architecture] has been linked to genetic dilated cardiomyopathy (DCM) caused by lamin A/C mutations8,9, and mutations in chromatin regulators are strongly enriched in de novo congenital heart defects (CHD)10, underscoring their pathogenic relevance11." The first studies to implicate chromatin structural changes in heart disease, including the role of CTCF in that process, were PMID: 28802249, a model of acquired, rather than genetic, disease.

      Can you quantify this statement: "the compartment switch coincided with progressive reduction of promoter-gene body interactions"?

      Regarding this statement: "six regions became less accessible in CMs, correlating with ChIP-seq signal for the ubiquitous architectural protein CTCF." I don't see 6 ATAC peaks in either TTN trace in Figure 1A.

      Western blots need molecular weight markers.

      Regarding this statement: "The decrease in CTCF protein levels may explain its selective detachment from TTN during cardiomyogenesis." At face value, these findings suggest the opposite: i.e. that a massive downregulation of CTCF at protein level should affect its binding across the genome, which is not tested and is hard to evaluate between ChIP-seq studies from different groups and from different developmental timeframes.

      A couple thoughts on the FISH experiments in Figure 2. A claim of 'impaired B-A transition' would be more convincing if you show, by FISH, that the relative distance of TTN from lamin B increases with differentiation. In the images: are you showing a total projection of all z planes? One assumes the quantitation is relative to a 3D reconstruction in which the lamin B signal is restricted to the periphery. Have you shown this? Lastly, these data are very interesting and important, provoking reexamination of your interpretation of the results in Figure 1. Figure 1 was interpreted to show that less CTCF binding led to decreased lamina (and thus B compartment) association during development. Figure 2 shows that depleting CTCF does not change association of TTN with lamina.

      A thought about this statement: "Altogether, these results suggest that GATA4 and CTCF function as positive and negative regulators of B-to-A compartment switching, likely acting through global and local chromatin remodeling, respectively." GATA4 induces TTN expression and its knockdown prevents TTN expression-the evidence that GATA4 affects compartmentalization is unclear. By activating the gene, GATA4 may shift TTN to B classification.

      I'm not sure what I am looking at in Figure 3C. Are those traces integration of interactions over a defined window? "Each [mutant is] clearly different from WT" is not obvious from the presentation. The histograms are plotting AUC of what? Interactions of those peaks with the mutated region? I genuinely appreciate how laborious this experiment must have been and encourage you to explain better what you are showing.

      Again acknowledging how challenging these experiments are: when you mutant a locus, you change CTCF binding but you also change the DNA. Thus, attributing the changes in interactions to presence/absence of CTCF binding is difficult, because the DNA substrate itself has changed. Perhaps you are presenting all of this as a negative result, given the modest effect on transcription, which is as important as a positive result, given the assumptions usually made about such things. But the results are not clearly described and your interpretation seems to go between implying the structural change causative and being agnostic.

      Figure 4C: since you have RNA-seq data, a much more objective way to present these data would be to show all data (again, A-B, up; A-B, down; B-A, up; B-A, down) and the effects of CTCF or GATA4. Regardless, you can still focus on the cardiac specific genes. But my guess is if you examine all genes, the pattern you show in panel C will not be present in the majority of cases. Furthermore, if this hypothesis is wrong, such an analysis will allow you to identify other genes affected by the mechanisms you describe and your analysis will test whether these mechanisms are in fact conserved at different loci.

      Significance

      This manuscript by Becca and others examines the relationship between GATA4 and CTCF in chromatin organization and cardiac maturation. There are several very interesting observations that lead to potentially new insights into the relationship between genome folding, gene expression and the relationship between transcription factors and chromatin structural proteins.

