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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.

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.

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

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)
5. Turner et al. Journal of Biological Chemistry (1998). DOI: 1074/jbc.273.48.32135 (PMID: 9822690)
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)
2. Ocbina et al. Nat Genet (2011). DOI: 1038/ng.832 (PMID: 21552265)
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4. Robert A. et al. J Cell Sci (2007). DOI: 1242/jcs.03366 (PMID: 17264151)
5. Singla et al, Developmental Cell (2010). DOI: 10.1016/j.devcel.2009.12.022 (PMID: 20230748) *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 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).

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 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).

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The 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

1. • 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

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. 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.

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'.*

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:

1. • 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

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

__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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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

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...

Referee #2

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

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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

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1. General Statements

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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.

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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.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

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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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. *

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 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.

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

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.

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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.