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      Reply to the reviewers

      Reviewer #1

      1. First, the authors have not convincingly shown that skin cells, or more specifically skin ECs, are a major source of circulating G-CSF in the psoriasis model as stated in the title and abstract. The data in Figure 4 show selective upregulation of Csf3 gene in skin ECs and their ability to secrete G-CSF upon IMQ treatment in vitro. However, the provided data do not address to what degree the skin EC-derived G-CSF contributes to the elevated level of circulating G-CSF. Additional experiments to selectively deplete G-CSF in skin ECs, or at least in skin cells of the affected site, are warranted to support the authors' claim. Does intradermal injection of G-CSF neutralizing antibody into the psoriatic skin reduce circulating levels of G-CSF?

      Author's response:

      Thank you for reviewer's comment. We agree with the Reviewer#1 that it is important to directly block G-CSF to the skin via intradermal injection and measure the G-CSF level in the serum afterwards. Therefore, we will perform intradermal injection of IgG-isotype or anti-G-CSF antibody into the IMQ-induced psoriatic mice.

      Another concern is insufficient demonstration of G-CSF-mediated emergency granulopoiesis in the psoriasis model. All data in Figure 5 were obtained from experiments with only n=3, and adding more replicates, in particular to those in Figure 5B, which show quite some variation in MPP numbers, is recommended. The relatively small reduction of BM granulocyte numbers (Figure 5C) compared to greater depletion of circulating granulocytes (Figure S5A) raises the possibility that it is the mobilization effect rather than granulopoiesis-stimulating effect that skin-derived G-CSF exerts to promote supply of circulating neutrophils that eventually infiltrate into the affected skin. This could also explain the negligible effect of IL-1blockade (Figure S4), which selectively shut off myelopoiesis-stimulating effect of IL-1 (Pietras et al. Nat Cell Biol 2016, PMID: 27111842). Are the HSPCs in the psoriasis model more cycling? Do they show myeloid-skewed differentiation when cultured ex vivo or upon transplantation?

      Author's response: Thank you for these critical comments. We agree to do the following experiments to address them:

      1) HSPCs quantification in Figure 5 especially the MPPs will be added with more replicates.

      2) We will assess cycling status of HSPCs by flow cytometric analysis of Ki67and Propidium Iodide to characterize G0, G1 and G2/M cell cycle phase.

      3) To test myeloid-skewed differentiation, Lin- c-Kit+ Sca-1+ cells containing HSPCs will be isolated from bone marrow of Vas/IMQ-treated mice and transplanted into lethally irradiated syngeneic mice.

      The authors' claim that skin-derived G-CSF "induces" neutrophil infiltration warrants further clarification. Alternative explanation is that the upregulated neutrophil-attracting chemokines (Figure S1D) could induce infiltration, whereas G-CSF increase the number of neutrophils to circulate in the vessels near the psoriatic skin. This notion seems supported elsewhere (Moos et al. J Invest Dermatol. 2019, PMID: 30684554). Can the infiltration be inhibited by systemically injecting neutralizing antibody of their receptor, CXCR2?

      Author's response: The manuscript focuses on the skin-derived G-CSF function as a long-distance signal for emergency granulopoiesis in the bone marrow upon psoriasis, not the chemoattractant property of it. The sentence of interest is "We found that upon psoriasis induction, skin-resident endothelial cells are activated to produce G-CSF which activates emergency granulopoiesis in bone marrow and induces cutaneous infiltration and accumulation of neutrophil that are functionally inflammatory." in line 28-30. In agreement with point #2 from Reviewer#2, the fact that neutrophil recruitment factors (CXCL1, CXCL2, and CXCL5) were upregulated in psoriatic skin (Figure S1D), suggesting a CXCL-mediated neutrophil recruitment. The sentence of concern need to be changed to "We found that upon psoriasis induction, skin-resident endothelial cells are activated to produce G-CSF which activates emergency granulopoiesis in bone marrow, leading to cutaneous accumulation of neutrophil that are functionally inflammatory.". This revised sentence has omitted the proposal that G-CSF directly dictates neutrophils mobilization to the skin, which is not the key message of the study. Therefore, we found that the CXCR2 (CXCLs receptor) blockade experiment may be of the benefit of future studies.

      It remains unclear how skin-derived G-CSF accumulates pathogenic neutrophils. The authors state "pathogenic granulopoiesis," but are the circulating neutrophils in the psoriatic mice already "pathogenic" or do they acquire pathogenic phenotype after cutaneous infiltration? Additional RNA-seq to compare circulating and infiltrated neutrophils would answer this question.

      Author's response: We appreciate this valuable comment. We will perform RNA-seq with the peripheral blood-circulating neutrophils (CD45+ CD11b+ Ly6G+ Ly6Cmid) versus skin-infiltrating neutrophils from both Vas/IMQ mice.

      In addition, how the accumulated pathogenic neutrophils exacerbate the psoriatic changes remains obscure. Although the authors have attempted to correlate Il17a gene expression in infiltrated neutrophils with psoriatic skin changes, the data do not address to what degree it contributes to cutaneous IL-17A protein levels. The data that cutaneous neutrophil depletion leads to subtle decrease in skin IL-17A expression (Figure 2H) rather supports alternative possibilities. For instance, as indicated elsewhere, IL-17A cutaneous tone could be enhanced by neutrophil-mediated augmentation of Th17 or gamma/delta T cell function (Lambert et al. J Invest Dermatol. 2019, PMID: 30528823). Does neutrophil depletion or G-CSF neutralization alter cell numbers or function of cutaneous Th17 and gamma/delta T cells?

      Author's response: Thank you for this insightful comment. To better understand the relative contribution of neutrophils to the cutaneous IL-17A tone in the psoriatic skin, we will perform flowcytometric analysis of Th17 and gamma/delta T cells which are widely known as the major source of IL-17 in psoriatic skin of IMQ-induced mice following injection of isotype-matched or anti-Ly6G antibody.

      Finally, as the above conclusions rely solely on the IMQ-induced acute psoriasis model, it would be informative if they could be derived from another psoriasis model. IMQ is known to induce unintended systemic inflammation due to grooming-associated ingestion (Gangwar et al. J Invest Dermatol. 2022, PMID: 34953514), and "pathological crosstalk between skin and BM in psoriatic inflammation" could be strengthened by an intradermal injection model.

      Author's response: We appreciate the reviewer for bringing this important point. Regarding the systemic inflammation upon psoriasis, the above-cited study reported increased IFN-B expression in the intestines of IMQ-ingested animal (Grine L et al. Sci Rep. 2016, PMID: 26818707 in Gangwar et al. J Invest Dermatol. 2022, PMID: 34953514). We examined several pro-inflammatory cytokines including IFN-b, IFN-g, and IL-6 and in contrast, found no systemic increase in all these cytokines, except for IFN-g downregulation (Explanation Figure 1), which suggests no evidence of grooming-associated ingestion.

      We also examined the Csf3 expression across several distinctively located tissues which showed a selective upregulation in the skin (Figure 4C), suggesting a skin-restricted perturbation. In addition, one study showed that IMQ-ingestion didn't alter number of gut injury-associated CXCR3+ macrophages nor did it aggravate skin inflammation (Pinget et al. Cell Reports. 2022, PMID: 35977500). Together, these findings support that IMQ-induced psoriasis by topical cutaneous application used in our study elicit a local inflammation but not systemic inflammation.

      The authors, however, realize that testing alternative psoriasis model such as intradermal injection of IL-23 (Chan et al. J Exp Med. 2006, PMID: 17074928) will strengthen the skin-local insults within the psoriasis model employed, and should be tested in the future.

      Minor comments

      Figure 1E shows multiple elongated Ly6G+ structures in d0-2 control and d0 IMQ skins that do not appear to be neutrophils.

      Author's response: We appreciate the Reviewer#1 pointing this issue. As mentioned by the Reviewer#1, the elongated structures detected in the intravital microscopy are not neutrophils, but autofluorescence from the skin bulge regions (Wun et al. J Invest Dermatol. 2005, PMID: 15816847). We have eliminated these unspecific signals from the transformation and quantification (Figure 1F, S1G, and S1H). We will also add an explanatory sentence in Materials and Methods section "Of note, the fluorescent signal with elongated structures resembling hair bulge were autofluorescence and thus removed from further analysis." to be more precise about our methods.

      In Figure 2C, the bottom GSEA seems to be showing type II IFN response, not type I IFN, according to the text.

      Author's response: Thank you for the comment, we will correct this misspelling.

      Author's response: We appreciate that Reviewer#1 bring up this point. We examined the kinetics of the bone marrow cellularity and GMPs across 4 days of psoriasis induction in mice. The bone marrow cell number was lowered along that span with lowermost count at 2 days. Consistent to the BM-cellularity, the GMP number was also lowered about one-third in the first 2 days of psoriasis. This kinetic is consistent with the previous report showing a rapid reduction of GMPs in the bone marrow within 2 days following systemic G-CSF administration driven emergency granulopoiesis (Hirai et al. Nat. Immunol. 2006, PMID: 16751774). From 2 days to 4 days, the GMP number rapidly increased to slightly above basal number (Explanation Figure 2). This timely coordinated expansion suggests a significant supply of GMPs from the differentiating upstream myeloid progenitors (Figure 3B).

      When the psoriatic mice with elevated G-CSF is injected with anti-G-CSF or IgG-isotype antibody, the bone marrow cellularity and GMP numbers at 4 days were (Explanation Figure 3). Firstly, as psoriasis reduced bone marrow cellularity (Explanation Figure 2), the unchanged number after anti-G-CSF injection indicates that administration of 10µg/day for 4 days does not significantly affect mobilization of psoriatic bone marrow cells. Secondly, the similar GMP numbers at 4 days psoriasis is plausibly due to snapshot analysis when it has already in the numerical recovery period (Explanation Figure 2). Importantly, the notion that anti-G-CSF injection to psoriatic mice reduced granulocytes in the bone marrow, peripheral blood, and skin suggesting G-CSF as a key mediator in psoriatic driven emergency granulopoiesis on top of unlikely case of ineffective anti-G-CSF treatment.

      Taken together, these data suggest a G-CSF mediated emergency granulopoiesis occurrence in the IMQ-induced psoriasis. We will put these data into a revised Figure.

      In Figures 6B, in which cluster of human skin cells IL-17A expression would be enriched?

      Author's response: Thank you for this important point. The IL-17A expression is found in the T-cell cluster (Explanation Figure 4). We also expected to see IL-17A contribution from other cell subset(s), in particular neutrophil. However, due to the fragile nature of neutrophils and thereby, technical difficulty to get their sequencing reads, this dataset (GSE173706) doesn't contain neutrophils, but rather monocytes, macrophages, and dendritic cells among the myeloid subset (Explanation Figure 5). With this, it leaves open the question on what potential contribution of IL-17A produced by neutrophils is in human psoriasis (Reich et al. Exp. Dermatol. 2015, PMID: 25828362).

      Figure 1E shows multiple elongated Ly6G+ structures in d0-2 control and d0 IMQ skins that do not appear to be neutrophils.

      Author's response: We appreciate the Reviewer#1 pointing this issue. As mentioned by the Reviewer#1, the elongated structures detected in the intravital microscopy are not neutrophils, but autofluorescence from the skin bulge regions (Wun et al. J Invest Dermatol. 2005, PMID: 15816847). We have eliminated these unspecific signals from the transformation and quantification (Figure 1F, S1G, and S1H). We will also add an explanatory sentence in Materials and Methods section "Of note, the fluorescent signal with elongated structures resembling hair bulge were autofluorescence and thus removed from further analysis." to be more precise about our methods.

      In Figure 2C, the bottom GSEA seems to be showing type II IFN response, not type I IFN, according to the text.

      Author's response: Thank you for the comment, we will correct this misspelling.

      Reviewer#2

      1. Interpretation of neutrophil transcriptomic changes (Figure 2)

      The RNA-seq analysis reveals substantial downregulation of several canonical pro inflammatory pathways in neutrophils from psoriatic skin, including IL-6, IL-1, and type II interferon signaling. The authors should discuss the functional relevance of this unexpected transcriptional repression. For example, does this indicate a shift toward specialized effector functions rather than classical cytokine responsiveness? More importantly, the most striking transcriptional change is the upregulation of NADPH oxidase-related genes (e.g., Nox1, Nox3, Nox4, Enox2). This suggests an oxidative stress-driven pathogenic mechanism, potentially more relevant than IL-17A production. Yet this aspect is not explored in the manuscript. Assessing ROS levels or oxidative neutrophil effector functions in this model would considerably strengthen the mechanistic link. Conversely, although IL-17A is upregulated in neutrophils, neutrophil depletion reduces total Il17a expression in skin only partially. This indicates that neutrophils are unlikely to be the dominant IL-17A source in the lesion. The authors' focus on neutrophil-derived IL 17A therefore seems overstated. A more rigorous assessment-e.g., conditional deletion of Il17a specifically in neutrophils-would be required to establish its true contribution. Taken together, the data suggest that oxidative programs, rather than IL-17A production, may represent the principal pathogenic axis downstream of neutrophils, and this deserves deeper discussion.

      Author's response: Thank you for raising this valuable views. We have agreed to address these critical points by the following approaches:

      1) To address the changes in NADPH oxidase-related gene signature, we will measure ROS production in the neutrophils from skin and peripheral blood with DHR123.

      2) Responding to the IL17A contribution by neutrophils, we will flow cytometrically assess the Th17 and gamma/delta T cell population in the skin of psoriatic mice treated with anti-Ly6G or isotype-matched antibody as was suggested by Reviewer#1.

      3) We will discuss downregulation of the canonical pro inflammatory and IL-17 pathways in the psoriatic neutrophils in the discussion.

      Human data reanalysis (Figure 6):

      The re-analysis of bulk and single-cell RNA-seq datasets is valuable but incomplete. Several mechanistically relevant questions could be addressed with the available data:

      2.1. GM-CSF (CSF2) is also strongly upregulated in psoriatic lesions (bulk RNA-seq). It would be informative to determine whether endothelial cells also express CSF2 in the scRNA-seq dataset, as this would suggest coordinated regulation of myeloid-supporting cytokines.

      2.2. Myeloid cell subsets should be examined more closely. A comparison of human myeloid transcriptomes with the mouse neutrophil RNA-seq would clarify whether similar IL-17A-related or NADPH oxidase-related signatures occur in human disease. In particular, which cell types express IL17A in human lesions?

      2.3. Chemokine production should be attributed to specific cell types. Bulk RNA-seq confirms strong induction of CXCL1, CXCL2, CXCL5, but the scRNA-seq dataset allows determining whether these chemokines originate from endothelial cells or other stromal/immune populations. This information is important for defining whether endothelial cells coordinate both neutrophil recruitment and granulopoiesis.

      Addressing these points would make the human-mouse comparison substantially stronger.

      Author's response: Thank you for pointing these important issues. By reanalyzing the dataset, we found several points regarding the comments, as follows:

      2.1) CSF2 is expressed by T-cell cluster in the human skin dataset (Explanation Figure 4), in agreement with previous murine study (Hartwig et al. Cell Reports. 2018, PMID: 30590032). We will add this data in the revised manuscript.

      2.2) In line with point#10 from Reviewer#1, the dataset clearly shows T-cell cluster as the main IL17A source (Explanation Figure 4 above). The dataset, however, doesn't contain phenotypic neutrophils (CEACAM (CD66b) and PGLYRP1) but monocytes, macrophages, and dendritic cells (Explanation Figure 5 above). This loss was probably due to a technical limitation given the difficulty in capturing sequencing reads from fragile neutrophils. Therefore, it is no longer possible to reanalyze IL-17 expression in the absence of neutrophils in the datapool.

      2.3) Reanalysis of CXCLs in the human scRNAseq dataset (GSE173706) clarified their secretion dynamics and cellular sources under normal and psoriatic condition. In normal skin, all examined cell subsets show only low CXCLs expression. In contrast, psoriatic skin exhibits significant CXCLs upregulation with distinct cell subsets clearly showing dramatic upregulation, potentially being the major CXCLs source. CXCL1 is markedly upregulated in fibroblasts, myeloid cells, and melanocyte and nerve cells. CXCL2 is strikingly upregulated to myeloid cells, while CXCL5 is hugely increased in fibroblasts, myeloid cells, and mast cells (Explanation Figure 7). Taken together, these results suggest that CXCLs upregulation in the psoriatic skin is coordinatively executed by both stromal and immune compartments. Of note, the endothelial cells show minimal changes in CXCLs expression, even downregulate CXCL2 in psoriasis, indicating that they are unlikely to be the major contributor to CXCL-mediated neutrophil recruitment.

      **Referees cross-commenting**

      I agree with Reviewer 1 that the contribution of EC-derived G-CSF to circulating G-CSF levels and to emergency myelopoiesis requires additional genetic or neutralization experiments to be fully established.

      Author's response: We appreciate that Reviewer#2 raised this key point. In addition to examining the serum G-CSF upon intradermal anti-G-CSF administration in point#1 from Reviewer#1 above, we will also examine the emergency myelopoiesis signs in vivo.

      Minor points

      1. Line 319: the text likely refers to Figure S4, not S3.

      Author's response: Thank you, we will correct the nomenclature.

      Line 338: "psoriatic" is misspelled.

      Author's response: Thank you, we will change this to "psoriatic".

      Reviewer #3

      • Place the work in the context of the existing literature (provide references, where appropriate).

      Psoriasis is extensively studied, a good recent reference- https://doi.org/10.1016/j.mam.2024.101306

      Author's response: Thank you for Reviewer#3's suggestion. The referenced study highlights the current paradigm that largely focus on skin-restricted mechanism and overlook potential cross-organ interaction in the psoriasis inflammation. Our findings provide a new insight into the skin-bone marrow crosstalk in the disease context. In addition, the suggested reference underscores the key roles of diverse innate immune cells including neutrophils, eosinophils, dendritic cells, etc. which is fundamental for our study and might also guide future exploration of additional innate cell subsets beyond neutrophils. We will therefore include the mentioned reference to our revised manuscript.

      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      It is all good. May add graphical-abstract.

      Author's response: Thank you for the reviewer's input, we agree that a graphical-abstract will help the readers more clearly grasp the key messages of our manuscript. We will include it in the revised manuscript.

      Major comments:

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      No. It is very solid.

      Author's response: We appreciate the reviewer's view that the claims in our paper are solid.

      • 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.

      Such a discovery clearly opens many options, and it is fascinating to suggest additional experiments for future studies. It is a complete study, best to publish as-is and let many to read and proceed with this new concept.

      Author's response: We thank the reviewer for noting that the current experimental evidence is complete that no additional experiments are necessary at this stage. We agree that the discovery opens prospective directions for future studies.

      • 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.

      N/A - I suggest no additional experiments at this point. Get it published and see how many will follow this new direction!

      Author's response: We thank the reviewer for recognizing that the experimental data has been sufficient to be a foundation for the future research.

      • Are the data and the methods presented in such a way that they can be reproduced?

      Yes.

      Author's response: We thank the reviewer for recognizing that our methods are reproducible.

      • Are the experiments adequately replicated, and is the statistical analysis adequate?

      Yes. The data are of very high quality.

      Author's response: We are grateful that the reviewer view our replication strategy and statistical analysis are of a high quality.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      None. It is good as-is. One may always suggest minor things- but this one is better published so many laboratories may rush for this new direction. I think it will be interesting studying some long-term impacts, and changes not only of neutrophils but also of other innate cells, such as DCs, Macrophages, and Eosinophils - so it is best to let laboratories that focus on these cells know of the discovery and pursue independent studies.

      Author's response: We appreciate the reviewer's assessment that our paper is already well set for the community to explore the newly proposed direction.

      • Are the text and figures clear and accurate?

      Yes.

      Author's response: We thank the reviewer's evaluation. We have ensured that the text and figures in our manuscript are clear and accurate. Once again, we thank the reviewer for the encouraging and constructive appraisal. We are pleased that the reviewer find the manuscript has already been strong and suitable for publication.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      Study titled: "Skin-derived G-CSF activates pathological granulopoiesis upon psoriasis" by Kosasih and Takizawa. Paper show establishment of psoriasis model in C57BL/6 mice. They focus on neutrophils infiltration following the Imiquimod cream induction. Importantly, authors show that the induction of psoriasis in the skin cause a robust enhancement of granulopoiesis in the bone marrow. Mechanistically, G-CSF is produced in the skin, especially by endothelial cells. Blocking of G-CSF gained clear inhibition of psoriatic pathology. They further add human data showing that patient with psoriasis have more neutrophils and more G-CSF in their skin endothelial cells.

      Parts of the study are simply in line with previous knowledge (e.g.- neutrophils infiltration into psoriatic skin, IL17a). authors show some data that largely confirm the model used. Major discovery: skin endothelial cells are secreting G-CSF that induce granulopoiesis in the bone-marrow. This is a conceptual advancement of this study: psoriatic skin not only recruit neutrophils from the blood, but also enhance the generation of new neutrophils in the bone-marrow. That a major- psoriasis at the level of the model used must not be considered as a confined-pathology. It affect systematically, and might also benefit new systemic treatments. There are plenty of follow-up experiments to pursue now, so it is critical to publish this finding and let many laboratories to know of this new direction. I expect this study to attract high interest and many citations.

      Major comments:

      • Are the key conclusions convincing?

      Yes. The study has excellent data, with good quantification, and very solid support for the discovery and interpretations. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      No. It is very solid. - 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.

      Such a discovery clearly opens many options, and it is fascinating to suggest additional experiments for future studies. It is a complete study, best to publish as-is and let many to read and proceed with this new concept. - 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.

      N/A - I suggest no additional experiments at this point. Get it published and see how many will follow this new direction! - Are the data and the methods presented in such a way that they can be reproduced?

      Yes. - Are the experiments adequately replicated, and is the statistical analysis adequate?

      Yes. The data are of very high quality.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      None. It is good as-is. One may always suggest minor things- but this one is better published so many laboratories may rush for this new direction. I think it will be interesting studying some long-term impacts, and changes not only of neutrophils but also of other innate cells, such as DCs, Macrophages, and Eosinophils - so it is best to let laboratories that focus on these cells know of the discovery and pursue independent studies. - Are prior studies referenced appropriately?

      Yes. I may suggest adding a recent review by Park and Jung, 2024, https://doi.org/10.1016/j.mam.2024.101306 to cover current concepts of innate immunity in psoriasis. - 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?

      It is all good. May add graphical-abstract.

      Significance

      • Describe the nature and significance of the advance (e.g., conceptual, technical, clinical) for the field.

      Conceptual advancement - discovery of a major impact of psoriasis on bone-marrow granulopoiesis. Explicit finding of endothelial-cells G-CSF as a major communication moiety.

      Neutrophil recruitment and IL17A are well established. G-CSF of endothelial cells brings the conceptual advancement- psoriasis at the level induced by IMQ develops local pathology, but is tightly linked to systemic changes. The impact on bone-marrow granulopoiesis may have many implications. So far, it was largely considered that chronic inflammation may affect hematopoiesis, but this study reveals an acute and specific communication between skin and bone marrow. The neutrophils are not only recruited from blood- they are made anew, so the disease is enhanced significantly! This discovery led to a novel basic understanding and suggests novel therapeutic options. - State what audience might be interested in and influenced by the reported findings.

      Dermatologist, immunologist, haematologist - this one goes for a broad audience. - 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.

      Immunology and hematology. I am not an expert of dermatology.